Lithologic Reservoirs ›› 2020, Vol. 32 ›› Issue (4): 98-106.doi: 10.12108/yxyqc.20200410

• EXPLORATION TECHNOLOGY • Previous Articles     Next Articles

Identification of complex carbonate lithology by logging based on XGBoost algorithm

SUN Yushu1,2, HUANG Yun3, LIANG Ting1,2, JI Hancheng1,2, XIANG Pengfei1,2, XU Xinrong1,2   

  1. 1. College of Geoscience, China University of Petroleum(Beijing), Beijing 102249, China;
    2. State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum(Beijing), Beijing 102249, China;
    3. Research Institute of Exploration & Development, PetroChina Huabei Oilfield Company, Renqiu 062550, Hebei, China
  • Received:2019-09-12 Revised:2019-11-29 Online:2020-08-01 Published:2020-06-16

PDF (PC)

566

Abstract: Carbonate reservoirs are affected by a variety of factors during the formation process,and the reservoir lithology is complex and diverse. Logging data are of great significance for carbonate lithology identification. In order to solve the problem that the traditional logging lithology identification method and traditional machine learning have low recognition accuracy for complex carbonate lithology,taking the Ordovician carbonate rocks in the northern Langgu Depression as an example, based on log data, XGBoost algorithm was appied to lithology identification of complex carbonate rocks,and the performance of the model was comparied with the decision tree C4.5 algorithm and the support vector machine algorithm. The results show that the lithology identification model based on XGBoost algorithm has an accuracy rate of 88.18% for the identification of carbonate lithology in the study area. Compared with decision tree C4.5 and support vector machine,the accuracy rate is increased by about 10%. And the XGBoost algorithm uses multi-threaded and distributed computing methods,the training time is greatly shortened. It shows that the lithology identification model established by XGBoost algorithm can effectively identify complex carbonate lithology and provide a new idea for logging identification of complex carbonate lithology.

Key words: XGBoost algorithm, machine learning, carbonate, lithology identification, log interpretation

CLC Number: 

