An Optimum Method of Early-Time Well Test Analysis - Genetic Algorithm

摘要

Conventional type curve fitting analysis of early-time transient presusre data influenced by wellbore storage effects can yield non-unique results due to similarity in the shape of the curves. In order to alleviate this problem, this paper proposed an optimum method based on adaptive genetic algorithm (AGA). This is a robust approach to get early-time well test interpretation. It is superior to the non-linear regression algorithms and standard genetic algorithm. Initial estimates of the parameters and operator probability (crossover and mutation probability) need not be specified. The diversity of the population and the convergence capacity of the GA can be maintained by using the adaptively varying probabilities of crossover and mutation depending on the fitness values of the solutions. By this way the optimum reserovir parameters can be obtained. Applicability of the proposed methods is demonstrated by analyzing a field example.