Data-Driven Proxy Models for Improving Advanced Well Completion Design under Uncertainty
In order to improve the design of advanced wells, the performance of such wells needs to be carefully assessed by taking the reservoir uncertainties into account. This research aimed to develop data-driven proxy models for the simulation and assessment of oil recovery through advanced wells under un...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/20/7484 |
| _version_ | 1797473611728027648 |
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| author | Ali Moradi Javad Tavakolifaradonbe Britt M. E. Moldestad |
| author_facet | Ali Moradi Javad Tavakolifaradonbe Britt M. E. Moldestad |
| author_sort | Ali Moradi |
| collection | DOAJ |
| description | In order to improve the design of advanced wells, the performance of such wells needs to be carefully assessed by taking the reservoir uncertainties into account. This research aimed to develop data-driven proxy models for the simulation and assessment of oil recovery through advanced wells under uncertainty. An artificial neural network (ANN) was employed to create accurate and computationally efficient proxy models as an alternative to physics-based integrated well–reservoir models created by the Eclipse<sup>®</sup> reservoir simulator. The simulation speed and accuracy of the data-driven proxy models compared to physic-driven models were then evaluated. The evaluation showed that while the developed proxy models are 350 times faster, they can predict the production of oil and unwanted fluids through advanced wells with a mean error of less than 1% and 4%, respectively. As a result, the data-driven proxy models can be considered an efficient tool for uncertainty analysis where several simulations need to be performed to cover all possible scenarios. In this study, the developed proxy models were applied for uncertainty quantification of oil recovery from advanced wells completed with different types of downhole flow control devices (FCDs). According to the obtained results, compared to other types of well completion design, advanced wells completed with autonomous inflow control valve (AICV) technology have the best performance in limiting the production of unwanted fluids and are able to reduce the associated risk by 91%. |
| first_indexed | 2024-03-09T20:18:56Z |
| format | Article |
| id | doaj.art-e8496d12aeb54a5aa738b2c382ac63e7 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T20:18:56Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-e8496d12aeb54a5aa738b2c382ac63e72023-11-23T23:55:23ZengMDPI AGEnergies1996-10732022-10-011520748410.3390/en15207484Data-Driven Proxy Models for Improving Advanced Well Completion Design under UncertaintyAli Moradi0Javad Tavakolifaradonbe1Britt M. E. Moldestad2Department of Process, Energy and Environmental Technology, University of South-Eastern Norway, 3918 Porsgrunn, NorwayDepartment of Process, Energy and Environmental Technology, University of South-Eastern Norway, 3918 Porsgrunn, NorwayDepartment of Process, Energy and Environmental Technology, University of South-Eastern Norway, 3918 Porsgrunn, NorwayIn order to improve the design of advanced wells, the performance of such wells needs to be carefully assessed by taking the reservoir uncertainties into account. This research aimed to develop data-driven proxy models for the simulation and assessment of oil recovery through advanced wells under uncertainty. An artificial neural network (ANN) was employed to create accurate and computationally efficient proxy models as an alternative to physics-based integrated well–reservoir models created by the Eclipse<sup>®</sup> reservoir simulator. The simulation speed and accuracy of the data-driven proxy models compared to physic-driven models were then evaluated. The evaluation showed that while the developed proxy models are 350 times faster, they can predict the production of oil and unwanted fluids through advanced wells with a mean error of less than 1% and 4%, respectively. As a result, the data-driven proxy models can be considered an efficient tool for uncertainty analysis where several simulations need to be performed to cover all possible scenarios. In this study, the developed proxy models were applied for uncertainty quantification of oil recovery from advanced wells completed with different types of downhole flow control devices (FCDs). According to the obtained results, compared to other types of well completion design, advanced wells completed with autonomous inflow control valve (AICV) technology have the best performance in limiting the production of unwanted fluids and are able to reduce the associated risk by 91%.https://www.mdpi.com/1996-1073/15/20/7484ANNproxy modelsadvanced wellsFCDAICV |
| spellingShingle | Ali Moradi Javad Tavakolifaradonbe Britt M. E. Moldestad Data-Driven Proxy Models for Improving Advanced Well Completion Design under Uncertainty Energies ANN proxy models advanced wells FCD AICV |
| title | Data-Driven Proxy Models for Improving Advanced Well Completion Design under Uncertainty |
| title_full | Data-Driven Proxy Models for Improving Advanced Well Completion Design under Uncertainty |
| title_fullStr | Data-Driven Proxy Models for Improving Advanced Well Completion Design under Uncertainty |
| title_full_unstemmed | Data-Driven Proxy Models for Improving Advanced Well Completion Design under Uncertainty |
| title_short | Data-Driven Proxy Models for Improving Advanced Well Completion Design under Uncertainty |
| title_sort | data driven proxy models for improving advanced well completion design under uncertainty |
| topic | ANN proxy models advanced wells FCD AICV |
| url | https://www.mdpi.com/1996-1073/15/20/7484 |
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