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http://dx.doi.org/10.7842/kigas.2017.21.3.61

Probabilistic Prediction of Estimated Ultimate Recovery in Shale Reservoir using Kernel Density Function  

Shin, Hyo-Jin (Dept. of Energy and Resources Engineering, Korea Maritime and Ocean University)
Hwang, Ji-Yu (Dept. of Energy and Resources Engineering, Korea Maritime and Ocean University)
Lim, Jong-Se (Dept. of Energy and Resources Engineering, Korea Maritime and Ocean University)
Publication Information
Journal of the Korean Institute of Gas / v.21, no.3, 2017 , pp. 61-69 More about this Journal
Abstract
The commercial development of unconventional gas is pursued in North America because it is more feasible owing to the technology required to improve productivity. Shale reservoir have low permeability and gas production can be carried out through cracks generated by hydraulic fracturing. The decline rate during the initial production period is high, but very low latter on, there are significant variations from the initial production behavior. Therefore, in the prediction of the production rate using deterministic decline curve analysis(DCA), it is not possible to consider the uncertainty in the production behavior. In this study, production rate of the Eagle Ford shale is predicted by Arps Hyperbolic and Modified SEPD. To minimize the uncertainty in predicting the Estimated Ultimate Recovery(EUR), Monte Carlo simulation is used to multi-wells analysis. Also, kernel density function is applied to determine probability distribution of decline curve factors without any assumption.
Keywords
shale gas; Monte Carlo simulation; kernel density function; decline curve analysis;
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