ABSTRACT

Accurate 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.

摘要

准确的煤层识别对于煤炭开采至关重要，可以防止资源浪费和爆炸物错放对煤层的潜在损害。当前的行业标准涉及钻过接缝并重新填充孔，这是一个资源密集型过程。手动煤层检测容易出错，而仅对一部分钻孔进行地球物理测井既昂贵又耗时。随钻监控 (MWD) 数据可捕获受当地地质影响的钻机响应指标，例如转速和扭矩。这些 MWD 测量提供了对地质学的深入了解，包括硬度和岩石类型；它们可用于使用先进的人工智能技术进行实时岩石识别。本研究的重点是开发利用随钻测井数据进行精确煤识别和煤层顶部识别的工具。采用了多个机器学习分类器，每个分类器都提供独特的数据解释，并且它们的结果被集成到更可靠的预测中。人工神经网络用于岩石密度回归，然后用于校正地球物理测井和钻探随钻测井数据之间的深度偏移。研究表明，MWD 数据可以实现实时煤层识别，减少对耗时且昂贵的地球物理测井的依赖。集成模型可以在 ± 20 厘米的范围内准确识别煤层顶部。然后用于校正地球物理测井和钻探 MWD 数据之间的深度偏移。研究表明，MWD 数据可以实现实时煤层识别，减少对耗时且昂贵的地球物理测井的依赖。集成模型可以在 ± 20 厘米的范围内准确识别煤层顶部。然后用于校正地球物理测井和钻探 MWD 数据之间的深度偏移。研究表明，MWD 数据可以实现实时煤层识别，减少对耗时且昂贵的地球物理测井的依赖。集成模型可以在 ± 20 厘米的范围内准确识别煤层顶部。