Research on the construction of a working fluid system model for ultra-high temperature dense pressurized leakage prevention and plugging combined with multivariate nonlinear regression and machine learning optimization

At present, drilling luid leakage in oil and gas drilling engineering in complex formations is a worldwide technical problem.The study explains the mechanism of dense pressure-bearing plugging at the bottom of the fracture, explores the in luencing factors of the pressure-bearing capacity of the leakage prevention and plugging working luid, and establishes a mathematical model by using multivariate nonlinear regression analysis.Based on the machine learning technology, the support vector machine algorithm is selected as the prediction method of the particle size of the working luid for leakage prevention and plugging, and the system model of the ultra-high-temperature dense pressurized leakage prevention and plugging working luid is constructed.It is found that the established multivariate nonlinear regression analysis has good it and accuracy, and the average relative error is only 2.9%, and the seam width (-0.694) and formation pressure (0.502) have the greatest in luence on the pressure-bearing capacity of the working luid for leakage prevention and plugging.The prediction accuracy of the support vector machine model for the working luid particle size was 95.36%, and the prediction F1 values on multiple datasets were all greater than 0.9, showing excellent prediction results.The constructed mathematical model can be used to guide the ield operation, which is conducive to the long-term stable plugging and scienti ic leakage prevention of issure-based leakage.