An Approach of Optimization Techniques for History Matching and Production Forecasting

Petroleum as a natural resource is depleting year after year, necessitating effective management of the resource and its reservoir. Reservoir modelling and production forecasting are critical inputs in this scenario for effective management. The spatial distribution of reservoir properties describing the reservoir and associated output profiles is difficult to estimate since naturally occurring reservoirs are extremely heterogeneous and nonlinear in nature. In terms of reservoir properties and history matching, an accurate model constructed with the aid of data obtained from the reservoir, can lead to more effective reservoir management, and such models can be created using mathematical modelling and optimization techniques. This chapter covers a variety of optimization strategies that can be used for output forecasting and background matching. Simulated Annealing (SA), Scatter Search (SS), Neighborhood algorithm (NA), Particle Swarm Optimization (PSO), and Ant Colony Optimization are examples of gradient-based and non-gradient-based optimization techniques (ACO), The application of Ensemble Kalman Filters (EnKF) and Genetic Algorithms (GA) to reservoir output history matching and efficiency. The chapter also goes into recent developments and adaptations of these techniques.

Author (s) Details

Giridhar Vadicharla
Department of Chemical Engineering, University of Petroleum and Energy Studies, Dehradun, India

Pushpa Sharma
Department of Petroleum Engineering and Earth Sciences, University of Petroleum and Energy Studies, Dehradun, India

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