Abstract
As a rotary-percussion system, the vibro-impact drilling (VID) system utilises resonantly induced high frequency periodic impacts alongside existing drill-string rotation to cut through downhole rock layers. Due to the inhomogeneous nature of the rock layers, the system often experiences multi-stability which generates different categories of impact motions as drilling continues downhole. Some impact motions yield better drilling performance in terms of rate of penetration (ROP) and bit life-span when compared to others. As an optimisation strategy, the present study adopts feature-based classification algorithms including multi-layer perceptron, support vector machine and long short-term memory network as intelligent models for categorising impact motions from a one-degree-of-freedom impact oscillator representing the percussive bit-rock impacts of the VID system. This way, high-performance impacts can be easily detected and maintained while undesirable low-performance impacts are well avoided to increase ROP, improve bit life-span and save cost. In this study, scarce and limited classes of experimental impact data are merged with inexhaustibly simulated impact data to train different network models. By means of cross-validation, the trained networks were tested on separate sets of only-simulation and only-experimental data. Results show that extracting appropriate features from raw impact data is essential for optimising the performance of each network model. About 42% of the feature-based networks yield accuracies greater than 91% while about 67% yield accuracies greater than 77% on both simulation and experimental impact motion data.(c) 2022 Elsevier Ltd. All rights reserved.
NSTL
Elsevier