Maximizing oil recovery in sandstone reservoirs through optimized ASP injection using the super learner algorithm
Optimizing the Alkaline-Surfactant-Polymer (ASP) injection process remains a persistent challenge in Enhanced Oil Recovery (EOR), particularly in heterogeneous sandstone reservoirs where traditional reservoir simulators are constrained by high computational demands and limited flexibility. This stud...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Komunitas Ilmuwan dan Profesional Muslim Indonesia
2025-07-01
|
| Series: | Communications in Science and Technology |
| Subjects: | |
| Online Access: | https://cst.kipmi.or.id/journal/article/view/1649 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850036729168265216 |
|---|---|
| author | Dike Fitriansyah Putra Mohd Zaidi Jaafar Ku Muhd Na’im Khalif Apri Siswanto Ichsan Lukman Ahmad Kurniawan |
| author_facet | Dike Fitriansyah Putra Mohd Zaidi Jaafar Ku Muhd Na’im Khalif Apri Siswanto Ichsan Lukman Ahmad Kurniawan |
| author_sort | Dike Fitriansyah Putra |
| collection | DOAJ |
| description | Optimizing the Alkaline-Surfactant-Polymer (ASP) injection process remains a persistent challenge in Enhanced Oil Recovery (EOR), particularly in heterogeneous sandstone reservoirs where traditional reservoir simulators are constrained by high computational demands and limited flexibility. This study introduces a novel application of the Super Learner (SL) ensemble, a stacking-based machine learning algorithm integrating multiple base models (XGBoost, SVR, BRR, and Decision Tree), to systematically predict and optimize ASP injection parameters. Unlike previous approaches, our method blends high-fidelity CMOST simulation data with machine learning precision in which it enables real-time optimization with field-scale relevance. Using 500 simulation scenarios validated by laboratory input, the SL model achieved exceptional predictive performance (R² = 0.988, RMSE = 0.304), outperforming all individual learners. The optimal recovery factor (RF) of 79.49% was obtained with the finely tuned concentrations of surfactant (5483.29 ppm), polymer (2242.61 ppm), SO?²? (5610.15 ppm), CO?²? (7053.59 ppm), and Na? (9939.35 ppm). Remarkably, the SL approach could reduce optimization time from 10 hours (CMOST) to under 1 minute; this underscored its potential for real-time operational deployment. The novelty of this work lies in its integrated use of ensemble learning to capture the complex and non-linear interactions between ionic chemistry and oil mobilization behavior, offering a field-ready AI framework for rapid and adaptive EOR design. This approach paves the way for the intelligent optimization of ASP schemes by minimizing the reliance on computationally intensive simulations while ensuring chemical and economic efficiency in marginal or complex reservoirs. |
| format | Article |
| id | doaj-art-ec1b7086732d468aabd466ee22abc95d |
| institution | DOAJ |
| issn | 2502-9258 2502-9266 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Komunitas Ilmuwan dan Profesional Muslim Indonesia |
| record_format | Article |
| series | Communications in Science and Technology |
| spelling | doaj-art-ec1b7086732d468aabd466ee22abc95d2025-08-20T02:57:04ZengKomunitas Ilmuwan dan Profesional Muslim IndonesiaCommunications in Science and Technology2502-92582502-92662025-07-0110114815910.21924/cst.10.1.2025.16491649Maximizing oil recovery in sandstone reservoirs through optimized ASP injection using the super learner algorithmDike Fitriansyah PutraMohd Zaidi JaafarKu Muhd Na’im KhalifApri SiswantoIchsan LukmanAhmad KurniawanOptimizing the Alkaline-Surfactant-Polymer (ASP) injection process remains a persistent challenge in Enhanced Oil Recovery (EOR), particularly in heterogeneous sandstone reservoirs where traditional reservoir simulators are constrained by high computational demands and limited flexibility. This study introduces a novel application of the Super Learner (SL) ensemble, a stacking-based machine learning algorithm integrating multiple base models (XGBoost, SVR, BRR, and Decision Tree), to systematically predict and optimize ASP injection parameters. Unlike previous approaches, our method blends high-fidelity CMOST simulation data with machine learning precision in which it enables real-time optimization with field-scale relevance. Using 500 simulation scenarios validated by laboratory input, the SL model achieved exceptional predictive performance (R² = 0.988, RMSE = 0.304), outperforming all individual learners. The optimal recovery factor (RF) of 79.49% was obtained with the finely tuned concentrations of surfactant (5483.29 ppm), polymer (2242.61 ppm), SO?²? (5610.15 ppm), CO?²? (7053.59 ppm), and Na? (9939.35 ppm). Remarkably, the SL approach could reduce optimization time from 10 hours (CMOST) to under 1 minute; this underscored its potential for real-time operational deployment. The novelty of this work lies in its integrated use of ensemble learning to capture the complex and non-linear interactions between ionic chemistry and oil mobilization behavior, offering a field-ready AI framework for rapid and adaptive EOR design. This approach paves the way for the intelligent optimization of ASP schemes by minimizing the reliance on computationally intensive simulations while ensuring chemical and economic efficiency in marginal or complex reservoirs.https://cst.kipmi.or.id/journal/article/view/1649eorasprecovery factormachine learningsuper learner |
| spellingShingle | Dike Fitriansyah Putra Mohd Zaidi Jaafar Ku Muhd Na’im Khalif Apri Siswanto Ichsan Lukman Ahmad Kurniawan Maximizing oil recovery in sandstone reservoirs through optimized ASP injection using the super learner algorithm Communications in Science and Technology eor asp recovery factor machine learning super learner |
| title | Maximizing oil recovery in sandstone reservoirs through optimized ASP injection using the super learner algorithm |
| title_full | Maximizing oil recovery in sandstone reservoirs through optimized ASP injection using the super learner algorithm |
| title_fullStr | Maximizing oil recovery in sandstone reservoirs through optimized ASP injection using the super learner algorithm |
| title_full_unstemmed | Maximizing oil recovery in sandstone reservoirs through optimized ASP injection using the super learner algorithm |
| title_short | Maximizing oil recovery in sandstone reservoirs through optimized ASP injection using the super learner algorithm |
| title_sort | maximizing oil recovery in sandstone reservoirs through optimized asp injection using the super learner algorithm |
| topic | eor asp recovery factor machine learning super learner |
| url | https://cst.kipmi.or.id/journal/article/view/1649 |
| work_keys_str_mv | AT dikefitriansyahputra maximizingoilrecoveryinsandstonereservoirsthroughoptimizedaspinjectionusingthesuperlearneralgorithm AT mohdzaidijaafar maximizingoilrecoveryinsandstonereservoirsthroughoptimizedaspinjectionusingthesuperlearneralgorithm AT kumuhdnaimkhalif maximizingoilrecoveryinsandstonereservoirsthroughoptimizedaspinjectionusingthesuperlearneralgorithm AT aprisiswanto maximizingoilrecoveryinsandstonereservoirsthroughoptimizedaspinjectionusingthesuperlearneralgorithm AT ichsanlukman maximizingoilrecoveryinsandstonereservoirsthroughoptimizedaspinjectionusingthesuperlearneralgorithm AT ahmadkurniawan maximizingoilrecoveryinsandstonereservoirsthroughoptimizedaspinjectionusingthesuperlearneralgorithm |