Design of Smart Wellhead Controllers for Optimal Fluid Injection Policy and Producibility in Petroleum Reservoirs: Neuro-Geometric Approach
byMasoud Nikravesh, Masoud Soroush, M. R. Johnston, Tadeusz W. Patzek
Nikravesh M., M. Soroush, M. Johnston and T. W. Patzek, “Design of Smart Wellhead Controllers for Optimal Fluid Injection Policy and Producibility in Petroleum Reservoirs: Neuro-Geometric Approach,” Paper SPE 37557, presented at the 1997 International Thermal Operations and Heavy Oil Symposium, 10-12 February, Bakersfield, CA.
In this paper, we present the next generation of "smart" controllers based on neural networks and geometric control techniques. In addition, we discuss an innovative Neural Network and Geometric Model-Based Control Strategy for developing and maintaining optimal fluid injection policy. First, the smart controller acquires data on wellhead pressures and rates continuously from the injectors. Second, a neural network model is "taught" the reservoir rate response to fluid injection pressure and vice versa. In the first case, the neural network learns how to predict the future injection rate based on injection pressure and rate. In the second case, the neural network learns how to predict the future injection pressure based on injection rate and pressure. The appropriately trained neural network can then recognize the symptoms of efficient or inefficient fluid injection around the operating point of the process. Third, feedback from the neural network models, in conjunction with geometric control, is used to design an optimal control strategy. In particular, the developed neural network-differential geometric models can be used to control and predict the behavior of individual and multiple fluid injectors.