Identifying the lithologies and thicknesses of coal seam roofs and floors based on multiparameter logging of boreholes
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摘要: 【背景】在煤层开采过程中，有效的地层特征探测方法对于井下工作开展至关重要，精确掌握煤层及其顶底板围岩的地层特征信息，有助于煤层瓦斯治理，保障矿井安全高效生产。【方法过程】采用底抽巷穿层钻孔进行多参数测井，重点分析了低位上行钻孔轨迹信息与自然伽马、自然电位和电阻率等参数的变化特性；通过综合对比多个钻孔的轨迹数据钻孔视频成像和测井结果，详细描述了工作面煤层及周围岩层的分布特征，并准确界定了各岩石类型及其真厚度；基于钻孔测井曲线特征和对应区域成孔视频，识别煤层中煤体结构异常区域和煤层含水区域。【结果和结论】研究结果表明：研究区域煤层顶底板岩性主要包括砂质泥岩、泥岩、细砂岩、煤岩四种岩石，不同岩性具有显著的测井响应特征，结合岩性划分结果和对应的钻孔轨迹信息，能有效划分煤层及其顶底板围岩层位。对单个钻孔开展重复性测井，提高了岩石岩性和厚度的识别精度，孔内厚度测量误差控制在0.2 m内，验证了测井结果的可靠性。煤层自然电位和电阻率曲线交会图表明，底抽巷第三、四组钻孔之间存在长8.4 m，厚1.1 m的煤体结构异常区域；自然伽马、电阻率曲线交会图曲线和钻孔视频表明，第6组钻孔间存在长3.4 m，厚0.9 m的含水区域，研究结果与工作面瞬变电磁法勘测结果相一致，区域表现为弱富水性。多参数测井为快速判别井下钻孔的目标层位提供了高效手段，在推进矿井地质信息的透明化建设上有着良好的应用前景。Abstract: [Background] In the process of coal mining, effective exploration methods for stratigraphic characteristics are crucial to underground operations. The reason is that the accurate stratigraphic characteristics of coal seams, along with the surrounding rocks of coal seam roofs and floors, facilitate the treatment of gas in coal seams, thereby ensuring safe and efficient coal mining. [Method] Based on the multiparameter logging of boreholes crossing strata in a bottom drainage roadway, this study highlighted the lowstand, upgoing borehole trajectories, as well as the variations in parameters including natural gamma rays, spontaneous potential, and resistivity. By comprehensive comparison of the trajectories, video-derived images, and log results of multiple boreholes, this study detailed the distributions of the coal seams and surrounding rocks of the mining face and determined the accurate types and true thicknesses of various rocks. Besides, using the log curves of boreholes and corresponding video-derived. images, this study identified zones with anomalous coal structures and the water-bearing zones within coal seams. [Results and conclusions] The results indicate that the coal seam roofs and floors in the study area consist primarily of sandy mudstones, mudstones, fine-grained sandstones, and coals, which exhibit significantly different log responses. The combination of the lithologic classification results and corresponding borehole trajectories allows for the effective definition of coal seams, as well as the horizons of the surrounding rocks of coal seam roofs and floors. Repeated logging of a single borehole can improve the accuracy of identified lithologies and thicknesses of rocks. With errors of thickness measurements within boreholes controlled at below 0.2 m, the log results proved valid. The cross plot of spontaneous potential and resistivity curves reveals a zone with anomalous coal structures measuring 8.4 m in length and 1.1 m in thickness between the third and fourth groups of boreholes in the bottom drainage roadway. Additionally, the cross plot of natural gamma ray and resistivity curves and borehole videos suggest the presence of a water-bearing zone with a length of 3.4 m and a thickness of 0.9 m between the boreholes of the sixth group. These results align with the results obtained using the transient electromagnetic method (TEM), suggesting regionally weak water-yield properties. Overall, multiparameter logging serves as an efficient method for rapidly determining target horizons in underground drilling, demonstrating promising prospects for advancing the geological transparency of coal mines.
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Keywords:
- multiparameter logging /
- drilling trajectory /
- rock type /
- lithologic classification /
- log curve
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[1] 贾建称，贾茜，桑向阳，等. 我国煤矿地质保障系统建设30年：回顾与展望[J]. 煤田地质与勘探，2023，51(1)：86–106. JIA JianCheng，JIA Qian，SANG Xiangyang，et al. Review and prospect of coal mine geological guarantee system in China during 30 years of construction[J]. Coal Geology & Exploration，2023，51(1)：86–106.
[2] 王海军，刘善德，马良，等. 面向智能化开采的矿井煤岩层综合对比技术[J]. 煤田地质与勘探，2022，50(2)：24–38. WANG Haijun，LIU Shande，MA Liang，et al. Comprehensive correlation technology of coal and rock layers in mines for intelligent mining[J]. Coal Geology & Exploration，2022，50(2)：24–38.
