Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part II
In recent years, Machine Learning (ML) has become a buzzword in the petroleum industry, with numerous applications which guide engineers in better decision making. The most powerful tool that most production development decisions rely on is reservoir simulation with applications in multiple modeling...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/16/18/6727 |
| _version_ | 1827726345297199104 |
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| author | Anna Samnioti Vassilis Gaganis |
| author_facet | Anna Samnioti Vassilis Gaganis |
| author_sort | Anna Samnioti |
| collection | DOAJ |
| description | In recent years, Machine Learning (ML) has become a buzzword in the petroleum industry, with numerous applications which guide engineers in better decision making. The most powerful tool that most production development decisions rely on is reservoir simulation with applications in multiple modeling procedures, such as individual simulation runs, history matching and production forecast and optimization. However, all of these applications lead to considerable computational time and computer resource-associated costs, rendering reservoir simulators as not fast and robust enough, and thus introducing the need for more time-efficient and intelligent tools, such as ML models which are able to adapt and provide fast and competent results that mimic the simulator’s performance within an acceptable error margin. In a recent paper, the developed ML applications in a subsurface reservoir simulation were reviewed, focusing on improving the speed and accuracy of individual reservoir simulation runs and history matching. This paper consists of the second part of that study, offering a detailed review of ML-based Production Forecast Optimization (PFO). This review can assist engineers as a complete source for applied ML techniques in reservoir simulation since, with the generation of large-scale data in everyday activities, ML is becoming a necessity for future and more efficient applications. |
| first_indexed | 2024-03-10T22:49:43Z |
| format | Article |
| id | doaj.art-df5f3df6036e4d308fe43c71e277f975 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T22:49:43Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-df5f3df6036e4d308fe43c71e277f9752023-11-19T10:29:14ZengMDPI AGEnergies1996-10732023-09-011618672710.3390/en16186727Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part IIAnna Samnioti0Vassilis Gaganis1School of Mining and Metallurgical Engineering, National Technical University of Athens, 15780 Athens, GreeceSchool of Mining and Metallurgical Engineering, National Technical University of Athens, 15780 Athens, GreeceIn recent years, Machine Learning (ML) has become a buzzword in the petroleum industry, with numerous applications which guide engineers in better decision making. The most powerful tool that most production development decisions rely on is reservoir simulation with applications in multiple modeling procedures, such as individual simulation runs, history matching and production forecast and optimization. However, all of these applications lead to considerable computational time and computer resource-associated costs, rendering reservoir simulators as not fast and robust enough, and thus introducing the need for more time-efficient and intelligent tools, such as ML models which are able to adapt and provide fast and competent results that mimic the simulator’s performance within an acceptable error margin. In a recent paper, the developed ML applications in a subsurface reservoir simulation were reviewed, focusing on improving the speed and accuracy of individual reservoir simulation runs and history matching. This paper consists of the second part of that study, offering a detailed review of ML-based Production Forecast Optimization (PFO). This review can assist engineers as a complete source for applied ML techniques in reservoir simulation since, with the generation of large-scale data in everyday activities, ML is becoming a necessity for future and more efficient applications.https://www.mdpi.com/1996-1073/16/18/6727reviewmachine learningreservoir simulationhistory matchingproduction optimizationproduction forecast |
| spellingShingle | Anna Samnioti Vassilis Gaganis Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part II Energies review machine learning reservoir simulation history matching production optimization production forecast |
| title | Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part II |
| title_full | Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part II |
| title_fullStr | Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part II |
| title_full_unstemmed | Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part II |
| title_short | Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part II |
| title_sort | applications of machine learning in subsurface reservoir simulation a review part ii |
| topic | review machine learning reservoir simulation history matching production optimization production forecast |
| url | https://www.mdpi.com/1996-1073/16/18/6727 |
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