A Modified Genetic Algorithm for Reservoir Characterisation

Abstract

Abstract This paper describes the implementation of a Genetic Algorithm (GA) to carry out hydrocarbon reservoir characterisation by conditioning the reservoir simulation model to production data (history matching) on a predefined geological and structural model. The proposed technique combines the advantages of the pilot point method for the description of petrophysical properties, with the advantages of GAs for global optimisation. The modified GA uses a complex genome, which is divided into seven separate chromosomes for different types of reservoir parameters. Chromosomes containing the pilot point information are three-dimensional real number structures which include information for the wells, while the chromosomes for all other parameters are one-dimensional arrays. Specially designed crossover and mutation operators have been created to work with the non-standard genome structure. Results from tests on several GA design issues are presented, including crossover and mutation operators, encoding, selection, and other population strategies such as elitism. In addition, a comparison is made with a standard Simulated Annealing algorithm.