Uncertainty Analysis of Factors Influencing Stimulated Fracture Volume in Layered Formation

Jingxuan Zhang, Xiangjun Liu, Xiaochen Wei, Lixi Liang, Jian Xiong and Wei Li
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Jingxuan Zhang: State Key Laboratory of Oil and Gas Reservoir Geology and Development Engineering, Southwest Petroleum University, Chengdu 610500, China
Xiangjun Liu: State Key Laboratory of Oil and Gas Reservoir Geology and Development Engineering, Southwest Petroleum University, Chengdu 610500, China
Xiaochen Wei: State Key Laboratory of Oil and Gas Reservoir Geology and Development Engineering, Southwest Petroleum University, Chengdu 610500, China
Lixi Liang: State Key Laboratory of Oil and Gas Reservoir Geology and Development Engineering, Southwest Petroleum University, Chengdu 610500, China
Jian Xiong: State Key Laboratory of Oil and Gas Reservoir Geology and Development Engineering, Southwest Petroleum University, Chengdu 610500, China
Wei Li: State Key Laboratory of Oil and Gas Reservoir Geology and Development Engineering, Southwest Petroleum University, Chengdu 610500, China

Energies, 2019, vol. 12, issue 23, 1-24

Abstract: Hydraulic fracture dimension is one of the key parameters affecting stimulated porous media. In actual fracturing, plentiful uncertain parameters increase the difficulty of fracture dimension prediction, resulting in the difficulty in the monitoring of reservoir productivity. In this paper, we established a three-dimensional model to analyze the key factors on the stimulated reservoir volume (SRV), with the response surface method (RSM). Considering the rock properties and fracturing parameters, we established a multivariate quadratic prediction equation. Simulation results show that the interactions of injection rate ( Q ), Young’s modulus ( E ) and permeability coefficient ( K ), and Poisson’s ratio ( μ ) play a relatively significant role on SRV. The reservoir with a high Young’s modulus typically generates high pressure, leading to longer fractures and larger SRV. SRV reaches the maximum value when E 1 and E 2 are high. SRV is negatively correlated with K 1. Moreover, maintaining a high injection rate in this layered formation with high E 1 and E 2, relatively low K 1, and μ 1 at about 0.25 would be beneficial to form a larger SRV. These results offer new perceptions on the optimization of SRV, helping to improve the productivity in hydraulic fracturing.

Keywords: fluid-driven fractures; reservoir modeling; finite element method; uncertainty 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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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