Advances in technology and optimisation are helping to improve decision making in the oil and gas industry.However,most of the traditional metaheuristic algorithms applied in well placement optimisation problems suffe...Advances in technology and optimisation are helping to improve decision making in the oil and gas industry.However,most of the traditional metaheuristic algorithms applied in well placement optimisation problems suffer from extensive parameter experimentations and local optimum trapping issues.This couples with the complex and heterogeneous nature of hydrocarbon reservoirs and increased decision variables poses severe simulation process demands.This study considered a functional composition integration approach to formulate a robust hybrid metaheuristic algorithm called HGWO-PSO.The HGWO-PSO leverages on the strengths of Grey Wolf Optimiser(GWO)and Particle Swarm Optimisation(PSO)and the Clerc's parameter setting considerations.A rigorous approach which enforces regulatory agreed minimum well spacing was incorporated in the optimisation process.Reservoir models ranging from unimodal to multimodal spatial systems were used as examples to test the explorative and exploitative capabilities of the algorithms.In this paper we show the performance curve and statistical analysis of HGWO-PSO as a well placement algorithm and compares its performance with that of standalone PSO,and GWO and the traditional Genetic Algorithm(GA).Results revealed that the HGWO-PSO demonstrated comparative performances in terms of exploration and exploitation obtaining the best optimal solutions which give highest contractor's NPVs in majority of cases considered.Again,the means and standard deviations for HGWO-PSO among the various runs showed consistent and efficient performance.The Wilcoxon signed rank test conducted gave very low p-values suggesting uniqueness of HGWO-PSO from the other metaheuristic variants.Additionally,the computational speed of HGWO-PSO was relatively better as compared to the individual GWO and PSO in most the test cases.The simulation results for all test cases confirm that implementation of HGWO-PSO can cause considerable improvement in locations of wells even in heterogeneous reservoirs.展开更多
文摘Advances in technology and optimisation are helping to improve decision making in the oil and gas industry.However,most of the traditional metaheuristic algorithms applied in well placement optimisation problems suffer from extensive parameter experimentations and local optimum trapping issues.This couples with the complex and heterogeneous nature of hydrocarbon reservoirs and increased decision variables poses severe simulation process demands.This study considered a functional composition integration approach to formulate a robust hybrid metaheuristic algorithm called HGWO-PSO.The HGWO-PSO leverages on the strengths of Grey Wolf Optimiser(GWO)and Particle Swarm Optimisation(PSO)and the Clerc's parameter setting considerations.A rigorous approach which enforces regulatory agreed minimum well spacing was incorporated in the optimisation process.Reservoir models ranging from unimodal to multimodal spatial systems were used as examples to test the explorative and exploitative capabilities of the algorithms.In this paper we show the performance curve and statistical analysis of HGWO-PSO as a well placement algorithm and compares its performance with that of standalone PSO,and GWO and the traditional Genetic Algorithm(GA).Results revealed that the HGWO-PSO demonstrated comparative performances in terms of exploration and exploitation obtaining the best optimal solutions which give highest contractor's NPVs in majority of cases considered.Again,the means and standard deviations for HGWO-PSO among the various runs showed consistent and efficient performance.The Wilcoxon signed rank test conducted gave very low p-values suggesting uniqueness of HGWO-PSO from the other metaheuristic variants.Additionally,the computational speed of HGWO-PSO was relatively better as compared to the individual GWO and PSO in most the test cases.The simulation results for all test cases confirm that implementation of HGWO-PSO can cause considerable improvement in locations of wells even in heterogeneous reservoirs.
