Optimization Techniques for History Matching and Production Forecasting

Abstract

Reservoir modelling and production forecasting can provide vital inputs to the efficient management of petroleum. Since the reservoirs are highly heterogeneous and nonlinear in nature, it is often difficult to obtain accurate estimates of the spatial distribution of reservoir properties representing the reservoir and corresponding production profiles. If an accurate model of a reservoir is built, it can lead to efficient management of the reservoir. This paper describes the mathematical modelling of oil reservoirs along with various optimization techniques applicable for history matching and production forecasting. Gradient based and non-gradient based optimization techniques viz. Simulated Annealing (SA), Scatter Search (SS), Neighborhood algorithm (NA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Ensemble Kalman Filters (EnKF) and Genetic Algorithm (GA) and their application to reservoir production history matching and performance are presented. The recent advancements and variants of these techniques applied for the purpose are also presented.