[3] 程建远，王保利，范涛，等. 煤矿地质透明化典型应用场景及关键技术[J]. 煤炭科学技术，2022，50(7)：1–12. CHENG Jianyuan，WANG Baoli，FAN Tao，et al. Typical application scenes and key technologies of coal mine geological transparency[J]. Coal Science and Technology，2022，50(7)：1–12.
[4] LAI Jin，WANG Guiwen，FAN Qixuan，et al. Geophysical well-log evaluation in the era of unconventional hydrocarbon resources：A review on current status and prospects[J]. Surveys in Geophysics，2022，43(3)：913–957.
[5] LI Cunlei，YANG Zhaobiao，TIAN Wenguang，et al. Accurate prediction of the macrolithotype of a high-rank coal reservoir by logging interpretation：A case study of the Junlian Block，Sichuan Province，China[J]. Natural Resources Research，2023，32(5)：2289–2311.
[6] CHEN Qiang，JING Jin，LIU Jun，et al. Productivity evaluation of coalbed methane well with geophysical logging-derived tectonically deformed coal[J]. Energies，2019，12(18)：3459.
[7] CUI Chao，CHANG Suoliang，YAO Yanbin，et al. Quantify coal macrolithotypes of a whole coal seam：A method combing multiple geophysical logging and principal component analysis[J]. Energies，2021，14(1)：213.
[8] 邵先杰，孙玉波，孙景民，等. 煤岩参数测井解释方法：韩城矿区为例[J]. 石油勘探与开发，2013，40(5)：559–565. SHAO Xianjie，SUN Yubo，SUN Jingmin，et al. Logging interpretation of coal petrologic parameters：A case study of Hancheng mining area[J]. Petroleum Exploration and Development，2013，40(5)：559–565.
[9] KHALIL M I，ISLAM F，AKON E. Gamma and resistivity logs for characterization of Gondwana coal seams at the Northwestern part of Bangladesh[J]. Arabian Journal of Geosciences，2015，8(9)：6497–6506.
[10] KHALIL M I，ISLAM F，AKON E. Nuclear geophysical logging for Gondwana coal exploration：A case study at Phulbari Coal Basin，northern part of Bangladesh[J]. Arabian Journal of Geosciences，2013，6(5)：1647–1655.
[11] LIU Shiming，LIU Rui，TANG Shuheng，et al. Quantitative measurement on coal components through the interpretation model of geophysical log：A case study from the Qaidam Basin，NW China[J]. Energy Exploration & Exploitation，2021，39(6)：2027–2044.
[12] KRISHNAMURTHY N S，ANANDA RAO V，KUMAR D，et al. Electrical Resistivity Imaging technique to delineate coal seam barrier thickness and demarcate water filled voids[J]. Journal of the Geological Society of India，2009，73(5)：639–650.
[13] 李力，欧阳春平. 基于超声相控阵的煤岩界面识别研究[J]. 中国矿业大学学报，2017，46(3)：485–492. LI Li，OUYANG Chunping. Research on coal-rock interface recognition based on ultrasonic phased array[J]. Journal of China University of Mining & Technology，2017，46(3)：485–492. [14] 董振国，赵伟，任玺宁等.声波测井在煤岩弹性力学分析中的应用[J].煤田地质与勘探，2019，47(增刊1)：104-112. DONGONG Zhenguo，ZHAO Wei，REN Xining，et al. The application of acoustic logging in the analysis of coal rock elasticity [J]. Coalfield Geology and Exploration，2019，47(Sup.1)：104-112.
[15] 李新，倪卫宁，米金泰，等. 一种基于非接触耦合原理的新型随钻微电阻率成像仪器[J]. 中国石油大学学报(自然科学版)，2020，44(6)：46–52. LI Xin，NI Weining，MI Jintai，et al. A novel high-resolution resistivity imaging while drilling tool based on contactless coupling[J]. Journal of China University of Petroleum (Edition of Natural Science)，2020，44(6)：46–52. [16] ROSLIN A，ESTERLE J S. Electrofacies analysis for coal lithotype profiling based on high-resolution wireline log data[J]. Computers & Geosciences，2016，91：1–10.
[17] 汪兴明，鲜林，陈璐思，等. 基于响应面法的煤矿钻孔随钻超声成像仪器受力部件优化设计[J]. 中国安全生产科学技术，2021，17(7)：97–102. WANG Xingming，XIAN Lin，CHEN Lusi，et al. Optimization design of forced parts in ultrasonic imaging instrument while drilling in coal mine based on response surface method[J]. Journal of Safety Science and Technology，2021，17(7)：97–102.
[18] 蒋必辞，田小超，张鹏，等. 随钻伽马对方位角测量的影响及校正方法研究[J]. 煤炭科学技术，2020，48(12)：175–181. JIANG Bici，TIAN Xiaochao，ZHANG Peng，et al. Study on influence and correction method of gamma while drilling on azimuth angle measurement[J]. Coal Science and Technology，2020，48(12)：175–181.
[19] LIU Xiugang，JIANG Zaibing，WANG Yi，et al. Research on trajectory control technology for L-shaped horizontal exploration wells in coalbed methane[J]. Scientific Reports，2024，14：11343.
