Recognizing Abnormal Shock Signatures During Drilling with Help of Machine Learning

Abstract

Drilling generated shocks and vibrations (torsional, axial, and lateral) are among the main causes of failures in the drilling industry; because they affect the rate of penetration, directional control, and wellbore quality. Rotary steerable system tools are equipped with measurement devices such as magnetometers, accelerometers, and shocks and vibration sensors from which statistical information is obtained, such as root-mean squared error, maximum peaks, and peak levels. From these statistics, whirl, bit bounce, and stick/slip severity are inferred. Often, the derived statistics are not enough to distinguish between normal drilling versus abnormal drilling for a location in the wellbore or to determine whether the shocks and vibrations are the result of poor drilling practice, formation disturbances, or mechanical failures of the bottomhole assembly, including the bit. Machine learning methods were used for analyzing the high-frequency radial shock burst data, which compresses and classifies the data; i.e., good drilling and abnormal drilling. The method is capable of further clustering the data into whirl or no whirl, bit-bounce or no bit-bounce, formation change or no change, and/or faulty equipment and parts; thus, assist in the robust post-failure analysis of existing data sets and prevent catastrophic failures in real time and improve the trajectory control.