Multiobjective Operation Optimization of Depleted Gas Storage Based on a Reference Vector–Guided Evolutionary Algorithm

Depleted gas reservoirs type underground gas storage (depleted-UGS) are important facilities used to solve the imbalance of natural gas supply and demand. However, gas storage has high energy consumption, and depleted-UGS faults are widely distributed and divided into multiple disconnected blocks. Excessive pressure variation between reservoir blocks (RBs) will affect the stability of depleted-UGS operation. A multiobjective optimization model is established to solve the economic security scheduling problem of depleted-UGS. The model takes the pressure variation of RB and the energy consumption of the compressor as the optimization objectives, coupled with the constraints of reservoir seepage pressure drop and wellbore flow pressure drop. To find a high-performance algorithm to solve the depleted-UGS operation optimization problem, three multiobjective algorithms are tested, and reference vector guided evolutionary algorithm (RVEA) is chosen as the solution. We apply this model to a large depleted-UGS in China and use the RVEA to solve the optimal gas injection scheme for the depleted-UGS under two scenarios of low RB pressure variation and high RB pressure variation. The findings suggest that the optimized solution is more inclined to allocate more gas injection to the block with lower pressure and higher elastic yield. For the scenario where the pressure of the RBs is relatively balanced, the optimization scheme can reduce the energy consumption by 5.2% and the pressure difference by 79.3%. For scenarios with large pressure differences among RBs, the optimization scheme can reduce energy consumption by 17.4% and pressure difference by 41.3%.