Big data analysis and research on fracturing construction parameters of shale gas horizontal wellsa case study of horizontal wells in fuling demonstration area, china

Big data analysis and research on fracturing construction parameters of shale gas horizontal wellsa case study of horizontal wells in fuling demonstration area, china

Petroleum industry

Shale gas

Big data

Fracture

Gases

Horizontal wells

Data handling

Information analysis

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 Pearsons 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. 2021 by the authors. Licensee MDPI, Basel, Switzerland.

24

14

Li, Minxuan

Cheng, Liang

Liu, Dehua

Hu, Jiani

Zhang, Wei

Li, Kuidong

Xiao, Jialin

Wang, Xiaojun

Zhang, Feng

Energies

2021

journalArticle

19961073

10.3390/en14248357