Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part I

Anna Samnioti and Vassilis Gaganis ()
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Anna Samnioti: School of Mining and Metallurgical Engineering, National Technical University of Athens, 15780 Athens, Greece
Vassilis Gaganis: School of Mining and Metallurgical Engineering, National Technical University of Athens, 15780 Athens, Greece

Energies, 2023, vol. 16, issue 16, 1-43

Abstract: In recent years, machine learning (ML) has become a buzzword in the petroleum industry with numerous applications that guide engineers toward better decision making. The most powerful tool that most production development decisions rely on is reservoir simulation with applications in numerous modeling procedures, such as individual simulation runs, history matching and production forecast and optimization. However, all these applications lead to considerable computational time- and resource-associated costs, and rendering reservoir simulators is not fast or robust, thus introducing the need for more time-efficient and smart tools like ML models which can adapt and provide fast and competent results that mimic simulators’ performance within an acceptable error margin. The first part of the present study (Part I) offers a detailed review of ML techniques in the petroleum industry, specifically in subsurface reservoir simulation, for cases of individual simulation runs and history matching, whereas ML-based production forecast and optimization applications are presented in Part II. This review can assist engineers as a complete source for applied ML techniques since, with the generation of large-scale data in everyday activities, ML is becoming a necessity for future and more efficient applications.

Keywords: review; machine learning; reservoir simulations; history matching; production optimization; production forecast (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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