Dynamical counterfactual inference under time-series model for waterflooding oilfield
The performances of numerical simulation and machine learning in production forecasting are severely dependent on precise geological modeling and high-quality history matching. To address these challenges, causal inference is an effective methodology since it can provide a causality for formalizing...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-02-01
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| Series: | Petroleum |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405656124000476 |
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| author | Guoquan Wen Chao Min Qingxia Zhang Guoyong Liao |
| author_facet | Guoquan Wen Chao Min Qingxia Zhang Guoyong Liao |
| author_sort | Guoquan Wen |
| collection | DOAJ |
| description | The performances of numerical simulation and machine learning in production forecasting are severely dependent on precise geological modeling and high-quality history matching. To address these challenges, causal inference is an effective methodology since it can provide a causality for formalizing causality in history, not statistical dependence. In this paper, to dynamically predict oil production from causality existed in waterflooding oilfield, a dynamical counterfactual inference framework is built to predict oil production. The proposed framework can forecast the oil production under non-observation of engineering factors, i.e., counterfactual, and provide the causal effect of engineering factors impacting on oil production. Meanwhile, combining with the practice exploitation in engineering factor impacting on production, a counterfactual experiment is designed to execute counterfactual prediction. Compared with general machine learning and statistical models, our results not only show better performance in oil production flooding but also guide the specific optimization in improving production, which holds more practical application significance. |
| format | Article |
| id | doaj-art-c4175b90c6e24a338b22b7e49dcfc437 |
| institution | DOAJ |
| issn | 2405-6561 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Petroleum |
| spelling | doaj-art-c4175b90c6e24a338b22b7e49dcfc4372025-08-20T03:15:28ZengKeAi Communications Co., Ltd.Petroleum2405-65612025-02-0111111312410.1016/j.petlm.2024.11.001Dynamical counterfactual inference under time-series model for waterflooding oilfieldGuoquan Wen0Chao Min1Qingxia Zhang2Guoyong Liao3School of Science, Southwest Petroleum University, Chengdu, 610500, China; Institute for Artificial Intelligence, Southwest Petroleum University, Chengdu, 610500, ChinaSchool of Science, Southwest Petroleum University, Chengdu, 610500, China; Institute for Artificial Intelligence, Southwest Petroleum University, Chengdu, 610500, China; State Key Laboratory of Oil and Gas Reservoir and Exploitation, Chengdu, 610500, China; Corresponding author.School of Science, Southwest Petroleum University, Chengdu, 610500, China; Institute for Artificial Intelligence, Southwest Petroleum University, Chengdu, 610500, ChinaSchool of Science, Southwest Petroleum University, Chengdu, 610500, China; Institute for Artificial Intelligence, Southwest Petroleum University, Chengdu, 610500, ChinaThe performances of numerical simulation and machine learning in production forecasting are severely dependent on precise geological modeling and high-quality history matching. To address these challenges, causal inference is an effective methodology since it can provide a causality for formalizing causality in history, not statistical dependence. In this paper, to dynamically predict oil production from causality existed in waterflooding oilfield, a dynamical counterfactual inference framework is built to predict oil production. The proposed framework can forecast the oil production under non-observation of engineering factors, i.e., counterfactual, and provide the causal effect of engineering factors impacting on oil production. Meanwhile, combining with the practice exploitation in engineering factor impacting on production, a counterfactual experiment is designed to execute counterfactual prediction. Compared with general machine learning and statistical models, our results not only show better performance in oil production flooding but also guide the specific optimization in improving production, which holds more practical application significance.http://www.sciencedirect.com/science/article/pii/S2405656124000476Waterflooding oilfieldSingle oil wellCounterfactual inferenceTime-series |
| spellingShingle | Guoquan Wen Chao Min Qingxia Zhang Guoyong Liao Dynamical counterfactual inference under time-series model for waterflooding oilfield Petroleum Waterflooding oilfield Single oil well Counterfactual inference Time-series |
| title | Dynamical counterfactual inference under time-series model for waterflooding oilfield |
| title_full | Dynamical counterfactual inference under time-series model for waterflooding oilfield |
| title_fullStr | Dynamical counterfactual inference under time-series model for waterflooding oilfield |
| title_full_unstemmed | Dynamical counterfactual inference under time-series model for waterflooding oilfield |
| title_short | Dynamical counterfactual inference under time-series model for waterflooding oilfield |
| title_sort | dynamical counterfactual inference under time series model for waterflooding oilfield |
| topic | Waterflooding oilfield Single oil well Counterfactual inference Time-series |
| url | http://www.sciencedirect.com/science/article/pii/S2405656124000476 |
| work_keys_str_mv | AT guoquanwen dynamicalcounterfactualinferenceundertimeseriesmodelforwaterfloodingoilfield AT chaomin dynamicalcounterfactualinferenceundertimeseriesmodelforwaterfloodingoilfield AT qingxiazhang dynamicalcounterfactualinferenceundertimeseriesmodelforwaterfloodingoilfield AT guoyongliao dynamicalcounterfactualinferenceundertimeseriesmodelforwaterfloodingoilfield |