An Approach of Optimization Techniques for History Matching and Production Forecasting

Petroleum as a natural reserve is depleting year-by-year and there is a firm need of efficient management of petroleum and its reservoir. In this scenario, reservoir modelling and production forecasting provide vital inputs to its efficient management. The naturally occurring reservoirs are highly heterogeneous and nonlinear in nature, which makes us difficult to obtain accurate estimates of the spatial distribution of reservoir properties representing the reservoir and corresponding production profiles. An accurate model built with the help of data obtained from the reservoir, in terms of reservoir properties and history matching, can lead us to efficient management of the reservoir and such models can be built with the help of mathematical modeling and optimization techniques. This chapter describes various optimization techniques applicable for history matching and production forecasting. It discusses 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. The chapter also discusses recent advancements and variations of these techniques used for this purpose.