Numerical simulation of fracking in shale rocks: current state and future approaches

Hattori, G.; Trevelyan, J.; Augarde, C.E.; Coombs, W.M.; Aplin, A.C.

Abstract

Extracting gas from shale rocks is one of the current engineering challenges but offers the prospect of cheap gas. Part of the development of an effective engineering solution for shale gas extraction in the future will be the availability of reliable and efficient methods of modelling the development of a fracture system, and the use of these models to guide operators in locating, drilling and pressurising wells. Numerous research papers have been dedicated to this problem, but the information is still incomplete, since a number of simplifications have been adopted such as the assumption of shale as an isotropic material. Recent works on shale characterisation have proved this assumption to be wrong. The anisotropy of shale depends significantly on the scale at which the problem is tackled (nano, micro or macroscale), suggesting that a multiscale model would be appropriate. Moreover, propagation of hydraulic fractures in such a complex medium can be difficult to model with current numerical discretisation methods. The crack propagation may not be unique, and crack branching can occur during the fracture extension. A number of natural fractures could exist in a shale deposit, so we are dealing with several cracks propagating at once over a considerable range of length scales. For all these reasons, the modelling of the fracking problem deserves considerable attention. The objective of this work is to present an overview of the hydraulic fracture of shale, introducing the most recent investigations concerning the anisotropy of shale rocks, then presenting some of the possible numerical methods that could be used to model the real fracking problem.

Citation

Hattori, G., Trevelyan, J., Augarde, C., Coombs, W., & Aplin, A. (2017). Numerical simulation of fracking in shale rocks: current state and future approaches. Archives of Computational Methods in Engineering, 24(2), 281-317. https://doi.org/10.1007/s11831-016-9169-0

• Journal Article Type: Article
• Acceptance Date: Jan 5, 2016
• Online Publication Date: Jan 29, 2016
• Publication Date: Apr 1, 2017
• Deposit Date: Jan 11, 2016
• Publicly Available Date: Jan 18, 2016
• Journal: Archives of Computational Methods in Engineering
• Print ISSN: 1134-3060
• Electronic ISSN: 1886-1784
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 24
• Issue: 2
• Pages: 281-317
• DOI: https://doi.org/10.1007/s11831-016-9169-0
• Public URL: https://durham-repository.worktribe.com/output/1415124