sample of results and discussion in quantitative research

To display these descriptive statistics in a paper, one might create a table like Table 2. Note that for discrete variables, we use the value label in the table, not the value.

If we were then to discuss our descriptive statistics in a quantitative paper, we might write something like this (note that we do not need to repeat every single detail from the table, as readers can peruse the table themselves):

This analysis relies on four variables from the 2021 General Social Survey: occupational prestige score, age, highest degree earned, and whether the respondent was born in the United States. Descriptive statistics for all four variables are shown in Table 2. The median occupational prestige score is 47, with a range from 16 to 80. 50% of respondents had occupational prestige scores scores between 35 and 59. The median age of respondents is 53, with a range from 18 to 89. 50% of respondents are between ages 37 and 66. Both variables have little skew. Highest degree earned ranges from less than high school to a graduate degree; the median respondent has earned an associate’s degree, while the modal response (given by 39.8% of the respondents) is a high school degree. 88.8% of respondents were born in the United States.

Crosstabulation

When presenting the results of a crosstabulation, we simplify the table so that it highlights the most important information—the column percentages—and include the significance and association below the table. Consider the SPSS output below.

Table 4 shows how a table suitable for include in a paper might look if created from the SPSS output in Table 3. Note that we use asterisks to indicate the significance level of the results: * means p < 0.05; ** means p < 0.01; *** means p < 0.001; and no stars mean p > 0.05 (and thus that the result is not significant). Also note than N is the abbreviation for the number of respondents.

If we were going to discuss the results of this crosstabulation in a quantitative research paper, the discussion might look like this:

A crosstabulation of respondent’s class identification and their highest degree earned, with class identification as the independent variable, is significant, with a Spearman correlation of 0.419, as shown in Table 4. Among lower class and working class respondents, more than 50% had earned a high school degree. Less than 20% of poor respondents and less than 40% of working-class respondents had earned more than a high school degree. In contrast, the majority of middle class and upper class respondents had earned at least a bachelor’s degree. In fact, 50% of upper class respondents had earned a graduate degree.

Correlation

When presenting a correlating matrix, one of the most important things to note is that we only present half the table so as not to include duplicated results. Think of the line through the table where empty cells exist to represent the correlation between a variable and itself, and include only the triangle of data either above or below that line of cells. Consider the output in Table 5.

Table 6 shows what the contents of Table 5 might look like when a table is constructed in a fashion suitable for publication.

If we were to discuss the results of this bivariate correlation analysis in a quantitative paper, the discussion might look like this:

Bivariate correlations were run among variables measuring age, occupational prestige, the highest year of school respondents completed, and family income in constant 1986 dollars, as shown in Table 6. Correlations between age and highest year of school completed and between age and family income are not significant. All other correlations are positive and significant at the p<0.001 level. The correlation between age and occupational prestige is weak; the correlations between income and occupational prestige and between income and educational attainment are moderate, and the correlation between education and occupational prestige is strong.

To present the results of a regression, we create one table that includes all of the key information from the multiple tables of SPSS output. This includes the R 2 and significance of the regression, either the B or the beta values (different analysts have different preferences here) for each variable, and the standard error and significance of each variable. Consider the SPSS output in Table 7.

The regression output in shown in Table 7 contains a lot of information. We do not include all of this information when making tables suitable for publication. As can be seen in Table 8, we include the Beta (or the B), the standard error, and the significance asterisk for each variable; the R 2 and significance for the overall regression; the degrees of freedom (which tells readers the sample size or N); and the constant; along with the key to p/significance values.

If we were to discuss the results of this regression in a quantitative paper, the results might look like this:

Table 8 shows the results of a regression in which age, occupational prestige, and highest year of school completed are the independent variables and family income is the dependent variable. The regression results are significant, and all of the independent variables taken together explain 15.6% of the variance in family income. Age is not a significant predictor of income, while occupational prestige and educational attainment are. Educational attainment has a larger effect on family income than does occupational prestige. For every year of additional education attained, family income goes up on average by $3,988.545; for every one-unit increase in occupational prestige score, family income goes up on average by $522.887. [1]

• Choose two discrete variables and three continuous variables from a dataset of your choice. Produce appropriate descriptive statistics on all five of the variables and create a table of the results suitable for inclusion in a paper.
• Using the two discrete variables you have chosen, produce an appropriate crosstabulation, with significance and measure of association. Create a table of the results suitable for inclusion in a paper.
• Using the three continuous variables you have chosen, produce a correlation matrix. Create a table of the results suitable for inclusion in a paper.
• Using the three continuous variables you have chosen, produce a multivariate linear regression. Create a table of the results suitable for inclusion in a paper.
• Write a methods section describing the dataset, analytical methods, and variables you utilized in questions 1, 2, 3, and 4 and explaining the results of your descriptive analysis.
• Write a findings section explaining the results of the analyses you performed in questions 2, 3, and 4.
• Note that the actual numberical increase comes from the B values, which are shown in the SPSS output in Table 7 but not in the reformatted Table 8. ↵

Social Data Analysis Copyright © 2021 by Mikaila Mariel Lemonik Arthur is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

• Privacy Policy

Home » Research Results Section – Writing Guide and Examples

Research Results Section – Writing Guide and Examples

Table of Contents

Research Results

Research results refer to the findings and conclusions derived from a systematic investigation or study conducted to answer a specific question or hypothesis. These results are typically presented in a written report or paper and can include various forms of data such as numerical data, qualitative data, statistics, charts, graphs, and visual aids.

Results Section in Research

The results section of the research paper presents the findings of the study. It is the part of the paper where the researcher reports the data collected during the study and analyzes it to draw conclusions.

In the results section, the researcher should describe the data that was collected, the statistical analysis performed, and the findings of the study. It is important to be objective and not interpret the data in this section. Instead, the researcher should report the data as accurately and objectively as possible.

Structure of Research Results Section

The structure of the research results section can vary depending on the type of research conducted, but in general, it should contain the following components:

• Introduction: The introduction should provide an overview of the study, its aims, and its research questions. It should also briefly explain the methodology used to conduct the study.
• Data presentation : This section presents the data collected during the study. It may include tables, graphs, or other visual aids to help readers better understand the data. The data presented should be organized in a logical and coherent way, with headings and subheadings used to help guide the reader.
• Data analysis: In this section, the data presented in the previous section are analyzed and interpreted. The statistical tests used to analyze the data should be clearly explained, and the results of the tests should be presented in a way that is easy to understand.
• Discussion of results : This section should provide an interpretation of the results of the study, including a discussion of any unexpected findings. The discussion should also address the study’s research questions and explain how the results contribute to the field of study.
• Limitations: This section should acknowledge any limitations of the study, such as sample size, data collection methods, or other factors that may have influenced the results.
• Conclusions: The conclusions should summarize the main findings of the study and provide a final interpretation of the results. The conclusions should also address the study’s research questions and explain how the results contribute to the field of study.
• Recommendations : This section may provide recommendations for future research based on the study’s findings. It may also suggest practical applications for the study’s results in real-world settings.

Outline of Research Results Section

The following is an outline of the key components typically included in the Results section:

I. Introduction

• A brief overview of the research objectives and hypotheses
• A statement of the research question

II. Descriptive statistics

• Summary statistics (e.g., mean, standard deviation) for each variable analyzed
• Frequencies and percentages for categorical variables

III. Inferential statistics

• Results of statistical analyses, including tests of hypotheses
• Tables or figures to display statistical results

IV. Effect sizes and confidence intervals

• Effect sizes (e.g., Cohen’s d, odds ratio) to quantify the strength of the relationship between variables
• Confidence intervals to estimate the range of plausible values for the effect size

V. Subgroup analyses

• Results of analyses that examined differences between subgroups (e.g., by gender, age, treatment group)

VI. Limitations and assumptions

• Discussion of any limitations of the study and potential sources of bias
• Assumptions made in the statistical analyses

VII. Conclusions

• A summary of the key findings and their implications
• A statement of whether the hypotheses were supported or not
• Suggestions for future research

Example of Research Results Section

An Example of a Research Results Section could be:

• This study sought to examine the relationship between sleep quality and academic performance in college students.
• Hypothesis : College students who report better sleep quality will have higher GPAs than those who report poor sleep quality.
• Methodology : Participants completed a survey about their sleep habits and academic performance.

II. Participants

• Participants were college students (N=200) from a mid-sized public university in the United States.
• The sample was evenly split by gender (50% female, 50% male) and predominantly white (85%).
• Participants were recruited through flyers and online advertisements.

III. Results

• Participants who reported better sleep quality had significantly higher GPAs (M=3.5, SD=0.5) than those who reported poor sleep quality (M=2.9, SD=0.6).
• See Table 1 for a summary of the results.
• Participants who reported consistent sleep schedules had higher GPAs than those with irregular sleep schedules.

IV. Discussion

• The results support the hypothesis that better sleep quality is associated with higher academic performance in college students.
• These findings have implications for college students, as prioritizing sleep could lead to better academic outcomes.
• Limitations of the study include self-reported data and the lack of control for other variables that could impact academic performance.

V. Conclusion

• College students who prioritize sleep may see a positive impact on their academic performance.
• These findings highlight the importance of sleep in academic success.
• Future research could explore interventions to improve sleep quality in college students.

Example of Research Results in Research Paper :

Our study aimed to compare the performance of three different machine learning algorithms (Random Forest, Support Vector Machine, and Neural Network) in predicting customer churn in a telecommunications company. We collected a dataset of 10,000 customer records, with 20 predictor variables and a binary churn outcome variable.

Our analysis revealed that all three algorithms performed well in predicting customer churn, with an overall accuracy of 85%. However, the Random Forest algorithm showed the highest accuracy (88%), followed by the Support Vector Machine (86%) and the Neural Network (84%).

Furthermore, we found that the most important predictor variables for customer churn were monthly charges, contract type, and tenure. Random Forest identified monthly charges as the most important variable, while Support Vector Machine and Neural Network identified contract type as the most important.

Overall, our results suggest that machine learning algorithms can be effective in predicting customer churn in a telecommunications company, and that Random Forest is the most accurate algorithm for this task.

Example 3 :

Title : The Impact of Social Media on Body Image and Self-Esteem

Abstract : This study aimed to investigate the relationship between social media use, body image, and self-esteem among young adults. A total of 200 participants were recruited from a university and completed self-report measures of social media use, body image satisfaction, and self-esteem.

Results: The results showed that social media use was significantly associated with body image dissatisfaction and lower self-esteem. Specifically, participants who reported spending more time on social media platforms had lower levels of body image satisfaction and self-esteem compared to those who reported less social media use. Moreover, the study found that comparing oneself to others on social media was a significant predictor of body image dissatisfaction and lower self-esteem.

Conclusion : These results suggest that social media use can have negative effects on body image satisfaction and self-esteem among young adults. It is important for individuals to be mindful of their social media use and to recognize the potential negative impact it can have on their mental health. Furthermore, interventions aimed at promoting positive body image and self-esteem should take into account the role of social media in shaping these attitudes and behaviors.

Importance of Research Results

Research results are important for several reasons, including:

• Advancing knowledge: Research results can contribute to the advancement of knowledge in a particular field, whether it be in science, technology, medicine, social sciences, or humanities.
• Developing theories: Research results can help to develop or modify existing theories and create new ones.
• Improving practices: Research results can inform and improve practices in various fields, such as education, healthcare, business, and public policy.
• Identifying problems and solutions: Research results can identify problems and provide solutions to complex issues in society, including issues related to health, environment, social justice, and economics.
• Validating claims : Research results can validate or refute claims made by individuals or groups in society, such as politicians, corporations, or activists.
• Providing evidence: Research results can provide evidence to support decision-making, policy-making, and resource allocation in various fields.

How to Write Results in A Research Paper

Here are some general guidelines on how to write results in a research paper:

• Organize the results section: Start by organizing the results section in a logical and coherent manner. Divide the section into subsections if necessary, based on the research questions or hypotheses.
• Present the findings: Present the findings in a clear and concise manner. Use tables, graphs, and figures to illustrate the data and make the presentation more engaging.
• Describe the data: Describe the data in detail, including the sample size, response rate, and any missing data. Provide relevant descriptive statistics such as means, standard deviations, and ranges.
• Interpret the findings: Interpret the findings in light of the research questions or hypotheses. Discuss the implications of the findings and the extent to which they support or contradict existing theories or previous research.
• Discuss the limitations : Discuss the limitations of the study, including any potential sources of bias or confounding factors that may have affected the results.
• Compare the results : Compare the results with those of previous studies or theoretical predictions. Discuss any similarities, differences, or inconsistencies.
• Avoid redundancy: Avoid repeating information that has already been presented in the introduction or methods sections. Instead, focus on presenting new and relevant information.
• Be objective: Be objective in presenting the results, avoiding any personal biases or interpretations.

When to Write Research Results

Here are situations When to Write Research Results”

• After conducting research on the chosen topic and obtaining relevant data, organize the findings in a structured format that accurately represents the information gathered.
• Once the data has been analyzed and interpreted, and conclusions have been drawn, begin the writing process.
• Before starting to write, ensure that the research results adhere to the guidelines and requirements of the intended audience, such as a scientific journal or academic conference.
• Begin by writing an abstract that briefly summarizes the research question, methodology, findings, and conclusions.
• Follow the abstract with an introduction that provides context for the research, explains its significance, and outlines the research question and objectives.
• The next section should be a literature review that provides an overview of existing research on the topic and highlights the gaps in knowledge that the current research seeks to address.
• The methodology section should provide a detailed explanation of the research design, including the sample size, data collection methods, and analytical techniques used.
• Present the research results in a clear and concise manner, using graphs, tables, and figures to illustrate the findings.
• Discuss the implications of the research results, including how they contribute to the existing body of knowledge on the topic and what further research is needed.
• Conclude the paper by summarizing the main findings, reiterating the significance of the research, and offering suggestions for future research.

Purpose of Research Results

The purposes of Research Results are as follows:

• Informing policy and practice: Research results can provide evidence-based information to inform policy decisions, such as in the fields of healthcare, education, and environmental regulation. They can also inform best practices in fields such as business, engineering, and social work.
• Addressing societal problems : Research results can be used to help address societal problems, such as reducing poverty, improving public health, and promoting social justice.
• Generating economic benefits : Research results can lead to the development of new products, services, and technologies that can create economic value and improve quality of life.
• Supporting academic and professional development : Research results can be used to support academic and professional development by providing opportunities for students, researchers, and practitioners to learn about new findings and methodologies in their field.
• Enhancing public understanding: Research results can help to educate the public about important issues and promote scientific literacy, leading to more informed decision-making and better public policy.
• Evaluating interventions: Research results can be used to evaluate the effectiveness of interventions, such as treatments, educational programs, and social policies. This can help to identify areas where improvements are needed and guide future interventions.
• Contributing to scientific progress: Research results can contribute to the advancement of science by providing new insights and discoveries that can lead to new theories, methods, and techniques.
• Informing decision-making : Research results can provide decision-makers with the information they need to make informed decisions. This can include decision-making at the individual, organizational, or governmental levels.
• Fostering collaboration : Research results can facilitate collaboration between researchers and practitioners, leading to new partnerships, interdisciplinary approaches, and innovative solutions to complex problems.

Advantages of Research Results

Some Advantages of Research Results are as follows:

• Improved decision-making: Research results can help inform decision-making in various fields, including medicine, business, and government. For example, research on the effectiveness of different treatments for a particular disease can help doctors make informed decisions about the best course of treatment for their patients.
• Innovation : Research results can lead to the development of new technologies, products, and services. For example, research on renewable energy sources can lead to the development of new and more efficient ways to harness renewable energy.
• Economic benefits: Research results can stimulate economic growth by providing new opportunities for businesses and entrepreneurs. For example, research on new materials or manufacturing techniques can lead to the development of new products and processes that can create new jobs and boost economic activity.
• Improved quality of life: Research results can contribute to improving the quality of life for individuals and society as a whole. For example, research on the causes of a particular disease can lead to the development of new treatments and cures, improving the health and well-being of millions of people.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Thesis Outline – Example, Template and Writing...

Research Methodology – Types, Examples and...

Dissertation Methodology – Structure, Example...

Research Paper Conclusion – Writing Guide and...

Scope of the Research – Writing Guide and...

Dissertation vs Thesis – Key Differences

Generate accurate APA citations for free

• Knowledge Base
• APA Style 7th edition
• How to write an APA results section

Reporting Research Results in APA Style | Tips & Examples

Published on December 21, 2020 by Pritha Bhandari . Revised on January 17, 2024.

The results section of a quantitative research paper is where you summarize your data and report the findings of any relevant statistical analyses.

The APA manual provides rigorous guidelines for what to report in quantitative research papers in the fields of psychology, education, and other social sciences.

Use these standards to answer your research questions and report your data analyses in a complete and transparent way.

Instantly correct all language mistakes in your text

Upload your document to correct all your mistakes in minutes

Table of contents

What goes in your results section, introduce your data, summarize your data, report statistical results, presenting numbers effectively, what doesn’t belong in your results section, frequently asked questions about results in apa.

In APA style, the results section includes preliminary information about the participants and data, descriptive and inferential statistics, and the results of any exploratory analyses.

Include these in your results section:

• Participant flow and recruitment period. Report the number of participants at every stage of the study, as well as the dates when recruitment took place.
• Missing data . Identify the proportion of data that wasn’t included in your final analysis and state the reasons.
• Any adverse events. Make sure to report any unexpected events or side effects (for clinical studies).
• Descriptive statistics . Summarize the primary and secondary outcomes of the study.
• Inferential statistics , including confidence intervals and effect sizes. Address the primary and secondary research questions by reporting the detailed results of your main analyses.
• Results of subgroup or exploratory analyses, if applicable. Place detailed results in supplementary materials.

