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Study on Mechanical Data Logging for Small Diameter Drilling using Neural Network Techniques

利用神经网络技术进行小直径钻井机械数据记录的研究

基本信息

批准号：
06651089
负责人：
ITAKURA Ken-ichi
金额：
$ 1.28万
依托单位：
Muroran Institute of Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (C)
财政年份：
1994
资助国家：
日本
起止时间：
1994 至 1995
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-06651089/
关键词：
Neural Network Adaptive Resonance Theory ART2 Drilling Logging Rockbolt MWD Mechanical Data

项目摘要

In underground mining it is important to obtain information about the rock structure, including distribution of discontinuities, before excavation and constructing supports. Mechanical data from a drilling machine at the working face such as torque, thrust, revolution and stroke can be useful for understanding the underground structure. Therefore, both laboratory and field experiments were carried out in order to develop a mechanical data logging system. A data analysis system using neural network techniques was also developed for objective interpretation of the logging data and to display the log of strata and discontinuities.1.From the laboratory experiments it was found that the level of torque or thrust of a drilling machine reflected the difference in rock types, and the separation of strata and cracks appeared as a specific pattern in the log of torque/thrust ratio or torque/drilling speed per one revolution.2.As an algorithm of neural network techniques, adaptive resonance theory (ART2) and back propagation network (BPN) were employed to extract the pattern corresponding to a rock layr boundary and cracks from mechanical data of a drilling machine. Using both algorithms sequentially the location of discontinuities were successfully estimated.3.For the field experiments, a data acquisition system was attached to a portable drilling machine for the collection of mechanical data. A feasibility study was conducted using the drilling machine at an underground coal mine. It was confirmed that the discontinuity log estimated by the analysis conformed to the core sample reconstructed from borehole TV data.4.Combining several sets of the logging data measured at a local area in the field study, a two-dimensional structure of the strata including discontinuities was reconstructed. From this structure it was possible to estimate the orientation of discontinuities, which was difficult to evaluate from the logging data obtained from only one drill hole.

在地下采矿中，在开挖和建造支护之前，获得有关岩石结构的信息（包括不连续面的分布）是很重要的。工作面钻机的扭矩、推力、转速和冲程等机械数据有助于了解地下结构。因此，为了开发机械数据记录系统，进行了实验室和现场实验。为了客观地解释测井资料，并显示地层和不连续面的测井曲线，还开发了一个利用神经网络技术的数据分析系统。1.通过室内实验发现，钻机扭矩或推力的大小反映了岩石类型的差异，岩层和裂缝的分离在扭矩/推力比或扭矩/2.采用自适应共振理论（ART 2）和反向传播网络（BPN）作为神经网络技术的一种算法，从钻机的力学数据中提取出对应于岩层边界和裂纹的模式。使用这两种算法顺序的不连续性的位置被成功地估计。3.现场实验，数据采集系统连接到便携式钻机的机械数据的收集。在一个地下煤矿使用钻孔机进行了可行性研究。通过分析得到的不连续性测井曲线与利用钻孔电视资料重建的岩心样品相吻合。4.结合现场研究中在局部区域测量的多组测井资料，重建了包含不连续性的地层二维结构。从这个结构，它是可能的，以估计的方向不连续性，这是很难评估的测井数据获得的只有一个钻孔。