Role of Natural Fractures Characteristics on the Performance of Hydraulic Fracturing for Deep Energy Extraction using Discrete Fracture Network (DFN)
Abstract
Extraction of geothermal energy, oil and gas, and coalbed methane (CBM) are all constrained by the rock's permeability. To stimulate the production, hydraulic fracturing has become a routine procedure, which is influenced by many factors especially the pre-existing natural fractures. The natural fractures have various characteristics, such as aperture, persistence, and density, which have different effects on the hydraulic performance. Hence, it is necessary to study the dependence of hydraulic fracturing on natural fracture parameters to improve its effectiveness. In this research, the distribution of natural fracture is generated using the discrete fracture network (DFN) to study these relationships. To verify the accuracy, the numerical model is calibrated at a particular case with observed data and then continued to different fragmentation characteristics. Results show that the hydraulically fractured area has an inverse relationship with the natural fracture aperture. However, the increase of pre-existing fracture persistence first causes the fractured zone to increase but increasing persistence of natural fractures further causes the area to decrease. Parametric study shows that pre-existing natural fractures play a critical role in hydraulic fracturing effectiveness, which ultimately affects the production.
Recommended Citation
W. Yao et al., "Role of Natural Fractures Characteristics on the Performance of Hydraulic Fracturing for Deep Energy Extraction using Discrete Fracture Network (DFN)," Engineering Fracture Mechanics, vol. 230, article no. 106962, Elsevier, May 2020.
The definitive version is available at https://doi.org/10.1016/j.engfracmech.2020.106962
Department(s)
Mining Engineering
Keywords and Phrases
DFN; Energy extraction; Fracture characteristics; Hydraulic fracturing; Rock mass permeability
International Standard Serial Number (ISSN)
0013-7944
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2020 Elsevier, All rights reserved.
Publication Date
01 May 2020
Comments
This work was financially supported by the Fundamental Research Funds for the Central Universities (2017CXNL01).