Write up the results in the past tense because you’re describing the outcomes of a completed research study.

Scribbr Citation Checker New

The AI-powered Citation Checker helps you avoid common mistakes such as:

• Missing commas and periods
• Incorrect usage of “et al.”
• Ampersands (&) in narrative citations
• Missing reference entries

Before diving into your research findings, first describe the flow of participants at every stage of your study and whether any data were excluded from the final analysis.

Participant flow and recruitment period

It’s necessary to report any attrition, which is the decline in participants at every sequential stage of a study. That’s because an uneven number of participants across groups sometimes threatens internal validity and makes it difficult to compare groups. Be sure to also state all reasons for attrition.

If your study has multiple stages (e.g., pre-test, intervention, and post-test) and groups (e.g., experimental and control groups), a flow chart is the best way to report the number of participants in each group per stage and reasons for attrition.

Also report the dates for when you recruited participants or performed follow-up sessions.

Missing data

Another key issue is the completeness of your dataset. It’s necessary to report both the amount and reasons for data that was missing or excluded.

Data can become unusable due to equipment malfunctions, improper storage, unexpected events, participant ineligibility, and so on. For each case, state the reason why the data were unusable.

Some data points may be removed from the final analysis because they are outliers—but you must be able to justify how you decided what to exclude.

If you applied any techniques for overcoming or compensating for lost data, report those as well.

Adverse events

For clinical studies, report all events with serious consequences or any side effects that occured.

Descriptive statistics summarize your data for the reader. Present descriptive statistics for each primary, secondary, and subgroup analysis.

Don’t provide formulas or citations for commonly used statistics (e.g., standard deviation) – but do provide them for new or rare equations.

Descriptive statistics

The exact descriptive statistics that you report depends on the types of data in your study. Categorical variables can be reported using proportions, while quantitative data can be reported using means and standard deviations . For a large set of numbers, a table is the most effective presentation format.

Include sample sizes (overall and for each group) as well as appropriate measures of central tendency and variability for the outcomes in your results section. For every point estimate , add a clearly labelled measure of variability as well.

Be sure to note how you combined data to come up with variables of interest. For every variable of interest, explain how you operationalized it.

According to APA journal standards, it’s necessary to report all relevant hypothesis tests performed, estimates of effect sizes, and confidence intervals.

When reporting statistical results, you should first address primary research questions before moving onto secondary research questions and any exploratory or subgroup analyses.

Present the results of tests in the order that you performed them—report the outcomes of main tests before post-hoc tests, for example. Don’t leave out any relevant results, even if they don’t support your hypothesis.

Inferential statistics

For each statistical test performed, first restate the hypothesis , then state whether your hypothesis was supported and provide the outcomes that led you to that conclusion.

Report the following for each hypothesis test:

• the test statistic value,
• the degrees of freedom ,
• the exact p- value (unless it is less than 0.001),
• the magnitude and direction of the effect.

When reporting complex data analyses, such as factor analysis or multivariate analysis, present the models estimated in detail, and state the statistical software used. Make sure to report any violations of statistical assumptions or problems with estimation.

Effect sizes and confidence intervals

For each hypothesis test performed, you should present confidence intervals and estimates of effect sizes .

Confidence intervals are useful for showing the variability around point estimates. They should be included whenever you report population parameter estimates.

Effect sizes indicate how impactful the outcomes of a study are. But since they are estimates, it’s recommended that you also provide confidence intervals of effect sizes.

Subgroup or exploratory analyses

Briefly report the results of any other planned or exploratory analyses you performed. These may include subgroup analyses as well.

Subgroup analyses come with a high chance of false positive results, because performing a large number of comparison or correlation tests increases the chances of finding significant results.

If you find significant results in these analyses, make sure to appropriately report them as exploratory (rather than confirmatory) results to avoid overstating their importance.

While these analyses can be reported in less detail in the main text, you can provide the full analyses in supplementary materials.

To effectively present numbers, use a mix of text, tables , and figures where appropriate:

• To present three or fewer numbers, try a sentence ,
• To present between 4 and 20 numbers, try a table ,
• To present more than 20 numbers, try a figure .

Since these are general guidelines, use your own judgment and feedback from others for effective presentation of numbers.

Tables and figures should be numbered and have titles, along with relevant notes. Make sure to present data only once throughout the paper and refer to any tables and figures in the text.

Formatting statistics and numbers

It’s important to follow capitalization , italicization, and abbreviation rules when referring to statistics in your paper. There are specific format guidelines for reporting statistics in APA , as well as general rules about writing numbers .

If you are unsure of how to present specific symbols, look up the detailed APA guidelines or other papers in your field.

It’s important to provide a complete picture of your data analyses and outcomes in a concise way. For that reason, raw data and any interpretations of your results are not included in the results section.

It’s rarely appropriate to include raw data in your results section. Instead, you should always save the raw data securely and make them available and accessible to any other researchers who request them.

Making scientific research available to others is a key part of academic integrity and open science.

Interpretation or discussion of results

This belongs in your discussion section. Your results section is where you objectively report all relevant findings and leave them open for interpretation by readers.

While you should state whether the findings of statistical tests lend support to your hypotheses, refrain from forming conclusions to your research questions in the results section.

Explanation of how statistics tests work

For the sake of concise writing, you can safely assume that readers of your paper have professional knowledge of how statistical inferences work.

In an APA results section , you should generally report the following:

• Participant flow and recruitment period.
• Missing data and any adverse events.
• Descriptive statistics about your samples.
• Inferential statistics , including confidence intervals and effect sizes.
• Results of any subgroup or exploratory analyses, if applicable.

According to the APA guidelines, you should report enough detail on inferential statistics so that your readers understand your analyses.

• the test statistic value
• the degrees of freedom
• the exact p value (unless it is less than 0.001)
• the magnitude and direction of the effect

You should also present confidence intervals and estimates of effect sizes where relevant.

In APA style, statistics can be presented in the main text or as tables or figures . To decide how to present numbers, you can follow APA guidelines:

• To present three or fewer numbers, try a sentence,
• To present between 4 and 20 numbers, try a table,
• To present more than 20 numbers, try a figure.

Results are usually written in the past tense , because they are describing the outcome of completed actions.

The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.

In qualitative research , results and discussion are sometimes combined. But in quantitative research , it’s considered important to separate the objective results from your interpretation of them.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Bhandari, P. (2024, January 17). Reporting Research Results in APA Style | Tips & Examples. Scribbr. Retrieved September 9, 2024, from https://www.scribbr.com/apa-style/results-section/

Is this article helpful?

Pritha Bhandari

Other students also liked, how to write an apa methods section, how to format tables and figures in apa style, reporting statistics in apa style | guidelines & examples, get unlimited documents corrected.

✔ Free APA citation check included ✔ Unlimited document corrections ✔ Specialized in correcting academic texts

Free Download

Research Results Template

The fastest (and smartest) way to craft a strong results section for your dissertation, thesis or research project.

Step-by-step instructions

Tried & tested academic format

Fill-in-the-blanks simplicity

Pro tips, tricks and resources

What It Covers

This template covers all the core components required in the results chapter of a typical dissertation, thesis or research project:

• The opening /overview section
• The body section for qualitative studies
• The body section for quantitative studies
• Concluding summary

The purpose of each section is explained in plain language, followed by an overview of the key elements that you need to cover. The template also includes practical examples to help you understand exactly what’s required, along with links to additional free resources (articles, videos, etc.) to help you along your research journey.

download your copy

100% Free to use. Instant access.

I agree to receive the free template and other useful resources.

Download Now (Instant Access)

FAQs: Thesis/Dissertation Results Template

Faq - thesis results chapter, what types of dissertations/theses can this be used for, is this template for an undergrad, master or phd-level thesis, how long should the results chapter be, can i share this template with my friends/colleagues, what format is the template (doc, pdf, ppt, etc.), do you have templates for the other chapters, can grad coach help me with my results and/or analysis, additional resources.

If you’re working on a dissertation or thesis, you’ll also want to check these out…

1-On-1 Private Coaching

Research Bootcamps

The Grad Coach YouTube Channel

The Grad Coach Podcast

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

7.1 Reading results in quantitative research

Learning objectives.

Learners will be able to…

• Describe how statistical significance and confidence intervals demonstrate which results are most important

Pre-awareness check (Knowledge)

What do you know about previously conducted research on your topic (e.g., statistical analyses, qualitative and quantitative results)?

If you recall, empirical journal articles are those that report the results of quantitative or qualitative data analyzed by the author. They follow a set structure—introduction, methods, results, discussion/conclusions. This chapter is about reading what is often the most challenging section: results.

Quantitative results

Quantitative articles often contain tables, and scanning them is a good way to begin reading the results. A table usually provides a quick, condensed summary of the report’s key findings. Tables are a concise way to report large amounts of data. Some tables present descriptive information about a researcher’s sample (often the first table in a results section). These tables will likely contain frequencies ( n ) and percentages (%). For example, if gender happened to be an important variable for the researcher’s analysis, a descriptive table would show how many and what percent of all study participants are of a particular gender. Frequencies or “how many” will probably be listed as n , while the percent symbol (%) might be used to indicate percentages. The symbol N is used for the entire sample size, and  n is used for the size of a portion of the entire sample.

In a table presenting a causal relationship, two sets of variables are represented. The independent variable , or cause, and the dependent variable , the effect. We’ll go into more detail on variables in Chapter 8. Independent variable attributes are typically presented in the table’s columns, while dependent variable attributes are presented in rows. This allows the reader to scan a table’s rows to see how values on the dependent variable change as the independent variable values change. Tables displaying results of quantitative analysis will also likely include some information about which relationships are significant or not. We will discuss the details of significance and p -values later in this section.

Let’s look at a specific example: Table 7.1 below.

Table 7.1 presents the association between gender and experiencing harassing behaviors at work. In this example, gender is the independent variable (the predictor) and the harassing behaviors listed are the dependent variables (the outcome). [1] Therefore, we place gender in the table’s columns and harassing behaviors in the table’s rows.

Reading across the table’s top row, we see that 2.9% of women in the sample reported experiencing subtle or obvious threats to their safety at work, while 4.7% of men in the sample reported the same. We can read across each of the rows of the table in this way. Reading across the bottom row, we see that 9.4% of women in the sample reported experiencing staring or invasion of their personal space at work while just 2.3% of men in the sample reported having the same experience. We’ll discuss  p- values later in this section.

While you can certainly scan tables for key results, they are often difficult to understand without reading the text of the article. The article and table were meant to complement each other, and the text should provide information on how the authors interpret their findings. The table is not redundant with the text of the results section. Additionally, the first table in most results sections is a summary of the study’s sample, which provides more background information on the study than information about hypotheses and findings. It is also a good idea to look back at the methods section of the article as the data analysis plan the authors outline should walk you through the steps they took to analyze their data which will inform how they report them in the results section.

Statistical significance

The statistics reported in Table 7.1 represent what the researchers found in their sample. The purpose of statistical analysis is usually to generalize from a the small number of people in a study’s sample to a larger population of people. Thus, the researchers intend to make causal arguments about harassing behaviors at workplaces beyond those covered in the sample.

Generalizing is key to understanding statistical significance . According to Cassidy et al. (2019), [2] 89% of research methods textbooks in psychology define statistical significance incorrectly. This includes an early draft of this textbook which defined statistical significance as “the likelihood that the relationships we observe could be caused by something other than chance.” If you have previously had a research methods class, this might sound familiar to you. It certainly did to me!

But statistical significance is less about “random chance” than more about the null hypothesis . Basically, at the beginning of a study a researcher develops a hypothesis about what they expect to find, usually that there is a statistical relationship between two or more variables . The null hypothesis is the opposite. It is the hypothesis that there is no relationship between the variables in a research study. Researchers then can hopefully reject the null hypothesis because they find a relationship between the variables.

For example, in Table 7.1 researchers were examining whether gender impacts harassment. Of course, researchers assumed that women were more likely to experience harassment than men. The null hypothesis, then, would be that gender has no impact on harassment. Once we conduct the study, our results will hopefully lead us to reject the null hypothesis because we find that gender impacts harassment. We would then generalize from our study’s sample to the larger population of people in the workplace.

Statistical significance is calculated using a p -value which is obtained by comparing the statistical results with a hypothetical set of results if the researchers re-ran their study a large number of times. Keeping with our example, imagine we re-ran our study with different men and women from different workplaces hundreds and hundred of times and we assume that the null hypothesis is true that gender has no impact on harassment. If results like ours come up pretty often when the null hypothesis is true, our results probably don’t mean much. “The smaller the p -value, the greater the statistical incompatibility with the null hypothesis” (Wasserstein & Lazar, 2016, p. 131). [3] Generally, researchers in the social sciences have set alpha at .05 for the value at which a result is significant ( p is less than or equal to .05) or not significant ( p is greater than .05). The p -value .05 refers to if less than 5% of those hypothetical results from re-running our study show the same or more extreme relationships when the null hypothesis is true. Researchers, however, may choose a stricter standard such as .01 in which 1% or less of those hypothetical results are more extreme or a more lenient standard like .1 in which 10% or less of those hypothetical results are more extreme than what was found in the study.

Let’s look back at Table 7.1. Which one of the relationships between gender and harassing behaviors is statistically significant? It’s the last one in the table, “staring or invasion of personal space,” whose p -value is .039 (under the p<.05 standard to establish statistical significance). Again, this indicates that if we re-ran our study over and over again and gender did not  impact staring/invasion of space (i.e., the null hypothesis was true), only 3.9% of the time would we find similar or more extreme differences between men and women than what we observed in our study. Thus, we conclude that for staring or invasion of space only , there is a statistically significant relationship.

For contrast, let’s look at “being pushed, hit, or grabbed” and run through the same analysis to see if it is statistically significant. If we re-ran our study over and over again and the null hypothesis was true, 48% of the time ( p =.48) we would find similar or more extreme differences between men and women. That means these results are not statistically significant.

This discussion should also highlight a point we discussed previously: that it is important to read the full results section, rather than simply relying on the summary in the abstract. If the abstract stated that most tests revealed no statistically significant relationships between gender and harassment, you would have missed the detail on which behaviors were and were not associated with gender. Read the full results section! And don’t be afraid to ask for help from a professor in understanding what you are reading, as results sections are often not written to be easily understood.

Statistical significance and p -values have been critiqued recently for a number of reasons, including that they are misused and misinterpreted (Wasserstein & Lazar, 2016) [4] , that researchers deliberately manipulate their analyses to have significant results (Head et al., 2015) [5] , and factor into the difficulty scientists have today in reproducing many of the results of previous social science studies (Peng, 2015). [6] For this reason, we share these principles, adapted from those put forth by the American Statistical Association, [7]  for understanding and using p -values in social science:

• p -values provide evidence against a null hypothesis.
• p -values do not indicate whether the results were produced by random chance alone or if the researcher’s hypothesis is true, though both are common misconceptions.
• Statistical significance can be detected in minuscule differences that have very little effect on the real world.
• Nuance is needed to interpret scientific findings, as a conclusion does not become true or false when the p -value passes from p =.051 to p =.049.
• Real-world decision-making must use more than reported p -values. It’s easy to run analyses of large datasets and only report the significant findings.
• Greater confidence can be placed in studies that pre-register their hypotheses and share their data and methods openly with the public.
• “By itself, a p -value does not provide a good measure of evidence regarding a model or hypothesis. For example, a p -value near .05 taken by itself offers only weak evidence against the null hypothesis. Likewise, a relatively large p -value does not imply evidence in favor of the null hypothesis; many other hypotheses may be equally or more consistent with the observed data” (Wasserstein & Lazar, 2016, p. 132).

Confidence intervals

Because of the limitations of p -values, scientists can use other methods to determine whether their models of the world are true. One common approach is to use a confidence interval , or a range of values in which the true value is likely to be found. Confidence intervals are helpful because, as principal #5 above points out, p -values do not measure the size of an effect (Greenland et al., 2016). [8] Remember, something that has very little impact on the world can be statistically significant, and the values in a confidence interval would be helpful. In our example from Table 7.1, imagine our analysis produced a confidence interval that women are 1.2-3.4 times more likely to experience “staring or invasion of personal space” than men. As with p -values, calculation for a confidence interval compares what was found in one study with a hypothetical set of results if we repeated the study over and over again. If we calculated 95% confidence intervals for all of the hypothetical set of hundreds and hundreds of studies, that would be our confidence interval. 

Confidence intervals are pretty intuitive. As of this writing, my wife and are expecting our second child. The doctor told us our due date was December 11th. But the doctor also told us that December 11th was only their best estimate. They were actually 95% sure our baby might be born any time in the 30-day period between November 27th and December 25th. Confidence intervals are often listed with a percentage, like 90% or 95%, and a range of values, such as between November 27th and December 25th. You can read that as: “we are 95% sure your baby will be born between November 27th and December 25th because we’ve studied hundreds of thousands of fetuses and mothers, and we’re 95% sure your baby will be within these two dates.”

Notice that we’re hedging our bets here by using words like “best estimate.” When testing hypotheses, social scientists generally phrase their findings in a tentative way, talking about what results “indicate” or “support,” rather than making bold statements about what their results “prove.” Social scientists have humility because they understand the limitations of their knowledge. In a literature review, using a single study or fact to “prove” an argument right or wrong is often a signal to the person reading your literature review (usually your professor) that you may not have appreciated the limitations of that study or its place in the broader literature on the topic. Strong arguments in a literature review include multiple facts and ideas that span across multiple studies.

You can learn more about creating tables, reading tables, and tests of statistical significance in a class focused exclusively on statistical analysis. We provide links to many free and openly licensed resources on statistics in Chapter 16. For now, we hope this brief introduction to reading tables will improve your confidence in reading and understanding the results sections in quantitative empirical articles.

