Show simple item record

dc.contributor.authorMioratina, Nomenjanahary Tanteliniaina
dc.contributor.authorOliver, Dean
dc.date.accessioned2024-05-02T11:01:22Z
dc.date.available2024-05-02T11:01:22Z
dc.date.created2023-05-23T12:12:49Z
dc.date.issued2023
dc.identifier.issn2949-8929
dc.identifier.urihttps://hdl.handle.net/11250/3128785
dc.description.abstractIn Bayesian approaches to history matching for subsurface inference, the prior model specifies the uncertain model parameters and the joint probability of those parameters before incorporating production-related data. A good prior model is generally complex enough to capture the future reservoir behavior in the long term, realistic enough to be plausible, consistent with geologic knowledge, and simple enough to allow calibration for data matching. Model complexity is often associated with the number of model parameters, thus the focus on finding the sufficient number of parameters needed for history matching and quantifying future uncertainty. This work explores model choice based on concepts of complexity and informativeness of models for subsurface reservoir models. It focuses on the effect of the misspecification of prior models for assimilating flow data and their predictive accuracy. The concept of the effective number of parameters is used to investigate the suitability of various types of prior models with different levels of complexity, ranging from a highly simplified polynomial trend model to a more realistic multipoint statistical model(MPS) and a family of isotropic Gaussian models and explore the effect of level of model complexity on the robustness of forecasting. The numerical experiments were performed with different combinations of data type, prior informativeness, forecast type, and model type to compare the effect of different prior models on the robustness of the results. The effective number of parameters was computed for each prior model and their accuracy for predicting future reservoir behavior was analyzed. The results suggest that effective model dimension is a useful measure of model complexity for history matching problems, although it is not independent of the data used for model calibration and the number of effective model parameters is generally much smaller than the number of model parameters. In a data-rich problem, realism of a model is much less important than the complexity of a model, while for a problem with few data, realism was beneficial for reliable forecasts.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleQuantifying prior model complexity for subsurface reservoir modelsen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.source.articlenumber211929en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2
dc.identifier.doi10.1016/j.geoen.2023.211929
dc.identifier.cristin2148695
dc.source.journalGeoenergy Science and Engineeringen_US
dc.relation.projectNorges forskningsråd: 295002en_US
dc.identifier.citationGeoenergy Science and Engineering. 2023, 227, 211929.en_US
dc.source.volume227en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

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