Multi-level Parallelization of Ensemble Kalman Filter for Reservoir History Matching

2011 ◽  
Author(s):  
Reza Tavakoli ◽  
Gergina Pencheva ◽  
Mary Fanett Wheeler
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
History Matching ◽  
Reservoir History Matching ◽  
Multi Level

scholarly journals Assessment of multilevel ensemble-based data assimilation for reservoir history matching

2019 ◽  
Vol 24 (1) ◽  
pp. 217-239
Author(s):  
Kristian Fossum ◽  
Trond Mannseth ◽  
Andreas S. Stordal
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
History Matching ◽  
Computational Cost ◽  
Ensemble Size ◽  
Matching Problems ◽  
Reservoir Models ◽  
Reservoir History Matching ◽  
Computational Resources

AbstractMultilevel ensemble-based data assimilation (DA) as an alternative to standard (single-level) ensemble-based DA for reservoir history matching problems is considered. Restricted computational resources currently limit the ensemble size to about 100 for field-scale cases, resulting in large sampling errors if no measures are taken to prevent it. With multilevel methods, the computational resources are spread over models with different accuracy and computational cost, enabling a substantially increased total ensemble size. Hence, reduced numerical accuracy is partially traded for increased statistical accuracy. A novel multilevel DA method, the multilevel hybrid ensemble Kalman filter (MLHEnKF) is proposed. Both the expected and the true efficiency of a previously published multilevel method, the multilevel ensemble Kalman filter (MLEnKF), and the MLHEnKF are assessed for a toy model and two reservoir models. A multilevel sequence of approximations is introduced for all models. This is achieved via spatial grid coarsening and simple upscaling for the reservoir models, and via a designed synthetic sequence for the toy model. For all models, the finest discretization level is assumed to correspond to the exact model. The results obtained show that, despite its good theoretical properties, MLEnKF does not perform well for the reservoir history matching problems considered. We also show that this is probably caused by the assumptions underlying its theoretical properties not being fulfilled for the multilevel reservoir models considered. The performance of MLHEnKF, which is designed to handle restricted computational resources well, is quite good. Furthermore, the toy model is utilized to set up a case where the assumptions underlying the theoretical properties of MLEnKF are fulfilled. On that case, MLEnKF performs very well and clearly better than MLHEnKF.

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

Processes ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1980
Author(s):  
Lihua Shen ◽  
Hui Liu ◽  
Zhangxin Chen
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Message Passing ◽  
History Matching ◽  
Message Passing Interface ◽  
Parallel Implementation ◽  
Computational Time ◽  
Ensemble Size ◽  
Three Phase ◽  
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 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.

History Matching Using the Ensemble Kalman Filter on a North Sea Field Case

2006 ◽  
Author(s):  
Vibeke Eilen Jensen Haugen ◽  
Lars-Jorgen Natvik ◽  
Geir Evensen ◽  
Aina Margrethe Berg ◽  
Kristin Margrethe Flornes ◽  
...  
Keyword(s):  
Kalman Filter ◽  
North Sea ◽  
Ensemble Kalman Filter ◽  
History Matching ◽  
Field Case

History Matching of the PUNQ-S3 Reservoir Model Using the Ensemble Kalman Filter

SPE Journal ◽  
2005 ◽  
Vol 10 (02) ◽  
pp. 217-224 ◽  
Author(s):  
Yaqing Gu ◽  
Dean S. Oliver
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
History Matching ◽  
Reservoir Model
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