Short-Term and Long-Term Optimizations for Reservoir Management with Intelligent Wells

Abstract Short-term optimization of an oil field has been used to increase economic value of oil recovery as compared to reactive control (shutting the well when water cut limit is reached, for instance), especially in the case of short-term production strategies. One way to improve the management of a field involves adjusting the production flow rates over a short time, maximizing the overall NPV during the life cycle of the field. Using intelligent wells (IW), the challenges include not only the optimization of well flow rates, but also the simultaneous adjustment of flow in each valve, controlling each aperture in a given production time. These optimal control strategies are often difficult to be realized in practice due to the large number of control variables involved in the optimization process, especially with larger number of wells and valves. To this end, this work proposes an efficient optimization framework employing a fast genetic algorithm (FGA) in order to adjust simultaneously the flow rates of wells and the valves aperture. We have used a commercial reservoir simulator whereby the flow rates of wells were optimized with an option available that calculates well rates when there is production constraint on the wells (platform capacity or other operational constraint) using production parameters in real time; and at the same time the flow in each valve was controlled through a keyword associated with the control of the aperture of valves by monitoring the pressure drop around of them. The FGA optimization algorithm employed is a global optimization method, which is robust and efficient for sweeping the solution space with many variables, and it is able to work with continuous and discrete variables simultaneously. We demonstrate the power of the FGA strategy by applying the methodology to a heterogeneous reservoir model based on Brazil's Namorado field, with four horizontal producers and four horizontal injector wells. Two producers were tested as intelligent, using two valves of continuous variation type. The rate of wells was determined using water cut values while there were constraints on the production of the platform. The valves were adjusted each 60 days, during the first four years of production, closing in the optimal time at the end of production. The results showed an improvement in reservoir management, increasing 3.7% of NPV, with additional gains around US$ 20 million (already discounted the costs of intelligent completion), increasing oil production and reducing water production. The combination of the tools available in a commercial simulator jointly with global optimization algorithm showed advantages of the operation of the wells and valves simultaneously.

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