Stochastic Resource Allocation for Well Control With Digital Oil Field Infrastructure

To investigate the flux patterns for a digital oil field system, the modeling and analysis need to include waterflood optimization, which is generated to maintain pressure and sweep oil to production. In this paper, a life-cycle well control optimization is integrated with proactive resource allocation to increase computational efficiency of mature fields. Based on a noninvasive and derivative-free approach, the combinatorial model is developed to address geological uncertainty by modifying injection and production rates at individual wells. With the derived model, the joint well control and field development algorithm is proposed based on their influence on the control variables. The possible benefits from the hybrid method and heuristics are clarified. In addition, the Benders decomposition method is adopted considering the computation burden with the optimal number of wells in a distributed manner at the cost of iteration. With the relaxation technique and acceleration technique, the optimal solutions are achieved to strengthen the cuts generated and to find better feasible solutions in less time. Simulation and experimental results are presented to verify the achievements of the proposed methods.