Remaining Life Analysis Of Oil And Gas Field Pipeline Based On Big Data

Oil and gas pipeline integrity management has always been a field of huge data accumulation.In recent years,the rise of big data technology has provided new ideas for pipeline integrity evaluation technology.Multi-dimensional pipeline data types,years of various sample records,and clear problem definitions provide an excellent big data analysis scenario for oil and gas pipeline life analysis.This paper systematically carried out a series of work based on pattern recognition and machine learning methods to explore the application mode of big data in the oil and gas field pipeline detection.It mainly includes the following aspects:Firstly,this paper systematically studies the key technologies related to the life analysis of oil and gas field pipelines and big data.The pipeline integrity data collection technology and management mode are expounded.The machine learning and pattern recognition algorithms involved in the topic and the Boosting and Bagging algorithms in the ensemble learning method are studied.At the same time,this paper also studies the next generation oil and gas pipeline big data system architecture,the development direction of big data architecture tends to be batch-stream unified and integrated.Secondly,this paper evaluates the pipeline datasets quality,and builds a series of pipeline life analysis models.The models include a series of single models(multivariate linear regression,minimum risk Bayesian decision,multi-class nonlinear SVM,BP neural network)and ensemble models(gradient boosting tree,random forest).This paper systematically completes the model establishment process from theoretical derivation,algorithm design,model training and result evaluation,and gives the key algorithm steps to accurately predict the pipeline inspection period.From the results,in the single models the minimum risk decision based on Naive Bayes is the best,the accuracy rate is 91.86%,and in the ensemble model the accuracy of GBDT is slightly better than that of the random forest,which accuracy rate reached 99.7%.In contrast,the ensemble learning method has a much better dataset fitting performance,which provides an idea for the selection of the algorithm in engineering applications.Finally,this paper designs a big data architecture integrating data cleaning,distributed learning and real-time query.Import various types of data from external data sources into big data systems to achieve data alignment and cleaning,support data interaction between external data sources and big data platforms,and serve subsequent distributed computing,analysis and query processes.