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Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/82533
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dc.contributor.authorXu, C.-
dc.contributor.authorDowd, P.-
dc.contributor.authorWyborn, D.-
dc.date.issued2013-
dc.identifier.citationTransactions of the Institutions of Mining and Metallurgy Section A: Mining Technology, 2013; 122(3):153-158-
dc.identifier.issn1474-9009-
dc.identifier.issn0967-8638-
dc.identifier.urihttp://hdl.handle.net/2440/82533-
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.language.isoen-
dc.publisherManey Publishing-
dc.rights©2013 The Australian Institute of Mining and Metalurgy-
dc.source.urihttp://dx.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-
dc.identifier.doi10.1179/1743286312Y.0000000023-
pubs.publication-statusPublished-
dc.identifier.orcidXu, C. [0000-0001-6662-3823]-
dc.identifier.orcidDowd, P. [0000-0002-6743-5119]-
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Civil and Environmental Engineering publications

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