Production Parameter Optimization for Gas Wells Using a Surrogate-Assisted Improved Particle Swarm Algorithm

Gas well production optimization is an effective way to improve the efficiency and economic performance of natural gas development. Multiphase flow in wellbores and reservoir seepage make the process highly nonlinear, strongly coupled, and complex. Traditional simulation software can provide accurate predictions, but the high computational cost limits its use in iterative and large-scale optimization. This paper presents an integrated framework that combines numerical simulation, surrogate modeling, and intelligent optimization for gas well production parameter optimization, particularly under continuous gas lift conditions. A simulator is used to generate datasets, which are then used to train a neural network surrogate model for fast prediction of gas well production response. An improved particle swarm optimization algorithm is applied to perform global search and obtain the optimal production parameter combination. Results show that the surrogate model can reliably replace the simulator in repeated prediction tasks while substantially reducing computational cost, and the improved algorithm performs better than traditional methods in both convergence speed and optimization accuracy. Case studies confirm that the optimized parameters effectively increase gas well production. The proposed framework provides an efficient and practical approach for intelligent production optimization in gas wells under complex wellbore multiphase-flow conditions.