A data-driven dynamic method of downhole rock characterisation for the vibro-impact drilling system
dc.contributor.author | Afebu, KO | |
dc.contributor.author | Liu, Y | |
dc.contributor.author | Papatheou, E | |
dc.date.accessioned | 2024-08-30T15:35:00Z | |
dc.date.issued | 2024-08-30 | |
dc.date.updated | 2024-08-30T15:15:03Z | |
dc.description.abstract | For the real-time characterisation of an inhomogeneous impact inhibiting constraint such as downhole rock layers, an unconventional method using machine learning (ML) and drill-bit vibrations is investigated. An impact oscillator with one-sided elastic constraint is employed in modelling the bit-rock impact actions. Measurable drill-bit dynamics, such as acceleration, were acquired and processed into features and 2D-images that were later used in developing ML models capable of predicting the stiffness of impacted rock constraint. Explored ML networks include Multilayer Perceptron (MLP), Convolutional Neural Network and Long Short-Term Memory Network. Both simulation and experimental studies have been presented to validate the proposed method while using coefficient of determination ( ) and normalised mean absolute error (NMAE) as the performance metrics of the ML models. Results showed that the feature-based models had better performances for both simulation and experiment compared to the raw signal and 2D-image based models. Aside being simple and computationally less expensive, the feature-based MLP models outperformed other models having values 0.7 and NMAE values 0.2 for both simulation and experiment, thus presenting them as the preferred ML model for dynamic downhole rock characterisation. In general, this study presents a new modality to achieving logging-while-drilling during deep-hole drilling operations such as carried out in hydrocarbon, mineral and geothermal exploration. | en_GB |
dc.description.sponsorship | Petroleum Technology Development Fund (PTDF) of Nigeria | en_GB |
dc.identifier.citation | Vol. 223, article 111880 | en_GB |
dc.identifier.doi | https://doi.org/10.1016/j.ymssp.2024.111880 | |
dc.identifier.grantnumber | PTDF/ED/PHD/AKO/1080/17 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/137296 | |
dc.identifier | ORCID: 0000-0003-3867-5137 (Liu, Yang) | |
dc.identifier | ORCID: 0000-0003-1927-1348 (Papatheou, Evangelos) | |
dc.language.iso | en | en_GB |
dc.publisher | Elsevier | en_GB |
dc.rights | © 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). | en_GB |
dc.subject | Rotary-percussive drilling | en_GB |
dc.subject | Rock characterisation | en_GB |
dc.subject | Impact oscillator | en_GB |
dc.subject | Signal processing | en_GB |
dc.subject | Impact durations | en_GB |
dc.subject | Artificial networks | en_GB |
dc.title | A data-driven dynamic method of downhole rock characterisation for the vibro-impact drilling system | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2024-08-30T15:35:00Z | |
dc.identifier.issn | 0888-3270 | |
exeter.article-number | 111880 | |
dc.description | This is the final version. Available on open access from Elsevier via the DOI in this record. | en_GB |
dc.description | Data availability: Data will be made available on request. | en_GB |
dc.identifier.eissn | 1096-1216 | |
dc.identifier.journal | Mechanical Systems and Signal Processing | en_GB |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2024-08-23 | |
dcterms.dateSubmitted | 2024-01-21 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2024-08-30 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2024-08-30T15:32:43Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2024-08-30T15:37:30Z | |
refterms.panel | B | en_GB |
refterms.dateFirstOnline | 2024-08-30 | |
exeter.rights-retention-statement | Yes |
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Except where otherwise noted, this item's licence is described as © 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/).