Unveiling the Feasibility of Coalbed Methane Production Adjustment in Area L through Native Data Reproduction Technology: A Study

Qifan Chang, Likun Fan, Lihui Zheng (), Xumin Yang, Yun Fu, Zixuan Kan and Xiaoqing Pan
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Qifan Chang: College of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, China
Likun Fan: Changqing Oilfield Company, China National Petroleum Corporation, Xi’an 710018, China
Lihui Zheng: College of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, China
Xumin Yang: College of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, China
Yun Fu: College of Safety and Ocean Engineering, China University of Petroleum (Beijing), Beijing 102249, China
Zixuan Kan: College of Information Engineering, Beijing Institute of Petrochemical Technology, Beijing 102617, China
Xiaoqing Pan: Beijing LihuiLab Energy Technology Co., Ltd., Beijing 102200, China

Energies, 2023, vol. 16, issue 15, 1-16

Abstract: 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.

Keywords: coalbed recover; yield optimization scheme; raw data; coalbed methane mining; native data reproduction technology (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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