Design of Smart Wellhead Controllers for Optimal Fluid Injection Policy and Producibility in Petroleum Reservoirs: A Neuro-Geometric Approach Masoud Nikravesh, Lawrence Berkeley National Laboratory, BISC Program, Computer Science Division, University of California at Berkeley, SPE, Masoud Soroush, Drexel University, M.R. Johnston, CalResources, LLC., SPE, and Tad W. Patzek, University of California at Berkeley, SPE


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.


Oil Recovery from Petroleum Reservoirs: Fluids, such as water, carbon dioxide or steam, are injected into reservoirs to maintain pressure and displace oil. The process of fluid injection into petroleum reservoirs is known to exhibit an inherently complex, nonlinear, time varying and nonstationary behavior. Although an oil field is a complex and highly coupled system, injectors are usually controlled individually on the basis of past experience and pilots. Usually, simple single-input single-output controllers, such as Proportional-Integral-Derivative (PID) are used.

Compared with carefully-controlled and maintained pilot injectors, field injectors have a more stochastic nature and their injectivity patterns are often different. These features have made the development of first-principle models for these injectors very challenging, if not impossible. Neural networks, however, have been able to provide sufficiently accurate models of fluid injectors by using historical process data such as flow rates and pressures. Despite our incomplete knowledge, neural network models are able to predict the complex behavior of petroleum reservoirs. These models can be used to synthesize model-based controllers that are capable of providing more effective control in these processes.

In this project, we are creating an innovative Computer Assisted Operations (CAO) tool for developing and maintaining optimal injection policy for water or steam. Figure 1 shows a simplified schematic of optimal fluid injection policy. First, the CAO system acquires continuously data from the injectors (wellhead pressures (WHPs), rates, and temperatures) and producers (WHPs, rates, etc.). Second, the pattern-by-pattern responses are inverted to "back-out" the evolution of reservoir properties such as permeability and the extent of fracturing. P. 405^

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