Key Takeaways

• The results section of empirical articles are often the most difficult to understand.
• To understand a quantitative results section, look for results that were statistically significant and examine the confidence interval, if provided.

Post-awareness check (Emotional)

On a scale of 1-10 (10 being excellent), how would you rate your confidence level in your ability to understand a quantitative results section in empirical articles on your topic of interest?

TRACK 1 (IF YOU ARE CREATING A RESEARCH PROPOSAL FOR THIS CLASS)

Select a quantitative empirical article related to your topic.

• Write down the results the authors identify as statistically significant in the results section.
• How do the authors interpret their results in the discussion section?
• Do the authors provide enough information in the introduction for you to understand their results?

TRACK 2 (IF YOU  AREN’T CREATING A RESEARCH PROPOSAL FOR THIS CLASS)

You are interested in researching the effects of race-based stress and burnout among social workers.

Select a quantitative empirical article related to this topic.

• It wouldn’t make any sense to say that people’s workplace experiences predict their gender, so in this example, the question of which is the independent variable and which are the dependent variables has a pretty obvious answer. ↵
• Cassidy, S. A., Dimova, R., Giguère, B., Spence, J. R., & Stanley, D. J. (2019). Failing grade: 89% of introduction-to-psychology textbooks that define or explain statistical significance do so incorrectly. Advances in Methods and Practices in Psychological Science ,  2 (3), 233-239. ↵
• Wasserstein, R. L., & Lazar, N. A. (2016). The ASA statement on p -values: context, process, and purpose. The American Statistician, 70 , p. 129-133. ↵
• Head, M. L., Holman, L., Lanfear, R., Kahn, A. T., & Jennions, M. D. (2015). The extent and consequences of p-hacking in science. PLoS biology, 13 (3). ↵
• Peng, R. (2015), The reproducibility crisis in science: A statistical counterattack. Significance , 12 , 30–32. ↵
• Greenland, S., Senn, S. J., Rothman, K. J., Carlin, J. B., Poole, C., Goodman, S. N., & Altman, D. G. (2016). Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations.  European journal of epidemiology ,  31 (4), 337-350. ↵

report the results of a quantitative or qualitative data analysis conducted by the author

a quick, condensed summary of the report’s key findings arranged by row and column

causes a change in the dependent variable

a variable that depends on changes in the independent variable

(as in generalization) to make claims about a large population based on a smaller sample of people or items

"Assuming that the null hypothesis is true and the study is repeated an infinite number times by drawing random samples from the same populations(s), less than 5% of these results will be more extreme than the current result" (Cassidy et al., 2019, p. 233).

the assumption that no relationship exists between the variables in question

“a logical grouping of attributes that can be observed and measured and is expected to vary from person to person in a population” (Gillespie & Wagner, 2018, p. 9)

summarizes the incompatibility between a particular set of data and a proposed model for the data, usually the null hypothesis. The lower the p-value, the more inconsistent the data are with the null hypothesis, indicating that the relationship is statistically significant.

a range of values in which the true value is likely to be, to provide a more accurate description of their data

Doctoral Research Methods in Social Work Copyright © by Mavs Open Press. All Rights Reserved.

Share This Book

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 05 September 2024

Quantitative classification evaluation model for tight sandstone reservoirs based on machine learning

• Xinglei Song 1 , 2 ,
• Congjun Feng 1 , 2 ,
• Teng Li 3 , 4 , 5 ,
• Qin Zhang 6 ,
• Xinhui Pan 1 , 2 ,
• Mengsi Sun 7 &
• Yanlong Ge 1 , 2  

Scientific Reports volume  14 , Article number:  20712 ( 2024 ) Cite this article

Metrics details

Tight sandstone reservoirs are a primary focus of research on the geological exploration of petroleum. However, many reservoir classification criteria are of limited applicability due to the inherent strong heterogeneity and complex micropore structure of tight sandstone reservoirs. This investigation focused on the Chang 8 tight reservoir situated in the Jiyuan region of the Ordos Basin. High-pressure mercury intrusion experiments, casting thin sections, and scanning electron microscopy experiments were conducted. Image recognition technology was used to extract the pore shape parameters of each sample. Based on the above, through grey relational analysis (GRA), analytic hierarchy process (AHP), entropy weight method (EWM) and comprehensive weight method, the relationship index Q1 between initial productivity and high pressure mercury injection parameters and the relationship index Q2 between initial productivity and pore shape parameters are obtained by fitting. Then a dual-coupled comprehensive quantitative classification prediction model for tight sandstone reservoirs was developed based on pore structure and shape parameters. A quantitative classification study was conducted on the target reservoir, analyzing the correlation between reservoir quality and pore structure and shape parameters, leading to the proposal of favourable exploration areas. The research results showed that when Q1 ≥ 0.5 and Q2 ≥ 0.5, the reservoir was classified as type I. When Q1 > 0.7 and Q2 > 0.57, it was classified as type I 1 , indicating a high-yield reservoir. When 0.32 < Q1 < 0.47 and 0.44 < Q2 < 0.56, was classified as type II. When 0.1 < Q1 < 0.32 and 0.3 < Q2 < 0.44, it was classified as type III. Type I reservoirs exhibit a zigzag pattern in the northwest part of the study area. Thus, the northwest should be prioritized in actual exploration and development. Additionally, the initial productivity of tight sandstone reservoirs showed a positive correlation with the porosity, permeability, sorting coefficient, coefficient of variation, and median radius. Conversely, it demonstrated a negative correlation with the median pressure and displacement pressure. The perimeters of pores, their circularity, and the length of the major axis showed a positive correlation with the porosity, permeability, sorting coefficient, coefficient of variation, and median radius. On the other hand, they exhibited a negative correlation with the median pressure and displacement pressure. This study quantitatively constructed a new classification and evaluation system for tight sandstone reservoirs from the perspective of microscopic pore structure, achieving an overall model accuracy of 93.3%. This model effectively predicts and evaluates tight sandstone reservoirs. It provides new guidance for identifying favorable areas in the study region and other tight sandstone reservoirs.

Similar content being viewed by others

A case study of petrophysical rock typing and permeability prediction using machine learning in a heterogenous carbonate reservoir in Iran

Hierarchical automated machine learning (AutoML) for advanced unconventional reservoir characterization

Permeability modelling in a highly heterogeneous tight carbonate reservoir using comparative evaluating learning-based and fitting-based approaches

Introduction.

With the depletion of conventional oil and gas reservoirs, tight oil reservoirs have gradually become a hot topic and a focal point for exploration and development, both domestically and internationally 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 . However, tight sandstone oil reservoirs exhibit complex reservoir characteristics, primarily manifested in their deep burial depths, wide distribution, and complex depositional processes. The reservoirs exhibit characteristics of low porosity, poor permeability, and high heterogeneity. The dominant pores are micro- and nano-scale, with narrow and dispersed throats, and are unfavorable for the migration and accumulation of oil and gas 10 , 11 , 12 , 13 , 14 , 15 . These factors necessitate considering the interdependent influences of multiple factors when classifying and evaluating tight sandstone reservoirs, which affects the accuracy of reservoir evaluation and hinders the selection of high-quality reservoirs. Therefore, the rapid and effective classification and evaluation of tight sandstone reservoirs has long been a focal point of scholarly research.

The quality of the reservoir is a key factor that determines the oil and gas production capacity. The classification and evaluation of reservoirs are central to reservoir studies and play a significant role in oilfield development. With the continuous advancement of oilfield development technologies, reservoir classification and evaluation methods have become increasingly diverse, gradually evolving from qualitative to quantitative research and from macro-parameter to micro-parameter evaluation. At present, both domestic and international scholars classify reservoirs using two main methods. The first is the traditional classification and evaluation method, which directly uses indicators such as the lithology, physical properties, pore structure, sedimentary facies, and oil and production experiments for classification. For example, Wei et al. classified the tight sandstone reservoirs of the Sha Creek Formation in the central Sichuan Basin based on the transverse relaxation (T 2 ) distribution of nuclear magnetic resonance 16 . Xu et al. studied the characteristics and controlling factors of tight sandstone using thin-section casting, scanning electron microscopy, X-ray diffraction (XRD), and spontaneous imbibition experiments 17 . Wu et al. analyzed the logging response characteristics using core data and electric imaging logging data and identified the reservoir type with the highest industrial production in the study area 18 . Zhang et al. established classification criteria for the third member of the Quan Formation based on mercury injection curves, core physical properties, and sedimentary facies characteristics 19 . Talib et al. quantitatively characterized tight oil and gas reservoirs through rock physics experiments and seismic inversion profiles 20 .

The second approach to reservoir classification involves initially choosing evaluation parameters that align with the geological conditions of the target area. Subsequently, machine learning techniques such as GRA the AHP, the EWM, and fuzzy analysis are employed to assign weight coefficients to each evaluation parameter. Finally, the reservoir is comprehensively scored. For example, Fang et al. proposed an automatic classification and verification method for reservoir types based on k-means clustering and Bayesian discriminant theory, using core logging and logging data from coring wells, combined with physical characteristics such as reservoir deposition and diagenesis 21 . Li et al. classified the Fuyu reservoir using GRA, Q clustering analysis, and discriminant analysis 22 . Wang et al.combined AHP and EWM, used the multi-factor superposition method, and established a new reservoir classification and evaluation method 23 . Fan et al. quantified the weight of evaluation parameters’ contribution to production by combining the relationships between variables and directional good production using the GRA 24 . Niu et al. proposed a new machine learning framework (GCA-CE-MGPK) for shale reservoirs, achieving efficient and accurate multi-scale evaluation of shale reservoirs 25 . In summary, traditional classification and evaluation methods are costly, inefficient and require extensive experimental data. They are mainly suitable for specific regions, making them inadequate for large-scale reservoir evaluation and prediction. Although machine learning techniques can improve efficiency and reduce costs, their accuracy often depends on the optimization of various mathematical methods, leading to high subjectivity in some models and lower overall precision, failing to meet the practical needs of production. Moreover, previous studies have primarily focused on evaluating single factors, lacking the integration of macro and micro perspectives. Based on these, this study combined multiple machine learning methods to directly link actual oilfield production data with micro-scale pore shape and structure parameters, effectively integrating macro and micro parameters.

Given the significant influence of subjective factors on the classification criteria for the quantitative evaluation of conventional reservoirs, adopting a new method for reservoir evaluation is essential. This study focuses on the Chang 8 tight sandstone reservoir in the Jiyuan area of the Ordos Basin, extracting pore shape parameters from 52 rock samples. Combined with the experimental data of high pressure mercury injection and the actual initial production capacity of the oil field. Through GRA, AHP, EWM and comprehensive weight method, the relationship index Q1 between initial productivity and high pressure mercury injection parameters and the relationship index Q2 between initial productivity and pore shape parameters are obtained by fitting. Then a dual-coupled comprehensive quantitative classification prediction model for tight sandstone reservoirs was developed based on pore structure and shape parameters. A quantitative classification study was conducted on the target reservoir, analyzing the correlation between reservoir quality and pore structure and shape parameters, leading to the proposal of favourable exploration areas. This method effectively combined the subjectivity-influenced AHP with the objectivity-influenced EWM to calculate the comprehensive weight coefficient, mitigating the impact of subjective factors and enhancing the model's accuracy. Validation results indicate that the model has an overall accuracy of 93.3%. Therefore, it was an effective tool for predicting and classifying tight sandstone reservoirs. It is significant for further exploration in the study area and other similar reservoirs.

Geological setting

The Ordos Basin is a large, multi-cycle, cratonic basin that formed on the crystalline basement during the Paleoproterozoic–Mesoproterozoic. The Ordos Basin, the second-largest sedimentary basin in China, has experienced five significant stages of sedimentary evolution. These stages include the middle to late Proterozoic rift valley, the early Paleozoic shallow marine platform, the late Paleozoic nearshore plain, the Mesozoic inland lake basin, and Cenozoic peripheral subsidence. This basin is known for its substantial reserves of oil and gas. The Ordos Basin extends across five provinces and regions, namely, Shaanxi, Gansu, Shanxi, Ningxia, and Inner Mongolia. Geographically, it stretches from the Yin Mountains in the north to the Qinling Mountains in the south, and from the Liupan Mountains in the west to the Lvliang Mountains in the east. The basin’s total area is 25 × 10 4 km 2 , with favorable areas covering 9.9 × 10 4 km 2 . The estimated resource volume is 6.2 × 10 12 m 3 , indicating significant exploration and development potential. Based on the basin’s geological nature, tectonic evolution, and structural pattern, the Ordos Basin can be divided into six primary tectonic units: the northern Shaanxi slope, the Tianhuan Depression, the western thrust fault zone, the Yimeng Uplift, the Weihebei Uplift, and the western Shanxi fold belt. The Jiyuan area, located in the central-western part of the Ordos Basin, covers a total area of 1302 km 2 (Fig. 1 a, c). This area spans the two primary tectonic units of the northern Shaanxi slope and the Tianhuan Depression, exhibiting a gently inclined monocline structure towards the west. Since the Mesozoic, the basin has developed thick fluvio-lacustrine deposits. In the Cenozoic, rift valleys were formed around the basin due to fault subsidence. The overall geological conditions are relatively complex, posing challenges for exploration. However, the area is rich in oil and gas resources, indicating favourable exploration prospects 26 , 27 , 28 , 29 . The proven petroleum geological reserves in this area amount to 800 × 10 6 t, with annual crude oil production of 700 × 10 4 t, making it the oilfield with the largest reserves and production levels in the Ordos Basin from the Mesozoic. Existing exploration results indicate that the Chang 8 oil-bearing formation is one of the most favourable hydrocarbon accumulation zones in the Jiyuan area, with a proven favourable oil-bearing area of 1500 km 2 .

( a ) Location of the study area(modified from Tong 29 ), ( b ) columnar diagram of the Chang 8 formation, ( c ) well location distribution map of the study area.

The Chang 8 reservoir is located in the lower part of the Upper Triassic Yan’an Formation. It is primarily composed of grey sandstone and dark black mudstone interbeds. These sedimentary microfacies are predominantly characterized by subaqueous distributary channels and underwater distributary bays, indicating a deposition pattern typical of a shallow-water deltaic environment (Fig.  1 b). Based on the thin-section identification of the study area (Fig.  2 ), the lithology of the Chang 8 reservoir is predominantly composed of fine-grained feldspathic sandstone, feldspathic lithic sandstone, and a small amount of feldspar sandstone. The detrital components in the study area mainly consist of quartz, feldspar, and detritus. The ranges of contents are as follows: the quartz content is 20.1% to 58.6%, with an average of 31.21%; the feldspar content is 23.56% to 57.62%, with an average of 34.43%; and the detritus content is 6.25% to 29.45%, with an average of 21.38%.

Triangular diagram and detrital composition diagram of the study area. ( a ) Triangular classification diagram of the sandstone in the Chang 8 reservoir, ( b ) histogram of the relative content of detrital components in the Chang 8 reservoir.

Materials and statistical methods

Materials and experiments.

In this study, 52 drilling core samples were obtained from the Chang 8 reservoir in Jiyuan, Ordos Basin, with all samples exhibiting a fine sandstone lithology. The samples underwent oil washing, gas permeability measurements, and the weight method for porosity calculation, allowing the determination of the reservoir’s petrophysical parameters (Table 1 ). The samples' average porosity was 8.23%, between 2.41 and 13.6%. The average permeability was 0.18 × 10 –3 µm 2 , ranging between 0.01 × 10 –3 µm 2 and 1.10 × 10 –3 µm 2 . Subsequently, thin-section casting and scanning electron microscopy experiments were conducted, resulting in 300 photographs. Additionally, high-pressure mercury intrusion was performed on the 52 samples to obtain the micropore throat characteristic parameters.

High pressure mercury intrusion and scanning electron microscopy

High pressure mercury intrusion experiment was used to evaluate the micropore throat characteristics of reservoirs quantitatively. This is achieved by observing the pressure changes during mercury injection into the pores, analyzing the characteristics of the capillary pressure curves, and studying the relationship between the intrusion volume of mercury and these characteristics 30 , 31 . In this experiment, the Auto Pore IV 9530 fully automated mercury porosimeter was utilized, with a pore diameter measurement range of 3 nm to 1100 μm. Continuous mercury injection was employed, with volume accuracy of less than 0.1 μl for both injection and withdrawal. The experimental procedure followed the national standard GB/T29171-2012, and the maximum mercury injection pressure reached 95.39 MPa.

Scanning electron microscopy (SEM) allows for high-resolution morphological observation and analysis of samples, as well as structural and compositional characterization. It also enables direct observation of the development characteristics of the micro-pore throats in the reservoir 32 , 33 , 34 . The experiment employed the Japanese Electron JSM-7500F field emission scanning electron microscope, which achieves a secondary-electron image resolution of 1 nm and magnification ranging from 20 to 300,000 times.

Pore parameter extraction technology

The ImageJ software, initially developed by Wayne Rasband at the National Institutes of Health in the United States, is a powerful open-source image processing system written in Java. It was initially applied in the fields of biomedical and agricultural sciences 35 . Recently, an increasing number of scholars have used it to identify and extract reservoir pores and fracture features 36 , 37 , 38 , 39 . In this study, the ImageJ software was used to process 210 scanning electron microscope images, extracting various pore parameters, including the perimeter, circularity, major axis length, aspect ratio, and solidity.

Statistical methodology

GRA is to address infinite space problems using finite sequences. It aims to evaluate the correlations between various factors within a system and determine the significance of each factor to the target function. This approach helps to avoid the subjective process of manually assigning weights to factor indicators 40 . In recent years, GRA has been applied in production forecasting and development plan optimization for tight sandstone reservoirs 41 , 42 , 43 , 44 . The specific steps are as follows.

