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Traditional optimizers require several experimentations to fine tune parameters and this makes them problem dependent and can compound the general simulation problem. Penalty method which is commonly used to solve constrained well placement optimization problem requires extensive experimentation to set up the right penalty parameter. Poor parameter set up affects performance of optimization tool. In this article, Grey Wolf Optimizer (GWO) is implemented as well placement optimizer. Superiority of feasible solutions method was proposed for handling the minimum well distance constraint. Powell-Skolnick (CPF-1) and Deb (CPF-2) constraint techniques were considered and their performances were compared to Unconstrained Problem Formulation (UCPF). Two field development strategies (DS-1 and DS-2) were considered and implemented in SPE1 (unimodal system) and PUNQ-S3 (multimodal system) reservoir models to evaluate the performances of CPF-1, CPF-2 and UCPF. Results revealed that under SPE1, UCPF gave the highest average Net Present Value (NPV) for DS-1 and DS-2 but with majority of the solutions violating the constraints whilst under PUNQ-S3, CPF-1 and CPF-2 showed superior performances against UCPF. Among the constraint satisfaction, CPF-2 showed relatively superior performance than CPF-1 in all cases except under DS-2 in PUNQ-S3.

Grey wolf optimizer for solving well placement optimization problem constrained to minimum well distance