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

Jaejun Kim, Joe M. Kang, Changhyup Park, Yongjun Park, Jihye Park and Seojin Lim
Additional contact information
Jaejun Kim: Department of Energy Systems Engineering, Seoul National University, Seoul 08826, Korea
Joe M. Kang: Department of Energy Systems Engineering, Seoul National University, Seoul 08826, Korea
Changhyup Park: Department of Energy and Resources Engineering, Kangwon National University, Chuncheon, Kangwon 24341, Korea
Yongjun Park: POSCO Daewoo Corporation, Incheon 21998, Korea
Jihye Park: POSCO Daewoo Corporation, Incheon 21998, Korea
Seojin Lim: AJU Global, Seoul 06626, Korea

Energies, 2017, vol. 10, issue 4, 1-16

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.

Keywords: multi-objective history matching; fast marching method; distance-based generalized sensitivity analysis; shale gas; hydraulic fracturing (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: 2017
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1996-1073/10/4/579/pdf (application/pdf)
https://www.mdpi.com/1996-1073/10/4/579/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:10:y:2017:i:4:p:579-:d:96592

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().