Determine the initial sequence:

where X 0 is the reference sequence, X i is the comparative sequence, i is the number of comparative sequences, m is the number of independent variables, and n is the number of samples.

Normalize the data using the extreme value method:

Calculate the gray correlation coefficient:

Obtain the gray correlation coefficient matrix:

where ρ is the resolution coefficient, which takes values between 0 and 1. A smaller resolution coefficient indicates greater differences between the correlation coefficients and stronger discriminatory power. Usually, ρ is set to 0.5.

Determine the correlation degree. Represent the correlation strength between the series using the average of the n correlation coefficients:

where \(\mathop \varepsilon \nolimits_{{\mathop o\nolimits_{i} }}\) represents the correlation degree between the i -th comparative sequence and the reference sequence.

Determine the weights and rank the correlation degrees. Normalize the correlation degrees to obtain the weight W i of each comparative sequence:

AHP is a methodology that categorizes the factors within a complex problem into interconnected and prioritized levels. This approach facilitates the process of making decisions based on multiple criteria. It is primarily used to determine the weighting coefficients for comprehensive evaluations 45 , 46 , 47 . The process is as follows.

Construction of a judgment matrix: a judgment matrix is constructed to compare the importance of different factors:

where A is the matrix of pairwise comparisons, W is the weight vector, and λ max is the maximum eigenvalue.

Calculation of weights: the weight vector W is determined using the sum-product method.

Consistency check:

where n is the number of elements, I c is the consistency index, I R is the random consistency index, I cR is the consistency ratio, and \(\lambda^{\prime } \max\) is the average of the maximum eigenvalues.

If I cR  < 0.10, the consistency of the judgment matrix is considered acceptable.

EWM is an objective weighting approach that comprehensively examines the underlying patterns and informational value of unprocessed data. It can determine the uncertainty in variables through entropy values, where larger information content corresponds to smaller uncertainty and smaller entropy, and vice versa. The entropy weighting method is characterized by high accuracy and strong objectivity, and many scholars have applied it to oilfield production with good results 48 , 49 . The basic steps are as follows.

Normalize the data and calculate the information entropy:

where E i is the information entropy of the i th indicator, X ij is the value of the i th indicator on the j th sample, and N is the number of samples.

Calculate the weights:

where W i is the weight of the i th indicator, E i is the information entropy of the i th indicator, and M is the number of indicators.

Comprehensive weight coefficient

Weight coefficients can be used to classify and evaluate the reservoir quality effectively, and several methods are currently available to determine the weight coefficients. These include GRA, the expert evaluation method, Q clustering analysis and discriminant analysis, and factor analysis 50 , 51 , 52 . In this research, a comprehensive weight analysis methodology that integrated AHP and EWM was employed. The key advantage of this approach lies in its amalgamation of the subjective AHP analysis and the objective numerical analysis of EWM. This combination helps to mitigate the influence of subjective factors to a certain extent, thereby enhancing the reliability of the data.

where W iAHP is the weight coefficient obtained from the AHP method, and W iEWM is the weight coefficient obtained from the EWM method.

Results and discussion

Evaluation parameter selection.

Tight sandstone reservoirs are influenced by deposition, tectonics, and diagenesis.. These reservoirs demonstrate significant heterogeneity and an intricate distribution of micropore throats. The pore structure plays a crucial role in governing the storage and flow behaviour of the reservoir, where the different shape parameters of the pores govern the micropore structure of the rock formation 53 , 54 , 55 , 56 , 57 . Considering the characteristics above, this study aimed to provide a quantitative characterization of the reservoir by assessing three key aspects: the pore structure, the physical properties, and the pore shape parameters. Twelve parameters were selected to establish the relationship between the initial production capacity index and the pore structure and shape parameters. The actual initial production capacity of the oilfield was used as the indicator.

Sensitivity parameter selection for pore structure characteristics

The selected 52 samples were subjected to high-pressure mercury intrusion experiments using an Auto Pore IV 9530 automatic mercury porosimeter. The sorting coefficient varied between 1.5 and 2.74, with an average of 2.10. The coefficient of variation ranged between 13.94 and 17.32, with a mean value of 15.54. With an average value of 13.86 MPa, the median pressure varied between 10.5 and 18.79 MPa. The average displacement pressure was 1.23 MPa, ranging between 0.09 and 2.57 MPa. The median radius had a mean value of 0.09 μm and varied from 0.05 to 0.15 μm. With a mean value of 84.52%, the maximum mercury saturation varied from 62.77 to 93.76%. With an average of 34.90%, the mercury withdrawal efficiency varied between 16.7 and 46.6%. Overall, the pore structure of the reservoir in the study area was poor, with uneven sorting and poor connectivity among the pore throats, indicating strong heterogeneity. Correlation analysis was conducted on the initial production and mercury intrusion parameters (Fig.  3 ), and it was found that the correlation between the production capacity and permeability and porosity was the strongest, with correlation coefficients (R 2 ) of 0.91 and 0.75, respectively. This is mainly because porosity plays a crucial role in determining the size of the pore space within a reservoir, while permeability governs its flow capacity. In the context of tight sandstone reservoirs, the reservoir quality often depends on favourable pore permeability. The sorting coefficient and coefficient of variation provide insights into the uniformity of the distribution of the pore throat sizes. Higher values of these parameters indicate an improved pore structure and increased reservoir productivity. The median radius and median pressure indicate the pore permeability of the reservoir. A larger median radius and smaller median pressure indicate a larger pore space and stronger flow capacity, resulting in a larger oil production capacity. Therefore, the median radius positively correlates with production, while the median pressure is inversely correlated. The displacement pressure is inversely correlated with production (R 2  = 0.65). This is because displacement pressure refers to the capillary pressure corresponding to the largest connected pore, and a higher displacement pressure means a higher capillary pressure, making it more difficult for fluid to flow through. This indicates that tight oil has poor flow capacity in the reservoir and is more difficult to accumulate and extract. In conclusion, the initial production capacity is sensitive to the porosity, permeability, sorting coefficient, coefficient of variation, median pressure, median radius, and displacement pressure.

Relationship between initial production and porosity, permeability, selectivity coefficient, coefficient of variation, median pressure, median radius, and displacement pressure.

Selection of pore-shape-sensitive parameters

A total of 210 high-resolution SEM images were captured for the 52 samples. The rock core pores were identified and extracted using ImageJ, obtaining pore shape parameters such as the perimeter, circularity, major axis length, aspect ratio, and solidity (Fig.  4 , Table 2 ). The average values of the identified pore shape parameters for each sample were then calculated. It was found that the pore perimeters of the 52 samples varied between 40.3 and 486.2 μm, with a mean value of 250.5 μm. The circularity ranged between 0.11 and 0.96, with a mean value of 0.31. The major axis lengths of the circumscribed ellipses spanned from 42.52 to 221.19 μm, with an average of 111.67 μm. The aspect ratios ranged from 1.14 to 2.92, and the average value was 2.32. The solidity values ranged between 0.09 and 0.89, with an average of 0.67. In general, the pore shape parameters of the tight sandstone reservoirs exhibited a wide range of variation, with relatively large average perimeters, average major axis lengths of the circumscribed ellipses, aspect ratios, and solidity, and with small average circularity (Fig.  5 ). This indicates that the pore shapes in tight sandstone are diverse, predominantly irregular and elongated, with few circular pores. Pearson correlation analysis was conducted between the most sensitive parameters for the prioritized pore structure characteristics and the extracted pore shape parameters (Fig.  6 ). The absolute value of the correlation coefficient always lies between −1 and 1. In this context, a value closer to 1 indicates a stronger positive relationship between the two independent variables, a value closer to -1 indicates a stronger negative relationship between the independent variables, and a value closer to 0 indicates a weak relationship between the variables. A significant and strong correlation (R 2  > 0.5) observed between the different shape parameters of the pores and the mercury injection parameters. This suggests that the shape parameters of the pores play a crucial role in determining the pore structures of tight sandstone reservoirs. In general, the perimeter, circularity, and major axis length of the pores displayed a positive correlation with the porosity (Φ), permeability (K), sorting coefficient (S p ), coefficient of variation (D r ), and median radius (R50). Conversely, they exhibited a negative correlation with the median pressure (P 50 ) and displacement pressure (Pd). On the other hand, the aspect ratio and solidity of the pores were inversely proportional to the porosity, permeability, sorting coefficient, coefficient of variation, and median radius. However, they were positively correlated with the median pressure and displacement pressure. Among them, there was a strong positive correlation (R 2  = 0.914) between the perimeter and porosity and a relatively strong negative correlation (R 2  = –0.766) with the displacement pressure. A larger pore perimeter results in a greater contact area between the reservoir fluid and the solid, facilitating fluid infiltration and storage. Circularity was strongly positively correlated with permeability (R 2  = 0.927) and negatively correlated with the displacement pressure (R 2  = –0.604). This is because larger circularity indicates a closer approximation to circular pores, which typically exhibit a uniform distribution, resulting in improved connectivity and fluid flow. The major axis length was strongly positively correlated with the permeability and porosity because the major axis length of the circumscribed ellipses of pores affects the connectivity and fluid flow path within the pores. A larger major axis length indicates better connectivity between pores, resulting in a more direct fluid flow path and higher permeability. Moreover, a longer major axis length corresponds to a larger pore size and higher porosity. The aspect ratio exhibited a strong negative correlation with the permeability and selectivity coefficient (R 2  = –0.866, R 2  = –0.754, respectively) and a strong positive correlation with the displacement pressure (R 2  = 0.652). As the aspect ratio increases, the pores become narrower and more uneven, resulting in longer and narrower flow channels, making fluid flow more difficult. As a result, the displacement pressure increases, the selectivity coefficient decreases, and the permeability decreases. Solidity exhibited a strong negative correlation with permeability (R 2  = –0.862) and a positive correlation with the displacement pressure (R 2  = 0.574). As the solidity increases, the pore shape becomes more concave, and the roundness deteriorates, making fluid flow between the pores more difficult. In conclusion, it can be observed that the perimeter, circularity, major axis of the circumscribed ellipse, aspect ratio, and solidity of the pores are sensitive to various parameters of mercury intrusion.

Visualization of pore extraction results for rock samples. ( A ) Pore identification (sample no. 1), ( B ) pore extraction (sample no. 1), ( C ) pore identification (sample no. 10), ( D ) pore extraction (sample no. 10), ( E ) pore identification (sample no. 25), ( F ) pore extraction (sample no. 25).

Distribution of pore shape parameters. ( a ) Distribution range of pore perimeter and major axis, ( b ) distribution range of pore circularity, solidity, and aspect ratio.

Correlations between pore structural parameters and pore shape parameters.

Reservoir classification evaluation

Quantitative classification prediction formula.

Based on the results of the GRA, AHP, and EWM, a comprehensive quantitative classification prediction formula was constructed using the superposition principle. This formula was then used to classify and evaluate tight sandstone reservoirs.

where Q is the productivity index, a i is the dimensionless weight coefficients of various parameters, b i,N is the dimensionless normalized parameters, and n is the number of parameters.

Determination of weight coefficients

In this study, the initial production rate directly reflecting the reservoir quality was taken as the fundamental sequence. Seven sensitive parameters, namely, the porosity, permeability, sorting coefficient, coefficient of variation, median pressure, median radius, and displacement pressure, were considered as sub-sequences. The principles and steps of GRA were employed to determine the weights of various parameters, thereby assessing the sensitivity of each factor to the initial production rate (Table 3 ). Combining the correlation degree between the sensitive parameters determined by the gray correlation method and the initial productivity. Then, the parameters were compared in pairs, and values were assigned based on the 9-point scale method. The judgment matrix was obtained by pairwise comparisons of the seven sensitive parameters (Table 4 ). Subsequently, the weight coefficients were determined using the weighted product method within the AHP (Table 5 ). Formula ( 14 ) shows that the judgment matrix I cR  = 0.093 is less than 0.1, meeting the consistency requirements. Subsequently, the EWM analysis method was employed to conduct an objective analysis of each sensitive parameter, resulting in objective weight indices. The comprehensive weight coefficients were calculated using Eq. ( 17 ) (Table 5 ). The formula for the initial productivity and the mercury intrusion sensitivity parameter can be obtained as follows:

where Φ N is the normalized porosity, K N is the normalized permeability, S P,N is the normalized sorting coefficient, Dr, N is the normalized coefficient of variation, P 50,N is the normalized median pressure, R 50,N is the normalized median radius, and P d,N is the normalized displacement pressure.

Then, using the mercury intrusion parameter as the fundamental sequence, five sensitive parameters related to the pore shape, namely, the perimeter, circularity, major axis length, aspect ratio, and solidity, were considered sub-sequences. The correlation between the mercury intrusion parameters and the pore-shape-sensitive parameters was determined using GRA. The comprehensive weight coefficients for each mercury intrusion parameter were calculated using a combination of the AHP and the EWM (Table 6 ). Based on these weight coefficients, the correlation formulas between each mercury intrusion parameter and the pore shape parameters were obtained as follows:

Combined with Formula ( 19 ), the relationship between the initial productivity and pore shape parameters can be obtained:

where P N is the normalized perimeter, C N is the normalized circularity, M N is the normalized major axis, A N is the normalized aspect ratio, and S N is the normalized solidity.

Classification scheme and feature evaluation

Based on the indices Q1, which relate initial productivity to high-pressure mercury intrusion sensitivity parameters, and Q2, which relate initial productivity to pore shape parameters, a classification and evaluation scheme for the Chang 8 tight sandstone reservoir have been determined. As depicted in Fig.  7 , Q1 for type III reservoirs ranges from 0.1 to 0.31, and Q2 ranges between 0.3 and 0.44. For type II reservoirs, Q1 ranges from 0.32 to 0.47, and Q2 ranges from 0.44 to 0.56. For type I reservoirs, Q1 ≥ 0.5 and Q2 ≥ 0.5. Moreover, type I reservoirs can be further divided into type I 1 , comprising high-yield reservoirs, and type I 2 , comprising high-quality reservoirs, with Q1 > 0.7 and Q2 > 0.57 indicating type I 1 high-yield reservoirs. Type I reservoirs are considered optimal for the Chang 8 formation, with 15 out of 52 samples belonging to this type, accounting for 28.8%. The characteristics associated with this type of reservoir include favourable pore permeability, featuring an average porosity of 11.1% and permeability of 0.4 × 10 –3  µm 2 . Additionally, these reservoirs possess a low displacement pressure of 0.62 MPa, a low median pressure of 11.79 MPa, and a relatively high median radius of 0.12 µm. The reservoir exhibits good pore throat selectivity, characterized by a large sorting coefficient (2.5) and variation coefficient (16.43). The average pore perimeter of the reservoir is relatively long (360.30 µm), with good circularity (0.50) and a small aspect ratio (1.92). This indicates that the pore shape is more regular and almost circular. Generally, type II displays moderate petrophysical characteristics, characterized by an average porosity of 8.43% and permeability of 0.1 × 10 –3 µm 2 . Within this classification, 19 samples contribute to 36.54% of the dataset. Compared to type I, this reservoir type has a somewhat higher average displacement pressure and median pressure (1.11 MPa and 13.48 MPa, respectively). The median radius is lower (0.10 µm), and the average sorting coefficient and coefficient of variation are 2.41 and 16.18, respectively, indicating moderate sorting. The average pore perimeter of this reservoir type is smaller than that of type I (261.61 µm), with smaller circularity (0.26) and a larger aspect ratio (2.41). Compared to type I, the pores of type II reservoirs exhibit irregular and more elongated shapes. Type III exhibits poorer petrophysical properties, with an average permeability of 0.06 × 10 –3 μm 2 and porosity of 5.7%, significantly lower than those of type I and type II. There were 18 samples belonging to this type, accounting for 34.62%. This reservoir type has an average displacement pressure of 1.89 MPa and a median pressure of 16.1 MPa, greater than type II. The median radius is the smallest (0.07 µm). The average sorting coefficient and coefficient of variation are 1.81 and 14.7, respectively, indicating poor pore throat sorting. The average pore perimeter is the smallest (147.37 µm), with the poorest circularity (0.19) and the largest aspect ratio (2.56). This indicates that the pores of type III reservoirs are more elongated and slender, making them unfavorable for fluid flow and leading to poor reservoir permeability. In summary, it can be observed that as the reservoir quality deteriorates, the pore structure becomes increasingly worse, and the pore shapes become more complex and variable.

Comprehensive quantitative classification prediction model for the research area of the Chang 8 reservoir.

According to the distribution maps of the well locations and sedimentary microfacies (Figs.  1 c, 8 ), it is observed that type I reservoir wells are mostly found in the northwest of the research region, within the subaqueous distributary channels, exhibiting a zigzag pattern. Most type II reservoir wells are located in the study area's centre, mainly within the middle portions of the subaqueous distributary channel's lateral sand bodies. On the other hand, the relatively poor type III reservoir wells are scattered around the type II reservoirs, with most of them located in the marginal areas adjacent to the interdistributary bay and the edge of the channel’s lateral sand bodies. Therefore, in practical exploration and development, the high-quality reservoirs (type I) in the study area's northwest part should be prioritised.

Planar distribution map of comprehensive quantitative classification for the research area of the Chang 8 reservoir.

