Research on Improving Accuracy of MWD Based on Support Vector Classifier and K-Proximity Method

Abstract

With the development of horizontal Wells and extended reach Wells in the field of oil and gas exploitation, it is of great significance to the development of MWD which can measure the drilling trajectory in real time. Therefore, in recent years, Measurement while drilling (MWD) has been widely recognized and rapidly developed in the field of precise target drilling. Accelerometers and fluxgates are related to well trajectory measurement in the MWD. The external environment and the processing and installation error of the sensor lead to some errors in the measured value. Deviation caused by environmental interference and installation error leads to the low control precision of drilling tool attitude. In view of the shortcomings of the current calibration methods and the time-consuming and complicated operation of the traditional tri-axis orthogonal inclinometer calibration, this paper proposes the attitude error calibration method of the MWD based on the support vector classifier and the K-proximity method. First, a neural network model is established based on the measured calibration results and the measurements errors. Then, the K-proximity method is used to further classify the drill tool attitude data, and a neural network model combining the K-proximity method is constructed. Secondly, the software and hardware experimental platform for on-line calibration of the slant meter while drilling is built by using the calibration frame and the geomagnetic sensor. Finally, the experimental results showed that the mean deviation of well inclination angle and tool face angle is controlled within ±0.2°, and the mean deviation of azimuth is controlled within ±0.5°, which verified that the algorithm can increase accurate of drilling tool attitude.