Research On Damage Identification And Life Prediction Of Injection-production String Based On Intelligent Algorithm

In recent years,with the increasing importance of oil and gas peak shaving and strategic reserves,the research and construction of underground gas storage facilities have experienced rapid development.The large-scale cyclic injection and extraction in underground gas storage put great challenges on the integrity of the injection-production string.Therefore,early health monitoring and lifetime prediction of the injection-production string,which are one of the most important components of underground gas storage,are required to make informed decisions and ensure the safe operation of underground gas storage facilities.In order to accurately identify defects in the injection-production string and predict their remaining life with high precision and reliability,this study utilizes degradation data and simulation data of the injection-production string,combines dynamic characteristics with intelligent algorithms to identify defects,and further predicts the remaining life of the injection-production string based on stochastic processes.The main work conducted in this paper is as follows:(1)In the case where high-order modal information is difficult to obtain in practical engineering,a new damage identification method based on modal flexibility curvature is proposed to fully utilize low-order modal information for accurate defect identification.By comparing and analyzing various dynamic parameters,the flexibility matrix parameter that carries more damage information is selected as the basis for damage identification.The grey system correlation theory is introduced to improve this parameter,and an improved grey correlation modal flexibility curvature difference damage identification index is proposed to achieve higher accuracy and sensitivity in identifying defects in the injection-production string.(2)In situations where the damage severity cannot be accurately obtained based on the dynamic characteristics method,a two-stage damage identification method combining intelligent algorithms is proposed to achieve higher accuracy and reliability in damage identification.Based on the premise of identifying the damage location using the dynamic characteristics method,considering that intelligent algorithms are prone to local optima and overfitting,a hybrid strategy and pre-training mechanism are used to improve the intelligent algorithms.An intelligent algorithm-based damage severity identification model for the injection-production string is established to achieve staged identification of damage.(3)To accurately predict the remaining life of the injection-production string,a prediction model based on Wiener stochastic process and Kalman filter with continuous-discrete systems is proposed.Analysis of the degradation process of injection-production string accords with Wiener process,and based on the damage identification results and historical operation data,considering the situation of continuous system and discrete measurement time,this study introduced the square root and continuous discrete system to establish a prediction model of the remaining life of injection-production string based on square root continuous discrete volume Kalman filter,and obtained the remaining life of injection-production string.In summary,improved optimization models for staged damage identification and remaining life prediction of injection-production string are proposed,providing references for subsequent health maintenance and informed decision-making.The focus of future research will be on establishing high-precision optimization models that consider multiple influencing factors and performance indicators simultaneously.