FEA-Based Prediction and Experimental Validation of Drilling Tool Lateral Motion Dynamics

Logging-while-drilling (LWD) tools are placed in the bottomhole assembly (BHA) at the lowest part of the drillstring, which are used for measuring formation properties during the excavation of the hole, or shortly thereafter. These formation properties can include spectral gamma ray, neutron, porosity, resistivity, and many others. With the tools being pushed to complete wells faster and cheaper, drilling conditions are becoming increasingly harsh. A variety of downhole vibration modes that hinder efficient drilling could occur and create wasted energy that is unusable to cut formation. Among them, BHA whirl is one of the undesired motions which can be faced during drilling operations, where the BHA follows an eccentric rotation about a point along the wellbore other than the geometric center. BHA whirl can be one of the most destructive types of drilling dynamics. For example, the lateral motion induced shock and vibration can be propagated from the outside collar to the inside chassis of the tool and damage the expensive electronics or other measurement devices. Therefore, having a reasonably accurate numerical model that can be used to understand and predict the lateral motion of a tool is highly demanded to mitigate the risks associated with the effect of whirl on measurement quality and health of the drilling tools. This study focuses on simulating the most seen backward whirl scenario where the center of the motion rotates in the opposite direction of the drill string. A high-fidelity 3D finite element analysis (FEA) model that can capture the contact interaction between the outside collar and internal chassis of the tool and between the tool and wellbore is developed to predict the lateral motion of the drilling tool in the presence of backward whirl. A roll test system (i.e., a tubular assembly rolls along the inner wall of an impact ring while spinning with a drive motor) instrumented with accelerometers, strain gauges and high-speed cameras is also developed to mimic such dynamics that the tool could experience while drilling in a controlled lab environment. The measurements collected from the accelerometer and strain gauge are analyzed and the videos recorded with the high speed camera is processed with computer vision to experimentally determine the motion trajectory and characteristics. Excellent agreement is observed between experimental motion data and the predictions from the developed FEA model, which validates the FEA model. The validated numerical model can be very useful for fit-for-basin virtual qualification of new drilling tools and for post-run root cause analysis of drilling tool failures.