Residual Life Prediction And Maintenance Cost Optimization Of Oil And Gas Pipeline Based On Stochastic Degradation Model

As a common way of energy transportation,pipelines play an important role in the industrialization process.Damage due to corrosion is one of the main causes of pipeline failure.Affected by various factors,in the pipeline operation process,more and more types of accidents caused by corrosion failure,resulting in huge economic losses and environmental pollution.Therefore,in order to ensure the safety of the pipeline,it is necessary to take relevant measures and formulate corresponding maintenance and maintenance strategies to achieve corrosion pipeline risk assessment.In this regard,this paper carried out the following research:(1)Aiming at the problem of inaccurate life prediction caused by small sample size of pipelines and insufficient degradation data,a Bayesian information fusion method based on Wiener Process is proposed to realize real-time prediction of residual life of corroded oil and gas pipelines.Firstly,the degradation data are obtained through the double stress accelerated degradation test,and the residual life prediction model is established based on the field measured data.Then,Markov chain Monte Carlo(MCMC)is used to estimate unknown parameters.Finally,taking a certain type of pipeline as an example,the rationality and correctness of the proposed method are verified.(2)A corrosion pipeline degradation analysis considering random effects is proposed.First,a general Bayesian theory-based inverse Gaussian process(IG)degradation analysis framework is established.Then,using the Bayesian method,a simple IG model and three IG models with random effects are studied.The appropriate model is selected by the relevant model optimization method.Finally,according to the selected model method,the Monte Carlo simulation method is used to predict the reliability of the pipeline,and the corrosion development trend is obtained.(3)By considering the generation of corrosion defects with time and the growth of singledefects with time,the optimal detection interval for corrosion of external metal loss in new onshore buried natural gas pipelines was studied.Firstly,the non-homogeneous Poisson process is used to model the generation of new defects,and the homogeneous gamma process is used to model the growth of single defects.Secondly,the detection rate(PoD)and size error of inspection tools,as well as failure cost are considered.A method based on simulation is proposed to numerically calculate the expected cost rate at a given check interval.Thirdly,the minimum expected cost criterion is used to determine the optimal test interval.Finally,through parametric analysis,the effects of failure cost,excavation repair criteria,defect depth growth rate,generation model instantaneous generation rate and defect generation model on the optimal detection interval are studied.This paper studies the degradation law of oil and gas pipelines from the perspective of stochastic process,and provides a reference for further deepening pipeline life prediction and maintenance strategy optimization research.The proposed stochastic degradation model based on Wiener process,inverse Gaussian process and Gamma process and cost-based maintenance model,the case analysis shows that it has certain practicability,and the research results will help engineers and technicians to gas pipelines.Make optimal maintenance decisions and promote reliability-based corrosion management.