A Big Data Method Based on Random BP Neural Network and Its Application for Analyzing Influencing Factors on Productivity of Shale Gas Wells

Qun Zhao, Leifu Zhang, Zhongguo Liu, Hongyan Wang, Jie Yao, Xiaowei Zhang, Rongze Yu, Tianqi Zhou and Lixia Kang
Additional contact information
Qun Zhao: PetroChina Research Institute of Petroleum Exploration and Development, Beijing 100083, China
Leifu Zhang: PetroChina Research Institute of Petroleum Exploration and Development, Beijing 100083, China
Zhongguo Liu: R&D Department, China National Petroleum Corporation, Beijing 100007, China
Hongyan Wang: PetroChina Research Institute of Petroleum Exploration and Development, Beijing 100083, China
Jie Yao: Downhole Testing Company of CNPC Bohai Drilling Engineering Company Limited, Langfang 065099, China
Xiaowei Zhang: PetroChina Research Institute of Petroleum Exploration and Development, Beijing 100083, China
Rongze Yu: PetroChina Research Institute of Petroleum Exploration and Development, Beijing 100083, China
Tianqi Zhou: PetroChina Research Institute of Petroleum Exploration and Development, Beijing 100083, China
Lixia Kang: PetroChina Research Institute of Petroleum Exploration and Development, Beijing 100083, China

Energies, 2022, vol. 15, issue 7, 1-13

Abstract: In recent years, big data and artificial intelligence technology have developed rapidly and are now widely used in fields of geophysics, well logging, and well test analysis in the exploration and development of oil and gas. The development of shale gas requires a large number of production wells, so big data and artificial intelligence technology have inherent advantages for evaluating the productivity of gas wells and analyzing the influencing factors for a whole development block. To this end, this paper combines the BP neural network algorithm with random probability analysis to establish a big data method for analyzing the influencing factors on the productivity of shale gas wells, using artificial intelligence and in-depth extraction of relevant information to reduce the unstable results from single-factor statistical analysis and the BP neural network. We have modeled and analyzed our model with a large amount of data. Under standard well conditions, the influences of geological and engineering factors on the productivity of a gas well can be converted to the same scale for comparison. This can more intuitively and quantitatively reflect the influences of different factors on gas well productivity. Taking 100 production wells in the Changning shale gas block as a case, random BP neural network analysis shows that maximum EUR can be obtained when a horizontal shale gas well has a fracture coefficient of 1.6, Type I reservoir of 18 m thick, optimal horizontal section of 1600 m long, and 20 fractured sections.

Keywords: shale gas; random probability; BP neural network; gas well productivity; big data analysis (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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