Machine learning algorithms for real-time coal recognition using monitor-while-drilling data

ABSTRACTAccurate coal seam identification is crucial in coal mining to prevent resource wastage and potential damage to coal seams from misplaced explosives. The current industry standard involves drilling past the seam and refilling the hole, a resource-intensive process. Manual seam detection is error-prone, and geophysical logging, performed for only a subset of drill holes, is costly and time-consuming. Monitor-While-Drilling (MWD) data captures drill response metrics like rotary speed and torque, influenced by local geology. These MWD measurements offer insights into geology, including hardness and rock type; They can be used for real-time rock recognition using advanced artificial intelligence techniques. This study focuses on developing tools for precise coal recognition and identification of the top of coal seams using MWD data. Several Machine Learning classifiers are employed, each providing unique data interpretations, and their results are integrated into a more reliable prediction. An artificial neural network is used for rock density regression, which is then used to correct depth offset between geophysical loggings and drill MWD data. The research demonstrates that MWD data can enable real-time coal seam identification, reducing the reliance on time-consuming and expensive geophysical logging. The integrated model accurately identifies the top of coal seams within a ± 20 cm margin.KEYWORDS: Artificial neural networkrock recognitionrock classificationmeasurement-while-drillingmachine learningensemble learning AcknowledgmentsThis project was also supported by the NSERC CRD program: RDCPJ53815-18.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work has been supported and partly funded by Peck Tech Consulting Ltd through the MITACS Accelerate program. The authors are grateful to the Peck Tech Consulting team and management for their valuable input and for providing the supporting data.