  • P618.13
[1] 江凯, 王守东, 胡永静, 等. 基于Boosting Tree算法的测井岩性识别模型.测井技术, 2018, 42(4):396. JIANG K, WANG S D, HU Y J, et al. Lithology identification model by well logging based on boosting tree algorithm. Well Logging Technology, 2018, 42(4):396.
[2] 王瑞, 朱筱敏, 王礼常.用数据挖掘方法识别碳酸盐岩岩性. 测井技术, 2012, 36(2):197. WANG R, ZHU X M, WANG L C. Using data mining to identify carbonate lithology. Well Logging Technology, 2012, 36(2):197.
[3] 吴施楷, 曹俊兴.基于连续限制玻尔兹曼机的支持向量机岩性识别方法.地球物理学进展, 2016, 31(2):821-828. WU S K, CAO J X. Lithology identification method based on continuous restricted Boltzmann machine and support vector machine. Progress in Geophysics, 2016, 31(2):821-828.
[4] 杨冬.BP神经网络技术在碳酸盐岩岩性识别中的应用.石化技术, 2016, 23(1):58. YANG D. Application of BP neural network technology in carbonate lithology identification. Petrochemical Industry Technology, 2016, 23(1):58.
[5] 张翔, 肖小玲, 严良俊, 等.基于模糊支持向量机方法的岩性识别. 石油天然气学报(江汉石油学院学报), 2009, 31(6):115-118. ZHANG X, XIAO X L, YAN L J, et al. Lithologic identification based on fuzzy support vector machines. Journal of Oil and Gas Technology(Journal of Jianghan Petroleum Institute), 2009, 31(6):115-118.
[6] 钟仪华, 李榕.基于主成分分析的最小二乘支持向量机岩性识别方法.测井技术, 2009, 33(5):425-429. ZHONG Y H, LI R. Application of principal component analysis and least square support vector machine to lithology identification. Well Logging Technology, 2009, 33(5):425-429.
[7] 赵忠军, 黄强东, 石林辉, 等.基于BP神经网络算法识别苏里格气田致密砂岩储层岩性.测井技术, 2015, 39(3):363-367. ZHAO Z J, HUANG Q D, SHI L H, et al. Identification of lithology in tight sandstone reservoir in Sulige Gas Field based on BP neural net algorithm. Well Logging Technology, 2015, 39(3):363-367.
[8] 范存辉, 梁则亮, 秦启荣, 等.基于测井参数的遗传BP神经网络识别火山岩岩性:以准噶尔盆地西北缘中拐凸起石炭系火山岩为例.石油天然气学报, 2012, 34(1):68-71. FAN C H, LIANG Z L, QIN Q R, et al. Identification of volcanic-rock lithology by using genetic BP neural network based on logging parameters:By taking carboniferous volcanic rocks in Zhongguai uplift of northwestern margin of Junggar Basin for instance. Journal of Oil and Gas Technology, 2012, 34(1):68-71.
[9] 王振洲, 张春雷, 高世臣.利用决策树方法识别复杂碳酸盐岩岩性:以苏里格气田苏东41-33区块为例.油气地质与采收率, 2017, 24(6):25-33. WANG Z Z, ZHANG C L, GAO S C. Lithology identification of complex carbonate rocks based on decision tree method:an example from block Sudong 41-33 in Sulige Gas Field. Petroleum Geology and Recovery Efficiency, 2017, 24(6):25-33.
[10] 仲鸿儒, 成育红, 林孟雄, 等.基于SOM和模糊识别的复杂碳酸盐岩岩性识别.岩性油气藏, 2019, 31(5):84-91. ZHONG H R, CHENG Y H, LIN M X, et al. Lithology identification of complex carbonate based on SOM and fuzzy recognition. Lithologic Reservoirs, 2019, 31(5):84-91.
[11] 马峥, 张春雷, 高世臣.主成分分析与模糊识别在岩性识别中的应用.岩性油气藏, 2017, 29(5):127-133. MA Z, ZHANG C L, GAO S C. Lithology identification based on principal component analysis and fuzzy recognition. Lithologic Reservoirs, 2017, 29(5):127-133.
[12] 宋延杰, 王团, 付健, 等.雷64区块砂砾岩储层岩性识别方法研究.哈尔滨商业大学学报(自然科学版), 2015, 31(1):73-78. SONG Y J, WANG T, FU J, et al. Research on technology of lithology identification of sand-conglomerate rock in Lei 64. Journal of Harbin University of Commerce(Natural Sciences Edition), 2015, 31(1):73-78.
[13] 李洪奇, 谭锋奇, 许长福, 等.基于决策树方法的砾岩油藏岩性识别:以克拉玛依油田六中区克下组油藏为例.石油天然气学报(江汉石油学院学报), 2010, 32(3):73-79. LI H Q, TAN F Q, XU C F, et al. Lithological identification of conglomerate reservoirs base on decision tree method. Journal of Oil and Gas Technology(Journal of Jianghan Petroleum Institute), 2010, 32(3):73-79.
