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A surrogate-assisted multi-objective evolutionary algorithm with dimension-reduction for production optimization.
- Source :
-
Journal of Petroleum Science & Engineering . Sep2020, Vol. 192, pN.PAG-N.PAG. 1p. - Publication Year :
- 2020
-
Abstract
- Multi-objective optimization (MOO), which involves more than one conflicting objective to be optimized simultaneously, is expected to provide efficient and comprehensive reservoir management (RM) solutions. The multi-objective production optimization problems are considered to be expensive due to the difficulties and cost of operations. Surrogate-assisted evolutionary algorithms (SAEAs), which has proved to be an effective way to solve expensive problems, design computationally cheap function to approximate each objective function. Meanwhile, the optimization process involves a large number of decision variables. However, building a high-quality surrogate model has become difficult due to the "curse of dimensionality". Base on characterization, an efficient multi-objective optimization framework called SA-RVEA-PCA is proposed to effectively deal with large-scale and computationally expensive simulation-based optimization problems, including three parts:1) Given a set of simulation results, a Gaussian process (GP) model based on Principal Component Analysis (PCA) for each objective function is trained so that the surrogate models can guide the optimization more accurately. 2) A reference vector guided evolutionary algorithm (RVEA) recently developed is employed as a multi-objective optimizer. 3) The information of uncertainty given by GP and angle-penalized penalized (APD) proposed in RVEA are used to update the surrogate models. To the best of our knowledge, the proposed algorithm is applied to a benchmark function, and two typical applications of MOO with synthetic reservoir models. Results show that the proposed method can provide more comprehensive and efficient RM with a higher convergence speed. • A new multi-objective framework is proposed based on surrogate-assisted algorithm and dimension-reduction method. • The new methodology is successfully applied to two general multi-objective production optimization problems. • The optimization results of two cases show the great performance of the proposed method. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09204105
- Volume :
- 192
- Database :
- Academic Search Index
- Journal :
- Journal of Petroleum Science & Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 143639599
- Full Text :
- https://doi.org/10.1016/j.petrol.2020.107192