Abstract
Well placement and parameter optimization (WPPO) is an essential step in hydrocarbon, geothermal and water resources development which has complexities and difficulties. In fact, high computational cost can be the most important obstacle in WPPO problems. Population-based metaheuristics algorithms (PBMAs) are the most widely utilized ones for WPPO problems. However, these methods suffer from the issue that at each iteration, the minimum number of simulation run is at least equal to the population size. Knowing this, in order to enhance the efficiency of these methods, in this study, we introduced a cell-based quality gate function (CBQGF) which is inspired from quantum gates in quantum computing. The CBQGF is incorporated in our previously introduced inter-distance algorithm (IDC) which we called CBQG-IDC. Since, CBQG sets a condition for each location during the optimization process, locations with poor cell properties will be filtered out to increase the rate of convergence considerably. We applied the CBQG-IDC to two universally popular global optimization methods, genetic algorithm (GA) and particle swarm optimization (PSO) and compared the results to the IDC limited algorithm. In all scenarios, net present value (NPV) was considered as the fitness value and all joint optimization of locations and well associated parameters were conducted simultaneously. The results showed CBQG-IDC with a much higher rate of convergence compared to IDC, while its performance is highly dependent on constant parameters. Ultimately, the proposed CBQG-IDC can be applied to any optimization algorithm for any placement optimization problem in Euclidian geometry to save the computational cost.
NSTL