To make prudent decisions regarding the exploitation and management of hydrocarbon reservoirs, we need to carry out history matching, a process for conditioning the reservoir simulation model to observation data collected over time. History matching is an inverse problem which requires an optimisation technique to match the simulation results to the measurements. Many techniques have been applied to address this optimisation problem effectively and in efficient time since reservoir simulation runs are computationally expensive.
Genetic algorithms (GAs) and Estimation of Distribution Algorithms (EDAs) are two popular types of evolutionary algorithms. In GAs, new candidate solutions are obtained by applying crossover and mutation operators to a population of feasible solutions according to the principle of ‘survival of the fittest’ in natural evolution. The Estimation of Distribution Algorithm (EDA) is a modern class of EA in which new candidate solutions are generating by sampling from a probability distribution inferred from the better members of the population.
A suitable hybrid of the GA and EDA algorithms can combine beneficial characteristics from each of GA and EDA, while addressing each other’s sources of inefficiency. The main difference between these two EAs is the way they generate new individuals, which results in different exploration/exploitation properties. GAs may sample bad representatives of good search regions and good representatives of bad regions, while the EDA may suffer from fitting a single probability distribution to diverse and distinct regions of good solutions. The hybrid algorithm performs a cooperative search that improves the exploitation and the exploration power of both algorithms.
In this paper, we applied GA, EDA, and a new hybrid GA-EDA algorithm to optimisation of three cases, a test function, the IC-Fault synthetic reservoir model, and one real reservoir, Teal South. The results show that each of these algorithms can be used for exploring the parameter search space in history matching problem. Depending on the problem type, GA, EDA, and Hybrid GA-EDA can achieve good quality matches while they perform a global seach in the space.