This paper evaluates the estimated ultimate recovery for 10-year operation at a shale gas reservoir, implementing FMM (Fast Marching Method) as a surrogate model of full-scale numerical simulation and Monte Carlo simulation as a tool for accessing the uncertainty of FMM-based proxy parameters. Sensitivity analysis shows the significant properties affecting the gas recovery that are enhanced permeability, matrix permeability, and porosity in sequence. Using the statistical distributions of these parameters, this study determines P10, P50, and P90 of the 10-year cumulative gas production and compares them with the values from full-physics simulations. The computing time based on the proxy model is much smaller than that of the full-scale simulations while the prediction accuracy is acceptable. FMM can forecast the production profiles reliably without time-consuming simulation and the integration of Monte-Carlo simulation is able to evaluate the uncertainty of gas recovery, quantitatively.
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ASME 2016 35th International Conference on Ocean, Offshore and Arctic Engineering
June 19–24, 2016
Busan, South Korea
Conference Sponsors:
- Ocean, Offshore and Arctic Engineering Division
ISBN:
978-0-7918-4999-6
PROCEEDINGS PAPER
Probabilistic Estimation of Shale Gas Reserves Implementing Fast Marching Method and Monte Carlo Simulation
Jaejun Kim,
Jaejun Kim
Seoul National University, Seoul, Korea
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Joe M. Kang,
Joe M. Kang
Seoul National University, Seoul, Korea
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Yongjun Park,
Yongjun Park
Seoul National University, Seoul, Korea
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Seojin Lim,
Seojin Lim
Seoul National University, Seoul, Korea
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Changhyup Park,
Changhyup Park
Kangwon National University, Chuncheon, Korea
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Jihye Park
Jihye Park
POSCO Daewoo Corporation, Incheon, Korea
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Jaejun Kim
Seoul National University, Seoul, Korea
Joe M. Kang
Seoul National University, Seoul, Korea
Yongjun Park
Seoul National University, Seoul, Korea
Seojin Lim
Seoul National University, Seoul, Korea
Changhyup Park
Kangwon National University, Chuncheon, Korea
Jihye Park
POSCO Daewoo Corporation, Incheon, Korea
Paper No:
OMAE2016-54167, V008T11A009; 6 pages
Published Online:
October 18, 2016
Citation
Kim, J, Kang, JM, Park, Y, Lim, S, Park, C, & Park, J. "Probabilistic Estimation of Shale Gas Reserves Implementing Fast Marching Method and Monte Carlo Simulation." Proceedings of the ASME 2016 35th International Conference on Ocean, Offshore and Arctic Engineering. Volume 8: Polar and Arctic Sciences and Technology; Petroleum Technology. Busan, South Korea. June 19–24, 2016. V008T11A009. ASME. https://doi.org/10.1115/OMAE2016-54167
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