An approximate dynamic programming approachto decision making in the presence of uncertainty for surfactant-polymer flooding
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- 作者：Ali Alkhatib (1)
Peter King (1) - 关键词：Decision making ; Surfactant ; polymer flooding ; Uncertainty ; PCM ; LSM ; Approximate dynamic programming ; 49L20 ; 65C99 ; 65D32 ; 65F25 ; 65K99 ; 90C15 ; 90C39 ; 93E20 ; 93E24
- 刊名：Computational Geosciences
- 出版年：2014
- 出版时间：April 2014
- 年：2014
- 卷：18
- 期：2
- 页码：243-263
- 全文大小：
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Peter King (1)
1. Earth Science and Engineering, Imperial College London, London, UK - ISSN：1573-1499
文摘
The least squares Monte Carlo method is a decision evaluation method that can capture the effect of uncertainty and the value of flexibility of a process. The method is a stochastic approximate dynamic programming approach to decision making. It is based on a forward simulation coupled with a recursive algorithm which produces the near-optimal policy. It relies on the Monte Carlo simulation to produce convergent results. This incurs a significant computational requirement when using this method to evaluate decisions for reservoir engineering problems because this requires running many reservoir simulations. The objective of this study was to enhance the performance of the least squares Monte Carlo method by improving the sampling method used to generate the technical uncertainties used in obtaining the production profiles. The probabilistic collocation method has been proven to be a robust and efficient uncertainty quantification method. By using the sampling methods of the probabilistic collocation method to approximate the sampling of the technical uncertainties, it is possible to significantly reduce the computational requirement of running the decision evaluation method. Thus, we introduce the least squares probabilistic collocation method. The decision evaluation considered a number of technical and economic uncertainties. Three reservoir case studies were used: a simple homogeneous model, the PUNQ-S3 model, and a modified portion of the SPE10 model. The results show that using the sampling techniques of the probabilistic collocation method produced relatively accurate responses compared with the original method. Different possible enhancements were discussed in order to practically adapt the least squares probabilistic collocation method to more realistic and complex reservoir models. Furthermore, it is desired to perform the method to evaluate high-dimensional decision scenarios for different chemical enhanced oil recovery processes using real reservoir data.