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dc.contributor.authorKjønsberg, Heidi
dc.contributor.authorHauge, Ragnar
dc.contributor.authorNilsen, Carl-Inge Colombo
dc.contributor.authorNdingwan, Abel Onana
dc.contributor.authorKolbjørnsen, Odd
dc.date.accessioned2025-01-08T07:20:06Z
dc.date.available2025-01-08T07:20:06Z
dc.date.created2024-12-02T10:57:52Z
dc.date.issued2024
dc.identifier.citationGeophysics. 2024, 89 (6), R551-R567.en_US
dc.identifier.issn0016-8033
dc.identifier.urihttps://hdl.handle.net/11250/3171441
dc.description.abstractSeismic data acquired at different times over the same area can provide insight into changes in an oil/gas reservoir. Probabilities for pore fluid will typically change, whereas the lithology remains stable over time. This implies significant correlations across the vintages. We develop a methodology for the Bayesian prediction of joint probabilities for discrete lithology-fluid classes (LFCs) for two vintages, simultaneously considering the seismic amplitude-variation-with-offset data of both vintages. By taking into account the cross-vintage correlations of elastic and seismic properties, the simultaneous inversion ensures that the individual results of both vintages, as well as their differences, are consistent and constrained by the seismic data of both vintages. The method relies on prior geologic knowledge of stratigraphic layering, the possible lithologies and fluids within each layer, and the possible cross-vintage changes in lithology and pore fluid. Multiple LFCs can be used to represent different strengths of dynamic cross-vintage changes. We test the algorithm on a synthetic data set and data from the Edvard Grieg field in the central North Sea. Synthetic results demonstrate that the algorithm is able to use dual-vintage data together with a prior model specifying their correlations to calculate joint LFC posterior probabilities for both vintages with a lower degree of uncertainty than independent single-vintage inversions. The Edvard Grieg results indicate that the underlying model is sufficiently general to explain 4D variations in seismic data using a reasonably simple prior model of 4D LFC changes.
dc.description.abstractBayesian seismic 4D inversion for lithology and fluid prediction
dc.language.isoengen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.subjectGeostatistikk
dc.subjectGeostatistics
dc.subjectBayesiansk inversjon
dc.subjectBayesian inversion
dc.titleBayesian seismic 4D inversion for lithology and fluid predictionen_US
dc.title.alternativeBayesian seismic 4D inversion for lithology and fluid predictionen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersion
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2
dc.identifier.doi10.1190/GEO2024-0092.1
dc.identifier.cristin2326014
dc.source.journalGeophysicsen_US
dc.source.volume89en_US
dc.source.issue6en_US
dc.source.pagenumberR551-R567en_US


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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal