Big Data Analysis and Research on Fracturing Construction Parameters of Shale Gas Horizontal Wells—A Case Study of Horizontal Wells in Fuling Demonstration Area, China

Minxuan Li, Liang Cheng, Dehua Liu, Jiani Hu, Wei Zhang, Kuidong Li, Jialin Xiao, Xiaojun Wang and Feng Zhang
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Minxuan Li: School of Petroleum Engineering, Yangtze University, Wuhan 430100, China
Liang Cheng: School of Petroleum Engineering, Yangtze University, Wuhan 430100, China
Dehua Liu: School of Petroleum Engineering, Yangtze University, Wuhan 430100, China
Jiani Hu: School of Petroleum Engineering, Yangtze University, Wuhan 430100, China
Wei Zhang: Sinopec Jianghan Oilfield Research Institute of Petroleum Engineering, Wuhan 430035, China
Kuidong Li: Sinopec Jianghan Oilfield Research Institute of Petroleum Engineering, Wuhan 430035, China
Jialin Xiao: Sinopec Jianghan Oilfield Research Institute of Petroleum Engineering, Wuhan 430035, China
Xiaojun Wang: Sinopec Jianghan Oilfield Research Institute of Petroleum Engineering, Wuhan 430035, China
Feng Zhang: Sinopec Jianghan Oilfield Research Institute of Petroleum Engineering, Wuhan 430035, China

Energies, 2021, vol. 14, issue 24, 1-17

Abstract: With the rapid development of computer science and technology, the Chinese petroleum industry has ushered in the era of big data. In this study, by collecting fracturing data from 303 horizontal wells in the Fuling Shale Gas Demonstration Area in China, a series of big data analysis studies was conducted using Pearson’s correlation coefficient, the unweighted pair group with arithmetic means method, and the graphical plate method to determine which is best. The fracturing parameters were determined through a series of big data analysis studies. The big data analysis process is divided into three main steps. The first is data preprocessing to screen out eligible, high-yielding wells. The second is a fracturing parameter correlation clustering analysis to determine the reasonableness of the parameters. The third is a big data panel method analysis of specific fracturing construction parameters to determine the optimal parameter range. The analyses revealed that the current amount of 100 mesh sand in the Fuling area is unreasonable; further, there are different preferred areas for different fracturing construction parameters. We have combined different fracturing parameter schemes by preferring areas. This analysis process is expected to provide new ideas regarding fracturing scheme design for engineers working on the frontline.

Keywords: shale gas; horizontal well; fracturing construction parameters; 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: 2021
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