Decision analysis under uncertainty for smart well deployment

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.

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Development of an effective completion schedule for a petroleum reservoir with strong aquifer to control water production

Azamipour

Assareh

Eshraghi

2022

J Petrol Explor Prod Technol

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AbstractsA reasonable solution, to deal with oil field water problem, is to minimize the amount of water associated with oil production using effective completion lengths. This work presents an effective method to optimize wells’ completion lengths in an oil reservoir with a strong aquifer. The suggested technique is formulated as a constrained optimization problem that defines a NPV objective function and a set of existing field/facility constraints. An effective algorithm translates the completion lengths to connections number in the dynamic simulation model. In this approach, a genetic algorithm (GA), an adaptive version of simulated annealing (ASA) and a particle swarm optimization (PSO) hybridized with polytope technique are applied to maximize NPV. A comparison is given for their performances in a strong water-drive reservoir where the combinatorial effects of wells’ completion lengths (decision variables) should be addressed. Optimizing the lengths of completions leads to an increased production period, total oil production, retarding water breakthrough, reducing total water production, and finally increasing ultimate recovery. The results showed that total oil production by GA, ASA and PSO algorithm is increased by 11.0%, 2.40% and 2.22%, respectively, related to the initial case. Total water productions are decreased by GA, 9.82%, by ASA 2.11%, and by PSO 1.82% relative to the initial schedule. The best performance belongs to the GA algorithm. Moreover, the average watercut of all wells is decreased through the optimization process. Besides, based on the numerical simulation, closing the worst connections with high watercut decreases total water production, and improves oil recovery, maximum well productivity, and NPV (oil–water ratio is increased 18.2%). Most connections are placed in the layers where water coning can occur later (considering near-well-bore permeability) and slightly far from full water zone.

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Decision analysis under uncertainty for smart well deployment

Cited by 22 publications

References 17 publications

Constraint Optimization

Constraint Optimization

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

Development of an effective completion schedule for a petroleum reservoir with strong aquifer to control water production

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