Research and Analysis on the Design and Tracking Algorithm of DST Downhole Safety Detection System

With the increasing frequency of oil and gas well development and downhole operation activities, downhole safety detection is becoming more and more important. The downhole environment is complex, and the detection system faces numerous challenges. The purpose of this research is to design a downhole safety detection system based on DST and optimize the tracking algorithm to improve the real-time performance and accuracy of downhole safety detection. A DST downhole safety detection system consisting of four modules, namely downhole sensors, ground data processing, communication, and a monitoring center, is constructed. A multi-level tracking algorithm based on Kalman filtering and particle filtering is proposed, and the algorithm is switched by monitoring the deviation through a change detection module. Simulation experiments are designed by combining downhole environmental data to test the data tracking ability under different pressure, temperature, and fluid flow situations. The results show that the fusion algorithm has a high data tracking accuracy in different downhole environments. Under the condition of nonlinear changes, the error is reduced by about 20% compared with the single Kalman filtering algorithm, and the root mean square error (RMSE) is lower. The fusion algorithm balances the computing requirements while ensuring accuracy. Compared with Kalman filtering, it has a smaller error in nonlinear scenarios, and compared with particle filtering, its computing demand is more reasonable. It is proved that the fusion algorithm has greatly improved the data tracking accuracy and adaptability to nonlinear changes. In the future, the real-time performance and anti-interference ability of the system will be optimized.