Research On Oil Pipeline Leak Detection Technology Based On Optimization Theory

With the increase of the service life of the pipeline,aging and corrosion problems have become increasingly prominent,and there is a great potential for leakage.The occurrence of third-party damage and drilling theft will seriously affect the normal transportation of oil products.Therefore,carrying out research on oil pipeline leakage detection technology and improving the accuracy of leakage detection are of great significance for ensuring the safety and stable transportation of oil pipelines.Combined with the idea of inverse transient analysis,this paper puts forward a leakage location model of oil pipeline,which provides a new idea for the research of leakage detection.Aiming at the problem of oil pipeline leakage detection,this paper carries out research on oil pipeline leakage detection technology based on optimization theory.First,the pipeline fluid dynamics model is established,which is used to accurately describe the distribution characteristics of pressure and temperature along the pipeline under steady-state conditions.Secondly,the change characteristics of the flow parameters when the oil pipeline leaks are analyzed,and combined with the idea of inverse transient analysis method,the leak location problem is converted into an optimization problem.An oil pipeline leak location model based on optimization theory is proposed,and genetic algorithm is selected to solve the model;the pressure and temperature weighting factor ratio is determined according to 108 sets of simulation experimental data provided by PNS software.When the pipeline is leaking,the calculation result of the leakage location model is the location of the leak;when the pipeline is not leaking,the calculation result of the location model tends to 0 or L(total length of the pipeline).Based on this feature,a method for judging pipeline leakage conditions is proposed.The feasibility of this method is verified by combining the experimental data(4235 groups in total)of 20 indoor loops(tube length 772 m,inner diameter 36mm)without noise reduction.The results of indoor loop experiments show that the method recognizes the leakage conditions 3-9s after the leakage occurs,and the positioning error is between 1.92% and 9.8%;the data noise and the inaccuracy of pipeline parameters have a greater impact on the positioning accuracy.Aiming at the problem that the accuracy of leak location is affected by the inaccuracy of pipeline parameters,this paper conducts research on pipeline parameter correction technology.Firstly,based on 240 sets of simulation experiment data,the influence degree of tube length,inner diameter,roughness and elevation difference deviation on simulation calculation is compared and analyzed.The study found that the influence of the deviation of tube length and inner diameter on the simulation calculation is far greater than the roughness and elevation difference.Secondly,taking the pipe length and inner diameter as the research object,the pipe parameter correction problem is converted into an optimization problem,the pipe parameter correction model is established,and the genetic algorithm is used to solve the model.Finally,the accuracy of the parameter correction model is proved based on the experimental data of 20 indoor loops.Based on the above research results,Python language is used to program the oil pipeline leak detection model and parameter correction model based on the optimization theory.The program judges whether the pipeline is leaking based on the measurement data of the start and end of the pipeline.When the pipeline does not leak,the pipeline parameters are corrected;when the pipeline leaks,the leak point is located.Combining 20 indoor loop leak test data and CR oil pipeline(pipe length 34.5km,inner diameter 204mm)3 times noise reduction oil discharge test data(1496 groups in total)to prove the reliability of the program.After parameter correction,the leak location error of the indoor loop test was 0.3%?7.5%;the leak location error of the on-site oil discharge experiment decreased from 6.56%?8.86% to0.64%?4.44%.