Development of HGAPSO-SVR corrosion prediction approach for offshore oil and gas pipelines

Abstract

Managing the oil and gas pipelines against corrosion is one of the major challenges of the oil and gas sector because of the complexities associated with the initiation, stabilization, and growth of the corrosion defects. The present research attempts to develop a model for predicting the maximum depth of pitting corrosion in oil and gas pipelines using SVM algorithm. In order to improve the SVM performance, Hybrid PSO and GA was utilized. Monte Carlo simulation was used to determine the time lapse for the pit depth growth. In order to implement the above modeling approaches and to prove their efficiency and accuracy against a large database, a total of 340 data samples for corrosion depth and rate are retrieved from the Iranian Oilfields. The performance of the new algorithm shows that it has higher stability and accuracy. In addition, the forecasting results of the new algorithm are compared with the 11 intelligent optimization algorithms, it shows that the novel hybrid algorithm has higher accuracy, better generalization ability, and stronger robustness. The coefficient of determination (R2) value in the testing phase for SVM-HGAPSO was estimated by 0.99. Proposed hybrid model and Monte-Carlo simulations pitting corrosion based on Poisson square wave process have been used to predict the time evolution of the mean value of the pit depth distribution for different categories of maximum pitting rates (low, moderate, high and sever). The models was validated with 4 field data for each of the pitting corrosion categories and the results agreed well. The pipelines under severe pitting corrosion rate were, more conservatively predicted by HGAPSO-SVR than those under low, moderate and high pitting corrosion rates. The results obtained demonstrate the potentials of this technique for the integrity management of corroded aged pipelines.