Fast Optimization of Injector Selection for Waterflood, CO 2 -EOR and Storage Using an Innovative Machine Learning Framework

Anand Selveindran, Zeinab Zargar, Seyed Mahdi Razavi and Ganesh Thakur
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Anand Selveindran: Department of Petroleum Engineering, University of Houston, Houston, TX 77204, USA
Zeinab Zargar: Department of Petroleum Engineering, University of Houston, Houston, TX 77204, USA
Seyed Mahdi Razavi: Department of Petroleum Engineering, University of Houston, Houston, TX 77204, USA
Ganesh Thakur: Department of Petroleum Engineering, University of Houston, Houston, TX 77204, USA

Energies, 2021, vol. 14, issue 22, 1-24

Abstract: Optimal injector selection is a key oilfield development endeavor that can be computationally costly. Methods proposed in the literature to reduce the number of function evaluations are often designed for pattern level analysis and do not scale easily to full field analysis. These methods are rarely applied to both water and miscible gas floods with carbon storage objectives; reservoir management decision making under geological uncertainty is also relatively underexplored. In this work, several innovations are proposed to efficiently determine the optimal injector location under geological uncertainty. A geomodel ensemble is prepared in order to capture the range of geological uncertainty. In these models, the reservoir is divided into multiple well regions that are delineated through spatial clustering. Streamline simulation results are used to train a meta-learner proxy. A posterior sampling algorithm evaluates injector locations across multiple geological realizations. The proposed methodology was applied to a producing field in Asia. The proxy predicted optimal injector locations for water and CO 2 EOR and storage floods within several seconds (94–98% R 2 scores). Blind tests with geomodels not used in training yielded accuracies greater than 90% (R 2 scores). Posterior sampling selected optimal injection locations within minutes compared to hours using numerical simulation. This methodology enabled the rapid evaluation of injector well location for a variety of flood projects. This will aid reservoir managers to rapidly make field development decisions for field scale injection and storage projects under geological uncertainty.

Keywords: injection optimization; waterflooding; CO 2 EOR; CO 2 storage; machine learning; proxy modelling; reservoir management (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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