Application of simulated annealing optimization algorithm to optimal operation of intelligent well completions in an offshore oil reservoir

Abstract

The latest intelligent fields include intelligent well completions capable of collecting downhole data, which allow the operator to selectively control completion intervals throughout the life of a reservoir. Performance can be monitored and optimized in real time and operations can be adjusted remotely using downhole equipment. To quantify and develop this potential, we deigned an algorithm-based system which is capable of optimizing intelligent well control to decide on whether or not to utilize intelligent well technology. Simulated annealing algorithm was used for obtaining an optimum control strategy and determining an operation that maximizes the net present value (NPV). In this article, we used more than 4,000 simulation runs which were performed automatically in a reservoir simulator to optimize a cyclic production scenario with intelligent well completions. The intelligent wells were equipped with on/off inflow control valves in each zone, which were opened and closed sequentially to maximize the oil rate while not exceeding limits for water production. The results show that the application of simulated annealing algorithm for optimization of intelligent well technology culminates in an increase in the NPV through 14 % increase in cumulative oil production and a significant reduction in water production.