1. School of Geological and Mining Engineering, Xinjiang University, Urumqi 830047, China 2. Research Institute of Petroleum Exploration and Development-Northwest (NWGI), PetroChina, Lanzhou 730020, China 3. Research Institute of Exploration and Development, PetroChina Xinjiang Oilfield Company, Karamay 834000, China
The accurate lithologyidentification of volcanic rocksserves as a significant foundation for the efficient exploration and exploitation of volcanic reservoirs. However, volcanic reservoirs exhibit intricate lithologies, longitudinalmultistagesuperimposition, and fast transverse phase transition, which reduce the accuracy of crossplots in lithologyidentification ofvolcanic reservoirs. Based on the optimal parameter combination of the model determined through grid search and orthogonal experiments, this study quantitatively evaluatedthe effects of conventional log curves on the lithologyidentification of volcanic rocks. Withthe natural gamma ray, compensated neutron, sonic interval transit time, and formation resistivity as lithologic indicators, this study builtan intelligent model for the lithology identification of Carboniferous volcanic rocks in the Dixi area in the Junggar Basin using therandom forest method. This study identified the lithologies of thecored intervalswith a cumulative thickness of 870 m infive cored wells in the study area, with the coincidence ratesof the identification results with thin section identification results and core description resultsreaching 76.67% and 85.98%, respectively. This suggestssignificant identification effects. Therefore, this studysets the stagefor the fine-scale evaluation of volcanic reservoirs in the study area.
尚亚洲, 张兆辉, 许多年, 赵雯雯, 陈华勇, 韩海波. 基于随机森林的火山岩岩性测井识别——以准噶尔盆地滴西地区石炭系为例[J]. 物探与化探, 2024, 48(4): 1025-1036.
SHANG Ya-Zhou, ZHANG Zhao-Hui, XU Duo-Nian, ZHAO Wen-Wen, CHEN Hua-Yong, HAN Hai-Bo. Log-based lithology identification of volcanic rocks using random forest method: A case study of Carboniferous strata in the Dixi area, Junggar Basin. Geophysical and Geochemical Exploration, 2024, 48(4): 1025-1036.
Zhang Y, Li D Q, Qiu P, et al. Study on fluid identification method of volcanic reservoir based on lithology classification:A case study of carboniferous volcanic rocks in kelameigasfield[J]. Journal of Xi’an Shiyou University:Natural Science Edition, 2020, 35(6):22-29.
Liu X H, Li N X, Feng M Y, et al. Fracture fillings and alteration halo in volcanic reservoirs as indicator of fluid activities in the dixi area in the kelameili gas field,Junggar Basin,northwestern China[J]. Bulletin of Mineralogy,Petrology and Geochemistry, 2019, 38(3):539-548.
Tan K J, Wei P S, Pan J G, et al. Volcanic seismic reservoir[J]. Lithologic Reservoirs, 2010, 22(4):8-13,19.
[4]
Zhang H X, Chen S J, Huang H, et al. Mixing ratios quantitative assessment of source-mixed gas reservoirs by carbon isotope analysis in Dinan Uplift,Junggar Basin,China[J]. Petroleum Science and Technology, 2016, 34(19):1636-1641.
[5]
Zhang Z H, Yu H G, Chen H Y, et al. Quantitative characterization of fracture-pore distribution and effects on production capacity of weathered volcanic crust reservoirs:Insights from volcanic gas reservoirs of the Dixi Area,Junggar Basin,Western China[J]. Marine and Petroleum Geology, 2022, 140:105651.
Zhou D L, Dong Q S. Analysis on features and formation of igneous reservoirs[J]. Lithologic Reservoirs, 2010, 22(4):80-84.
[7]
Zhang K L, Wang Z L, Jiang Y Q, et al. Effects of weathering and fracturing on the physical properties of different types of volcanic rock:Implications for oil reservoirs of the Zhongguai relief,Junggar Basin,NW China[J]. Journal of Petroleum Science and Engineering, 2020, 193:107351.
