Journal of the Korean Institute of Gas (한국가스학회지)

The Korean Institute of GAS (한국가스학회)
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Development of the Efficiency-Evaluation Model for the Mechanism of CO2 Sequestration in a Deep Saline Aquifer

심부 대염수층 CO2 격리 메커니즘에 관한 효율성 평가 모델 개발

  • Kim, Jung-Gyun (Dept. of Energy and Resources Engineering, Chonnam National University) ;
  • Lee, Young-Soo (R&D Division, Korea Gas Corporation) ;
  • Lee, Jeong-Hwan (Dept. of Energy and Resources Engineering, Chonnam National University)
  • 김정균 (전남대학교 에너지자원공학과) ;
  • 이영수 (한국가스공사 연구개발원) ;
  • 이정환 (전남대학교 에너지자원공학과)
  • Received : 2012.12.03
  • Accepted : 2012.12.26
  • Published : 2012.12.31
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Abstract

The practical way to minimize the greenhouse gas is to reduce the emission of carbon dioxide. For this reason, CCS(Carbon Capture and Storage) technology, which could reduce carbon dioxide emission, has risen as a realistic alternative in recent years. In addition, the researcher is recently working into ways of applying CCS technologies with deep saline aquifer. In this study, the evaluation model on the feasibility of $CO_2$ sequestration in the deep saline aquifer using ANN(Artificial Neural Network) was developed. In order to develop the efficiency-evaluation model, basic model was created in the deep saline aquifer and sensitivity analysis was performed for the aquifer characteristics by utilizing the commercial simulator of GEM. Based on the sensitivity analysis, the factors and ranges affecting $CO_2$ sequestration in the deep saline aquifer were chosen. The result from ANN training scenario were confirmed $CO_2$ sequestration by solubility trapping and residual trapping mechanism. The result from ANN model evaluation indicated there is the increase of correlation coefficient up to 0.99. It has been confirmed that the developed model can be utilized in feasibility of $CO_2$ sequestration at deep saline aquifer.

$CO_2$ 감축은 최근 문제되고 있는 온실가스를 감축시킬 수 있는 직접적인 수단이 되고 있으며, 이러한 방법으로는 CCS 기술이 현실적인 대안기술로 부상하고 있다. 특히 전 세계적으로 널리 분포되어 있고 많은 양의 $CO_2$ 를 격리할 수 있는 심부 대염수층을 대상으로 활발한 연구가 진행 중이다. 이에 본 연구에서는 심부 대염수층에 대한 $CO_2$ 지중격리시 예비 타당성 평가 수행을 위하여 인공신경망을 이용한 효율성 평가 모델을 개발하였다. 모델 개발에 앞서 심부 대염수층을 대표할 수 있는 기본 모델을 선정하고 상용시뮬레이터 GEM을 활용하여 민감도 분석을 수행하였으며, 분석 결과를 바탕으로 심부 대염수층에 영향을 미치는 주요 인자 및 영향범위를 선정하였다. 인공신경망 학습을 위한 격리 시나리오 구성 결과 용해트랩과 잔류트랩에 의한 $CO_2$ 격리를 확인할 수 있었으며, 인공신경망 모델 자체 검증 결과 0.99이상의 높은 상관계수를 나타내어 심부 대염수층에서의 $CO_2$ 지중격리 효율성 평가에 활용 가능함을 확인하였다.

