A surrogate-assisted multi-objective evolutionary algorithm with dimension-reduction for production optimization

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.