Additionally, the main reason for the high productivity of type I 1 reservoirs is the higher content of dissolved pores in type I reservoirs. According to Table 7 and Fig.  9 , samples 3, 15, 16, and 20 from type I reservoirs exhibit significant development of feldspar dissolution pores, intergranular pores, and a small number of rock particles that dissolve pores. The average absolute contents of feldspar dissolution and intergranular pores are 1.2% and 5.15%, respectively. The average face rate is 0.8%, higher than the other samples. The greater the development of feldspar dissolution and intergranular pores, the larger the flow channels and storage space they provide, thus improving the reservoir’s porosity and permeability, resulting in high-productivity reservoirs. The pore shape parameters of samples 3, 15, 16, and 20 were compared with those of the other samples (Table 2 ). It was found that these four samples have longer pore perimeters and major axes, larger shape factor (roundness) coefficients, and relatively smaller aspect ratios and concavity. This indicates that high-productivity reservoirs (type I 1 ) have larger pore perimeters, an increased contact area between the pores and reservoir fluids, higher pore circularity, and more circular shapes favourable for fluid flow and storage. Furthermore, as shown in Fig.  8 , the four high-productivity wells (JY-3, JY-15, JY-16, JY-20) are all located on the main channel of the subaqueous distributary channel. Therefore, from a macro perspective, thicker sand bodies may be another reason for their high productivity.

Porosity structure of type I 1 reservoir. ( A ) Intergranular pores, developed dissolution pores (sample no. 3), ( B ) feldspar dissolution pores (sample no. 20), ( C ) rock fragment dissolution pores (sample no. 15), ( D ) intergranular pores, locally developed dissolution pores (sample no. 16).

Model validation

In order to verify the model, 15 coring wells in Jiyuan Chang 8 reservoir were selected. High-pressure mercury intrusion tests, scanning electron microscopy, and thin-section casting experiments were conducted on corresponding samples to extract the pore shape parameters. Next, the comprehensive indices Q1 and Q2, for reservoir categorization, were determined using the GRA, the AHP, and the EWM. Finally, the accuracy of the classification results was compared with that of the existing oil test parameters. As shown in Fig.  10 , three wells were classified as type I reservoirs, with an average initial yield of 5.73 t/d. Six wells were classified as type II reservoirs, with an average initial yield lower than type I at 3.52 t/d. One well was misclassified, deviating from the expected value. Five wells were classified as type III reservoirs, with the lowest average initial yield of 1.32 t/d. The quantitative evaluation of the comprehensive parameters matched the actual production capacity results, demonstrating a high matching rate of 93.3%. Compared to conventional models by other scholars for tight sandstone reservoirs, this model establishes a direct connection between actual oilfield production data, microscale pore shape parameters, and pore structure parameters, leading to quantitative reservoir classification evaluation 58 , 59 , 60 . It demonstrates higher and more stable classification accuracy.

Comparative analysis of the integrated quantitative classification prediction for the Chang 8 reservoir.

Conclusions

Tight sandstone reservoirs display significant heterogeneity and intricate microscopic pore structures, which impact the accuracy of reservoir assessment. This study employed scanning electron microscopy, thin section analysis, and high-pressure mercury intrusion data as samples. It utilized image recognition technology and machine learning methods to develop a novel classification and evaluation system for tight sandstone reservoirs based on microscopic pore structures. This method utilizes minimal experimental data, is cost-effective, demonstrates relatively high model accuracy, and is particularly suitable for tight sandstone reservoirs. The research conclusions are as follows:

By analyzing high pressure mercury parameters, scanning electron microscopy images, and thin sections of the study area in the Chang 8 reservoir, a comprehensive quantitative classification prediction model for tight sandstone reservoirs was established. The model was constructed using twelve sensitive parameters: porosity, permeability, sorting coefficient, coefficient of variation, median pressure, median radius, displacement pressure, pore perimeter, circularity, major axis length, aspect ratio, and solidity, all extracted using image recognition technology.

The case study based on the comprehensive quantitative classification prediction model showed that Q1 ≥ 0.5 and Q2 ≥ 0.5 corresponded to type I reservoirs, while Q1 > 0.7 and Q2 > 0.57 corresponded to type I 1 high-yield reservoirs. When 0.32 < Q1 < 0.47 and 0.44 < Q2 < 0.56, a type II reservoir was identified. When 0.1 < Q1 < 0.32 and 0.3 < Q2 < 0.44, a type III reservoir was identified. Additionally, the presence of high-content dissolution pores, intergranular pores, and larger pore perimeters, as well as higher pore circularity, were the main factors contributing to high-yield reservoirs (type I 1 ). The model was validated, achieving an overall accuracy of 93.3%, which indicates its effectiveness in predicting the classification and evaluation of tight reservoirs.

Reservoir quality is influenced by the pore structure characteristics and shape parameters. In tight sandstone reservoirs, the productivity is positively correlated with the porosity, permeability, sorting coefficient, coefficient of variation, and median radius, but negatively correlated with the median pressure and displacement pressure. The perimeter, circularity, and major axis length of the pores are positively correlated with the porosity, permeability, sorting coefficient, coefficient of variation, and median radius, but negatively correlated with the median pressure and displacement pressure.

Type I reservoir wells were primarily found in the northwest of the research region, within the subaqueous distributary channels, exhibiting a zigzag pattern. The majority of type II reservoir wells were located in the study area's center, mostly within the middle portions of the subaqueous distributary channel’s lateral sand bodies. In contrast, the relatively inferior type III reservoir wells were dispersed among the type II reservoirs, primarily situated in the marginal zones bordering the interdistributary bay and the periphery of the channel’s lateral sand bodies. Therefore, in terms of practical exploration and development, priority should be given to the superior reservoirs (type I) in the northwestern sector of the research region.

The evaluation results of the quantitative classification of tight sandstone reservoirs using machine learning are generally consistent with previous multiparameter conventional evaluation studies. However, this approach effectively integrates macroscopic and microscopic parameters, resulting in higher model accuracy, easier operation, and lower costs. It is particularly suitable for large-scale quality assessments of tight sandstone reservoirs, offering essential guidance for further exploration in the study area and other similar reservoirs.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Abbreviations

Analytic hierarchy process

Grey relational analysis

Entropy weight method

X-ray diffraction

Scanning electron microscopy

Fine-grained lithic feldspar sandstone

Fine-grained feldspar lithic sandstone

Fine-grained feldspar sandstone

Grey correlation analysis, clustering ensemble, and the Kriging model combined with macro geological parameters

Ledingham, & Glen, W. Santigo, pool. California: Geological note. AAPG Bull. 31 (11), 2063–2067 (1947).

Google Scholar  

Wang, Q. R., Tao, S. Z. & Guan, P. Progress in research and exploration & development of shale oil in continental basins in China. Nat. Gas Geosci. 31 (3), 417–427 (2020).

Zou, C. N. et al. Geological concepts, characteristics, resource potential and key techniques of unconventional hydro-carbon: On unconventional petroleum geology. Pet. Explor. Dev. 40 (4), 385–399 (2013).

Article   Google Scholar  

Zou, C. N. et al. Progress in China’s unconventional oil & gas exploration and development and theoretical technologies. Acta Geol. Sin. 89 (6), 979–1007 (2015).

CAS   Google Scholar  

Zhou, N. et al. Limits and grading evaluation criteria of tight oil reservoirs in typical continental basins of China. Petrol. Explor. Dev. 48 (05), 1089–1100 (2021).

Zhao, W. et al. Types and resource potential of continental shale oil in China and its boundary with tight oil. Petrol. Explor. Dev. 47 (01), 1–11 (2020).

Article   CAS   Google Scholar  

Sun, L. et al. Development characteristics and orientation of tight oil and gas in China. Petrol. Explor. Dev. 46 (06), 1073–1087 (2019).

Xiang, F. et al. Classification evaluation criteria and exploration potential of tight oil resources in key basins of China. J. Nat. Gas Geosci. 4 (6), 309–319 (2019).

Gao, X., Chen, J., Xu, R., Zhen, Z., Zeng, X., Chen, X. & Cui, L. Research progress and prospect of the materials of bipolar plates for proton exchange membrane fuel cells (PEMFCs)[J]. International Journal of Hydrogen Energy . 50 , 711–743 (2024).

Article   ADS   CAS   Google Scholar  

Wang, J., Wu, S., Li, Q. & Guo, Q. An investigation into pore structure fractal characteristics in tight oil reservoirs: A case study of the Triassic tight sandstone with ultra-low permeability in the Ordos Basin, China. Arab. J. Geosci. 13 (18), 961 (2020).

Gao, H., Cao, J., Wang, C., He, M., Dou, L., Huang, X. & Li, T. Comprehensive characterization of pore and throat system for tight sandstone reservoirs and associated permeability determination method using SEM, rate controlled mercury and high pressure mercury. J. Petrol. Sci. Eng . 174 (2018).

Gao, H. et al. Effect of pressure pulse stimulation on imbibition displacement within a tight sandstone reservoir with local variations in porosity. Geoenergy Sci. Eng. 226 , 211811 (2023).

Wang, C., Gao, H., Gao, Y. & Fan, H. Influence of pressure on spontaneous imbibition in tight sandstone reservoirs. Energy Fuels 34 (8), 9275–9282 (2020).

Wang, C., Li, T., Gao, H., Zhao, J. & Gao, Y. Quantitative study on the blockage degree of pores due to asphaltene precipitation in low-permeability reservoirs with NMR technique. J. Petrol. Sci. Eng. 163 , 703–711 (2018).

Gao, H. et al. Effects of pore structure and salinity on the imbibition of shale samples using physical simulation and NMR technique: A case from Chang 7 shale, Ordos basin. Simulation. 97 (2), 167–173 (2021).

Wei, H. et al. Classification of tight sandstone reservoirs based on the nuclear magnetic resonance T 2 distribution: A case study on the Shaximiao Formation in Central Sichuan, China. Energy Fuels 36 , 10803–10812 (2022).

Xu, J. et al. Characteristics and controlling factors of tight gas sandstones from the Upper Shanxi and Lower Shihezi Formations in the Northern Sulige Area, Ordos Basin, China. Energy Fuels 37 (20), 15712–15729 (2023).

Wu, X. et al. A novel evaluation method of dolomite reservoir using electrical image logs: The Cambrian dolomites in Tarim Basin, China. Geoenergy Sci. Eng. 233 , 212509 (2024).

Zhang, Q. et al. Comprehensive evaluation and reservoir classification in the Quan 3 member of the Cretaceous Quantou Formation in the Fuxin Uplift, Songliao Basin. Front. Earth Sci. 10 , 1016924 (2022).

Article   ADS   Google Scholar  

Talib, M., Durrani, M. Z. A., Palekar, A. H., Sarosh, B. & Rahman, S. A. Quantitative characterization of unconventional (tight) hydrocarbon reservoir by integrating rock physics analysis and seismic inversion: A case study from the Lower Indus Basin of Pakistan. Acta Geophys. 70 (6), 2715–2731 (2022).

Fang, X., Zhu, G., Yang, Y., Li, F. & Feng, H. Quantitative method of classification and discrimination of a porous carbonate reservoir integrating k-means clustering and Bayesian theory. Acta Geol. Sin. (Beijing) 97 (1), 176–189 (2023).

Li, Y. et al. Microscopic pore-throat grading evaluation in a tight oil reservoir using machine learning: A case study of the Fuyu oil layer in Bayanchagan area, Songliao Basin central depression. Earth Sci. Inform. 14 (2), 601–617 (2021).

Wang, Z. et al. Quantitative evaluation of unconsolidated sandstone heavy oil reservoirs based on machine learning. Geol. J. (Chichester, England). 58 (6), 2321–2341 (2023).

Fan, J., Shi, J., Wan, X., Xie, Q. & Wang, C. Classification evaluation method for Chang 7 oil group of Yanchang formation in Ordos Basin. J. Pet. Explor. Prod. Te. 12 , 825–834 (2021).

Niu, D. et al. Multi-scale classification and evaluation of shale reservoirs and “sweet spot” prediction of the second and third members of the Qingshankou Formation in the Songliao Basin based on machine learning. J. Petrol Sci. Eng. 216 , 110678 (2022).

Li, C. et al. Oil charging pore throat threshold and accumulation effectiveness of tight sandstone reservoir using the physical simulation experiments combined with NMR. J. Petrol. Sci. Eng. 208 , 109–338 (2022).

Li, S. et al. The dissolution characteristics of the Chang 8 tight reservoir and its quantitative influence on porosity in the Jiyuan area, Ordos Basin, China. J. Nat. Gas Geosci. 3 (2), 95–108 (2018).

Song, X. et al. Analysis of the influence of micro-pore structure on oil occurrence using nano-CT scanning and nuclear magnetic resonance technology: An example from Chang 8 tight sandstone reservoir, Jiyuan, Ordos Basin. Processes 11 , 11274 (2023).

Tong, Q. et al. Research on sand body architecture at the intersection of a bidirectional sedimentary system in the Jiyuan area of Ordos Basin. Sci. Rep. 13 , 12261 (2023).

Fu, S. et al. Accurate characterization of full pore size distribution of tight sandstones by low-temperature nitrogen gas adsorption and high-pressure mercury intrusion combination method. Energy Sci. Eng. 9 (1), 80–100 (2021).

Li, P. et al. Occurrence characteristics and main controlling factors of movable fluids in Chang 81 reservoir, Maling Oilfield, Ordos Basin, China. J. Petrol. Explor. Prod. Technol. 9 (1), 17–29 (2018).

Li, C., Chen, G., Li, X., Zhou, Q. & Sun, Z. The occurrence of tight oil in the Chang 8 lacustrine sandstone of the Huaqing area, Ordos Basin, China: Insights into the content of adsorbed oil and its controlling factors. J. Nat. Gas Geosci. 7 (1), 27–37 (2022).

Gong, Y. & Liu, K. Pore throat size distribution and oiliness of tight sands-A case study of the Southern Songliao Basin, China. J. Petrol. Sci. Eng. 184 , 106508 (2020).

Liu, Y. et al. A novel experimental investigation on the occurrence state of fluids in microscale pores of tight reservoirs. J. Petrol. Sci. Eng. 196 , 107656 (2021).

Sandhya, N. & Baviskar, A. A quick & automated method for measuring. Am. Biol. Teach. 73 (9), 554–556 (2011).

Curtis, M. E., Cardott, B. J. & Sondergeld, C. H. Development to for organic porosity in the Woodford shale with increasing thermal maturity. Int. J. Coal Geol. 26 (31), 26–30 (2012).

Keller, L. M., Schuetz, P. & Erni, R. Characterization of multi-scale micro-structural features in opalinus clay. Microporous Mesoporous Mater. 83 , 84–90 (2013).

Jin, L. et al. Evolution of porosity and geochemistry in Mar cell us formation black shale during weathering. Chem. Geol. 50 , 51–56 (2013).

Rine, J. M. et al. Comparison of porosity distribution with in selected north American shale units by SEM examination of argon-ion-milled samples. Electron Microsc. Shale Hydrocarbon Reserv. AAPG Memoir. 102 , 137–152 (2013).

Zhao, J. Y. et al. A quantitative evaluation for well pattern adaptability in ultra-low permeability oil reservoirs: A case study of Triassic Chang 6 and Chang 8 reservoirs in Ordos Basin. Pet. Explor. Dev. 45 (3), 482–488 (2018).

Dong, Q., Dai Yin, Y. & Ya Zhou, Z. Fine classification of ultra-low permeability reservoirs around the Placanticline of Da Qing oilfield (PR of China). J. Petrol. Sci. Eng. 174 , 1042–1052 (2019).

Gao, Y. et al. Application of an analytic hierarchy process to hydro-carbon accumulation coefficient estimation. Petrol. Sci. 7 (3), 337–346 (2010).

Liu, Y. et al. A reservoir quality evaluation approach for tight sandstone reservoirs based on the gray correlation algorithm: A case study of the Chang 6 layer in the W area of the as oilfield, Ordos Basin. Energy Explor. Exploit. 39 (4), 1027–1056 (2021).

Shi, B., Chang, X., Yin, W., Li, Y. & Mao, L. Quantitative evaluation model for tight sandstone reservoirs based on statistical methods—A case study of the Triassic Chang 8 tight sandstones, Zhenjing area, Ordos Basin, China. J. Petrol. Sci. Eng. 173 , 601–616 (2019).

Liu, B. The analytic hierarchy process for the reservoir evaluation in Chaoyanggou oilfield. Adv. Petrol. Explor. Dev. 6 , 46–50 (2014).

Shang, Y. Z. Application of analytical hierarchy process in the low-grade oil reservoirs evaluation. Daqing Petrol. Geol. Oilfield Dev. 33 , 55–59 (2014).

Xi, Y. et al. Application of analytic hierarchy process in mineral prospecting prediction based on an integrated geology—aerogeophysics—geochemistry model. Minerals 13 (7), 978 (2023).

Lai, F. et al. Crushability evaluation of shale gas reservoir based on analytic hierarchy process. Spec. Oil Gas Reserv. 25 (3), 154–159 (2018).

Elhaj, M.A., Imtiaz, S. A., Naterer, G. F. & Zendehboudi, S. Production optimization of hydrocarbon reservoirs by entropy generation minimization. J. Nat. Gas Sci. Eng . 83 , 103538 (2020).

Szabo, N. P. et al. Cluster analysis of core measurements using heterogeneous data sources: An application to complex Miocene reservoirs. J. Petrol. Sci. Eng. 178 , 575–585 (2019).

Oliveira, G. P., Santos, M. D. & Roque, W. L. Constrained clustering approaches to identify hydraulic flow units in petroleum reservoirs. J. Petrol. Sci. Eng. 186 , 106732 (2020).

Jia, A., Wei, Y. & Jin, Y. Progress in key technologies for evaluating marine shale gas development in China. Petrol. Explor. Dev. 43 (6), 1035–1042 (2016).

Xiao, L., Bi, L., Yi, T., Lei, Y. & Wei, Q. Pore structure characteristics and influencing factors of tight reservoirs controlled by different provenance systems: A case study of the Chang 7 members in Heshui and Xin’anbian of the Ordos Basin. Energies 16 , 34108 (2023).

Dong, J. et al. Pore structure and fractal characteristics of tight sandstone: A case study for Huagang Formation in the Xihu Sag, East China Sea Basin, China. Energies 16 , 20134 (2023).