Keywords

References

  1. Raj, P., Mathur, B., Kumar, S., Jhalani, S., "CCS: Opportunutues and Obstacles for India", paper SPE 137717 presented at Canadian Unconventional Resources & International Petroleum Conference, Calgary, Canada, (2010)
  2. Basbug, B., and Gumrah, F., "Simulating the Effects of Deep Saline Aquifer Properties on $CO_{2}$ Sequestration", paper SPE 07-10-03 presented at 2005 Petroleum Society's 6th, Calgary, Alberta, Canada, (2007)
  3. Bachu, S., "Sequestratio of $CO_{2}$ in Geological Media: Criteria and Approach for Site Selection in Response to Climate Change", Energy Convertsion & Management, 41, 953-970, (2000) https://doi.org/10.1016/S0196-8904(99)00149-1
  4. Franklin, M., and Orr, Jr., "Storage of Carbon Dioxide in Geologic Formations", paper SPE 88842 presented at 2004 Distinguished Author Series, Standford Univ, (2004)
  5. Sengul, M., "$CO_{2}$ Sequestration-A Safe Transition Technology", paper SPE 98617 presented at the SPE International Conference, Abu Dhabi, UAE, (2006)
  6. IEA(International Energy Agency), http://www.iea.org/techno/essentials1.pdf, (2009)
  7. Kim, S. H., Lee, J. M., Yoon, E. S., "Reservoir Modelling for Carbon Dioxide Sequestration and Enhanced Oil Recovery", Journal of the Korean Institute of Gas, 16(3), 35-41, (2012) https://doi.org/10.7842/kigas.2012.16.3.035
  8. Lee, Y. S., A Simulation Study on the Analysis of $CO_{2}$-Water Flow for $CO_{2}$ Sequestration in Deep Saline Aquifer, Doctoral Dissertation, Hanyang University, Seoul, (2009)
  9. Son, H. Y., Kwon, S. I., Sung, W. M., "Analytical Approach of $CO_{2}$ Sequestration in a Deep Confined Aquifer", Journal of Korean Society for Geosystem Engineering, 43(4), 275-281, (2006)
  10. Kumar, A., Noh, M., Pope, G.A., Sepehrnoori, K., Bryant, S., Lake, L.W., "Reservoir Simulation of $CO_{2}$ Storage in Deep Saline Aquifers," paper SPE 89343 presented at 2004 SPE/DOE Fourteenth Symposium, Tulsa, Oklahoma, USA, (2004)
  11. Liner, C.L., Geng, P., Zeng, J., King, H., Li, J., "A $CO_{2}$ Sequestration Simulation Case Study at the Dickman Field, Ness Co., Kansas," paper SPE 145791 presented at 2011 SPE Annual Technical Conference and Exhibition, Denver, Colorado, USA, (2011)
  12. Lim, J. S., "Reservoir Permeability Determination using Artificial Neural Network", Journal of Korean Society for Geosystem Engineering, 40(4), 232-238, (2003)
  13. Mohaghegh, S., "Virtual-Intelligence Applications in Petroleum Engineering: Part I-Artificial Neural Networks", Journal of Petroleum Technology, 52(9), 64-73, (2000) https://doi.org/10.2118/58046-JPT
  14. IPCC, IPCC Special Report on Carbon Dioxide Capture and Storage, Cambridge Univ., Press., New York, (2005)
  15. Nghiem, L., Yang, C., Shrivatave, V., Koshe, B., Hassam, M., Chen, D. and Card, C., "Optimization of Residual Gas and Solubility Trapping for CO2 Stroage in Saline Aquifer", paper SPE 119080 presented at 2009 SPE Reservoir Simulation Symposium, Woodlands, TX, (2009)
  16. Kim, K. H., The Analysis of $CO_{2}$ Injectivity in Saline Aquifer Using the Experimental Results of $CO_{2}$ Relative Permeability, Master Dissertation, Hanyang University, Seoul, (2008)
  17. Land, C.S., "Calculation of Imbibition Relative Permeability for Two- and Three-Phase Flow from Rock Properties", Soc. Pet. Eng. J., 8(2), 149-156, (1968) https://doi.org/10.2118/1942-PA
  18. Law, D., and Bachu, S., "Hydrogeological and Numerical Analysis of $CO_{2}$ Disposal in Deep Aquifers in the Alberta Sedimentary Basin", Energy Conversion & Management, 37, 1167-1174, (1996) https://doi.org/10.1016/0196-8904(95)00315-0
  19. Carter, R. D., and Tracy, G.W., "An Improved Method for Calculating Water Influx", Petroleum Transactions, AIME, 219, 415-417, (1960)
  20. Park, H. Y., Lee, G. Y., Pattern Recognition, EHAN Media, Seoul, 241-251, (2011)
  21. Kim, D. S., Neural Network Theory and Background I, Jinhan M&B, Seoul, 92-109, (1993)

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