Data Assimilation for a Geological Process Model Using the Ensemble Kalman Filter

We consider the problem of conditioning a geological process-based computer simulation, which produces basin models by simulating transport and deposition of sediments, to data. Emphasising uncertainty quantification, we frame this as a Bayesian inverse problem, and propose to characterize the poste...

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Bibliographic Details
Main Authors: Skauvold, Jacob, Eidsvik, Jo
Format: Report
Language:unknown
Published: arXiv 2017
Subjects:
Applications stat.AP
FOS Computer and information sciences
north slope
Alaska
Online Access:https://dx.doi.org/10.48550/arxiv.1711.07763
https://arxiv.org/abs/1711.07763
id ftdatacite:10.48550/arxiv.1711.07763
record_format openpolar
spelling ftdatacite:10.48550/arxiv.1711.07763 2023-05-15T17:40:14+02:00 Data Assimilation for a Geological Process Model Using the Ensemble Kalman Filter Skauvold, Jacob Eidsvik, Jo 2017 https://dx.doi.org/10.48550/arxiv.1711.07763 https://arxiv.org/abs/1711.07763 unknown arXiv arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Applications stat.AP FOS Computer and information sciences Preprint Article article CreativeWork 2017 ftdatacite https://doi.org/10.48550/arxiv.1711.07763 2022-04-01T10:16:28Z We consider the problem of conditioning a geological process-based computer simulation, which produces basin models by simulating transport and deposition of sediments, to data. Emphasising uncertainty quantification, we frame this as a Bayesian inverse problem, and propose to characterize the posterior probability distribution of the geological quantities of interest by using a variant of the ensemble Kalman filter, an estimation method which linearly and sequentially conditions realisations of the system state to data. A test case involving synthetic data is used to assess the performance of the proposed estimation method, and to compare it with similar approaches. We further apply the method to a more realistic test case, involving real well data from the Colville foreland basin, North Slope, Alaska. : 34 pages, 10 figures, 4 tables Report north slope Alaska DataCite Metadata Store (German National Library of Science and Technology)
institution Open Polar
collection DataCite Metadata Store (German National Library of Science and Technology)
op_collection_id ftdatacite
language unknown
topic Applications stat.AP
FOS Computer and information sciences
spellingShingle Applications stat.AP
FOS Computer and information sciences
Skauvold, Jacob
Eidsvik, Jo
Data Assimilation for a Geological Process Model Using the Ensemble Kalman Filter
topic_facet Applications stat.AP
FOS Computer and information sciences
description We consider the problem of conditioning a geological process-based computer simulation, which produces basin models by simulating transport and deposition of sediments, to data. Emphasising uncertainty quantification, we frame this as a Bayesian inverse problem, and propose to characterize the posterior probability distribution of the geological quantities of interest by using a variant of the ensemble Kalman filter, an estimation method which linearly and sequentially conditions realisations of the system state to data. A test case involving synthetic data is used to assess the performance of the proposed estimation method, and to compare it with similar approaches. We further apply the method to a more realistic test case, involving real well data from the Colville foreland basin, North Slope, Alaska. : 34 pages, 10 figures, 4 tables
format Report
author Skauvold, Jacob
Eidsvik, Jo
author_facet Skauvold, Jacob
Eidsvik, Jo
author_sort Skauvold, Jacob
title Data Assimilation for a Geological Process Model Using the Ensemble Kalman Filter
title_short Data Assimilation for a Geological Process Model Using the Ensemble Kalman Filter
title_full Data Assimilation for a Geological Process Model Using the Ensemble Kalman Filter
title_fullStr Data Assimilation for a Geological Process Model Using the Ensemble Kalman Filter
title_full_unstemmed Data Assimilation for a Geological Process Model Using the Ensemble Kalman Filter
title_sort data assimilation for a geological process model using the ensemble kalman filter
publisher arXiv
publishDate 2017
url https://dx.doi.org/10.48550/arxiv.1711.07763
https://arxiv.org/abs/1711.07763
genre north slope
Alaska
genre_facet north slope
Alaska
op_rights arXiv.org perpetual, non-exclusive license
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
op_doi https://doi.org/10.48550/arxiv.1711.07763
_version_ 1766141101710770176

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