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Energies 2017, 10(4), 579; doi:10.3390/en10040579

Multi-Objective History Matching with a Proxy Model for the Characterization of Production Performances at the Shale Gas Reservoir

1
Department of Energy Systems Engineering, Seoul National University, Seoul 08826, Korea
2
Department of Energy and Resources Engineering, Kangwon National University, Chuncheon, Kangwon 24341, Korea
3
POSCO Daewoo Corporation, Incheon 21998, Korea
4
AJU Global, Seoul 06626, Korea
*
Author to whom correspondence should be addressed.
Academic Editors: Mofazzal Hossain and Frede Blaabjerg
Received: 10 January 2017 / Revised: 7 March 2017 / Accepted: 18 April 2017 / Published: 23 April 2017
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Abstract

This paper presents a fast, reliable multi-objective history-matching method based on proxy modeling to forecast the production performances of shale gas reservoirs for which all available post-hydraulic-fracturing production data, i.e., the daily gas rate and cumulative-production volume until the given date, are honored. The developed workflow consists of distance-based generalized sensitivity analysis (DGSA) to determine the spatiotemporal-parameter significance, fast marching method (FMM) as a proxy model, and a multi-objective evolutionary algorithm to integrate the dynamic data. The model validation confirms that the FMM is a sound surrogate model working within an error of approximately 2% for the estimated ultimate recovery (EUR), and it is 11 times faster than a full-reservoir simulation. The predictive accuracy on future production after matching 1.5-year production histories is assessed to examine the applicability of the proposed method. The DGSA determines the effective parameters with respect to the gas rate and the cumulative volume, including fracture permeability, fracture half-length, enhanced permeability in the stimulated reservoir volume, and average post-fracturing porosity. A comparison of the prediction accuracy for single-objective optimization shows that the proposed method accurately estimates the recoverable volume as well as the production profiles to within an error of 0.5%, while the single-objective consideration reveals the scale-dependency problem with lesser accuracy. The results of this study are useful to overcome the time-consuming effort of using a multi-objective evolutionary algorithm and full-scale reservoir simulation as well as to conduct a more-realistic prediction of the shale gas reserves and the corresponding production performances. View Full-Text
Keywords: multi-objective history matching; fast marching method; distance-based generalized sensitivity analysis; shale gas; hydraulic fracturing multi-objective history matching; fast marching method; distance-based generalized sensitivity analysis; shale gas; hydraulic fracturing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Kim, J.; Kang, J.M.; Park, C.; Park, Y.; Park, J.; Lim, S. Multi-Objective History Matching with a Proxy Model for the Characterization of Production Performances at the Shale Gas Reservoir. Energies 2017, 10, 579.

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