Application Research of Hybrid Optimization Algorithm in Predicting the Remaining Corrosion Lifespan of Oilfield Water Injection Pipelines

Aiming at the problem of low prediction accuracy of the corrosion lifespan of oilfield water injection pipelines, a hybrid optimization model based on the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Simulated Annealing (SA) is proposed. Through the dynamic integration of global and local searches, combined with the dynamic weight adjustment and adaptive constraint penalty strategies, the convergence speed and prediction accuracy of the model are improved. The monitoring data of the water injection pipeline system in Oilfield H is used for modeling and training. The experimental results show that the Root Mean Squared Error (RMSE) of the model is 2.35mm and the Maximum Relative Error (MRE) is 4.8%. Both the prediction accuracy and the convergence speed are superior to those of traditional methods.