Gao, J. et al. Study on the coupling law between pore-scale fluid flow capacity and pore-throat configuration in tight sandstone reservoirs. Geofluids 2023 (1), 1693773 (2023).

Zhang, R. et al. Microscopic pore structures and their controlling factors of the lower carboniferous Luzhai Shale in Guizhong depression, China. Geofluids 2023 , 8890709 (2023).

Du, M. et al. Study on the quantitative characterization and heterogeneity of pore structure in deep ultra-high pressure tight glutenite reservoirs. Minerals 13 , 6015 (2023).

Wu, B. H. et al. Integrated classification method of tight sandstone reservoir based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means. Petrol. Sci. 20 (5), 2747–2758 (2023).

Lu, X., Xing, X., Hu, K. & Zhou, B. Classification and evaluation of tight sandstone reservoirs based on MK-SVM. Processes. 11 (9), 2678 (2023).

Qiu, X. et al. Quantitative evaluation of reservoir quality of tight oil sandstones in Chang 7 member of Ordos Basin. Front. Earth Sci. 10 , 1046489 (2023).

Download references

Acknowledgements

This research was sponsored by Natural Science Basic Research Plan in Shaanxi Province of China (Grant No. 2017JM4013; Grant No. 2020JQ-798).

Author information

Authors and affiliations.

State Key Laboratory of Continental Dynamics, Northwest University, Xi’an, 710069, China

Xinglei Song, Congjun Feng, Xinhui Pan & Yanlong Ge

Department of Geology, Northwest University, No. 229, Taibai North Road, Xi’an, 710069, Shaanxi, China

School of Petroleum Engineering, Xi’an Shiyou University, Xi’an, 710065, China

Engineering Research Center of Development and Management for Low to Ultra-Low Permeability Oil & Gas Reservoirs in West China, Ministry of Education, Xi’an, 710065, China

Xi’an Key Laboratory of Tight Oil (Shale Oil) Development, Xi’an, 710065, China

PetroChina Research Institute of Petroleum Exploration & Development, Beijing, 100083, People’s Republic of China

School of Petroleum Engineering and Environmental Engineering, Yan’an University, Yan’an, 716000, China

You can also search for this author in PubMed   Google Scholar

Contributions

Xinglei Song: Investigation, Formal analysis, Conceptualization, Data Curation, Writing-Original Draft; Congjun Feng: Writing-Review & Editing, Supervision, Funding acquisition,Methodology; Teng Li: Investigation, Resources, Data Curation, Writing-Review & Editing; Qin Zhang: Investigation, Resources, Data Curation; Xinhui Pan: Supervision, Project administration; Mengsi Sun: Supervision, Writing-Review & Editing, Project administration; Yanlong Ge: Investigation, Resources, Data Curation. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Congjun Feng .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Song, X., Feng, C., Li, T. et al. Quantitative classification evaluation model for tight sandstone reservoirs based on machine learning. Sci Rep 14 , 20712 (2024). https://doi.org/10.1038/s41598-024-71351-0

Download citation

Received : 22 April 2024

Accepted : 27 August 2024

Published : 05 September 2024

DOI : https://doi.org/10.1038/s41598-024-71351-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Tight sandstone
• Pore structure
• Quantitative evaluation
• High-pressure mercury injection
• Image recognition
• Machine learning

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

• Harvard Library
• Research Guides
• Faculty of Arts & Sciences Libraries

Library Support for Qualitative Research

• Data Analysis
• Types of Interviews
• Recruiting & Engaging Participants
• Interview Questions
• Conducting Interviews
• Recording & Transcription

QDA Software

Coding and themeing the data, data visualization, testing or generating theories.

• Managing Interview Data
• Finding Extant Interviews
• Past Workshops on Interview Research
• Methodological Resources
• Remote & Virtual Fieldwork
• Data Management & Repositories
• Campus Access
• Free download available for Harvard Faculty of Arts and Sciences (FAS) affiliates
• Desktop access at Lamont Library Media Lab, 3rd floor
• Desktop access at Harvard Kennedy School Library (with HKS ID)
• Remote desktop access for Harvard affiliates from  IQSS Computer Labs . Email them at  [email protected] and ask for a new lab account and remote desktop access to NVivo.
• Virtual Desktop Infrastructure (VDI) access available to Harvard T.H. Chan School of Public Health affiliates.

Qualitative data analysis methods should flow from, or align with, the methodological paradigm chosen for your study, whether that paradigm is interpretivist, critical, positivist, or participative in nature (or a combination of these). Some established methods include Content Analysis, Critical Analysis, Discourse Analysis, Gestalt Analysis, Grounded Theory Analysis, Interpretive Analysis, Narrative Analysis, Normative Analysis, Phenomenological Analysis, Rhetorical Analysis, and Semiotic Analysis, among others. The following resources should help you navigate your methodological options and put into practice methods for coding, themeing, interpreting, and presenting your data.

• Users can browse content by topic, discipline, or format type (reference works, book chapters, definitions, etc.). SRM offers several research tools as well: a methods map, user-created reading lists, a project planner, and advice on choosing statistical tests.  
• Abductive Coding: Theory Building and Qualitative (Re)Analysis by Vila-Henninger, et al.  The authors recommend an abductive approach to guide qualitative researchers who are oriented towards theory-building. They outline a set of tactics for abductive analysis, including the generation of an abductive codebook, abductive data reduction through code equations, and in-depth abductive qualitative analysis.  
• Analyzing and Interpreting Qualitative Research: After the Interview by Charles F. Vanover, Paul A. Mihas, and Johnny Saldana (Editors)   Providing insight into the wide range of approaches available to the qualitative researcher and covering all steps in the research process, the authors utilize a consistent chapter structure that provides novice and seasoned researchers with pragmatic, "how-to" strategies. Each chapter author introduces the method, uses one of their own research projects as a case study of the method described, shows how the specific analytic method can be used in other types of studies, and concludes with three questions/activities to prompt class discussion or personal study.   
• "Analyzing Qualitative Data." Theory Into Practice 39, no. 3 (2000): 146-54 by Margaret D. LeCompte   This article walks readers though rules for unbiased data analysis and provides guidance for getting organized, finding items, creating stable sets of items, creating patterns, assembling structures, and conducting data validity checks.  
• "Coding is Not a Dirty Word" in Chapter 1 (pp. 1–30) of Enhancing Qualitative and Mixed Methods Research with Technology by Shalin Hai-Jew (Editor)   Current discourses in qualitative research, especially those situated in postmodernism, represent coding and the technology that assists with coding as reductive, lacking complexity, and detached from theory. In this chapter, the author presents a counter-narrative to this dominant discourse in qualitative research. The author argues that coding is not necessarily devoid of theory, nor does the use of software for data management and analysis automatically render scholarship theoretically lightweight or barren. A lack of deep analytical insight is a consequence not of software but of epistemology. Using examples informed by interpretive and critical approaches, the author demonstrates how NVivo can provide an effective tool for data management and analysis. The author also highlights ideas for critical and deconstructive approaches in qualitative inquiry while using NVivo. By troubling the positivist discourse of coding, the author seeks to create dialogic spaces that integrate theory with technology-driven data management and analysis, while maintaining the depth and rigor of qualitative research.   
• The Coding Manual for Qualitative Researchers by Johnny Saldana   An in-depth guide to the multiple approaches available for coding qualitative data. Clear, practical and authoritative, the book profiles 32 coding methods that can be applied to a range of research genres from grounded theory to phenomenology to narrative inquiry. For each approach, Saldaña discusses the methods, origins, a description of the method, practical applications, and a clearly illustrated example with analytic follow-up. Essential reading across the social sciences.  
• Flexible Coding of In-depth Interviews: A Twenty-first-century Approach by Nicole M. Deterding and Mary C. Waters The authors suggest steps in data organization and analysis to better utilize qualitative data analysis technologies and support rigorous, transparent, and flexible analysis of in-depth interview data.  
• From the Editors: What Grounded Theory is Not by Roy Suddaby Walks readers through common misconceptions that hinder grounded theory studies, reinforcing the two key concepts of the grounded theory approach: (1) constant comparison of data gathered throughout the data collection process and (2) the determination of which kinds of data to sample in succession based on emergent themes (i.e., "theoretical sampling").  
• “Good enough” methods for life-story analysis, by Wendy Luttrell. In Quinn N. (Ed.), Finding culture in talk (pp. 243–268). Demonstrates for researchers of culture and consciousness who use narrative how to concretely document reflexive processes in terms of where, how and why particular decisions are made at particular stages of the research process.   
• The Ethnographic Interview by James P. Spradley  “Spradley wrote this book for the professional and student who have never done ethnographic fieldwork (p. 231) and for the professional ethnographer who is interested in adapting the author’s procedures (p. iv) ... Steps 6 and 8 explain lucidly how to construct a domain and a taxonomic analysis” (excerpted from book review by James D. Sexton, 1980). See also:  Presentation slides on coding and themeing your data, derived from Saldana, Spradley, and LeCompte Click to request access.  
• Qualitative Data Analysis by Matthew B. Miles; A. Michael Huberman   A practical sourcebook for researchers who make use of qualitative data, presenting the current state of the craft in the design, testing, and use of qualitative analysis methods. Strong emphasis is placed on data displays matrices and networks that go beyond ordinary narrative text. Each method of data display and analysis is described and illustrated.  
• "A Survey of Qualitative Data Analytic Methods" in Chapter 4 (pp. 89–138) of Fundamentals of Qualitative Research by Johnny Saldana   Provides an in-depth introduction to coding as a heuristic, particularly focusing on process coding, in vivo coding, descriptive coding, values coding, dramaturgical coding, and versus coding. Includes advice on writing analytic memos, developing categories, and themeing data.   
• "Thematic Networks: An Analytic Tool for Qualitative Research." Qualitative Research : QR, 1(3), 385–405 by Jennifer Attride-Stirling Details a technique for conducting thematic analysis of qualitative material, presenting a step-by-step guide of the analytic process, with the aid of an empirical example. The analytic method presented employs established, well-known techniques; the article proposes that thematic analyses can be usefully aided by and presented as thematic networks.  
• Using Thematic Analysis in Psychology by Virginia Braun and Victoria Clark Walks readers through the process of reflexive thematic analysis, step by step. The method may be adapted in fields outside of psychology as relevant. Pair this with One Size Fits All? What Counts as Quality Practice in Reflexive Thematic Analysis? by Virginia Braun and Victoria Clark

Data visualization can be employed formatively, to aid your data analysis, or summatively, to present your findings. Many qualitative data analysis (QDA) software platforms, such as NVivo , feature search functionality and data visualization options within them to aid data analysis during the formative stages of your project.

For expert assistance creating data visualizations to present your research, Harvard Library offers Visualization Support . Get help and training with data visualization design and tools—such as Tableau—for the Harvard community. Workshops and one-on-one consultations are also available.

The quality of your data analysis depends on how you situate what you learn within a wider body of knowledge. Consider the following advice:

A good literature review has many obvious virtues. It enables the investigator to define problems and assess data. It provides the concepts on which percepts depend. But the literature review has a special importance for the qualitative researcher. This consists of its ability to sharpen his or her capacity for surprise (Lazarsfeld, 1972b). The investigator who is well versed in the literature now has a set of expectations the data can defy. Counterexpectational data are conspicuous, readable, and highly provocative data. They signal the existence of unfulfilled theoretical assumptions, and these are, as Kuhn (1962) has noted, the very origins of intellectual innovation. A thorough review of the literature is, to this extent, a way to manufacture distance. It is a way to let the data of one's research project take issue with the theory of one's field.

- McCracken, G. (1988), The Long Interview, Sage: Newbury Park, CA, p. 31

Once you have coalesced around a theory, realize that a theory should  reveal  rather than  color  your discoveries. Allow your data to guide you to what's most suitable. Grounded theory  researchers may develop their own theory where current theories fail to provide insight.  This guide on Theoretical Models  from Alfaisal University Library provides a helpful overview on using theory.

If you'd like to supplement what you learned about relevant theories through your coursework and literature review, try these sources:

• Annual Reviews   Review articles sum up the latest research in many fields, including social sciences, biomedicine, life sciences, and physical sciences. These are timely collections of critical reviews written by leading scientists.  
• HOLLIS - search for resources on theories in your field   Modify this example search by entering the name of your field in place of "your discipline," then hit search.  
• Oxford Bibliographies   Written and reviewed by academic experts, every article in this database is an authoritative guide to the current scholarship in a variety of fields, containing original commentary and annotations.  
• ProQuest Dissertations & Theses (PQDT)   Indexes dissertations and masters' theses from most North American graduate schools as well as some European universities. Provides full text for most indexed dissertations from 1990-present.  
• Very Short Introductions   Launched by Oxford University Press in 1995, Very Short Introductions offer concise introductions to a diverse range of subjects from Climate to Consciousness, Game Theory to Ancient Warfare, Privacy to Islamic History, Economics to Literary Theory.
• << Previous: Recording & Transcription
• Next: Managing Interview Data >>

Except where otherwise noted, this work is subject to a Creative Commons Attribution 4.0 International License , which allows anyone to share and adapt our material as long as proper attribution is given. For details and exceptions, see the Harvard Library Copyright Policy ©2021 Presidents and Fellows of Harvard College.

Dust arrestment in subways: analysis and technique design

• Original Paper
• Published: 10 September 2024

Cite this article

• I. Lugin   ORCID: orcid.org/0000-0002-5287-3589 1 ,
• L. Kiyanitsa   ORCID: orcid.org/0000-0001-6436-1997 1 ,
• A. Krasyuk   ORCID: orcid.org/0000-0001-7579-3015 1 &
• T. Irgibayev   ORCID: orcid.org/0000-0003-2948-2683 2  

The research is devoted to solve the problem of elevated dust levels in subway air through the implementation of a proposed dust collection system. A comprehensive experiment to determine the fractional and chemical compositions, as well as dust density, in the existing metro systems of Almaty (Kazakhstan) and Novosibirsk (Russian Federation) was conducted. The experiment results led to hypotheses about the sources of dust emission in subways. An innovative method for de-dusting tunnel air has been developed. The method is based on the use of air flows generated by the piston action of trains and the installation of labyrinth filters in the ventilation joints of stations. The parameters of the computational model of a subway line on the basis of decomposition approach to mathematical modeling of aerodynamic processes methods of computational aerodynamics by transition from a full model of a subway line to an open-ended periodic one have been substantiated. The research also justifies the geometric parameters of the labyrinth filters, determining their effectiveness based on air velocity and the number of filter element rows. Additionally, potential energy savings achievable with the proposed system were assessed. The scope of application of the results of the presented study of air distribution from the piston effect in subway structures and the effectiveness of the proposed air filtration system are limited to subways with single-track tunnels and open-type stations equipped with ventilation joints.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Study on temporal and spatial evolution law for dust pollution in double roadway ventilation system of short wall continuous mining face

Acknowledgements

The study was carried out within the framework of the Project of Fundamental Scientific Research of the Russian Federation (state registration number is 121052500147-6) and was supported by the Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan, Grant No. AP09260842.

Author information

Authors and affiliations.

Chinakal Institute of Mining, Siberian Branch, Russian Academy of Sciences, Krasny Prospect, Novosibirsk, Russia, 630091

I. Lugin, L. Kiyanitsa & A. Krasyuk

Satbayev University, Almaty, Republic of Kazakhstan

T. Irgibayev

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to I. Lugin .

Ethics declarations

Conflict of interest.

All authors declare that they have no conflict of interest.

Additional information

Editorial responsibility: Samareh Mirkia.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Lugin, I., Kiyanitsa, L., Krasyuk, A. et al. Dust arrestment in subways: analysis and technique design. Int. J. Environ. Sci. Technol. (2024). https://doi.org/10.1007/s13762-024-05970-5

Download citation

Received : 26 June 2023

Revised : 25 April 2024

Accepted : 13 August 2024

Published : 10 September 2024

DOI : https://doi.org/10.1007/s13762-024-05970-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Ventilation
• Numerical modelling
• Air distribution
• Air purification
• Find a journal
• Publish with us
• Track your research

• Previous Article
• Next Article

Insights on feline infectious peritonitis risk factors and sampling strategies from polymerase chain reaction analysis of feline coronavirus in large-scale nationwide submissions

• Download PDF

This nationwide study aimed to investigate risk factors associated with FIP and determine optimal sample submission strategies for its diagnosis.

A total of 14,035 clinical samples from cats across the US were analyzed by means of reverse transcriptase quantitative PCR to detect replicating feline coronavirus (FCoV). χ 2 and logistic regression analyses were conducted to assess the association between FCoV detection rates and risk factors such as age, gender, breed, and types of submitted samples.

Higher FCoV detection rates were observed in younger cats, particularly those aged 0 to 1 year, and in male cats. Purebred cats, notably British Shorthairs [OR: 2.81; P < .001], showed a higher incidence of FCoV infection than other cats. Peritoneal fluid (OR, 7.51; P < .001) exhibited higher FCoV detection rates than other samples, while lower rates were seen in blood samples (OR, 0.08; P < .001) than in other samples. High FCoV detection rates were found in urine, kidney, and lymph node samples.

CONCLUSIONS

The study identified significant risk factors associated with FIP. Optimal sample submission strategies, particularly emphasizing the use of peritoneal fluid, kidney, and lymph node, were identified to improve FIP detection rates. Urine yielded a relatively high frequency of infection and viral loads compared with most other samples.

CLINICAL RELEVANCE

Understanding the risk factors and optimizing sample selection for FIP diagnosis can aid in the early detection and management of the disease, ultimately improving outcomes for affected cats. These findings contribute valuable insights to FIP epidemiology and underscore the importance of continued research to enhance diagnostic strategies and disease management approaches.

