Global optimization strategies for well tests in dual porosity reservoirs

This study presents an investigation of the performance of multiple stochastic optimization algorithms in performing automatic type-curve matching for pressure transient well test analysis. The pressure transient responses of a vertical well in a dual porosity reservoir were generated. A synthetic reservoir model that shows all flow regimes for the model was created. Gaussian White Noise data was added to the typical response to imitate measured data. In addition to the Levenberg-Marquardt algorithm, four stochastic algorithms were used to estimate the reservoir model from the noisy data. These algorithms are Deferential Evolution, Particle Swarm Optimization, Local Unimodal Sampling and Many Optimizing Liaisons. Behavioral parameters of each algorithm were investigated by comparing the performance of recommended values in the literature. Each algorithm was run for 25 realizations. The results of the runs were ordered in terms of the best achieved result. The performance was compared by comparing the best 1st, 7th, 19th and 25th results of each algorithm. The result showed that the algorithms performance is affected by the model and the unknowns. Differential Evolution algorithm showed the best performance in Dual Porosity Reservoir when Φ_m, λ, ω, skin, r_e & k_f are the unknowns. All the other stochastic algorithms performed better than Levenberg-Marquardt optimization algorithm.