Study On Corrosion Behavior And Corrosion Rate Prediction Of Oilfield Produced Water

Oil and gas pipeline corrosion is one of the important causes of safety accidents in the process of oil and gas transportation,which has always been the focus of scholars’research.With the continuous exploitation of the oilfield,the water content of crude oil is also rising,and the corrosion in the oilfield water treatment system is becoming more and more serious.The monitoring and prediction of corrosion of oilfield produced water has become an urgent problem in the production process.In this paper,the water quality analysis results of Shengli oilfield produced water are taken as the reference object,combined with chemical means,the effects of different temperature,flow rate and CO₂ concentration on corrosion are studied,and the corrosion rate data are obtained.The indoor experimental corrosion data and field corrosion data are taken as the sample data,and the least square vector machine（LSSVM）is used as the core algorithm of prediction model to establish the corrosion model KPCA is used to reduce the dimension of the original data samples,eliminate the noise factors in the sample parameters,and reduce the model input while retaining the original parameters as much as possible.The LSSVM model is improved,and the KPCA-LSSVM corrosion prediction model is established.In order to further improve the accuracy of the prediction model,the latest computer bionic algorithms,such as Taurus optimization algorithm（BAS）and whale optimization algorithm（WOA）,are used to optimize the model.The optimal model parameters are selected and KPCA-BAS-LSSVM model and KPCA-WOA-LSSVM model are obtained.It can be seen from the results that the two improved prediction models are better than the unoptimized prediction models in prediction accuracy,generalization ability and operation speed,and can make a good prediction of corrosion.