History matching of petroleum reservoirs employing adaptive genetic algorithm

Abstract

History matching is an important phase in reservoir modeling and simulation process, where one aims to find a reservoir description that minimizes difference between the observed performance and the simulator output during historic production period. For the automatic history-matching problem through reservoir characterization, a global optimization method called adaptive genetic algorithm (AGA) has been employed. AGA is a relatively new optimization technique which has adaptive genetic operators that dynamically update the crossover and mutation probabilities in each generation according to fitness of population to reach optimal solutions. Only critical parameters such as porosity and permeability distributions have been found by the optimization route, the rest being adjusted manually, if necessary, in the present study. History-matching results from AGA were also compared to those from conventional simple genetic algorithm (SGA). The AGA and SGA techniques were utilized to determine permeability map that resulted in a good match for past field history. The methodology was tested and validated by implementing it on a known 2D synthetic black-oil reservoir, which was subsequently used for a real-field reservoir situated in Cambay Basin, Gujarat, India. AGA methodology was able to outperform the SGA in terms of reduced computation load and improved history match.