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Optimisation of a stochastic rock fracture model using Markov Chain Monte Carlo simulation

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dc.contributor.authorXu, C.
dc.contributor.authorDowd, P.
dc.contributor.authorWyborn, D.
dc.date.issued2013
dc.description.abstractThe characterisation of rock fracture networks is an important component of rock engineering applications involving stability assessment or fluid flow analysis. However, the derivation of a reliable rock fracture model remains a very challenging problem in practice. This paper describes a Bayesian framework, in the form of Markov Chain Monte Carlo (MCMC) simulation, for the construction of such a model. Model conditioning using different data sources is discussed including seismic events recorded during hydraulic fracture stimulation, rock face fracture mapping data and downhole geophysical survey data. The freeware FracSim3D is used for the simulations.
dc.description.statementofresponsibilityC. Xu, P. A. Dowd and D. Wyborn
dc.identifier.citationTransactions of the Institutions of Mining and Metallurgy Section A: Mining Technology, 2013; 122(3):153-158
dc.identifier.doi10.1179/1743286312Y.0000000023
dc.identifier.issn1474-9009
dc.identifier.issn0967-8638
dc.identifier.orcidXu, C. [0000-0001-6662-3823]
dc.identifier.orcidDowd, P. [0000-0002-6743-5119]
dc.identifier.urihttp://hdl.handle.net/2440/82533
dc.language.isoen
dc.publisherManey Publishing
dc.rights©2013 The Australian Institute of Mining and Metalurgy
dc.source.urihttps://doi.org/10.1179/1743286312y.0000000023
dc.subjectFracture modelling
dc.subjectMarkov Chain Monte Carlo
dc.subjectGeothermal energy
dc.titleOptimisation of a stochastic rock fracture model using Markov Chain Monte Carlo simulation
dc.typeJournal article
pubs.publication-statusPublished

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