Predicting asphaltene precipitation during natural depletion of oil reservoirs by integrating SARA fractions with advanced intelligent models
Abstract Asphaltene precipitation during natural depletion can lead to substantial issues in oil transport and production systems, requiring close assessment of crude oil stability through thermodynamic and structural factors. This precipitation can alter wettability, reduce permeability, and block...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-11966-z |
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| author | Behnam Amiri-Ramsheh Sara Sahebalzamani Reza Zabihi Abdolhossein Hemmati-Sarapardeh |
| author_facet | Behnam Amiri-Ramsheh Sara Sahebalzamani Reza Zabihi Abdolhossein Hemmati-Sarapardeh |
| author_sort | Behnam Amiri-Ramsheh |
| collection | DOAJ |
| description | Abstract Asphaltene precipitation during natural depletion can lead to substantial issues in oil transport and production systems, requiring close assessment of crude oil stability through thermodynamic and structural factors. This precipitation can alter wettability, reduce permeability, and block flow, causing increased pressure drops in wells, upstream facilities, and pipelines. Determining asphaltene precipitation experimentally is time-consuming and costly. Thus, it is essential to find fast and precise techniques for determining asphaltene precipitation. This research aims to accurately predict asphaltene precipitation values using an extensive databank containing 380 experimental data points. Pressure, bubble point pressure, temperature, oil °API gravity, and saturated-aromatic-resin-asphaltene (SARA) fractions of the crude oil samples were treated as input variables, while asphaltene precipitation was the output of the models. Four smart models, namely, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), cascade forward neural network (CFNN), and generalized regression neural network (GRNN) were constructed. To train the CFNN, the Levenberg-Marquardt (LM) algorithm was implemented. Various statistical and visual assessments were conducted to validate the reliability and precision of constructed models. Findings revealed that the developed LightGBM technique could outperform other models in estimating output values with the lowest absolute error value of 8.57% and the highest correlation coefficient (R2) of 0.9904. Besides, all developed models in this study could forecast asphaltene precipitation values with an absolute error of less than 10%. Also, trend assessment indicated that predicted output values by the LightGBM paradigm have a satisfactory agreement with actual asphaltene precipitation values. Sensitivity evaluation indicated that pressure and the amount of asphaltene exert the most significant negative and positive effects on the output, respectively. Lately, outlier discovery utilizing the Leverage technique illustrated that almost 96% of the used data points seem to be valid and reliable, statistically. |
| format | Article |
| id | doaj-art-7d5baf9f6f294a7591f4dff85109f5b7 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-7d5baf9f6f294a7591f4dff85109f5b72025-08-20T03:42:35ZengNature PortfolioScientific Reports2045-23222025-07-0115111610.1038/s41598-025-11966-zPredicting asphaltene precipitation during natural depletion of oil reservoirs by integrating SARA fractions with advanced intelligent modelsBehnam Amiri-Ramsheh0Sara Sahebalzamani1Reza Zabihi2Abdolhossein Hemmati-Sarapardeh3Department of Petroleum Engineering, Shahid Bahonar University of KermanDepartment of Petroleum Engineering, Shahid Bahonar University of KermanDepartment of Petroleum Engineering, Shahid Bahonar University of KermanDepartment of Petroleum Engineering, Shahid Bahonar University of KermanAbstract Asphaltene precipitation during natural depletion can lead to substantial issues in oil transport and production systems, requiring close assessment of crude oil stability through thermodynamic and structural factors. This precipitation can alter wettability, reduce permeability, and block flow, causing increased pressure drops in wells, upstream facilities, and pipelines. Determining asphaltene precipitation experimentally is time-consuming and costly. Thus, it is essential to find fast and precise techniques for determining asphaltene precipitation. This research aims to accurately predict asphaltene precipitation values using an extensive databank containing 380 experimental data points. Pressure, bubble point pressure, temperature, oil °API gravity, and saturated-aromatic-resin-asphaltene (SARA) fractions of the crude oil samples were treated as input variables, while asphaltene precipitation was the output of the models. Four smart models, namely, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), cascade forward neural network (CFNN), and generalized regression neural network (GRNN) were constructed. To train the CFNN, the Levenberg-Marquardt (LM) algorithm was implemented. Various statistical and visual assessments were conducted to validate the reliability and precision of constructed models. Findings revealed that the developed LightGBM technique could outperform other models in estimating output values with the lowest absolute error value of 8.57% and the highest correlation coefficient (R2) of 0.9904. Besides, all developed models in this study could forecast asphaltene precipitation values with an absolute error of less than 10%. Also, trend assessment indicated that predicted output values by the LightGBM paradigm have a satisfactory agreement with actual asphaltene precipitation values. Sensitivity evaluation indicated that pressure and the amount of asphaltene exert the most significant negative and positive effects on the output, respectively. Lately, outlier discovery utilizing the Leverage technique illustrated that almost 96% of the used data points seem to be valid and reliable, statistically.https://doi.org/10.1038/s41598-025-11966-zAsphaltene precipitationSARA fractionLightGBMOutlier discovery |
| spellingShingle | Behnam Amiri-Ramsheh Sara Sahebalzamani Reza Zabihi Abdolhossein Hemmati-Sarapardeh Predicting asphaltene precipitation during natural depletion of oil reservoirs by integrating SARA fractions with advanced intelligent models Scientific Reports Asphaltene precipitation SARA fraction LightGBM Outlier discovery |
| title | Predicting asphaltene precipitation during natural depletion of oil reservoirs by integrating SARA fractions with advanced intelligent models |
| title_full | Predicting asphaltene precipitation during natural depletion of oil reservoirs by integrating SARA fractions with advanced intelligent models |
| title_fullStr | Predicting asphaltene precipitation during natural depletion of oil reservoirs by integrating SARA fractions with advanced intelligent models |
| title_full_unstemmed | Predicting asphaltene precipitation during natural depletion of oil reservoirs by integrating SARA fractions with advanced intelligent models |
| title_short | Predicting asphaltene precipitation during natural depletion of oil reservoirs by integrating SARA fractions with advanced intelligent models |
| title_sort | predicting asphaltene precipitation during natural depletion of oil reservoirs by integrating sara fractions with advanced intelligent models |
| topic | Asphaltene precipitation SARA fraction LightGBM Outlier discovery |
| url | https://doi.org/10.1038/s41598-025-11966-z |
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