Global and Local Surrogate-Model-Assisted Differential Evolution for Waterflooding Production Optimization
- Guodong Chen (China University of Petroleum (East China)) | Kai Zhang (China University of Petroleum (East China)) | Liming Zhang (China University of Petroleum (East China)) | Xiaoming Xue (China University of Petroleum (East China)) | Dezhuang Ji (China University of Petroleum (East China)) | Chuanjin Yao (China University of Petroleum (East China)) | Jun Yao (China University of Petroleum (East China)) | Yongfei Yang (China University of Petroleum (East China))
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- February 2020
- Document Type
- Journal Paper
- 105 - 118
- 2020.Society of Petroleum Engineers
- differential evolution, radial basis function, RBF, production optimization, surrogate
- 18 in the last 30 days
- 206 since 2007
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Surrogate models, which have become a popular approach to oil-reservoir production-optimization problems, use a computationally inexpensive approximation function to replace the computationally expensive objective function computed by a numerical simulator. In this paper, a new optimization algorithm called global and local surrogate-model-assisted differential evolution (GLSADE) is introduced for waterflooding production-optimization problems. The proposed method consists of two parts: (1) a global surrogate-model-assisted differential-evolution (DE) part, in which DE is used to generate multiple offspring, and (2) a local surrogate-model-assisted DE part, in which DE is used to search for the optimum of the surrogate. The cooperation between global optimization and local search helps the production-optimization process become more efficient and more effective. Compared with the conventional one-shot surrogate-based approach, the developed method iteratively selects data points to enhance the accuracy of the promising area of the surrogate model, which can substantially improve the optimization process. To the best of our knowledge, the proposed method uses a state-of-the-art surrogate framework for production-optimization problems. The approach is tested on two 100-dimensional benchmark functions, a three-channel model, and the egg model. The results show that the proposed method can achieve higher net present value (NPV) and better convergence speed in comparison with the traditional evolutionary algorithm and other surrogate-assisted optimization methods for production-optimization problems.
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