[8]
Guo Y H, Pan B Z, Zhang L H, et al. A study on water saturation predictions in igneous reservoirs based on the relationship between the transverse relaxation time and the resistivity index[J]. Journal of Petroleum Science and Engineering, 2022, 208:109519.
[9]
Han R Y, Wang Z W, Wang W H, et al. Lithology identification of igneous rocks based on XGboost and conventional logging curves,a case study of the eastern depression of Liaohe Basin[J]. Journal of Applied Geophysics, 2021, 195:104480.
[10]
Ao Y L, Zhu L P, Guo S, et al. Probabilistic logging lithology characterization with random forest probability estimation[J]. Computers & Geosciences, 2020, 144:104556.
[11]
Ma X H, Yang Y, Zhang J, et al. A major discovery in Permian volcanic rock gas reservoir exploration in the Sichuan Basin and its implications[J]. Natural Gas Industry B, 2019, 6(4):419-425.
[12]
Yu Y L, Xu H B, Bai Y, et al. CT-based 3D pore-fracture network analysis of volcanic reservoirs of Lower Cretaceous Yingcheng Formation in southern Songliao Basin,China:Impact on natural gas migration[J]. Geoenergy Science and Engineering, 2023, 223:211581.
Zhou P. Identification of volcanics reservoir lithology and lithofacies in Huoshiling Formation of Wangfu fault depression[J]. Fault-Block Oil & Gas Field, 2020, 27(2):188-192.
Kuang L C, Dong Z, Sun Z C, et al. Method for calculation of volcanic reservoir porosity based on ECS log[J]. Xinjiang Petroleum Geology, 2009, 30(3):287-288,296.
Huang L, Zhang Y L, Wang Y H, et al. Application of velocity optimization and modeling technology in volcanic rock imaging in Dehui fault depression[J]. Oil Geophysical Prospecting, 2017, 52(S1):34-40,6.
Ye T, Wei A J, Deng H, et al. Study on volcanic lithology identification methods based on the data of conventional well logging data:A case from Mesozoic volcanic rocks in Bohai Bay Area[J]. Progress in Geophysics, 2017, 32(4):1842-1848.
Zhang L H, Zhang G B, Qi Y P, et al. Lithology identification of carboniferous volcanic rock with logging data in xiquan area,Junggar Basin[J]. Xinjiang Petroleum Geology, 2017, 38(4):427-431.
Feng Z Y, Yin W, Zhong Y T, et al. Lithology identification method of igneous rock based on conventional logging—Takingcarboniferous igneous rock reservoir of Hongche fault zone in northwest margin of Junggar Basin as an example[J]. Journal of Northeast Petroleum University, 2021, 45(5):95-108.
[19]
Liu M, Hu S, Zhang J, et al. Methods for identifying complex lithologies from log data based on machine learning[J]. Unconventional Resources, 2023, 3:20-29.
Xu D N, Pan J G, Chen Y B, et al. Application of seismic reservoir to volcanic reservoir exploration in Wuxia Area of Junggar Basin[J]. Lithologic Reservoirs, 2010, 22(3):5-8.
[21]
Li Z R, Wu Y P, Kang Y, et al. Feature-depth smoothness based semi-supervised weighted extreme learning machine for lithology identification[J]. Journal of Natural Gas Science and Engineering, 2021, 96:104306.
[22]
Lan X X, Zou C C, Kang Z H, et al. Log facies identification in carbonate reservoirs using multiclass semi-supervised learning strategy[J]. Fuel, 2021, 302:121145.
[23]
Ren Q, Zhang H B, Zhang D L, et al. A framework of active learning and semi-supervised learning for lithology identification based on improved naive Bayes[J]. Expert Systems with Applications, 2022, 202:117278.
