Intelligent identification and real-time warning method of diverse complex events in horizontal well fracturing
The existing approaches for identifying events in horizontal well fracturing are difficult, time-consuming, inaccurate, and incapable of real-time warning. Through improvement of data analysis and deep learning algorithm, together with the analysis on data and information of horizontal well fracturi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2023-12-01
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| Series: | Petroleum Exploration and Development |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1876380424604829 |
| _version_ | 1827578732500484096 |
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| author | Bin YUAN Mingze ZHAO Siwei MENG Wei ZHANG He ZHENG |
| author_facet | Bin YUAN Mingze ZHAO Siwei MENG Wei ZHANG He ZHENG |
| author_sort | Bin YUAN |
| collection | DOAJ |
| description | The existing approaches for identifying events in horizontal well fracturing are difficult, time-consuming, inaccurate, and incapable of real-time warning. Through improvement of data analysis and deep learning algorithm, together with the analysis on data and information of horizontal well fracturing in shale gas reservoirs, this paper presents a method for intelligent identification and real-time warning of diverse complex events in horizontal well fracturing. An identification model for “point” events in fracturing is established based on the Att-BiLSTM neural network, along with the broad learning system (BLS) and the BP neural network, and it realizes the intelligent identification of the start/end of fracturing, formation breakdown, instantaneous shut-in, and other events, with an accuracy of over 97%. An identification model for “phase” events in fracturing is established based on enhanced Unet++ network, and it realizes the intelligent identification of pump ball, pre-acid treatment, temporary plugging fracturing, sand plugging, and other events, with an error of less than 0.002. Moreover, a real-time prediction model for fracturing pressure is built based on the Att-BiLSTM neural network, and it realizes the real-time warning of diverse events in fracturing. The proposed method can provide an intelligent, efficient and accurate identification of events in fracturing to support the decision-making. |
| first_indexed | 2024-03-08T21:50:37Z |
| format | Article |
| id | doaj.art-05a224f2ddc0445f921d42dd1e8ec178 |
| institution | Directory Open Access Journal |
| issn | 1876-3804 |
| language | English |
| last_indexed | 2024-03-08T21:50:37Z |
| publishDate | 2023-12-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Petroleum Exploration and Development |
| spelling | doaj.art-05a224f2ddc0445f921d42dd1e8ec1782023-12-20T07:34:04ZengKeAi Communications Co., Ltd.Petroleum Exploration and Development1876-38042023-12-0150614871496Intelligent identification and real-time warning method of diverse complex events in horizontal well fracturingBin YUAN0Mingze ZHAO1Siwei MENG2Wei ZHANG3He ZHENG4School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China;School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China;PetroChina Research Institute of Petroleum Exploration & Development, Beijing 100083, China; Corresponding author.School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China;School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China;The existing approaches for identifying events in horizontal well fracturing are difficult, time-consuming, inaccurate, and incapable of real-time warning. Through improvement of data analysis and deep learning algorithm, together with the analysis on data and information of horizontal well fracturing in shale gas reservoirs, this paper presents a method for intelligent identification and real-time warning of diverse complex events in horizontal well fracturing. An identification model for “point” events in fracturing is established based on the Att-BiLSTM neural network, along with the broad learning system (BLS) and the BP neural network, and it realizes the intelligent identification of the start/end of fracturing, formation breakdown, instantaneous shut-in, and other events, with an accuracy of over 97%. An identification model for “phase” events in fracturing is established based on enhanced Unet++ network, and it realizes the intelligent identification of pump ball, pre-acid treatment, temporary plugging fracturing, sand plugging, and other events, with an error of less than 0.002. Moreover, a real-time prediction model for fracturing pressure is built based on the Att-BiLSTM neural network, and it realizes the real-time warning of diverse events in fracturing. The proposed method can provide an intelligent, efficient and accurate identification of events in fracturing to support the decision-making.http://www.sciencedirect.com/science/article/pii/S1876380424604829horizontal well fracturingfracturing eventsintelligent identificationreal-time warningdeep learning |
| spellingShingle | Bin YUAN Mingze ZHAO Siwei MENG Wei ZHANG He ZHENG Intelligent identification and real-time warning method of diverse complex events in horizontal well fracturing Petroleum Exploration and Development horizontal well fracturing fracturing events intelligent identification real-time warning deep learning |
| title | Intelligent identification and real-time warning method of diverse complex events in horizontal well fracturing |
| title_full | Intelligent identification and real-time warning method of diverse complex events in horizontal well fracturing |
| title_fullStr | Intelligent identification and real-time warning method of diverse complex events in horizontal well fracturing |
| title_full_unstemmed | Intelligent identification and real-time warning method of diverse complex events in horizontal well fracturing |
| title_short | Intelligent identification and real-time warning method of diverse complex events in horizontal well fracturing |
| title_sort | intelligent identification and real time warning method of diverse complex events in horizontal well fracturing |
| topic | horizontal well fracturing fracturing events intelligent identification real-time warning deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S1876380424604829 |
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