[14] 李百强, 张小莉, 王起琮, 等.低渗-特低渗白云岩储层成岩相分析及测井识别:以伊陕斜坡马五段为例. 岩性油气藏, 2019, 31(5):70-83. LI B Q, ZHANG X L, WANG Q C, et al. Analysis and logging identification of diagenetic facies of dolomite reservoir with low and ultra-low permeability:a case study from Ma 5 memberin Yishan slope, Ordos Basin. Lithologic Reservoirs, 2019, 31(5):70-83.
[15] CHEN T Q, GUESTRIN C. XGBoost:a scalable tree boosting system. Proceedings of the 22 nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, California, USA, 2016:785-794.
[16] 刘宇, 乔木.基于聚类和XGboost算法的心脏病预测.计算机系统应用, 2019, 28(1):229. LIU Y, QIAO M. Heart disease prediction based on clustering and XGboost. Computer Systems & Applications, 2019, 28(1):229.
[17] FRIEDMAN J H. Greedy function approximation:a gradient boosting machine. The Annals of Statistics, 2001, 29(5):1189-1232.
[18] 李超, 张文辉, 林基明.基于XGBoost算法的恒星/星系分类研究.天文学报, 2019, 60(2):75. LI C, ZHANG W H, LIN J M. Research on star/galaxy classification based on XGBoost algorithm. Acta Astronomica Sinica, 2019, 60(2):75.
[19] 沈倩倩, 邵峰晶, 孙仁诚.基于XGBoost的乳腺癌预测模型. 青岛大学学报(自然科学版), 2019, 32(1):97. SHEN Q Q, SHAO F J, SUN R C. Prediction model of breast cancer based on XGBoost. Journal of Qingdao University (Natural Science Edition), 2019, 32(1):97.
[20] 罗菊兰, 陈彦竹, 高波, 等.基于矿物组合分类的碳酸盐岩储层岩性识别模型的建立.国外测井技术, 2018, 39(2):21-26. LUO J L, CHEN Y Z, GAO B, et al. Establishment of lithology recognition model for carbonate reservoir based on mineral assemblage classification. World Well Logging Technology, 2018, 39(2):21-26.
[21] 高雅琴, 谢润成, 吕志洲, 等.基于多元概率因子识别复杂碳酸盐岩岩性方法的应用.石化技术, 2018, 25(2):122-123. GAO Y Q, XIE R C, LYU Z Z, et al. The application of methods of identifying lithology of complex carbonate rocks based on multiple probability factor. Petrochemical Industry Technology, 2018, 25(2):122-123.
[22] 孙哲, 韦阿娟, 江尚昆, 等.元素录井技术在渤海潜山岩性识别中的应用.特种油气藏, 2017, 24(5):78-84. SUN Z, WEI A J, JIANG S K, et al. Application of element logging technology in identifying buried hill lithologies in Bohai Sea. Special Oil and Gas Reservoirs, 2017, 24(5):78-84.
[23] 关新, 陈世加, 苏旺, 等.四川盆地西北部栖霞组碳酸盐岩储层特征及主控因素.岩性油气藏, 2018, 30(2):67-76. GUAN X, CHEN S J, SU W, et al. Carbonate reservoir characteristics and main controlling factors of Middle Permian Qixia Formation in NW Sichuan Basin. Lithologic Reservoirs, 2018, 30(2):67-76.
[24] 刘冬冬, 杨东旭, 张子亚, 等.基于常规测井和成像测井的致密储层裂缝识别方法:以准噶尔盆地吉木萨尔凹陷芦草沟组为例.岩性油气藏, 2019, 31(3):76-85. LIU D D, YANG D X, ZHANG Z Y, et al. Fracture identification for tight reservoirs by conventional and imaging logging:a case study of Permian Lucaogou Formation in Jimsar Sag, Junggar Basin. Lithologic Reservoirs, 2019, 31(3):76-85.
[25] 杨柳, 王钰.泛化误差的各种交叉验证估计方法综述.计算机应用研究, 2015, 32(5):1288-1289. YANG L, WANG Y. Survey for various cross-validation estimators of generalization error. Application Research of Computers, 2015, 32(5):1288-1289.
[26] KHALID S, KHALIL T, NASREEN S. A survey of feature selection and feature extraction techniques in machine leaing. 2014 Science and Information Conference. London, UK, 2014:372-378.
[27] RAMASUBRAMANIAN K, SINGH A. Machine learning using R. New York:Apress, 2017:181-184.