[24]
Abdulaziz A M, Mahdi H A, Sayyouh M H. Prediction of reservoir quality using well logs and seismic attributes analysis with an artificial neural network:A case study from Farrud Reservoir,Al-Ghani Field,Libya[J]. Journal of Applied Geophysics, 2019, 161:239-254.
doi: 10.1016/j.jappgeo.2018.09.013
[25]
Xia L, Zhao Y W, Yu C L, et al. Key factors controlling deep Carboniferous volcanic reservoirs in the east slope of Mahu Sag,Junggar Basin,NW China[J]. Journal of Petroleum Science and Engineering, 2023, 220:111223.
[26]
Saporetti C M, da Fonseca L G, Pereira E, et al. Machine learning approaches for petrographic classification of carbonate-siliciclastic rocks using well logs and textural information[J]. Journal of Applied Geophysics, 2018, 155:217-225.
[27]
Xiang M, Qin P B, Zhang F W. Research and application of logging lithology identification for igneous reservoirs based on deep learning[J]. Journal of Applied Geophysics, 2020, 173:103929.
[28]
Yu Z C, Wang Z Z, Zeng F C, et al. Volcanic lithology identification based on parameter-optimized GBDT algorithm:A case study in the Jilin Oilfield,Songliao Basin,NE China[J]. Journal of Applied Geophysics, 2021, 194:104443.
[29]
Mou D, Wang Z W, Tan X L, et al. A variational inequality approach with SVM optimization algorithm for identifying mineral lithology[J]. Journal of Applied Geophysics, 2022, 204:104747.
[30]
Houshmand N, GoodFellow S, Esmaeili K, et al. Rock type classification based on petrophysical,geochemical,and core imaging data using machine and deep learning techniques[J]. Applied Computing and Geosciences, 2022, 16:100104.
[31]
Wang W F, Wang Z Z, Leung J Y, et al. Petrophysical rock typing based on deep learning network and hierarchical clustering for volcanic reservoirs[J]. Journal of Petroleum Science and Engineering, 2022, 210:110017.
[32]
Gu Y F, Zhang D Y, Lin Y B, et al. Data-driven lithology prediction for tight sandstone reservoirs based on new ensemble learning of conventional logs:A demonstration of a Yanchang member,Ordos Basin[J]. Journal of Petroleum Science and Engineering, 2021, 207:109292.
Wang M, Yang J L, Wang X, et al. Identification of shale lithofacies by well logs based on random forest algorithm[J]. Earth Science, 2023, 48(1):130-142.
Shi X P, Hu Q X, Xie Z W, et al. Identification methods of volcanic lithology,lithofacies:Taking the volcanic rocks of Dinan alient in the Junggar Basin as an instance[J]. Natural Gas Geoscience, 2016, 27(10):1808-1816.
[35]
Lu J G, Luo Z Y, Zou H L, et al. Geochemical characteristics,origin,and mechanism of differential accumulation of natural gas in the carboniferous kelameili gas field in Junggar Basin,China[J]. Journal of Petroleum Science and Engineering, 2021, 203:108658.
Dai C M, Zeng Q C, Guo X L, et al. Establishment and application of trinity interpretation technology on volcanic rock:A case study of the Kelamieli gas field in Xinjiang[J]. Progress in Geophysics, 2018, 33(3):1035-1042.
Liu X H, Li N X, Feng M Y, et al. Fracture fillings and alteration halo in volcanic reservoirs as indicator of fluid activities in the dixi area in the kelameili gas field,Junggar Basin,northwestern China[J]. Bulletin of Mineralogy,Petrology and Geochemistry, 2019, 38(3):539-548.
Liu J Y, Xi A H, Ran Q Q, et al. Petrography characteristics and main controlling factors of secondary pores in Carboniferous volcanic reservoir in Dixi Area,Junggar Basin[J]. Lithologic Reservoirs, 2012, 24(3):51-55.
Liu S Q, Cao Y T, Zhao G L, et al. Influencing factors of Carboniferous volcanic reservoir in Ludong-Wucaiwan Area,Junggar Basin[J]. Lithologic Reservoirs, 2014, 26(5):23-29.
