Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/109138
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dc.contributor.authorPsaltis, S.-
dc.contributor.authorFarrell, T.-
dc.contributor.authorBurrage, K.-
dc.contributor.authorBurrage, P.-
dc.contributor.authorMcCabe, P.-
dc.contributor.authorMoroney, T.-
dc.contributor.authorTurner, I.-
dc.contributor.authorMazumder, S.-
dc.contributor.authorBednarz, T.-
dc.date.issued2017-
dc.identifier.citationApplied Mathematical Modelling: simulation and computation for engineering and environmental systems, 2017; 49:338-353-
dc.identifier.issn0307-904X-
dc.identifier.issn1872-8480-
dc.identifier.urihttp://hdl.handle.net/2440/109138-
dc.description.abstractAbstract Not Available-
dc.description.statementofresponsibilitySteven Psaltis, Troy Farrell, Kevin Burrage, Pamela Burrage, Peter McCabe, Timothy Moroney, Ian Turner, Saikat Mazumder, Tomasz Bednarz-
dc.language.isoen-
dc.publisherElsevier-
dc.rights© 2017 Elsevier Inc. All rights reserved.-
dc.source.urihttp://dx.doi.org/10.1016/j.apm.2017.05.005-
dc.subjectMathematical modelling; coal seam gas; Latin hypercube sampling; population of models; validation-
dc.titleUsing population of models to investigate and quantify gas production in a spatially heterogeneous coal seam gas field-
dc.typeJournal article-
dc.identifier.doi10.1016/j.apm.2017.05.005-
dc.relation.granthttp://purl.org/au-research/grants/arc/CE140100049-
pubs.publication-statusPublished-
dc.identifier.orcidMcCabe, P. [0000-0001-5262-1018]-
Appears in Collections:Aurora harvest 8
Australian School of Petroleum publications

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