Introduction

Feline infectious peritonitis is a fatal and progressive illness caused by feline coronavirus (FCoV), affecting domestic and wild felids globally. 3 Feline coronavirus manifests in 2 forms: the low-virulence feline enteric coronavirus (FECV) and the high-virulence FIP virus (FIPV). 1 Feline infectious peritonitis can be exhibited in 2 forms: the “wet” form, characterized by fluid accumulation in body cavities, and the “dry” form, which involves granulomatous lesions in various organs. 2 Despite being extensively studied, FIP remains without a definitive diagnostic test, an approved efficacious treatment, or a dependable vaccine. It is widely believed that FIPV arises from the accumulation of mutations in FECV, which are favored by high frequencies of FCoV replication and transmission, particularly in multicat environments. 3 , 4 This enigmatic pathogenesis of FIP creates substantial diagnostic difficulties in distinguishing FIPV infections from common mild FECV ones.

Studies 5 , 6 investigating the epidemiology of FIP in cats have identified risk factors for the development of FIP such as age, breed, sex, seasons, coinfection, and multicat environments. All these identified risk factors play a crucial role in guiding diagnostic strategies, therapy, and disease control strategies.

Early and accurate diagnosis is crucial for improving the quality of life for those infected with FIPV. However, definitively diagnosing FIP can be extremely challenging, especially antemortem, due to the limitations of available diagnostic tests and the overlapping clinical signs with other feline diseases. The choice of the samples may vary depending on the clinical access, the preferences of the veterinarian, and the clinical presentation of the cat. 7

In cases where FIP is suspected, a combination of tests and appropriate samples may be necessary to obtain an accurate diagnosis. Polymerase chain reaction testing for FCoV RNA has become one of the most reliable and rapid diagnostic indicators for FIP in suspected cases. 8 , 9 However, it has been argued that the detection of FCoV genomic RNA using PCRs may not always indicate a definite diagnosis of FIP, as FCoV viremia has been observed in clinically healthy cats. 10 This argument is grounded in the understanding that FIPV replicates mainly within monocytes/macrophages, unlike the less virulent FECV counterpart. The diagnostic usefulness of PCR was evaluated in different types of samples 11 ; however, the reliability of this test for FIP diagnosis depends largely on the choice of test specimens.

Among the key questions asked about FIP by clinicians, the 2 most important ones concern the risk factors and optimal choice of tissue or fluid samples for viral detection. While most studies addressing these 2 essential questions have been conducted with small sample sizes, 10 , 12 – 14 our work comprehensively analyzed the viral presence of FCoV in 14,035 submissions from across the US. The statistical analysis of this extensive data set confirmed some previous findings and also shed novel light on FIP risk factors. The results also suggested an optimal choice of samples for diagnostic testing.

Clinical sample collection

Convenience samples (n = 14,035) submitted to the Molecular Diagnostic Laboratory at the Auburn University College of Veterinary Medicine from 2016 to 2023, originating from 47 US states, were utilized in this study. These samples had been submitted for molecular diagnosis due to clinical signs and tests suggestive of FIP. Information such as age, breed, and types of submitted feline samples was recorded.

Extraction of nucleic acids

Total nucleic acid extraction from the submitted samples was performed with glass fiber matrix binding and elution with a commercial kit (High-Pure PCR Template Preparation Kit; Roche Diagnostic) following the manufacturer’s instructions and described previously. 15 For each specimen, 400 μL of fluid or biopsy tissue in saline was mixed with an equal volume of binding buffer and eluted in a final volume of 100 μL.

Reverse transcriptase quantitative PCR

The FCoV MN gene PCR utilized in this study followed the original approach reported by Simons et al 16 with minor modifications. The reverse transcriptase quantitative PCR (RT-qPCR) was designed to quantify the replicating FCoV and amplify a 281-bp FCoV genomic region that spans the junction of M and N genes, as described. 10

The assay was performed with 25-ng standardized cDNA input as a 1-step RT-qPCR modeled on the proprietary Auburn University Molecular Diagnostics PCR thermal design (US patent 7,252,937). The sensitivity of this assay was validated by serial dilution of cDNA standard templates. The limit of detection was a single mRNA copy per reaction as evident in the Poisson distribution of positive and negative amplification reactions at the limiting dilution. Validation of the specificity was performed by sequence determination of positive amplifications in this study.

Fluorescence resonance energy transfer RT-qPCR was performed on a Roche light cycler 480 II system (Roche Molecular Biochemicals) containing 2.0-U Platinum Taq DNA polymerase (Invitrogen) and 0.0213-U ThermoScript reverse transcriptase (Invitrogen). Thermal cycling was preceded by a 10-minute reverse transcription reaction at 55 °C followed by a 4-minute denaturation at 95 °C and 30 fluorescence acquisition cycles of 10 seconds at 95 °C, 8 seconds at 58 °C with fluorescence acquisition, 30 seconds at 67 °C, and 30 seconds at 72°C with the melting curve determined by 1 minute at 95 °C and 2 minutes at 42 °C and increasing to 74 °C with continuous fluorescence reading. The reference FIPV as a quantitative standard used in this study was FIPV strain 79-1146 (American Type Culture Collection).

Analysis of multiple types of submitted samples from individual cats

Among 14,035 submissions included in this study, multiple types of samples were submitted from 389 individual cats. These samples encompassed a variety of sources, with whole blood (n = 330), peritoneal fluid (286), lymph node (63), feces (50), kidney (19), spleen (14), pleural fluid (8), liver (8), colon (7), CSF (4), aqueous humor (3), lung (3), omentum (2), intestine (1), bone marrow (1), and testicle (1) being among them. This study specifically compared the FCoV positivity among multiple submissions when they originated from the same cats.

Statistical analysis

All data were analyzed with STATISTICA 7.1 software (Statsoft). Summary statistics describing the overall FCoV detection rates associated with different risk factors (sex, age, and types of samples) were performed. The data were presented as mean and ± SD or CI. χ 2 tests were employed for preliminary univariate analyses to ascertain the significance of the relationships between sex, age groups, neutered status, various types of samples, breeds, and the presence of FCoV. The univariate logistic regression model used age, gender, breed, and kinds of submitted samples as independent and categorical variables (age, gender, breed, and types of samples) to assess the risk factor and existence of FCoV. Odds ratios with 95% CI were calculated to quantify the strength of the associations between the risk factors and FCoV detection. Out of the 74 distinct breeds, we specifically focused on 24 breeds that had a sample size of 30 or greater to calculate the odds ratio. A P value < .05 was considered statistically significant.

Demographic information of the cats and the submitted samples

The average age of the cats from which the samples were submitted in this study was 3.51 years, ranging from 1 month to 17 years old (SD, 3.88 years; Supplementary Table S1 ).

In total, the submitted samples represented 77 different feline breeds, including domestic shorthair (n = 7,891), followed by domestic longhair (729), domestic mediumhair (529), Siamese (348), Maine Coon (291), Ragdoll (275), Persian (189), Bengal (160), Siberian (155), Sphynx (147), British Shorthair (143), Scottish Fold (113), and unknown mixed breeds (172). These samples, as well as other samples with < 100 submissions, are listed in Supplementary Table S2 .

The main types of submitted samples included peritoneal fluid (n = 7,720), whole blood (4,496), lymph node (360), pleural fluid (197), urine (145), kidney (124), intestine (98), spleen (92), liver (83), cerebrospinal fluid (CSF) (73), aqueous humor (40), lung (22), and bone marrow (12; Table 1 ). Other samples with < 10 submissions included the brain, eye, omentum, colon, testicle, and skin.

Positivity of feline coronavirus detection in different types of submitted samples.

*The viral copy number in the feline coronavirus–positive samples.

Higher detection rate of FCoV in young cats than in old ones

Fluorescence resonance energy transfer PCR detected replicating FCoV in 39.1% of submitted samples (5,491 of 14,035) in this study. Overall, significantly higher detection rates of FCoV were observed in younger cats compared to older ones, with detection rates of FCoV declining as cats aged ( Figure 1 ; Supplementary Table S1 and Supplementary Table S3 ). Cats aged 2 to 10 years exhibited a significantly lower FCoV detection rate (1,762 of 5,167 [34.1%]) than cats aged 0 to 1 year (3,250 of 6,979 [46.6%]; P < .001) and a rate significantly higher than the cats above 10 years old (268 of 1,238 [21.6%] P < .001). Cats aged 0 to 1 year have a 2-fold higher possibility of being FCoV positive than cats ≥ 2 years old.

Detection rates of feline coronavirus (FCoV) in cats across various age groups. A—The detection rates of FCoV declined as cats aged. Logistic regression analysis was employed to analyze the FCoV detection rates among cats of different age groups. It was defined that 1 year means ages 0 to 1 year, 2 years mean 1.1 to 2 years old, and so forth. The P values from logistic regression analysis can be found in Supplementary Table S3 . B—Cats aged 2 to 10 years exhibited a significantly lower FCoV detection rate (1,762 of 5,167 samples [34.1%]) than cats aged 0 to 1 year (3,250 of 6,979 samples [46.6%]) and a rate significantly higher than the cats above 10 years old (268 of 1,238 samples [21.6%]; P < .001; χ 2 test). Error bar, mean ± 95% CI.

Citation: Journal of the American Veterinary Medical Association 2024; 10.2460/javma.24.03.0208

• Download Figure
• Download figure as PowerPoint slide

Higher FCoV detection rates in male than female cats

The detection rate of FCoV in male cats was 42.4% (3,536 of 8,324), statistically significantly higher than the 34.3% in female cats (1,799 of 5,242; P < .001; Figure 2 ). The FCoV detection rate was statistically significantly higher in male cats than in female ones. However, no statistically significant association was observed between castrated and intact male cats (2,645 of 6,361 [41.6%] vs 138 of 328 [42.0%]) and between spayed female and intact cats (1,308 of 3,887 [33.7%] vs 48 of 129 [37.2%]). While male cats showed an overall higher detection rate of FCoV than female cats across different ages in this study, the difference was significant specifically for cats under 4 years of age ( Figure 3 ) .

Comparison of the detection rates of FCoV in male and female cats. The detection rate of FCoV in male cats was 42.4% (3,536 of 8,324 cats), significantly higher than the 34.3% in female cats (1,799 of 5,242 cats; A). However, no significant difference was observed between intact male and castrated cats (138 of 328 vs 2,645 of 6,361 cats) and intact female cats and spayed cats (48 of 129 vs 1,308 of 3,887 cats; C). Error bar, mean ± 95% CI.

Detection rates of FCoV in male and female cats across various age groups. While the male cats showed an overall higher detection rate of FCoV than female cats across different ages in this study, the difference was significant specifically for cats under 4 years of age. Error bar, mean ± 95% CI.

Lower detection rates of FCoV in mixed-breed cats compared to the purebred cats

Among the 77 feline breeds included in this study, 25 breeds with more than 30 submissions were analyzed by means of logistic regression analysis. The statistical analysis demonstrated that British Shorthair cats (purebred) demonstrated a significantly higher detection rate of FCoV (positivity rate, 64.3%; OR, 2.81; 95% CI, 1.99 to 3.96; P < .001) than other cats. Domestic shorthair cats (mixed breed) had a significantly lower FCoV detection rate (positivity rate, 37.7%; OR, 0.81; 95% CI, 0.75 to 0.88; P < .001) than other cats ( Figure 4 ; Supplementary Table S2 and Supplementary Table S4 ).

Differences in FCoV detection rates among different cat breeds. Logistic regression analysis was conducted to compare the detection rates of FCoV across 25 feline breeds with more than 30 submissions in this study. Particularly, a significant lower FCoV detection rate was found in domestic shorthairs (OR, 0.81). In a comparison, a significantly higher detection rate of FCoV was found in British Shorthairs (OR, 2.81), Birmans (OR, 2.13), Siberians (OR, 2.10), Persians (OR, 1.53), and Ragdolls (OR, 1.29). The p values and OR from logistic regression analysis can be found in Supplementary Table S4 . Error bar, mean ± 95% CI.

Significantly higher FCoV in peritoneal fluids and tissues than in whole blood

We employed logistic regression analysis to analyze the FCoV detection rates across 14 distinct sample types, each with over 10 submissions ( Figure 5 ; Table 1 ; Supplementary Table S5 ). The highest detection rates were observed in peritoneal fluid samples (4,431 of 7,720 [57.4%]) and urine samples (76 of 145 [52.4%]), significantly higher than in samples of whole blood (400 of 4,496 [8.9%]), CSF (7 of 73 [9.6%]), lung (3 of 22 [13.6%]), spleen (15 of 92 [16.3%]), liver (14 of 83 [16.9%]), aqueous humor (7 of 40 [17.5%]), feces (43 of 140 [30.7%]), kidney (48 of 124 [38.7%]), pleural fluid (78 of 197 [39.6%]), lymph node (162 of 360 [45.0%]) and bone marrow (0 of 12 [0%]). Additionally, the detection rate in whole blood (positivity, 8.9%; OR, 0.08; 95% CI, 0.07 to 0.09; P < .001) was significantly lower than in most tissue samples, while kidney and lymph nodes exhibited higher detection rates compared to other tissue samples.

Comparison of FCoV positivity and viral burdens in different types of samples. A—Logistic regression analysis was utilized to compare the detection rates of FCoV across 14 types of submitted samples with more than 30 submissions in this study. The significantly higher detection rates were observed in peritoneal fluid (OR, 7.51), urine (OR, 1.72), and lymph node (OR, 1.28). In comparison, a significantly lower detection rate was observed in blood (OR, 0.09), CSF (OR, 0.16), lung (OR, 0.24), spleen (OR, 0.30), liver (OR, 0.31), aqueous humor (OR, 0.33), and feces (OR, 0.69). The P values and OR from logistic regression analysis can be found in Supplementary Table S5 . B—Error bar, mean ± 95% CI.

The comparison of the viral copies among the FCoV-positive samples showed that the viral burden in whole blood (10 1.63 ) was significantly lower than in urine (10 3.19 ), peritoneal fluid (10 2.97 ), kidney (10 2.95 ), CSF (10 2.83 ), feces (10 2.78 ), pleural fluid (10 2.70 ), lymph node (10 2.54 ), intestine (10 2.46 ), and bone marrow (0; Table 1 ). In addition, the viral copy numbers in urine (10 3.19 ) and peritoneal fluid (10 2.97 ) were significantly higher than in lymph node (10 2.54 ; Figure 5 ).

Analysis of FCoV in multiple sample submissions from the same cats

Among 389 cats with multiple sample submissions, the FCoV positivity (77 of 331 [23%]; P < .01) was significantly lower than those of any other types of samples (body fluids, 93%; aqueous humor, 2 of 2; tissues, 131 of 155 [85%]; feces, 34 of 50 [68%]). In addition, the FCoV positivity was the highest among all types of submitted samples, and tissue had a significant higher FCoV positivity than in feces ( P < .01). Among the multiple samples, both whole blood and peritoneal fluids were submitted from 234 cats. The blood samples positive for FCoV had an average copy number of 10 2.93 (SD, 10 2.91 ) per sample, significantly lower than the 10 5.39 (SD, 10 6.31 ) in the peritoneal fluids ( P = .02).

Further analysis of these 234 cats demonstrated a negative correlation between FCoV detection rates in feces and other organs and tissues. Interestingly, out of 47 cats that were positive in blood and peritoneal fluids, all 3 submitted fecal submissions were found to be FCoV negative. Furthermore, out of 7 cats that were negative in both blood and peritoneal fluids, all 3 submitted fecal samples were found to be FCoV positive. Among the total 50 of fecal samples in multiple submissions, 34 were found to be FCoV positive. For these 34 cats that had FCoV-positive feces, 23.4% of other submitted samples were found to be positive for FCoV, including 3 of 31 in the whole blood, 7 of 11 in peritoneal fluid, 1 of 3 in the lymph node, 0 of 1 in the kidney, and 0 of 1 in the lymph node. On the other hand, for the 16 cats that had negative fecal tests for FCoV, 69.0% of other submitted samples were FCoV positive (6 of 15 in blood, 9 of 9 in peritoneal fluid, 1 of 1 in CSF, and 4 of 4 in lymph node).

The data from this nationwide study, comprising 14,035 clinical sample submissions for FIP diagnosis, reaffirmed the findings of previous studies and shed novel insights into associated risk factors and optimal choice of sample selection for diagnosis.

In our investigation, we found that cats aged 0 to 1 year exhibited a greater likelihood of testing positive for FCoV compared to cats aged ≥ 2 years, aligning with previous studies. 17 This suggests that young cats may contact FCoV before their immune systems reach full maturity, facilitating efficient virus replication and favoring mutations from FECV to FIPV. 3 , 12 , 13 , 18 Young cats may also experience greater stress due to factors such as relocation, vaccination, neutering, and separation from parent cats. 6 , 14 These stressors could render young cats more susceptible to FIPV compared to their adult counterparts. Stress has been implicated in the increased risk of FCoV shedding and subsequent FIP development. 1 , 17 Stress triggers the release of glucocorticoids, which likely suppress cell-mediated immunity and facilitate increased FCoV replication. 1 , 19

Interestingly, our study also detected a significantly higher positivity of FCoV in male cats compared to females, mirroring trends similarly observed in COVID-19 cases in male and female individuals. 20 Research into FCoV and gender predisposition suggests that males may be at a higher risk of developing more severe manifestations of FIP. 12 , 21 – 24 However, other reports have stated that males and females have similar risks of developing FIP. 25 Sex-based differences may be related to sex hormones, particularly androgens, which could negatively impact the immune system, potentially increasing the risk of virus replication and mutation. 26 This gender predisposition aligns with findings from other infections affecting cats such as FIV 27 and FeLV, 28 indicating a potential behavioral, hormonal, or physiological basis for the differences in susceptibility and severity between male and female cats. Results of this study suggested that male cats may be at a higher risk of developing FIP than female ones, likely due to the differences in sex hormone and the derived immune responses.

