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dc.contributor.authorYenwongfai, Honore Dzekamelive
dc.contributor.authorMondol, Nazmul Haque
dc.contributor.authorLecomte, Isabelle
dc.contributor.authorFaleide, Jan Inge
dc.contributor.authorLeutscher, Johan
dc.date.accessioned2019-07-12T11:39:17Z
dc.date.available2019-07-12T11:39:17Z
dc.date.created2018-05-21T21:08:10Z
dc.date.issued2018
dc.identifier.issn0016-8025
dc.identifier.urihttp://hdl.handle.net/11250/2605150
dc.description.abstractSeismic petro‐facies characterization in low net‐to‐gross reservoirs with poor reservoir properties such as the Snadd Formation in the Goliat field requires a multidisciplinary approach. This is especially important when the elastic properties of the desired petro‐facies significantly overlap. Pore fluid corrected endmember sand and shale depth trends have been used to generate stochastic forward models for different lithology and fluid combinations in order to assess the degree of separation of different petro‐facies. Subsequently, a spectral decomposition and blending of selected frequency volumes reveal some seismic fluvial geomorphological features. We then jointly inverted for impedance and facies within a Bayesian framework using facies‐dependent rock physics depth trends as input. The results from the inversion are then integrated into a supervised machine learning neural network for effective porosity discrimination. Probability density functions derived from stochastic forward modelling of endmember depth trends show a decreasing seismic fluid discrimination with depth. Spectral decomposition and blending of selected frequencies reveal a dominant NNE trend compared to the regional SE–NW pro‐gradational trend, and a local E–W trend potentially related to fault activity at branches of the Troms‐Finnmark Fault Complex. The facies‐based inversion captures the main reservoir facies within the limits of the seismic bandwidth. Meanwhile the effective porosity predictions from the multilayer feed forward neural network are consistent with the inverted facies model, and can be used to qualitatively highlight the cleanest regions within the inverted facies model. A combination of facies‐based inversion and neural network improves the seismic reservoir delineation of the Snadd Formation in the Goliat Field.
dc.description.abstractIntegrating facies-based Bayesian inversion and supervised machine learning for petro-facies characterization in the Snadd Formation of the Goliat Field, south-western Barents Sea
dc.language.isoeng
dc.titleIntegrating facies-based Bayesian inversion and supervised machine learning for petro-facies characterization in the Snadd Formation of the Goliat Field, south-western Barents Sea
dc.typePeer reviewed
dc.typeJournal article
dc.description.versionsubmittedVersion
dc.description.versionacceptedVersion
dc.source.journalGeophysical Prospecting
dc.identifier.doi10.1111/1365-2478.12654
dc.identifier.cristin1585767
dc.relation.projectNorges forskningsråd: 234152
cristin.unitcode7452,4,5,0
cristin.unitnamePetroleumsgeomekanikk og geofysikk (PGG)
cristin.ispublishedtrue
cristin.fulltextpreprint
cristin.fulltextpostprint
cristin.qualitycode1


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