Error Correction Method of Sensor Based on Fuzzy-KF for Measurement of Drilling Cuttings

Abstract

In oil and gas development operation, for horizontal wells and extended reach wells, monitoring cuttings flow rate is an important method for analyzing wellbore cleanliness. However, due to complex working environment on site, the accuracy of cuttings flow rate monitoring is low. In order to solve this problem, based on the analysis of a cuttings flow rate real-time measurement method, combined with the classic Kalman filter (KF) algorithm, an adaptive KF model based on fuzzy control rules (Fuzzy-KF) is proposed, it realize real-time update of process noise <inline-formula> <tex-math notation="LaTeX">$({Q})$ </tex-math></inline-formula> and measurement noise <inline-formula> <tex-math notation="LaTeX">$({R})$ </tex-math></inline-formula> and realize real-time correction of data collected by sensors of Cuttings Flow Meters (CFM). Through the comparison of experiments, the research results show that the mean square error (MSE) of the Fuzzy-KF model is 76.2919, and the posterior error estimate be less than 0.3, and all aspects of performance are better than the KF and Sage-Husa KF models. The effects of different initial values of <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${R}$ </tex-math></inline-formula> on the experimental results were studied, the research result shows that the difference of the initial value of <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${R}$ </tex-math></inline-formula> will affect the data correction result, but does not change the change trend of each parameter. The Fuzzy-KF model established in this paper can effectively reduce the environmental interference to CFM, and it provides new technical support for improving the accuracy of cuttings flow rate monitoring.