Unveiling the Feasibility of Coalbed Methane Production Adjustment in Area L through Native Data Reproduction Technology: A Study
In the L Area, big data techniques are employed to manage the principal controlling factors of coalbed methane (CBM) production, thereby regulating single-well output. Nonetheless, conventional data cleansing and the use of arbitrary thresholds may result in an overemphasis on certain controlling fa...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/15/5709 |
| _version_ | 1797586817658126336 |
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| author | Qifan Chang Likun Fan Lihui Zheng Xumin Yang Yun Fu Zixuan Kan Xiaoqing Pan |
| author_facet | Qifan Chang Likun Fan Lihui Zheng Xumin Yang Yun Fu Zixuan Kan Xiaoqing Pan |
| author_sort | Qifan Chang |
| collection | DOAJ |
| description | In the L Area, big data techniques are employed to manage the principal controlling factors of coalbed methane (CBM) production, thereby regulating single-well output. Nonetheless, conventional data cleansing and the use of arbitrary thresholds may result in an overemphasis on certain controlling factors, compromising the design and feasibility of optimization schemes. This study introduces a novel approach that leverages raw data without data cleaning and eschews artificial threshold setting for controlling factor identification. The methodology supplements previously overlooked controlling factors, proposing a more pragmatic CBM production adjustment scheme. In addition to the initial five controlling factors, this approach incorporates three additional ones, namely, dynamic fluid level state, drainage velocity, and fracturing displacement. This study presents a practical application case study of the proposed approach, demonstrating its ability to reduce reservoir damage during the coal fracturing process and enhance output through seal adjustments. Utilizing the full spectrum of original data and minimizing human intervention thresholds enriches the information available for model training, thereby facilitating the development of a more efficacious model. |
| first_indexed | 2024-03-11T00:28:32Z |
| format | Article |
| id | doaj.art-8e770e3fb488413a8b736c0da7e8aaad |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T00:28:32Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-8e770e3fb488413a8b736c0da7e8aaad2023-11-18T22:51:48ZengMDPI AGEnergies1996-10732023-07-011615570910.3390/en16155709Unveiling the Feasibility of Coalbed Methane Production Adjustment in Area L through Native Data Reproduction Technology: A StudyQifan Chang0Likun Fan1Lihui Zheng2Xumin Yang3Yun Fu4Zixuan Kan5Xiaoqing Pan6College of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, ChinaChangqing Oilfield Company, China National Petroleum Corporation, Xi’an 710018, ChinaCollege of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, ChinaCollege of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, ChinaCollege of Safety and Ocean Engineering, China University of Petroleum (Beijing), Beijing 102249, ChinaCollege of Information Engineering, Beijing Institute of Petrochemical Technology, Beijing 102617, ChinaBeijing LihuiLab Energy Technology Co., Ltd., Beijing 102200, ChinaIn the L Area, big data techniques are employed to manage the principal controlling factors of coalbed methane (CBM) production, thereby regulating single-well output. Nonetheless, conventional data cleansing and the use of arbitrary thresholds may result in an overemphasis on certain controlling factors, compromising the design and feasibility of optimization schemes. This study introduces a novel approach that leverages raw data without data cleaning and eschews artificial threshold setting for controlling factor identification. The methodology supplements previously overlooked controlling factors, proposing a more pragmatic CBM production adjustment scheme. In addition to the initial five controlling factors, this approach incorporates three additional ones, namely, dynamic fluid level state, drainage velocity, and fracturing displacement. This study presents a practical application case study of the proposed approach, demonstrating its ability to reduce reservoir damage during the coal fracturing process and enhance output through seal adjustments. Utilizing the full spectrum of original data and minimizing human intervention thresholds enriches the information available for model training, thereby facilitating the development of a more efficacious model.https://www.mdpi.com/1996-1073/16/15/5709coalbed recoveryield optimization schemeraw datacoalbed methane miningnative data reproduction technology |
| spellingShingle | Qifan Chang Likun Fan Lihui Zheng Xumin Yang Yun Fu Zixuan Kan Xiaoqing Pan Unveiling the Feasibility of Coalbed Methane Production Adjustment in Area L through Native Data Reproduction Technology: A Study Energies coalbed recover yield optimization scheme raw data coalbed methane mining native data reproduction technology |
| title | Unveiling the Feasibility of Coalbed Methane Production Adjustment in Area L through Native Data Reproduction Technology: A Study |
| title_full | Unveiling the Feasibility of Coalbed Methane Production Adjustment in Area L through Native Data Reproduction Technology: A Study |
| title_fullStr | Unveiling the Feasibility of Coalbed Methane Production Adjustment in Area L through Native Data Reproduction Technology: A Study |
| title_full_unstemmed | Unveiling the Feasibility of Coalbed Methane Production Adjustment in Area L through Native Data Reproduction Technology: A Study |
| title_short | Unveiling the Feasibility of Coalbed Methane Production Adjustment in Area L through Native Data Reproduction Technology: A Study |
| title_sort | unveiling the feasibility of coalbed methane production adjustment in area l through native data reproduction technology a study |
| topic | coalbed recover yield optimization scheme raw data coalbed methane mining native data reproduction technology |
| url | https://www.mdpi.com/1996-1073/16/15/5709 |
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