Adaptive Proxy-based Robust Production Optimization with Multilayer Perceptron
Machine learning (ML) has been a technique employed to build data-driven models that can map the relationship between the input and output data provided. ML-based data-driven models offer an alternative path to solving optimization problems, which are conventionally resolved by applying simulation m...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-12-01
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| Series: | Applied Computing and Geosciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590197422000258 |
| _version_ | 1811310877175971840 |
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| author | Cuthbert Shang Wui Ng Ashkan Jahanbani Ghahfarokhi |
| author_facet | Cuthbert Shang Wui Ng Ashkan Jahanbani Ghahfarokhi |
| author_sort | Cuthbert Shang Wui Ng |
| collection | DOAJ |
| description | Machine learning (ML) has been a technique employed to build data-driven models that can map the relationship between the input and output data provided. ML-based data-driven models offer an alternative path to solving optimization problems, which are conventionally resolved by applying simulation models. Higher computational cost is induced if the simulation model is computationally intensive. Such a situation aptly applies to petroleum engineering, especially when different geological realizations of numerical reservoir simulation (NRS) models are considered for production optimization. Therefore, data-driven models are suggested as a substitute for NRS. In this work, we demonstrated how multilayer perceptron could be implemented to build data-driven models based on 10 realizations of the Egg Model. These models were then coupled with two nature-inspired algorithms, viz. particle swarm optimization and grey wolf optimizer to solve waterflooding optimization. These data-driven models were adaptively re-trained by applying a training database that was updated via the addition of extra samples retrieved from optimization with the proxy models. The details of the methodology will be divulged in the paper. According to the results obtained, we could deduce that the methodology generated reliable data-driven models to solve the optimization problem, as justified by the excellent performance of the ML-based proxy model (with a coefficient of determination, R2 exceeding 0.98 in training, testing, and blind validation) and accurate optimization result (less than 1% error between the Expected Net Present Values optimized using NRS and proxy models). This study aids in an enhanced understanding of implementing adaptive training in tandem with optimization in ML-based proxy modeling. |
| first_indexed | 2024-04-13T10:06:48Z |
| format | Article |
| id | doaj.art-cb99478d4b464bd39c3158ba8d836675 |
| institution | Directory Open Access Journal |
| issn | 2590-1974 |
| language | English |
| last_indexed | 2024-04-13T10:06:48Z |
| publishDate | 2022-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Applied Computing and Geosciences |
| spelling | doaj.art-cb99478d4b464bd39c3158ba8d8366752022-12-22T02:51:03ZengElsevierApplied Computing and Geosciences2590-19742022-12-0116100103Adaptive Proxy-based Robust Production Optimization with Multilayer PerceptronCuthbert Shang Wui Ng0Ashkan Jahanbani Ghahfarokhi1Corresponding author.; Department of Geoscience and Petroleum, Norwegian University of Science and Technology, Trondheim, NorwayDepartment of Geoscience and Petroleum, Norwegian University of Science and Technology, Trondheim, NorwayMachine learning (ML) has been a technique employed to build data-driven models that can map the relationship between the input and output data provided. ML-based data-driven models offer an alternative path to solving optimization problems, which are conventionally resolved by applying simulation models. Higher computational cost is induced if the simulation model is computationally intensive. Such a situation aptly applies to petroleum engineering, especially when different geological realizations of numerical reservoir simulation (NRS) models are considered for production optimization. Therefore, data-driven models are suggested as a substitute for NRS. In this work, we demonstrated how multilayer perceptron could be implemented to build data-driven models based on 10 realizations of the Egg Model. These models were then coupled with two nature-inspired algorithms, viz. particle swarm optimization and grey wolf optimizer to solve waterflooding optimization. These data-driven models were adaptively re-trained by applying a training database that was updated via the addition of extra samples retrieved from optimization with the proxy models. The details of the methodology will be divulged in the paper. According to the results obtained, we could deduce that the methodology generated reliable data-driven models to solve the optimization problem, as justified by the excellent performance of the ML-based proxy model (with a coefficient of determination, R2 exceeding 0.98 in training, testing, and blind validation) and accurate optimization result (less than 1% error between the Expected Net Present Values optimized using NRS and proxy models). This study aids in an enhanced understanding of implementing adaptive training in tandem with optimization in ML-based proxy modeling.http://www.sciencedirect.com/science/article/pii/S2590197422000258Machine learningData-driven modelingMultilayer perceptronNature-inspired algorithmsAdaptive trainingRobust production optimization |
| spellingShingle | Cuthbert Shang Wui Ng Ashkan Jahanbani Ghahfarokhi Adaptive Proxy-based Robust Production Optimization with Multilayer Perceptron Applied Computing and Geosciences Machine learning Data-driven modeling Multilayer perceptron Nature-inspired algorithms Adaptive training Robust production optimization |
| title | Adaptive Proxy-based Robust Production Optimization with Multilayer Perceptron |
| title_full | Adaptive Proxy-based Robust Production Optimization with Multilayer Perceptron |
| title_fullStr | Adaptive Proxy-based Robust Production Optimization with Multilayer Perceptron |
| title_full_unstemmed | Adaptive Proxy-based Robust Production Optimization with Multilayer Perceptron |
| title_short | Adaptive Proxy-based Robust Production Optimization with Multilayer Perceptron |
| title_sort | adaptive proxy based robust production optimization with multilayer perceptron |
| topic | Machine learning Data-driven modeling Multilayer perceptron Nature-inspired algorithms Adaptive training Robust production optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2590197422000258 |
| work_keys_str_mv | AT cuthbertshangwuing adaptiveproxybasedrobustproductionoptimizationwithmultilayerperceptron AT ashkanjahanbanighahfarokhi adaptiveproxybasedrobustproductionoptimizationwithmultilayerperceptron |