TDRM을 이용한 캐나다 자마광구 시뮬레이션 연구

이태훈^*

Simulation study of Canada Zama reservoir using TDRM

Taehun Lee^*

^*Corresponding Author : aehun Lee, Tel: +82-42-868-3076, Fax: +82-42-868-3417, E-mail: thlee@kigam.re.kr

ⓒ Copyright 2018 Korean Society of Petroleum and Sedimentary Geology. This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Received: Jun 22, 2018 ; Revised: Jul 03, 2018 ; Accepted: Jul 06, 2018

Published Online: Oct 31, 2018

요약

인공지능은 인간의 삶에 다양한 분야에서 적용되고 있는 실정이며, 석유 분야에서도 활발하게 연구 및 적용이 되고 있는 중이다. 특히, 최근에 각광받고 있는 디지털 오일필드에는 인공지능을 이용한 저류층 시뮬레이션 기술이 필수적이다. 그러나 현재까지 이에 대한 연구는 거의 전무하다. 따라서 본 연구에서는 인공지능 기술을 이용한 TDRM을 이용하여 캐나다 육상에 위치한 자마광구에 적용하고자 하였다. 필요한 정적․동적 자료는 캐나다 유정 정보 S/W인 Accumap을 이용하였다. 적용 결과 성공적으로 저류층 모델을 구축하였으며, 이를 이용하여 단시간에 민감도 분석도 수행이 가능하였다.

ABSTRACT

Artificial intelligence is applied in various fields of human life and is being actively studied and applied in the oil fields. Especially, the digital oil field, which has recently been spotlighted, is required to simulate the reservoir using artificial intelligence. However, there is almost no research to date. Therefore, in this study, we applied TDRM using artificial intelligence technology to Zama field located on the land of Canada. The required static and dynamic data were obtained from Accumap, a Canadian well information S/W. As a result, the reservoir model was constructed successfully and the sensitivity analysis could be performed in a short time.

Keywords: 인공지능; 디지털오일필드; TDRM; 캐나다 자마광구

Keywords: artificial intelligence; digital oil field; TDRM; Canada Zama reservoir

References

Allanin, O.F. and Horne, R.N., 1990, Use of artificial intelligence in Well Test Interpretation. JPT, 342-349 .

Chang, S., 1999, Applications for Expert Systems in the Petroleum World:Present and Perspective. Korea Intelligent Information System Society Conference .

Intelligent Solutions, 2010, AI-based reservoir management .

Kalantari-Dahaghi, A., Mohaghegh, S.D, and Khazaeni, Y., 2010, New Insight into Integrated Reservoir Management using Top-Down, Intelligent Reservoir Modeling Technique; Application to a Giant and Complex Oil Field in the Middle East. SPE Western Regional Meeting, Anaheim, California, USA .

Kim, S.S., Son, J.Y. and Park, J.H., 2013, IT convergence technology trends based on big data analytics in the digital oil field. ETRI, 28, 4, 1-9 .

Lim, J.-T., Park, H.-W. and Lim, J.-S., 2014, Classification and application of digital oil field system. Journal of Korean Society of Mineral and Energy Resource Engineers, 51, 750-756 .

Smith, S.A, Sorensen, J.A., Steadman, E.N., Harju, J.A., Jackson, B. and Nimchuk, D., 2009, Plains CO2 Reduction (PCOR) Partnership (Phase II) - Zama Field Validation Test regional technology implementation plan. Report to U.S. Department of Energy, Grand Forks, North Dakota: Energy & Environmental Research Center, pp. 11 .

Smith, S.A., Sorenson, J.A., Steadman, E.N., Harju, J.A. and Ryan, D., 2009, Zama acid gas EOR, CO2 sequestration, and monitoring project. Energy Procedia 4, 3957-3964 .

Undeerc, 2009, Plains CO2 Reduction (PCOR) Partnership (Phase II) - Zama Field Validation Test Regional Technology Implementation Plan. http://www.undeerc.org/ PCOR/newsandpubs/pdf/RTIP-Zama-Acid-Gas-EOR-CO2-Sequestration-and-Monitoring-Project.pdf[accessed 1th July 2016] .