Feature-based intelligent models for optimisation of percussive drilling
Afebu, KO; Liu, Y; Papatheou, E
Date: 3 February 2022
Journal
Neural Networks
Publisher
Elsevier
Publisher DOI
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 the rock layers. Due to
the inhomogeneous nature of underlying rock layers, the system often experiences multi-stability which
generates different ...
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 the rock layers. Due to
the inhomogeneous nature of underlying rock layers, the system often experiences multi-stability which
generates different categories of impact motions as drilling continues dowhnhole. 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 neural networks including
multi-layer perceptron, support vector machine and long short-term memory as intelligent models in
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
lifespan 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.
Engineering
Faculty of Environment, Science and Economy
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