Random Weighting Adaptive Estimation of Model Errors on Attitude Measurement for Rotary Steerable System

Abstract

During drilling, a rotary steerable system (RSS) is affected by vibration, rotation, and other random noises. This paper presents a random weighting adaptive estimation of model errors on attitude measurement for RSS drilling tools. The algorithm is used to estimate the covariance matrix of the dynamic model errors and the state prediction vectors for RSS tools, to reduce the effect of abnormal noises of the dynamic model on the estimation of state parameters. The random weighting estimations of observation's systematic error and covariance matrices of observation error vectors to control the effect of observation model noises anomaly on state parameter estimation, enhance the use efficiency of the latest observation information, indirectly weaken the impact of model error on state parameter vector, and improve the accuracy of attitude calculation of RSS. Experimental results and comparison analysis demonstrate that the proposed algorithm achieves better outcomes than the Kalman filtering and Extended Kalman filtering. The maximum solution error is controlled at 0.15°, and the tool face angle error is less than 3°. It cannot only control the covariance matrix of the observation error vector and predicted residual vector, but it can also effectively resist the disturbances of kinematic model error. It also can improve the dynamic measurement accuracy of attitude parameters, and solves the problem of uncertainty in dynamic attitude measurement of rotary steerable drilling tools.

Chaotic Effect-Based Array Duffing Systems with Improved Nonlinear Restoring Force for Weak Signal Detection in Dynamic MWD

“…However, the model of the array Duffing system is first given in this section and the amplitude of its driving signals are determined by observing the output state of the array system. Finally, the amplitude and phase values of the characteristic signal are determined by solving Equation (16).…”

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“…Although the method of combining wavelet neural network with Kalman improves the accuracy and stability of attitude calculation, the training time of its neural network increased with the increase in the amount of test data. Gao et al [16] proposed a stochastic weighted adaptive method to improve the attitude calculation accuracy of a rotary steering system and compared it with the EKF method, which proved the superiority of the proposed method in calculating the attitude of drilling tools. Although the above research has achieved certain results in drilling tool attitude calculation, improvements can still be made to further enhance the calculation accuracy.…”

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“…To extract oil and gas resources more effectively, precise control of the wellbore trajectory is necessary [1]. Accurate prediction of the wellbore trajectory contributes to efficient resource extraction, reduces the impact on the surface environment, ensures the safety of workers, and maximizes resource recovery [2,3]. Wellbore trajectory prediction is not just an engineering challenge, it is a comprehensive consideration involving efficiency, sustainability, and safety.…”