Parallel Implementation of the Deterministic Ensemble Kalman Filter for Reservoir History Matching

In this paper, the deterministic ensemble Kalman filter is implemented with a parallel technique of the message passing interface based on our in-house black oil simulator. The implementation is separated into two cases: (1) the ensemble size is greater than the processor number and (2) the ensemble...

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Autores principales:	Lihua Shen, Hui Liu, Zhangxin Chen
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	history matching DEnKF relative permeability power-law model parallel computing Chemical technology TP1-1185 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/fa3835f9c8d041129f38e3e78229f1f6
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id	oai:doaj.org-article:fa3835f9c8d041129f38e3e78229f1f6
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spelling	oai:doaj.org-article:fa3835f9c8d041129f38e3e78229f1f62021-11-25T18:51:11ZParallel Implementation of the Deterministic Ensemble Kalman Filter for Reservoir History Matching10.3390/pr91119802227-9717https://doaj.org/article/fa3835f9c8d041129f38e3e78229f1f62021-11-01T00:00:00Zhttps://www.mdpi.com/2227-9717/9/11/1980https://doaj.org/toc/2227-9717In this paper, the deterministic ensemble Kalman filter is implemented with a parallel technique of the message passing interface based on our in-house black oil simulator. The implementation is separated into two cases: (1) the ensemble size is greater than the processor number and (2) the ensemble size is smaller than or equal to the processor number. Numerical experiments for estimations of three-phase relative permeabilities represented by power-law models with both known endpoints and unknown endpoints are presented. It is shown that with known endpoints, good estimations can be obtained. With unknown endpoints, good estimations can still be obtained using more observations and a larger ensemble size. Computational time is reported to show that the run time is greatly reduced with more CPU cores. The MPI speedup is over 70% for a small ensemble size and 77% for a large ensemble size with up to 640 CPU cores.Lihua ShenHui LiuZhangxin ChenMDPI AGarticlehistory matchingDEnKFrelative permeabilitypower-law modelparallel computingChemical technologyTP1-1185ChemistryQD1-999ENProcesses, Vol 9, Iss 1980, p 1980 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	history matching DEnKF relative permeability power-law model parallel computing Chemical technology TP1-1185 Chemistry QD1-999
spellingShingle	history matching DEnKF relative permeability power-law model parallel computing Chemical technology TP1-1185 Chemistry QD1-999 Lihua Shen Hui Liu Zhangxin Chen Parallel Implementation of the Deterministic Ensemble Kalman Filter for Reservoir History Matching
description	In this paper, the deterministic ensemble Kalman filter is implemented with a parallel technique of the message passing interface based on our in-house black oil simulator. The implementation is separated into two cases: (1) the ensemble size is greater than the processor number and (2) the ensemble size is smaller than or equal to the processor number. Numerical experiments for estimations of three-phase relative permeabilities represented by power-law models with both known endpoints and unknown endpoints are presented. It is shown that with known endpoints, good estimations can be obtained. With unknown endpoints, good estimations can still be obtained using more observations and a larger ensemble size. Computational time is reported to show that the run time is greatly reduced with more CPU cores. The MPI speedup is over 70% for a small ensemble size and 77% for a large ensemble size with up to 640 CPU cores.
format	article
author	Lihua Shen Hui Liu Zhangxin Chen
author_facet	Lihua Shen Hui Liu Zhangxin Chen
author_sort	Lihua Shen
title	Parallel Implementation of the Deterministic Ensemble Kalman Filter for Reservoir History Matching
title_short	Parallel Implementation of the Deterministic Ensemble Kalman Filter for Reservoir History Matching
title_full	Parallel Implementation of the Deterministic Ensemble Kalman Filter for Reservoir History Matching
title_fullStr	Parallel Implementation of the Deterministic Ensemble Kalman Filter for Reservoir History Matching
title_full_unstemmed	Parallel Implementation of the Deterministic Ensemble Kalman Filter for Reservoir History Matching
title_sort	parallel implementation of the deterministic ensemble kalman filter for reservoir history matching
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/fa3835f9c8d041129f38e3e78229f1f6
work_keys_str_mv	AT lihuashen parallelimplementationofthedeterministicensemblekalmanfilterforreservoirhistorymatching AT huiliu parallelimplementationofthedeterministicensemblekalmanfilterforreservoirhistorymatching AT zhangxinchen parallelimplementationofthedeterministicensemblekalmanfilterforreservoirhistorymatching
_version_	1718410689992720384

Parallel Implementation of the Deterministic Ensemble Kalman Filter for Reservoir History Matching

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