Effect Evaluation of Staged Fracturing and Productivity Prediction of Horizontal Wells in Tight Reservoirs

Yuan Zhang, Jianyang Chen, Zhongbao Wu, Yuxiang Xiao, Ziyi Xu, Hanlie Cheng () and Bin Zhang
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Yuan Zhang: Research Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China
Jianyang Chen: Research Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China
Zhongbao Wu: Research Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China
Yuxiang Xiao: Research Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China
Ziyi Xu: Research Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China
Hanlie Cheng: School of Energy Resource, China University of Geosciences (Beijing), Beijing 100083, China
Bin Zhang: Petrochina Company Limited, Downhole Services Company, Bohai Driling Engineering Company, Renqiu 062550, China

Energies, 2024, vol. 17, issue 12, 1-10

Abstract: In this paper, the effect evaluation and production prediction of staged fracturing for horizontal wells in tight reservoirs are studied. Firstly, the basic characteristics and value of horizontal wells in tight reservoirs are introduced, their geological characteristics, flow mechanism and permeability model are analyzed and the application of grey theory in effect analysis is discussed. Considering the problems of staged fracturing effect evaluation and the production prediction of horizontal wells in tight reservoirs, a BP neural network model based on deep learning is proposed. Due to the interference of multiple physical parameters and the complex functional relationship in the development of tight reservoir fracturing, the traditional prediction method has low accuracy and it is difficult to establish an accurate mapping relationship. In this paper, a BP neural network is used to simulate multivariable nonlinear mapping by modifying the model, and its advantages in solving the coupling relationship of complex functions are brought into play. A neural network model with fracturing parameters as input and oil and gas production as output is designed. Through the training and testing of data sets, the accuracy and applicability of the proposed model for effect evaluation and yield prediction are verified. The research results show that the model can fit the complex mapping relationship between fracturing information and production and provide an effective evaluation and prediction tool for the development of the staged fracturing of horizontal wells in tight reservoirs.

Keywords: horizontal wells in tight reservoirs; staged fracturing; effect evaluation; productivity prediction (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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