A one-step Bayesian inversion framework for 3D reservoir characterization based on a Gaussian mixture model-A Norwegian Sea demonstration
We have developed a one-step approach for Bayesian prediction and uncertainty quantification of lithology/fluid classes, petrophysical properties, and elastic attributes conditional on prestack 3D seismic amplitude-variation-with-offset data. A 3D Markov random field prior model is assumed for the l...
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Society of Exploration Geophysicists (SEG)
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Online Access: | https://hdl.handle.net/11250/3032132 https://doi.org/10.1190/geo2020-0094.1 |
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ftntnutrondheimi:oai:ntnuopen.ntnu.no:11250/3032132 2023-05-15T17:47:03+02:00 A one-step Bayesian inversion framework for 3D reservoir characterization based on a Gaussian mixture model-A Norwegian Sea demonstration Fjeldstad, Torstein Avseth, Per Åge Omre, Henning 2021 application/pdf https://hdl.handle.net/11250/3032132 https://doi.org/10.1190/geo2020-0094.1 eng eng Society of Exploration Geophysicists (SEG) Norges forskningsråd: 294404 Geophysics. 2021, 86 (2), R221-R236. urn:issn:0016-8033 https://hdl.handle.net/11250/3032132 https://doi.org/10.1190/geo2020-0094.1 cristin:1894753 R221-R236 86 Geophysics 2 Peer reviewed Journal article 2021 ftntnutrondheimi https://doi.org/10.1190/geo2020-0094.1 2022-11-23T23:42:31Z We have developed a one-step approach for Bayesian prediction and uncertainty quantification of lithology/fluid classes, petrophysical properties, and elastic attributes conditional on prestack 3D seismic amplitude-variation-with-offset data. A 3D Markov random field prior model is assumed for the lithology/fluid classes to ensure spatially coupled lithology/fluid class predictions in the lateral and vertical directions. Conditional on the lithology/fluid classes, we consider Gauss-linear petrophysical and rock-physics models including depth trends. Then, the marginal prior models for the petrophysical properties and elastic attributes are multivariate Gaussian mixture models. The likelihood model is assumed to be Gauss-linear to allow for analytic computation. A recursive algorithm that translates the Gibbs formulation of the Markov random field into a set of vertical Markov chains is proposed. This algorithm provides a proposal density in a Markov chain Monte Carlo algorithm such that efficient simulation from the posterior model of interest in three dimensions is feasible. The model is demonstrated on real data from a Norwegian Sea gas reservoir. We evaluate the model at the location of a blind well, and we compare results from the proposed model with results from a set of 1D models in which each vertical trace is inverted independently. At the blind well location, we obtain at most a 60% reduction in the root-mean-square error for the proposed 3D model compared to the model without lateral spatial coupling. acceptedVersion Article in Journal/Newspaper Norwegian Sea NTNU Open Archive (Norwegian University of Science and Technology) Norwegian Sea GEOPHYSICS 86 2 R221 R236 |
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Open Polar |
collection |
NTNU Open Archive (Norwegian University of Science and Technology) |
op_collection_id |
ftntnutrondheimi |
language |
English |
description |
We have developed a one-step approach for Bayesian prediction and uncertainty quantification of lithology/fluid classes, petrophysical properties, and elastic attributes conditional on prestack 3D seismic amplitude-variation-with-offset data. A 3D Markov random field prior model is assumed for the lithology/fluid classes to ensure spatially coupled lithology/fluid class predictions in the lateral and vertical directions. Conditional on the lithology/fluid classes, we consider Gauss-linear petrophysical and rock-physics models including depth trends. Then, the marginal prior models for the petrophysical properties and elastic attributes are multivariate Gaussian mixture models. The likelihood model is assumed to be Gauss-linear to allow for analytic computation. A recursive algorithm that translates the Gibbs formulation of the Markov random field into a set of vertical Markov chains is proposed. This algorithm provides a proposal density in a Markov chain Monte Carlo algorithm such that efficient simulation from the posterior model of interest in three dimensions is feasible. The model is demonstrated on real data from a Norwegian Sea gas reservoir. We evaluate the model at the location of a blind well, and we compare results from the proposed model with results from a set of 1D models in which each vertical trace is inverted independently. At the blind well location, we obtain at most a 60% reduction in the root-mean-square error for the proposed 3D model compared to the model without lateral spatial coupling. acceptedVersion |
format |
Article in Journal/Newspaper |
author |
Fjeldstad, Torstein Avseth, Per Åge Omre, Henning |
spellingShingle |
Fjeldstad, Torstein Avseth, Per Åge Omre, Henning A one-step Bayesian inversion framework for 3D reservoir characterization based on a Gaussian mixture model-A Norwegian Sea demonstration |
author_facet |
Fjeldstad, Torstein Avseth, Per Åge Omre, Henning |
author_sort |
Fjeldstad, Torstein |
title |
A one-step Bayesian inversion framework for 3D reservoir characterization based on a Gaussian mixture model-A Norwegian Sea demonstration |
title_short |
A one-step Bayesian inversion framework for 3D reservoir characterization based on a Gaussian mixture model-A Norwegian Sea demonstration |
title_full |
A one-step Bayesian inversion framework for 3D reservoir characterization based on a Gaussian mixture model-A Norwegian Sea demonstration |
title_fullStr |
A one-step Bayesian inversion framework for 3D reservoir characterization based on a Gaussian mixture model-A Norwegian Sea demonstration |
title_full_unstemmed |
A one-step Bayesian inversion framework for 3D reservoir characterization based on a Gaussian mixture model-A Norwegian Sea demonstration |
title_sort |
one-step bayesian inversion framework for 3d reservoir characterization based on a gaussian mixture model-a norwegian sea demonstration |
publisher |
Society of Exploration Geophysicists (SEG) |
publishDate |
2021 |
url |
https://hdl.handle.net/11250/3032132 https://doi.org/10.1190/geo2020-0094.1 |
geographic |
Norwegian Sea |
geographic_facet |
Norwegian Sea |
genre |
Norwegian Sea |
genre_facet |
Norwegian Sea |
op_source |
R221-R236 86 Geophysics 2 |
op_relation |
Norges forskningsråd: 294404 Geophysics. 2021, 86 (2), R221-R236. urn:issn:0016-8033 https://hdl.handle.net/11250/3032132 https://doi.org/10.1190/geo2020-0094.1 cristin:1894753 |
op_doi |
https://doi.org/10.1190/geo2020-0094.1 |
container_title |
GEOPHYSICS |
container_volume |
86 |
container_issue |
2 |
container_start_page |
R221 |
op_container_end_page |
R236 |
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1766151294780702720 |