Gas Production Prediction Model of Volcanic Reservoir Based on Data-Driven Method

Haijie Zhang, Junwei Pu, Li Zhang, Hengjian Deng, Jihao Yu, Yingming Xie, Xiaochang Tong, Xiangjie Man and Zhonghua Liu ()
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Haijie Zhang: Chong Qing Shale Gas Exploration and Development, Co., Ltd., Chongqing 401121, China
Junwei Pu: Chong Qing Shale Gas Exploration and Development, Co., Ltd., Chongqing 401121, China
Li Zhang: Chong Qing Shale Gas Exploration and Development, Co., Ltd., Chongqing 401121, China
Hengjian Deng: Chong Qing Shale Gas Exploration and Development, Co., Ltd., Chongqing 401121, China
Jihao Yu: School of Petroleum and Natural Gas Engineering, Chongqing University of Science and Technology, Chongqing 401331, China
Yingming Xie: CNOOC EnerTech-Drilling & Production, Co., Beijing 100028, China
Xiaochang Tong: Pancasia Holding Co., Ltd., Chongqing 400000, China
Xiangjie Man: Pancasia Holding Co., Ltd., Chongqing 400000, China
Zhonghua Liu: School of Petroleum and Natural Gas Engineering, Chongqing University of Science and Technology, Chongqing 401331, China

Energies, 2024, vol. 17, issue 21, 1-13

Abstract: Based on on-site construction experience, considering the time-varying characteristics of gas well quantity, production time, effective reservoir thickness, controlled reserves, reserve abundance, formation pressure, and the energy storage coefficient, a data-driven method was used to establish a natural gas production prediction model based on differential simulation theory. The calculation results showed that the average error between the actual production and predicted production was 12.49%, and the model determination coefficient was 0.99, indicating that the model can effectively predict natural gas production. Additionally, we observed that the influence of factors such as reserve abundance, the number of wells in operation, controlled reserves, the previous year’s gas production, formation pressure, the energy storage coefficient, effective matrix thickness, and annual production time on the annual gas production increases progressively as the F-values decrease. These insights are pivotal to a more profound understanding of gas production dynamics in volcanic reservoirs and are instrumental in optimizing stimulation treatments and enhancing resource recovery in such reservoirs and other unconventional hydrocarbon formations.

Keywords: production prediction model; volcanic reservoir; data-driven method; data nondimensionalization; dimension recovery (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: 2024
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