Auto-tuning PVT data using multi-objective optimization: Application of NSGA-II algorithm
Reservoir simulation is known as perhaps the most widely used, accurate, and reliable method for field development in the petroleum industry. An integral part of a reliable reservoir simulation process is to consider robust and rigorous tuned EOS models. Traditionally, EOS models are tuned iterative...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-03-01
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| Series: | Petroleum |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405656123000226 |
| _version_ | 1797238004538933248 |
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| author | Abdolhadi Zarifi Mohammad Madani Mohammad Jafarzadegan |
| author_facet | Abdolhadi Zarifi Mohammad Madani Mohammad Jafarzadegan |
| author_sort | Abdolhadi Zarifi |
| collection | DOAJ |
| description | Reservoir simulation is known as perhaps the most widely used, accurate, and reliable method for field development in the petroleum industry. An integral part of a reliable reservoir simulation process is to consider robust and rigorous tuned EOS models. Traditionally, EOS models are tuned iteratively through arduous workflows against experimental PVT data. However, this comes with a number of drawbacks such as forcingly using weight factors, which upon alteration adversely affects the optimization process. The objective of the current work is thus to introduce an auto-tune PVT matching tool using NSGA-II multi-objective optimization. In order to illustrate the robustness of the presented technique, three different PVT samples are used, including two black-oil and one gas condensate sample. We utilize Peng-Robinson EOS during all the manual and auto-tuning processes. Comparison of auto-tuned EOS-generated results with those of experimental and computed statistical error values for these samples clearly show that the proposed method is robust. In addition, the proposed method, contrary to the manual matching process, provides the engineer with several matched solutions, which allows them to select a match based on the engineering background to be best amenable to the problem at hand. In addition, the proposed technique is fast, and can output several solutions within less time compared to the traditional manual matching method. |
| first_indexed | 2024-04-24T17:28:45Z |
| format | Article |
| id | doaj.art-fc88fa8ea068485ab239c2722cb86929 |
| institution | Directory Open Access Journal |
| issn | 2405-6561 |
| language | English |
| last_indexed | 2024-04-24T17:28:45Z |
| publishDate | 2024-03-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Petroleum |
| spelling | doaj.art-fc88fa8ea068485ab239c2722cb869292024-03-28T06:38:20ZengKeAi Communications Co., Ltd.Petroleum2405-65612024-03-01101135149Auto-tuning PVT data using multi-objective optimization: Application of NSGA-II algorithmAbdolhadi Zarifi0Mohammad Madani1Mohammad Jafarzadegan2Reservoir Engineering Systems, Petroleum Engineering Department, Main Office Building of National Iranian South Oil Company (NISOC), Ahvaz, Iran; Combined Planning Management, National Iranian Oil Company (NIOC), Tehran, IranReservoir Engineering Systems, Petroleum Engineering Department, Main Office Building of National Iranian South Oil Company (NISOC), Ahvaz, Iran; Reservoir Engineering Division, Petroleum Engineering Department, Petroleum Engineering and Development Company (PEDEC), Tehran, Iran; Combined Planning Management, National Iranian Oil Company (NIOC), Tehran, Iran; Corresponding author.Reservoir Engineering Systems, Petroleum Engineering Department, Main Office Building of National Iranian South Oil Company (NISOC), Ahvaz, IranReservoir simulation is known as perhaps the most widely used, accurate, and reliable method for field development in the petroleum industry. An integral part of a reliable reservoir simulation process is to consider robust and rigorous tuned EOS models. Traditionally, EOS models are tuned iteratively through arduous workflows against experimental PVT data. However, this comes with a number of drawbacks such as forcingly using weight factors, which upon alteration adversely affects the optimization process. The objective of the current work is thus to introduce an auto-tune PVT matching tool using NSGA-II multi-objective optimization. In order to illustrate the robustness of the presented technique, three different PVT samples are used, including two black-oil and one gas condensate sample. We utilize Peng-Robinson EOS during all the manual and auto-tuning processes. Comparison of auto-tuned EOS-generated results with those of experimental and computed statistical error values for these samples clearly show that the proposed method is robust. In addition, the proposed method, contrary to the manual matching process, provides the engineer with several matched solutions, which allows them to select a match based on the engineering background to be best amenable to the problem at hand. In addition, the proposed technique is fast, and can output several solutions within less time compared to the traditional manual matching method.http://www.sciencedirect.com/science/article/pii/S2405656123000226Auto-tuningPVTEquation of stateNSGA-IIMulti-objective optimization |
| spellingShingle | Abdolhadi Zarifi Mohammad Madani Mohammad Jafarzadegan Auto-tuning PVT data using multi-objective optimization: Application of NSGA-II algorithm Petroleum Auto-tuning PVT Equation of state NSGA-II Multi-objective optimization |
| title | Auto-tuning PVT data using multi-objective optimization: Application of NSGA-II algorithm |
| title_full | Auto-tuning PVT data using multi-objective optimization: Application of NSGA-II algorithm |
| title_fullStr | Auto-tuning PVT data using multi-objective optimization: Application of NSGA-II algorithm |
| title_full_unstemmed | Auto-tuning PVT data using multi-objective optimization: Application of NSGA-II algorithm |
| title_short | Auto-tuning PVT data using multi-objective optimization: Application of NSGA-II algorithm |
| title_sort | auto tuning pvt data using multi objective optimization application of nsga ii algorithm |
| topic | Auto-tuning PVT Equation of state NSGA-II Multi-objective optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2405656123000226 |
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