Research For Internal Corrosion Rate Prediction Of Submarine Oil And Gas Pipelines In Service

In recent years,with the increase of service time of the undersea oil and gas pipeline,the problem of corrosion leakage in the pipeline has become more and more obvious,which has seriously threatened the safe operation of the pipeline and the ecological stability of the ocean.How to make a reasonable prediction of corrosion in the undersea oil and gas pipeline,and accurately determine the sensitive area of the corrosion of the undersea oil and gas pipeline is an urgent need for the normal operation of oil and gas field and the exertion of economic benefits.In this paper,from the perspective of system engineering safety,intelligent learning algorithms are introduced for the prediction of corrosion rate in undersea oil and gas pipelines,with a view to improving the increasingly severe corrosion situation in the undersea oil and gas pipeline and and promoting the improvement of the level of safety production management of enterprises.Based on the current situation of marine oil and gas resources development at this stage,first of all,through a large number of reading domestic and foreign literature and related oil and gas pipeline operation data to grasp the characteristics of the seafloor oil and gas pipeline and corrosion mechanism,secondly,according to the principle of index selection and internal corrosion rate factors to establish the primary internal corrosion rate prediction index system,using entropy right gray correlation method to sequence the correlation index of the factors,and relying on the analysis of the main components of the nuclear main component to achieve the optimization of corrosion index system The data in the optimized corrosion rate prediction index system are introduced as training sets,the RBF neural network algorithm,Support Vector Machine algorithm and Random Forest Regression algorithm in machine learning are introduced to construct the internal corrosion rate prediction model to explore its applicability in the research of oil and gas pipeline corrosion on the seafloor,and finally the application analysis of the CO₂ corrosion pipeline of SP74-FPSO pipe segment in an oil field in the South China Sea is applied.Results The optimized internal corrosion rate prediction index system:CO₂ sub-pressure,CO₂ concentration,temperature,pH and medium flow rate.At the same time,the prediction accuracy of the three types of undersea oil and gas pipeline corrosion rate prediction models reached more than 85%,which proved the good application ability of these three intelligent learning algorithms in the modeling and prediction of corrosion rate of the undersea oil and gas pipeline,in which the average square root error and fit superiority of the random forest regression corrosion prediction model were 3.59%,0.9746,and the prediction effect was better than the RBF neural network model and Support Vector Machine model,which showed the advantages of prediction accuracy and robustness.Due to the limitation of time and academic level,there will be certain limitations in this research,and it is the focus of the follow-up research to continue to explore the more perfect system of corrosion rate prediction index system in the undersea oil and gas pipeline and to improve the prediction performance of the existing prediction model sands.