[20] 王楠，秦其明，陈理，等. 天然源超低频电磁探测技术在煤储层识别中的应用[J]. 煤炭学报，2014，39(1)：141–146. WANG Nan，QIN Qiming，CHEN Li，et al. Natural source super-low frequency electromagnetic prospecting in the application of coal-bed methane reservoir identification[J]. Journal of China Coal Society，2014，39(1)：141–146. [21] Liu Z D，Zhao J Z，Zhang P，et al. Evaluating the CBM reservoirs using NMR logging data[J]. Open Geosciences，10(1)：544-553.
[22] LI Mengqi，LU Jun，XIONG Shu. Prediction of fractures in coal seams with multi-component seismic data[J]. Scientific Reports，2019，9：6488.
[23] LU Jun，WANG Yun，CHEN Jingyi. Detection of tectonically deformed coal using model-based joint inversion of multi-component seismic data[J]. Energies，2018，11(4)：829.
[24] 潘辉，印兴耀，李坤，等. 地震反演驱动的改进匹配追踪煤层识别方法[J]. 地球物理学报，2022，65(6)：2276–2293. PAN Hui，YIN Xingyao，LI Kun，et al. An improved matching pursuit method for coal seam identification driven by seismic inversion[J]. Chinese Journal of Geophysics，2022，65(6)：2276–2293.
[25] 傅雪海，姜波，秦勇，等. 用测井曲线划分煤体结构和预测煤储层渗透率[J]. 测井技术，2003，27(2)：140–143. FU Xuehai，JIANG Bo，QIN Yong，et al. Classification of coalbody structure and prediction of coal reservoir permeability with log curves[J]. Well Logging Technology，2003，27(2)：140–143.
[26] 李存磊，杨兆彪，孙晗森，等. 多煤层区煤体结构测井解释模型构建[J]. 煤炭学报，2020，45(2)：721–730. LI Cunlei，YANG Zhaobiao，SUN Hansen，et al. Construction of a logging interpretation model for coal structure from multi-coal seams area[J]. Journal of China Coal Society，2020，45(2)：721–730. [27] REN Pengfei，XU Hao，TANG Dazhen，et al. The identification of coal texture in different rank coal reservoirs by using geophysical logging data in Northwest Guizhou，China：Investigation by principal component analysis[J]. Fuel，2018，230：258–265.
[28] 彭苏萍，魏文希，杜文凤，等. 基于AVO反演与叠前同步反演预测构造煤分布[J]. 中国矿业大学学报，2018，47(5)：929–934. PENG Suping，WEI Wenxi，DU Wenfeng，et al. AVO inversion associated with prestack simultaneous inversion in determining the distribution of tectonic coal[J]. Journal of China University of Mining & Technology，2018，47(5)：929–934.
[29] 鲁秀芹，汪关妹，杨延辉，等.基于叠前AVO数据的波形指示反演预测煤体结构[J].石油地球物理勘探，2022，57(增刊1)：117-121. Lu Xiuqin，Wang Guanmei，Yang Yanhui，et al. Waveform indication inversion prediction of coal structure based on prestack AVO data [J]. petroleum geophysical exploration，2022，57(Sup.1)：117-121+12. [30] 王小龙，蒋必辞，汪凯斌. 矿用电磁随钻伽马测井仪标定与试验[J]. 煤田地质与勘探，2019，47(5)：208–212. WANG Xiaolong，JIANG Bici，WANG Kaibin. Calibration and experiment of mine electromagnetic LWD gamma logging tool[J]. Coal Geology & Exploration，2019，47(5)：208–212.
[31] 和郑翔，卢才武，居培，等. 基于PCA-BP神经网络的随钻参数岩性智能感知方法研究[J]. 矿业研究与开发，2022，42(7)：155–159.HE Zhengxiang，LU Caiwu，JU Pei，et al. Research on lithology intelligent sensing method of drilling parameters based on PCA-BP neural network[J]. Mining Research and Development，2022，42(7)：155–159. [32] 王杰. 基于钻进参数的煤岩界面识别系统研究[J]. 煤田地质与勘探，2023，51(9)：72–80. WANG Jie. Research on coal-rock interface recognition system based on drilling parameters[J]. Coal Geology & Exploration，2023，51(9)：72–80. [33] 张幼振，张宁，邵俊杰，等. 基于钻进参数聚类的含煤地层岩性模糊识别[J]. 煤炭学报，2019，44(8)：2328–2335. ZHANG Youzhen，ZHANG Ning，SHAO Junjie，et al. Fuzzy identification of coal-bearing strata lithology based on drilling parameter clustering[J]. Journal of China Coal Society，2019，44(8)：2328–2335. [34] 梁栋才，汤华，吴振君，等. 基于多钻进参数和概率分类方法的地层识别研究[J]. 岩土力学，2022，43(4)：1123–1134. LIANG Dongcai，TANG Hua，WU Zhenjun，et al. Stratum identification based on multiple drilling parameters and probability classification[J]. Rock and Soil Mechanics，2022，43(4)：1123–1134.
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