Comprehensive application of borehole log data of the Pulang copper deposit, Yunnan Province
YANG Chao-Yi1,2(), ZHU Qian-Kun1,2, JIE Shao-Peng3, KONG Chui-Ai1,2, SHA You-Cai1,2, ZHONG Zhi-Yong1,2, SHEN Qi-Wu1,2, CHEN Zhi-Jun2,4, MA Huo-Lin2,3()
1. Yunnan Diqing Non-ferrous Metal Co., Ltd., Shangri-La 674400, China 2. Practical Teaching and Innovative Talents Training Base in the Pulang Copper Deposit, Shangri-La 674400, China 3. Institute of Geophysics and Geomatics, China University of Geosciences (Wuhan), Wuhan 430074, China 4. School of Earth Sciences, China University of Geosciences (Wuhan), Wuhan 430074, China
The copper mineralized bodies and orebodies of the Pulang copper deposit in Yunnan Province are mainly distributed in the Pulang complex porphyry body and were formed through complex multi-stage development. This study aims to detail the geophysical response and fractures of copper reservoirs and provide detailed orebody characteristics, fractures, and horizon burial depth to be referenced in the exploration and exploitation of the Pulang copper deposit. First, the borehole-log data in the Pulang copper deposit were sampled for comprehensive evaluation. Then, in combination with the drilling reports and data on partial core samples, this study analyzed the log response characteristics and fractures and identified the lithology of the Pulang copper deposit using mathematical statistics, three-dimensional cross plots, convolutional neural networks (CNNs), and fracture parameter calculation. The log response characteristics of the three major strata of quartz monzonite porphyries, quartz diorite porphyrites, and hornstones in the study area are as follows. The hornstone strata have relatively high resistivity, followed by the quartz diorite porphyrite strata and the quartz monzonite porphyry strata in sequence. The resistivity decreases significantly at the intervals with fractures occurring or at the relatively fractured intervals. The quartz monzonite porphyry strata have a relatively high charge rate (polarization rate) of up to about 10%. The hornstone strata have relatively high radioactive intensity than the quartz diorite porphyrite strata and the quartz monzonite porphyry strata. CNNs were used to identify and analyze the lithology of the three major types of strata based on log data, with an accuracy rate of 97.94%. Finally, this study identified fractures in these strata using dual laterolog data. The resistivity significantly decreases at intervals with fractures occurring and differs greatly between deep and shallow lateral resistivity. The quartz monzonite porphyry strata with a high copper grade have relatively low resistivity and relatively well-developed high-angle fractures. The results of this study are of significance for the identification of ore body characteristics and the exploitation of ore bodies in the Pulang copper deposit.
Zhou X P, Xiang B, Zou C C, et al. Integrated geophysical logging study on the borehole NLSD-2 of the polymetallic ore in the Nanling District[J]. Acta Geologica Sinica, 2014, 88(4): 686-694.
doi: 10.1111/1755-6724.12375_4
Guo J H, Du T, Zhang Z S, et al. The coal structure identification method based on support vector machine and geophysical logging data[J]. Geophysical and Geochemical Exploration, 2021, 45(3):768-777.
[7]
Konate A A, Pan H P, Ma H L, et al. Use of spectral gamma ray as a lithology guide for fault rocks: A case study from the Wenchuan Earthquake Fault Scientific Drilling project Borehole 4 (WFSD-4)[J]. Applied Radiation and Isotopes, 2017, 128: 75-85. https://doi.org/10.1016/j.apradiso.2017.06.038.
doi: S0969-8043(17)30025-8
pmid: 28688249
[8]
Xu Z X, Ma W, Peng L, et al. Deep learning of rock images for intelligent lithology identification[J]. Computers & Geosciences, 2021, 154: 104799. https://doi.org/10.1016/j.cageo.2021.104799.
doi: 10.1016/j.cageo.2021.104799
Chen G H, Liang S S, Wang J, et al. Application of convolutional neural network in lithology identification[J]. Well Logging Technology, 2019, 43(2): 129-134.
[10]
Yang L Q, He W Y, Gao X, et al. Mesozoic multiple magmatism and porphyry-skarn Cu-polymetallic systems of the Yidun Terrane, Eastern Tethys: Implications for subduction- and transtension-related metallogeny[J]. Gondwana Research, 2018, 62: 144-162. https://doi.org/10.1016/j.gr.2018.02.009.
doi: 10.1016/j.gr.2018.02.009
Li W C, Zeng P S. Characteristics and metallogenic model of the Pulang super large porphyry copper deposit in Yunnan, China[J]. Journal of Chengdu University of Technology:Science & Technology Edition, 2007, 34(4):436-446.
Li W C, Yu H J, Yin G H. Porphyry metallogenic system of Geza arc in the Sanjiang region, southwestern China[J]. Acta Petrologica Sinica, 2013, 29(4): 1129-1144.
[13]
LeCun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324. https://doi.org/10.1109/5.726791.
doi: 10.1109/5.726791
[14]
Sibbit A M, Faivre Q. The dual laterolog response in fractured rocks[C]// Dallas:SPWLA 26th Annual Logging Symposium, 1985: 17-20.
