Unlocking Ensemble History Matching Potential with Parallelism and Careful Data Management

Abstract

Improvements in reservoir simulation computational time thanks to GPU-based simulators and the increasing computational power of modern HPC systems, are paving the way for a massive employment of Ensemble History Matching (EHM) techniques which are intrinsically parallel. Here we present the results of a comparative study between a newly developed EHM tool that aims at leveraging the GPU parallelism, and a commercial third-party EHM software as a benchmark. Both are tested on a real case. The reservoir chosen for the comparison has a production history of 3 years with 15 wells between oil producers, and water and gas injectors. The EHM algorithm used is the Ensemble Smoother with Multiple Data Assimilations (ESMDA) and both tools have access to the same computational resources. The EHM problem was stated in the same way for both tools. The objective function considers well oil productions, water cuts, bottom-hole pressures, and gas-oil-ratios. Porosity and horizontal permeability are used as 3D grid parameters in the update algorithm, along with nine scalar parameters for anisotropy ratios, Corey exponents, and fault transmissibility multipliers. Both the presented tool and the benchmark obtained a satisfactory history match quality. The benchmark tool took around 11.2 hours to complete, while the proposed tool took only 1.5 hours. The two tools performed similar updates on the scalar parameters with only minor discrepancies. Updates on the 3D grid properties instead show significant local differences. The updated ensemble for the benchmark reached extreme values for porosity and permeability which are also distributed in a heterogeneous way. These distributions are quite unlikely in some model regions given the initial geological characterization of the reservoir. The updated ensemble for the presented tool did not reach extreme values in neither porosity nor permeability. The resulting property distributions are not so far off from the ones of the initial ensemble, therefore we can conclude that we were able to successfully update the ensemble while persevering the geological characterization of the reservoir. Analysis suggests that this discrepancy is due to the different way by which our EHM code consider inactive cells in the grid update calculations compared to the benchmark highlighting the fact that statistics including inactive cells should be carefully managed to correctly preserve the geological distribution represented in the initial ensemble. The presented EHM tool was developed from scratch to be fully parallel and to leverage on the abundantly available computational resources. Moreover, the ESMDA implementation was tweaked to improve the reservoir update by carefully managing inactive cells. A comparison against a benchmark showed that the proposed EHM tool achieved similar history match quality while improving the computation time and the geological realism of the updated ensemble.