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Title: Assisted history matching using pattern recognition technology

Authors: Alireza Shahkarami; Shahab D. Mohaghegh; Yasin Hajizadeh

Addresses: School of Sciences, Saint Francis University, Science Center 003, P.O. Box 600, Loretto, PA, 15940, USA ' Benjamin M. Statler College of Engineering and Mineral Resources, West Virginia University, 345-E Mineral Resources Bldg., P.O. Box 6070, Morgantown, WV, 26506, USA ' University of Calgary, 2500 University Dr. NW, Calgary, Alberta, T2N 1N4, Canada

Abstract: This study examines the application of pattern recognition technologies to improve the time and effort required for completing successful history matching projects. The pattern recognition capabilities of artificial intelligence and data mining techniques are used to develop a surrogate reservoir model (SRM), which is then employed to perform the assisted history matching process. A well-known reservoir model, PUNQ-S3, was selected to study the potentials of the SRM in an assisted history matching process. The SRM is a prototype of a full-field reservoir simulation model that demands a low development cost and has a high implementation pace. SRMs are built based on a spatio-temporal database, which includes different types of data extracted from a few realisations of the simulation model. The SRM was coupled with the differential evolution optimisation method to construct an automated history matching workflow. The results of this study prove the SRMs' capability in assisting history matching processes. [Received: December 3, 2015; Accepted: June 17, 2016]

Keywords: history matching; pattern recognition; surrogate reservoir models; PUNQ-S3; differential evolution.

DOI: 10.1504/IJOGCT.2018.090966

International Journal of Oil, Gas and Coal Technology, 2018 Vol.17 No.4, pp.412 - 442

Received: 10 Dec 2015
Accepted: 17 Jun 2016

Published online: 05 Apr 2018 *

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