Although we reported gender bias for FCoV detection in the present study, we observed no statistically significant difference in FCoV detection rates between intact cats and those that had been castrated/spayed. Gender, specifically intact males, has been identified as a risk factor for FIP in previous studies. 18 , 24 , 29 Contrary to expectation, certain earlier studies failed to observe any form of gender bias for FIP. 14 , 30 Discrepancies may arise from various factors, including differences in sample sizes and different populations.

In this study, an increased FCoV detection rate has been observed in certain purebred varieties, particularly the British Shorthair, which demonstrated a significantly higher detection rate of FCoV than other cats. Pesteanu-Somogyi et al 18 reported the increased risk of developing FIP in certain breeds, notably the Birman, Ragdoll, Bengal, Rex, Abyssinian, and Himalayan breeds. Reduced genetic diversity in purebred cats may result in decreased disease resistance and environmental adaptability compared to mixed-breed cats, potentially explaining the high prevalence of FCoV infection in purebred cats, including certain breeds. 12 , 13 , 25 , 26

Our findings indicated a markedly lower detection rate and copy number of FCoV in whole blood compared to most tissue samples, which supports the previous findings. 31 Pedersen et al reported that even in cats with highly fulminant experimentally induced FIP, viremia is either absent or falls below the reliable detection limits of highly sensitive RT-qPCR throughout all stages of the infection. 31 Other studies 32 reported on inconsistent FCoV detection in the blood of kittens inoculated with different doses of 2 independent FECV field strains, UCD and RM. In a virus persistence study, cats infected with FCoV type I were all found to be positive for FCoV for at least one of the examined tissues with or without blood viremia. 33 Therefore, efforts for virus detection should focus on tissues and effusions presumably containing FIPV-infected macrophages.

An unexpected and interesting finding in this study was the high detection rate and viral burdens of FCoV in urine and kidney samples. Generally, urine is reported to be an unlikely source of infection. 3 Feline infectious peritonitis virus is strongly cell and tissue bound, with shedding in urine typically occurring only in specific scenarios, such as when lesions disrupt the renal collecting ducts or intestinal wall, leading to the potential shedding of the virus in urine. 3 Urine is more likely diagnosed for abnormalities (proteinuria) in support of a diagnosis if other clinical signs and test results are consistent with FIP. 3 Interestingly, a handful of SARS-CoV-2 studies 34 , 35 have also reported viral shedding in urine and explored its potential correlation with disease severity. The findings of the present study provide compelling evidence to explore the possibility of urine as a convenient and valuable sample for the FIP diagnosis. A correlation between renal involvement and urine positivity needs to be studied. Nevertheless, further studies are warranted to evaluate urine for its diagnostic value in FIP.

Among 14,035 submissions included in this study, multiple types of samples were submitted from 389 individual cats. We additionally analyzed these multiple samples submitted from the same cats, and the results further confirmed the findings from the analysis of the nationwide samples. Significantly higher FCoV detection rates and viral loads were found in peritoneal fluids and urine, compared to the blood when submitted at the same time in multiple sample submissions. One key finding from multiple submissions was the negative correlations in the FCoV detection rates in feces and other types of submitted samples. When FCoV was detected in feces, it was less likely to be detected in other organs and tissues, and vice versa. As fecal detection more likely indicates the shedding of FCoV in cats rather than being connected to ongoing FIP infection, 4 , 9 further studies are warranted to identify the genotypes of FCoV in feces and other types of submitted samples, correlating the results of FIP diagnosis by other methods such as immunohistochemistry.

One significant limitation of this study is the lack of confirmed diagnosis of FIP for the cats from which samples were included, due to the nature of convenience sample submission in this study. However, consistent communication with clinicians during reporting of results indicates strong correlations between replicating mRNA PCR and FIP diagnosis. Another limitation of this study is the detection of the whole population of FCoV via subgenomic mRNA detection that amplified all RNA species with maximum sensitivity and specificity. It is well-known that infection by a specific variant can quickly result in the emergence of genetically diverse clades of coronavirus. 4 , 36 Cats in laboratory settings, when inoculated with a mixture of 2 closely related variants originating from the same FIP-infected cat, exhibited illness caused by either one of the variants, but not both simultaneously. 4 As a result, the RT-qPCR used in our study might be beneficial and valuable to identify quasispecies of virulent FCoV rather than only targeting classical FIPV. In fact, many studies report the identification of diverse FCoV strains in FIP cats. 37 , 38 Nevertheless, the results of the present study should be further confirmed with immunohistochemistry as the gold standard test, and consideration should be given to the clinical presentation of the cats and other tests.

In this study, statistical analyses were performed on the positivity of FCoV in cats of different ages (1 to 17 years old), different sexes, 23 feline breeds, and 14 different types of submitted samples. Ideally, a hierarchical approach for multivariable comparison should be used to analyze the data, taking into account the interactions of different variables. However, given the multiple groups for each variable in this work, we decided to focus on the analysis of individual variables.

In conclusion, this nationwide study sheds new light on the risk factors associated with FIP and the optimal choice of sample submission for its diagnosis. Our findings highlighted that young cats aged 0 to 1 year exhibit a greater likelihood of testing positive for FCoV, potentially due to stressors and immature immune systems. Additionally, similar to COVID-19 in humans, male cats appear to have a higher positivity of FCoV detection, suggesting a gender predisposition. Purebred cats, especially British Shorthairs, show increased susceptibility to FCoV infection, emphasizing the importance of genetic factors. Detection of FCoV in tissues such as peritoneal fluid, urine, kidney, and lymph node proved valuable in our study, potentially aiding in diagnosis. However, limitations such as the lack of confirmed FIP diagnoses and the need for further validation of detection methods underscored the necessity for continued research. Overall, this study contributes to our understanding of FIP epidemiology and underscores the need for improved diagnostic strategies and management approaches.

Supplementary Materials

Supplementary materials are posted online at the journal website: avmajournals.avma.org .

Acknowledgments

The authors thank Dr. Laura Huber for her valuable advice on the statistical analysis in this work.

Disclosures

The authors have nothing to disclose. No AI-assisted technologies were used in the generation of this manuscript.

The authors have nothing to disclose.

Vennema H , Poland A , Foley J , Pedersen NC . Feline infectious peritonitis viruses arise by mutation from endemic feline enteric coronaviruses . Virology . 1998 ; 243 ( 1 ): 150 - 157 . doi: 10.1006/viro.1998.9045

• Search Google Scholar
• Export Citation

Hartmann K . Feline infectious peritonitis . Vet Clin North Am Small Anim Pract . 2005 ; 35 ( 1 ): 39 - 79 . doi: 10.1016/j.cvsm.2004.10.011

Pedersen NC . A review of feline infectious peritonitis virus infection: 1963-2008 . J Feline Med Surg . 2009 ; 11 ( 4 ): 225 - 258 . doi: 10.1016/j.jfms.2008.09.008

Pedersen NC , Liu H , Dodd KA , Pesavento PA . Significance of coronavirus mutants in feces and diseased tissues of cats suffering from feline infectious peritonitis . Viruses . 2009 ; 1 ( 2 ): 166 - 184 . doi: 10.3390/v1020166

Pedersen NC . An update on feline infectious peritonitis: virology and immunopathogenesis . Vet J . 2014 ; 201 ( 2 ): 123 - 132 . doi: 10.1016/j.tvjl.2014.04.017

Klein-Richers U , Hartmann K , Hofmann-Lehmann R , et al. Prevalence of feline coronavirus shedding in German catteries and associated risk factors . Viruses . 2020 ; 12 ( 9 ): 12 . doi: 10.3390/v12091000

Thayer V , Gogolski S , Felten S , Hartmann K , Kennedy M , Olah GA . 2022 AAFP/EveryCat feline infectious peritonitis diagnosis guidelines . J Feline Med Surg . 2022 ; 24 ( 9 ): 905 - 933 . doi: 10.1177/1098612X221118761 . Published correction appears in J Feline Med Surg . 2022;24(12):e676. doi:10.1177/1098612X221126448

Addie D , Belák S , Boucraut-Baralon C , et al. Feline infectious peritonitis: ABCD guidelines on prevention and management . J Feline Med Surg . 2009 ; 11 ( 7 ): 594 - 604 . doi: 10.1016/j.jfms.2009.05.008

Herrewegh AA , de Groot RJ , Cepica A , Egberink HF , Horzinek MC , Rottier PJ . Detection of feline coronavirus RNA in feces, tissues, and body fluids of naturally infected cats by reverse transcriptase PCR . J Clin Microbiol . 1995 ; 33 ( 3 ): 684 - 689 . doi: 10.1128/jcm.33.3.684-689.1995

Fish EJ , Diniz PPV , Juan YC , et al. Cross-sectional quantitative RT-PCR study of feline coronavirus viremia and replication in peripheral blood of healthy shelter cats in Southern California . J Feline Med Surg . 2018 ; 20 ( 4 ): 295 - 301 . doi: 10.1177/1098612X17705227

Tasker S . Diagnosis of feline infectious peritonitis: update on evidence supporting available tests . J Feline Med Surg . 2018 ; 20 ( 3 ): 228 - 243 . doi: 10.1177/1098612X18758592

Benetka V , Kübber-Heiss A , Kolodziejek J , Nowotny N , Hofmann-Parisot M , Möstl K . Prevalence of feline coronavirus types I and II in cats with histopathologically verified feline infectious peritonitis . Vet Microbiol . 2004 ; 99 ( 1 ): 31 - 42 . doi: 10.1016/j.vetmic.2003.07.010

Dong B , Zhang X , Zhong X , et al. Prevalence of natural feline coronavirus infection in domestic cats in Fujian, China . Virol J . 2024 ; 21 ( 1 ): 2 . doi: 10.1186/s12985-023-02273-y

Foley JE , Poland A , Carlson J , Pedersen NC . Risk factors for feline infectious peritonitis among cats in multiple-cat environments with endemic feline enteric coronavirus . J Am Vet Med Assoc . 1997 ; 210 ( 9 ): 1313 - 1318 . doi: 10.2460/javma.1997.210.09.1313

Barua S , Bailey M , Zhong K , et al. Research Note: role of darkling beetles (Alphitobius diaperinus) and litter in spreading and maintaining Salmonella Enteritidis and Campylobacter jejuni in chicken flocks . Poult Sci . 2023 ; 102 ( 11 ): 103061 . doi: 10.1016/j.psj.2023.103061

Simons FA , Vennema H , Rofina JE , et al. A mRNA PCR for the diagnosis of feline infectious peritonitis . J Virol Methods . 2005 ; 124 ( 1-2 ): 111 - 116 . doi: 10.1016/j.jviromet.2004.11.012

Pedersen NC , Allen CE , Lyons LA . Pathogenesis of feline enteric coronavirus infection . J Feline Med Surg . 2008 ; 10 ( 6 ): 529 - 541 . doi: 10.1016/j.jfms.2008.02.006

Pesteanu-Somogyi LD , Radzai C , Pressler BM . Prevalence of feline infectious peritonitis in specific cat breeds . J Feline Med Surg . 2006 ; 8 ( 1 ): 1 - 5 . doi: 10.1016/j.jfms.2005.04.003

Drechsler Y , Alcaraz A , Bossong FJ , Collisson EW , Diniz PP . Feline coronavirus in multicat environments . Vet Clin North Am Small Anim Pract . 2011 ; 41 ( 6 ): 1133 - 1169 . doi: 10.1016/j.cvsm.2011.08.004

Pijls BG , Jolani S , Atherley A , et al. Demographic risk factors for COVID-19 infection, severity, ICU admission and death: a meta-analysis of 59 studies . BMJ Open . 2021 ; 11 ( 1 ): e044640 . doi: 10.1136/bmjopen-2020-044640

Norris JM , Bosward KL , White JD , Baral RM , Catt MJ , Malik R . Clinicopathological findings associated with feline infectious peritonitis in Sydney, Australia: 42 cases (1990-2002) . Aust Vet J . 2005 ; 83 ( 11 ): 666 - 673 . doi: 10.1111/j.1751-0813.2005.tb13044.x

Rohrbach BW , Legendre AM , Baldwin CA , Lein DH , Reed WM , Wilson RB . Epidemiology of feline infectious peritonitis among cats examined at veterinary medical teaching hospitals . J Am Vet Med Assoc . 2001 ; 218 ( 7 ): 1111 - 1115 . doi: 10.2460/javma.2001.218.1111

Riemer F , Kuehner KA , Ritz S , Sauter-Louis C , Hartmann K . Clinical and laboratory features of cats with feline infectious peritonitis—a retrospective study of 231 confirmed cases (2000-2010) . J Feline Med Surg . 2016 ; 18 ( 4 ): 348 - 356 . doi: 10.1177/1098612X15586209

Potkay S , Bacher JD , Pitts TW . Feline infectious peritonitis in a closed breeding colony . Lab Anim Sci . 1974 ; 24 ( 2 ): 279 - 289 .

Pedersen NC , Liu H , Gandolfi B , Lyons LA . The influence of age and genetics on natural resistance to experimentally induced feline infectious peritonitis . Vet Immunol Immunopathol . 2014 ; 162 ( 1-2 ): 33 - 40 . doi: 10.1016/j.vetimm.2014.09.001

Coolen RL , Cambier JC , van Asselt E , Blok BFM . Androgen receptors in the forebrain: a study in adult male cats . J Morphol . 2023 ; 284 ( 2 ): e21553 . doi: 10.1002/jmor.21553

Ravi M , Wobeser GA , Taylor SM , Jackson ML . Naturally acquired feline immunodeficiency virus (FIV) infection in cats from western Canada: prevalence, disease associations, and survival analysis . Can Vet J . 2010 ; 51 ( 3 ): 271 - 276 .

Powers JA , Chiu ES , Kraberger SJ , et al. Feline leukemia virus (FeLV) disease outcomes in a domestic cat breeding colony: relationship to endogenous FeLV and other chronic viral infections . J Virol . 2018 ; 92 ( 18 ): 92 . doi: 10.1128/JVI.00649-18

Flook M , Jackson C , Vasileiou E , et al. Informing the public health response to COVID-19: a systematic review of risk factors for disease, severity, and mortality . BMC Infect Dis . 2021 ; 21 ( 1 ): 342 . doi: 10.1186/s12879-021-05992-1

Kharroubi SA , Diab-El-Harake M . Sex-differences in COVID-19 diagnosis, risk factors and disease comorbidities: a large US-based cohort study . Front Public Health . 2022 ; 10 : 1029190 . doi: 10.3389/fpubh.2022.1029190

Pedersen NC , Eckstrand C , Liu H , Leutenegger C , Murphy B . Levels of feline infectious peritonitis virus in blood, effusions, and various tissues and the role of lymphopenia in disease outcome following experimental infection . Vet Microbiol . 2015 ; 175 ( 2-4 ): 157 - 166 . doi: 10.1016/j.vetmic.2014.10.025

Vogel L , Van der Lubben M , te Lintelo EG , et al. Pathogenic characteristics of persistent feline enteric coronavirus infection in cats . Vet Res . 2010 ; 41 ( 5 ): 71 . doi: 10.1051/vetres/2010043

Meli M , Kipar A , Müller C , et al. High viral loads despite absence of clinical and pathological findings in cats experimentally infected with feline coronavirus (FCoV) type I and in naturally FCoV-infected cats . J Feline Med Surg . 2004 ; 6 ( 2 ): 69 - 81 . doi: 10.1016/j.jfms.2003.08.007

Anjos D , Fiaccadori FS , Servian CDP , et al. SARS-CoV-2 loads in urine, sera and stool specimens in association with clinical features of COVID-19 patients . J Clin Virol Plus . 2022 ; 2 ( 1 ): 100059 . doi: 10.1016/j.jcvp.2021.100059

Roshandel MR , Nateqi M , Lak R , et al. Diagnostic and methodological evaluation of studies on the urinary shedding of SARS-CoV-2, compared to stool and serum: a systematic review and meta-analysis . Cell Mol Biol (Noisy-le-grand) . 2020 ; 66 ( 6 ): 148 - 156 . doi: 10.14715/cmb/2020.66.6.26

Battilani M , Coradin T , Scagliarini A , et al. Quasispecies composition and phylogenetic analysis of feline coronaviruses (FCoVs) in naturally infected cats . FEMS Immunol Med Microbiol . 2003 ; 39 ( 2 ): 141 - 147 . doi: 10.1016/S0928-8244(03)00237-2

Le SJ , Xin GY , Wu WC , Shi M . Genetic diversity and evolution of viruses infecting Felis catus: a global perspective . Viruses . 2023 ; 15 ( 6 ): 15 . doi: 10.3390/v15061338

Zhou Q , Li Y , Huang J , et al. Prevalence and molecular characteristics of feline coronavirus in southwest China from 2017 to 2020 . J Gen Virol . 2021 ; 102 ( 9 ): 102 . doi: 10.1099/jgv.0.001654

• Supplementary Table S1 (PDF 110 KB)
• Supplementary Table S2 (PDF 126 KB)
• Supplementary Table S3 (PDF 110 KB)
• Supplementary Table S4 (PDF 111 KB)
• Supplementary Table S5 (PDF 110 KB)

• PubMed Citation
• Article by Subarna Barua
• Article by Shila Sarkar
• Article by Kelly Chenoweth
• Article by Calvin Johnson
• Article by Diane Delmain
• Article by Chengming Wang
• Similar articles in PubMed

Follow JAVMA

Follow AJVR

Follow the AVMA

Subscribe to newsletters

© 2024 American Veterinary Medical Association. All rights reserved.

Powered by KGL PubFactory

• [185.194.105.172]
• 185.194.105.172

Character limit 500 /500