Title: Development of Surrogate Reservoir Model (SRM) for fast track analysis of a complex reservoir

Authors: Shahab D. Mohaghegh, Abi Modavi, Hafez Hafez, Masoud Haajizadeh, Srikant Guruswamy

Addresses: Department of Petroleum & Natural Gas Engineering, West Virginia University, Morgantown, West Virginia 26506, USA. ' ExxonMobil/ZADCO, Zakum Development Company, P.O. Box 46808, Abu Dhabi, United Arab Emirates. ' ADCO-PDD, Abu Dhabi Company for Onshore Oil Operation (ADCO), P.O. Box 270, Abu Dhabi, United Arab Emirates. ' BP Exploration (Norway), BP Norge AS, P.O. Box 197, 4065 Stavanger, Norway. ' ADCO-PDD, Abu Dhabi Company for Onshore Oil Operation (ADCO), P.O. Box 270, Abu Dhabi, United Arab Emirates

Abstract: Reservoir simulation is the industry standard for reservoir management that is used in all phases of field development. As the main source of information, prediction and decision-making, the Full Field Models (FFM) is regularly updated to include the latest measurements and interpretations. A typical FFM consists of large number of grid blocks and usually takes hours for each run. This makes comprehensive analysis of the solution space and incorporation of the FFM in smart fields impractical. Surrogate Reservoir Models (SRMs) are introduced as a bridge to make Real-Time Reservoir Management possible. SRMs are replicas of FFM that can run in fractions of a second. They accurately mimic the capabilities of FFM and are used for automatic history matching, real-time optimisation, real-time decision-making and quantification of uncertainties. This paper presents the development of SRM using the state of the art Artificial Intelligence and Data Mining (AI&DM) techniques. An example application to giant oil field in the Middle East and the accuracy of SRM predictions are presented. SRM is used in order to identify the wells that are prime candidate for rate relaxation with the objective of higher oil production without an increase in water cut. After 30 months of production all SRM predictions were proved accurate. [Received: July 6, 2008; Accepted: November 4, 2008]

Keywords: surrogate models; Middle East; neural networks; reservoir modelling; reservoir simulation; fast track analysis; complex reservoirs; surrogate reservoir models; data mining; oil fields.

DOI: 10.1504/IJOGCT.2009.023627

International Journal of Oil, Gas and Coal Technology, 2009 Vol.2 No.1, pp.2 - 23

Published online: 03 Mar 2009 *

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