Predictive data analytics application for enhanced oil recovery in a mature field in the Middle East
Top-Down Modeling (TDM) was developed through four main steps of data gathering and preparation, model build-up, model training and validation, and model prediction, based on more than 8 years of development and production/injection data and well tests and log data from more than 37 wells in a carbo...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2020-04-01
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| Series: | Petroleum Exploration and Development |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1876380420600568 |
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| author | Alklih Mohamad YOUSEF Ghahfarokhi Payam KAVOUSI Marwan ALNUAIMI Yara ALATRACH |
| author_facet | Alklih Mohamad YOUSEF Ghahfarokhi Payam KAVOUSI Marwan ALNUAIMI Yara ALATRACH |
| author_sort | Alklih Mohamad YOUSEF |
| collection | DOAJ |
| description | Top-Down Modeling (TDM) was developed through four main steps of data gathering and preparation, model build-up, model training and validation, and model prediction, based on more than 8 years of development and production/injection data and well tests and log data from more than 37 wells in a carbonate reservoir of onshore Middle-East. The model was used for production prediction and sensitivity analysis. The TDM involves 5 inter-connected data-driven models, and the output of one model is input for the next model. The developed TDM history matched the blind dataset with a high accuracy, it was validated spatially and applied on a temporal blind test, the results show that the developed TDM is capable of generalization when applied to new dataset and can accurately predict reservoir performance for 3 months in future. Production forecasting by the validated history matched TDM model suggest that the water production increases while oil production decreases under the given operation condition. The injection analysis of the history matched model is also examined by varying injection amounts and injection period for water and gas (WAG) process. Results reveal that higher injection volume does not necessarily translate to higher oil production in this field. Moreover, we show that a WAG process with 3 months period would result in higher oil production and lower water production and gas production than a 6 months process. The developed TDM provides a fast and robust alternative to WAG parameters, and optimizes infill well location. Key words: Top-Down Modeling, reservoir modeling, artificial intelligence, neural network, data-driven model, enhanced oil recovery, reservoir management |
| first_indexed | 2024-12-14T16:36:52Z |
| format | Article |
| id | doaj.art-9fe21b4f186d4903859fe0bf46e3ebbc |
| institution | Directory Open Access Journal |
| issn | 1876-3804 |
| language | English |
| last_indexed | 2024-12-14T16:36:52Z |
| publishDate | 2020-04-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Petroleum Exploration and Development |
| spelling | doaj.art-9fe21b4f186d4903859fe0bf46e3ebbc2022-12-21T22:54:25ZengKeAi Communications Co., Ltd.Petroleum Exploration and Development1876-38042020-04-01472393399Predictive data analytics application for enhanced oil recovery in a mature field in the Middle EastAlklih Mohamad YOUSEF0Ghahfarokhi Payam KAVOUSI1Marwan ALNUAIMI2Yara ALATRACH3ADNOC Onshore, P.O. Box 270, Abu Dhabi, United Arab Emirates; Corresponding authorIntelligent Solutions Inc. and West Virginia University, Morgantown, WV 26506, USAADNOC Offshore, P.O. Box 46808, Abu Dhabi, United Arab EmiratesADNOC Offshore, P.O. Box 46808, Abu Dhabi, United Arab EmiratesTop-Down Modeling (TDM) was developed through four main steps of data gathering and preparation, model build-up, model training and validation, and model prediction, based on more than 8 years of development and production/injection data and well tests and log data from more than 37 wells in a carbonate reservoir of onshore Middle-East. The model was used for production prediction and sensitivity analysis. The TDM involves 5 inter-connected data-driven models, and the output of one model is input for the next model. The developed TDM history matched the blind dataset with a high accuracy, it was validated spatially and applied on a temporal blind test, the results show that the developed TDM is capable of generalization when applied to new dataset and can accurately predict reservoir performance for 3 months in future. Production forecasting by the validated history matched TDM model suggest that the water production increases while oil production decreases under the given operation condition. The injection analysis of the history matched model is also examined by varying injection amounts and injection period for water and gas (WAG) process. Results reveal that higher injection volume does not necessarily translate to higher oil production in this field. Moreover, we show that a WAG process with 3 months period would result in higher oil production and lower water production and gas production than a 6 months process. The developed TDM provides a fast and robust alternative to WAG parameters, and optimizes infill well location. Key words: Top-Down Modeling, reservoir modeling, artificial intelligence, neural network, data-driven model, enhanced oil recovery, reservoir managementhttp://www.sciencedirect.com/science/article/pii/S1876380420600568 |
| spellingShingle | Alklih Mohamad YOUSEF Ghahfarokhi Payam KAVOUSI Marwan ALNUAIMI Yara ALATRACH Predictive data analytics application for enhanced oil recovery in a mature field in the Middle East Petroleum Exploration and Development |
| title | Predictive data analytics application for enhanced oil recovery in a mature field in the Middle East |
| title_full | Predictive data analytics application for enhanced oil recovery in a mature field in the Middle East |
| title_fullStr | Predictive data analytics application for enhanced oil recovery in a mature field in the Middle East |
| title_full_unstemmed | Predictive data analytics application for enhanced oil recovery in a mature field in the Middle East |
| title_short | Predictive data analytics application for enhanced oil recovery in a mature field in the Middle East |
| title_sort | predictive data analytics application for enhanced oil recovery in a mature field in the middle east |
| url | http://www.sciencedirect.com/science/article/pii/S1876380420600568 |
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