Surrogate-assisted differential evolution for production optimization with nonlinear state constraints

• Published in: Journal of petroleum science & engineering Vol. 194; p. 107441
• Main Authors: Zhao, Xinggang, Zhang, Kai, Chen, Guodong, Xue, Xiaoming, Yao, Chuanjin, Wang, Jian, Yang, Yongfei, Zhao, Hui, Yao, Jun
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2020
• Subjects: Differential evolution; Nonlinear state constraint; Production optimization; Radial basis function (RBF); Surrogate model; Differential evolution; Production optimization; Radial basis function (RBF); Surrogate model; Nonlinear state constraint
• ISSN: 0920-4105; 1873-4715
• DOI: 10.1016/j.petrol.2020.107441

In recent years, evolutionary computation (EC) has gained increasing attention in the field of production optimization due to its powerful global search ability and derivative-free characteristic. However, state constraints are much more challenging to handle in comparison with the explicit constraints. To alleviate this difficulty, this paper presents a framework called surrogate-assisted differential evolution with an effective constraint-handling technique—the feasibility rule with the incorporation of objective function information (SADE-FROFI). The novel constraint-handling technique used in this work achieves an effective balance between constraints and objective function by revising the well-known feasibility rule (FR), which can effectively maintain the diversity of population and help the population jump out of local optimal solution. Moreover, to address the computationally time-consuming issue of numerical simulator, a multi-surrogate strategy is introduced specially for state constraints. Two benchmark functions are tested to verify the effectiveness of new constraint-handling technique (FROFI). The efficacy of the proposed method is validated on two synthetic reservoir models, named three-channel model and PUNQ-S3 model, respectively. The experimental results demonstrated that the proposed method can find better solutions on the basis of satisfying all constraints in comparison with several existing methods. •A new constrained production optimization framework is proposed based on surrogate-assisted algorithm and novel constraint-handling technique.•The new methodology is successfully applied to two constrained production optimization problems.•The optimization results of two reservoir models show the great performance of the proposed method.