Liu X H, Feng M Y, Xi A H, et al. Diagenesis and pore evolution of Carboniferous volcanic reservoirs in Dixi Area,Kelameili Gas Field[J]. Lithologic Reservoirs, 2016, 28(1):38-48.
Zhang Z H, Du S K, Chen H Y, et al. Quantitative characterization of volcanic fracture distribution based on electrical imaging logging:A case study of Carboniferous in Dixi Area,Junggar Basin[J]. Acta Petrolei Sinica, 2018, 39(10):1130-1140.
doi: 10.7623/syxb201810005
Liu X H, Wang W W, Feng M Y, et al. Hydrothermal process and duration of carboniferous altered tuff reservoir in well dixi 14 area of kelameili gas field(Junggar basin),NW China[J]. Earth Science, 2022, 47(5):1694-1710.
Xiong Y X, Xi A H, Ran Q Q, et al. Characteristics and significance of Carboniferous volcanic rocks in Dinan uplift[J]. Lithologic Reservoirs, 2011, 23(6):62-68.
Zhang Z H, Chen H Y, Gao Y L, et al. Study on lithology identification of igneous rocks in Kelameili Gasfield by well logging[J]. Geophysical Prospecting for Petroleum, 2016, 55(5):764-770.
doi: 10.3969/j.issn.1000-1441.2016.05.016
Zhang D Q, Zou N N, Jiang Y, et al. Logging identification method of volcanic rock lithology:A case study from volcanic rock in Junggar Basin[J]. Lithologic Reservoirs, 2015, 27(1):108-114.
Yin W, Feng C, Zhou F X, et al. Lithology and lithofacies characteristics analysis and reservoir identification of Carboniferous volcanic rocks in Hongche fault zone[J]. Journal of China University of Petroleum:Natural Science Edition, 2023, 47(1):50-61.
Huang A, Cai W Y, Wei X L, et al. Lithology identification of volcanic logging based on improved random forest[J]. Science Technology and Engineering, 2023, 23(9):3696-3704.
Lai Q, Wei B Y, Wu Y Y, et al. Classification of igneous rock lithology with K-nearest neighbor algorithm based on random forest (RF-KNN)[J]. Special Oil & Gas Reservoirs, 2021, 28(6):62-69.
Ma D L, Cai Y Q, Sun Y Q. Random forest algorithm based lithology identification of geophysical logging data in tarangaole area[J]. World Nuclear Geoscience, 2023, 40(1):43-50,67.
Ji W L, Liu Y X, Chai J, et al. Mine pressure prediction method based on random forest[J]. Journal of Mining and Strata Control Engineering, 2021, 3(3):71-81.
[52]
Alkhasawneh M S, Ngah U K, Tay L T, et al. Modeling and testing landslide hazard using decision tree[J]. Journal of Applied Mathematics, 2014, 2014:929768.
[53]
Xue L, Liu Y T, Xiong Y F, et al. A data-driven shale gas production forecasting method based on the multi-objective random forest regression[J]. Journal of Petroleum Science and Engineering, 2021, 196:107801.
[54]
Xu Z H, Shi H, Lin P, et al. Integrated lithology identification based on images and elemental data from rocks[J]. Journal of Petroleum Science and Engineering, 2021, 205:108853.
[55]
Bhattacharya S, Carr T R, Pal M. Comparison of supervised and unsupervised approaches for mudstone lithofacies classification:Case studies from the Bakken and Mahantango-Marcellus Shale,USA[J]. Journal of Natural Gas Science and Engineering, 2016, 33:1119-1133.
[56]
Zhao C J, Jiang Y L, Wang L J. Data-driven diagenetic facies classification and well-logging identification based on machine learning methods:A case study on Xujiahe tight sandstone in Sichuan Basin[J]. Journal of Petroleum Science and Engineering, 2022, 217:110798.
