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Sampling-based stochastic analysis of the PKN model for hydraulic fracturing

Author
Garikapati, H.; V. Verhoosel, .; van Brummelen, .; Zlotnik, S.; Diez, P.
Type of activity
Journal article
Journal
Computational geosciences
Date of publication
2019-02
Volume
23
Number
1
First page
81
Last page
105
DOI
https://doi.org/10.1007/s10596-018-9784-y Open in new window
Project funding
Data assimilation for credible engineering simulations
Repository
http://hdl.handle.net/2117/125346 Open in new window
URL
https://link.springer.com/article/10.1007%2Fs10596-018-9784-y Open in new window
Abstract
Hydraulic fracturing processes are surrounded by uncertainty, as available data is typically scant. In this work, we present a sampling-based stochastic analysis of the hydraulic fracturing process by considering various system parameters to be random. Our analysis is based on the Perkins-Kern-Nordgren (PKN) model for hydraulic fracturing. This baseline model enables computation of high fidelity solutions, which avoids pollution of our stochastic results by inaccuracies in the deterministic solu...
Citation
Garikapati, H., V. Verhoosel, ., van Brummelen, ., Zlotnik, S., Diez, P. Sampling-based stochastic analysis of the PKN model for hydraulic fracturing. "Computational geosciences", Febrer 2019, vol. 23, núm. 1, p. 81-105.
Keywords
Finite element method, Hydraulic fracturing, Monte-Carlo method, Moving-boundary problem, Perkins-Kern-Nordgren model, Random fields, Sensitivity analysis, Stochastic analysis
Group of research
LACÀN - Numerical Methods for Applied Sciences and Engineering

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