A review of artificial intelligence techniques applied to subsurface oil and gas reservoir simulation; Applied Soft Computing; Vol. 198
| Parent link: | Applied Soft Computing.— .— Amsterdam: Elsevier Science Publishing Company Inc. Vol. 198.— 2026.— Article number 115222, 20 p. |
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| Other Authors: | , , , , , , , |
| Summary: | Title screen The integration of artificial intelligence (AI) into reservoir simulation (RS) has rapidly evolved, offering new solutions to long-standing challenges in predicting subsurface behavior. Although recent advances have improved the accuracy of fluid-flow modeling and reservoir performance forecasting, issues related to computational cost, data complexity, and model scalability persist. This review, therefore, explores the transformative role of AI in enhancing RS, with a particular focus on three core areas: history matching (HM), production optimization, and computational acceleration. To achieve this, the study systematically analyzes AI-driven approaches, including the historical development of RS and its multiple challenges with data obstacles. These include data volume and quality, input variable prioritization through feature selection, and objective function refinement. Reservoir property assessment across clastic and carbonate formations, alongside AI-enabled strategies for optimizing well placement and well production and injection planning pose additional challenges. The review finds that AI significantly enhances prediction accuracy and reduces simulation time while offering scalable solutions to big-data challenges in RS workflows, leading to better decision-making. Despite these advancements, obstacles remain in the real field deployment of AI models, the interpretation of their output results, and the assurance of consistent performance across various reservoirs. This highlights the necessity for hybrid physics and AI methodologies, in conjunction with automated, high-performance computing workflows, and learning from real field applications to address these deficiencies. This review provides a structured perspective on leveraging AI to advance reservoir engineering practices, enhance predictive capabilities, and maximize hydrocarbon recovery in an economically and operationally viable manner. It also identifies key challenges and research gaps and offers recommendations for future directions Текстовый файл AM_Agreement |
| Language: | English |
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2026
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| Subjects: | |
| Online Access: | https://doi.org/10.1016/j.asoc.2026.115222 |
| Format: | Electronic Book Chapter |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=687225 |