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Bilevel Optimization of Well Placement and Control Settings Assisted by Capacitance-Resistance Models


 Well control and well placement optimization have typically been considered as separate problems. More recently, there have been a number of works which have shown improved results when these two problems are considered in a joint manner. However, this joint optimization problem, whether in a sequential or simultaneous manner, is more computationally demanding. In light of this, we propose the use of capacitance-resistance models (CRMs) to assist the computational demand of the joint optimization of well controls and well placement. Specifically, we use a bilevel (or nested) approach, where the outer loop is the well placement problem and the inner loop is the well control problem assisted by CRMs. The well placement problem is solved using particle swarm optimization (PSO), and the well control problem is solved using Adam-simultaneous perturbation stochastic approximation (SPSA). The proposed approach is compared with the conventional implementation using only high fidelity full-physics simulations on two reservoir models of varying complexity. We also investigate the accuracy of the CRMs during the optimization procedure. The proposed approach resulted in solutions for the joint optimization problems with objective function values of up to 21.8% higher than the conventional approach and up to a 99.6% decrease in the number of required reservoir simulations.