Formation Prediction Model based on Drill String Vibration Measurements Using Laboratory Scale Rig

Abstract

Characteristics of formations usually cause changes in the drilling parameters such as weight on bit and drill string rotational speed as well as drill string dynamic behavior. When the drill bit breaks up formations, some of the energy dedicated to drilling is transmitted to the drill string as axial, lateral and torsional vibrations. Although these vibrations are affected by the drilling parameters, it has been experienced that vibration measurements can be used to evaluate formations during drilling a well. For this purpose a laboratory scale drilling rig was used to perform numerous experiments. During the drilling operations, several drilling parameters as well as vibrations with three-component accelerometers attached to the drill string were recorded continuously. Numerous uniform concrete and rock samples were drilled by a double roller cone bit. The uniaxial compressive strength of all concretes and rocks were measured prior to the experiments. The tests were executed using different combinations of weight on bit and rotary speed.For a subsequent evaluation statistical features were extracted in a first step from the recorded data in both, the time domain and the frequency domain. Those features formed the base for the formation classification by linear and non-linear models. Although linear models like Bayesian inference are not suited for that challenge, it turned out that non-linear models like neural networks provide excellent results in both, the training and the test data-set. Finally, the application of parameter selection methods showed that mechanical specific energy and rate of penetration in combination with higher order frequency moments of all recorded vibration types play the major role in the formation classification.