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Energies 2017, 10(2), 197;

Development of a General Package for Resolution of Uncertainty-Related Issues in Reservoir Engineering

3,* and 4
Department of Oil-Gas Field Development Engineering, College of Petroleum Engineering, China University of Petroleum, Beijing 102249, China
State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing 102249, China
State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development, SINOPEC Group, Beijing 050021, China
Department of Energy and Resources Engineering, College of Engineering, Peking University, Beijing 100871, China
Author to whom correspondence should be addressed.
Academic Editor: Mofazzal Hossain
Received: 20 November 2016 / Revised: 31 January 2017 / Accepted: 2 February 2017 / Published: 10 February 2017
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Reservoir simulations always involve a large number of parameters to characterize the properties of formation and fluid, many of which are subject to uncertainties owing to spatial heterogeneity and insufficient measurements. To provide solutions to uncertainty-related issues in reservoir simulations, a general package called GenPack has been developed. GenPack includes three main functions required for full stochastic analysis in petroleum engineering, generation of random parameter fields, predictive uncertainty quantifications and automatic history matching. GenPack, which was developed in a modularized manner, is a non-intrusive package which can be integrated with any existing commercial simulator in petroleum engineering to facilitate its application. Computational efficiency can be improved both theoretically by introducing a surrogate model-based probabilistic collocation method, and technically by using parallel computing. A series of synthetic cases are designed to demonstrate the capability of GenPack. The test results show that the random parameter field can be flexibly generated in a customized manner for petroleum engineering applications. The predictive uncertainty can be reasonably quantified and the computational efficiency is significantly improved. The ensemble Kalman filter (EnKF)-based automatic history matching method can improve predictive accuracy and reduce the corresponding predictive uncertainty by accounting for observations. View Full-Text
Keywords: reservoir simulation; uncertainty quantification; automatic history matching reservoir simulation; uncertainty quantification; automatic history matching

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Xue, L.; Dai, C.; Wang, L. Development of a General Package for Resolution of Uncertainty-Related Issues in Reservoir Engineering. Energies 2017, 10, 197.

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