History Matching with Ensemble Kalman Filter Using Fast Marching Method in Shale Gas Reservoir

2015 ◽  
Author(s):  
Jihun Leem ◽  
Kyungbook Lee ◽  
Joe M. Kang ◽  
Yonjun Park ◽  
Jihye Park
Keyword(s):  
Kalman Filter ◽  
Shale Gas ◽  
Ensemble Kalman Filter ◽  
History Matching ◽  
Fast Marching Method ◽  
Fast Marching ◽  
Gas Reservoir ◽  
Shale Gas Reservoir ◽  
Marching Method

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

Energies ◽  
2017 ◽  
Vol 10 (4) ◽  
pp. 579 ◽  
Author(s):  
Jaejun Kim ◽  
Joe Kang ◽  
Changhyup Park ◽  
Yongjun Park ◽  
Jihye Park ◽  
...  
Keyword(s):  
Shale Gas ◽  
History Matching ◽  
Gas Reservoir ◽  
Multi Objective ◽  
Shale Gas Reservoir ◽  
Proxy Model

Probabilistic Estimation of Shale Gas Reserves Implementing Fast Marching Method and Monte Carlo Simulation

2016 ◽  
Author(s):  
Jaejun Kim ◽  
Joe M. Kang ◽  
Yongjun Park ◽  
Seojin Lim ◽  
Changhyup Park ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Shale Gas ◽  
Computing Time ◽  
Gas Production ◽  
Full Scale ◽  
Fast Marching Method ◽  
Fast Marching ◽  
Gas Recovery ◽  
Marching Method

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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