[1] ZHANG Tianze, WANG Hongjun, ZHANG Liangjie, ZHANG Wenqi, XIE Mingxian, LEI Ming, GUO Qiang, ZHANG Xuerui. Application of ray-path elastic impedance inversion in carbonate gas reservoir prediction of the right bank of Amu Darya River [J]. Lithologic Reservoirs, 2024, 36(6): 56-65.
[2] GUI Jinyong, LI Shengjun, GAO Jianhu, LIU Bingyang, GUO Xin. A random forests prediction method for gas saturation based on feature variable extension [J]. Lithologic Reservoirs, 2024, 36(2): 65-75.
[3] LI Changhai, ZHAO Lun, LIU Bo, ZHAO Wenqi, WANG Shuqin, LI Jianxin, ZHENG Tianyu, LI Weiqiang. Connectivity of fracture networks of Carboniferous carbonate reservoirs in North Truva Oilfield，eastern margin of Precaspian Basin [J]. Lithologic Reservoirs, 2024, 36(2): 113-123.
[4] CHEN Shuyang, HE Yunfeng, WANG Lixin, SHANG Haojie, YANG Xinrui, YIN Yanshu. Architecture characterization and 3D geological modeling of Ordovician carbonate reservoirs in Shunbei No. 1 fault zone，Tarim Basin [J]. Lithologic Reservoirs, 2024, 36(2): 124-135.
[5] YANG Wuyang, WEI Xinjian, LI Haishan. The past，present and future of intelligent geophysical technology [J]. Lithologic Reservoirs, 2024, 36(2): 170-188.
[6] SUN Hanxiao, XING Fengcun, XIE Wuren, QIAN Hongshan. Lithofacies paleogeography evolution of Late Ordovician in Sichuan Basin and its surrounding areas [J]. Lithologic Reservoirs, 2024, 36(1): 121-135.
[7] LUO Beiwei, YIN Jiquan, HU Guangcheng, CHEN Hua, KANG Jingcheng, XIAO Meng, ZHU Qiuying, DUAN Haigang. Characteristics and controlling factors of high porosity and permeability limestone reservoirs of Cretaceous Cenomanian in the western United Arab Emirates [J]. Lithologic Reservoirs, 2023, 35(6): 63-71.
[8] FAN Rui, LIU Hui, YANG Peiguang, SUN Xing, MA Hui, HAO Fei, ZHANG Shanshan. Identification of carbonate dissolution valleys filled with mudstones of Cretaceous in block A,Oman Basin [J]. Lithologic Reservoirs, 2023, 35(6): 72-81.
[9] GUO Haifeng, XIAO Kunye, CHENG Xiaodong, DU Yebo, DU Xudong, NI Guohui, LI Xianbing, JI Ran. Determination of effective permeability of granitic buried-hill fractured reservoirs in Bongor Basin,Chad [J]. Lithologic Reservoirs, 2023, 35(6): 117-126.
[10] LIU Yaming, WANG Dandan, TIAN Zuoji, ZHANG Zhiwei, WANG Tongkui, WANG Chaofeng, YANG Xiaofa, ZHOU Yubing. Characteristics and prediction methods of igneous rocks in complex carbonate oilfields in Santos Basin,Brazil [J]. Lithologic Reservoirs, 2023, 35(6): 127-137.
[11] TANG Yuzhe, CHAI Hui, WANG Hongjun, ZHANG Liangjie, CHEN Pengyu, ZHANG Wenqi, JIANG Lingzhi, PAN Xingming. Characteristics and new prediction methods of Jurassic subsalt carbonate reservoirs in the eastern right bank of Amu Darya,Central Asia [J]. Lithologic Reservoirs, 2023, 35(6): 147-158.
[12] WANG Xueke, WANG Zhen, JI Zhifeng, YIN Wei, JIANG Ren, HOU Yu, ZHANG Yiqiong. Hydrocarbon accumulation rules and exploration technologies of Carboniferous subsalt carbonate reservoirs in the eastern margin of Pre-Caspian Basin [J]. Lithologic Reservoirs, 2023, 35(6): 54-62.
[13] XU Xin, YANG Wuyang, ZHANG Kai, WEI Xinjian, ZHANG Xiangyang, LI Haishan. Optimization of 3D first-arrival traveltime tomography inversion [J]. Lithologic Reservoirs, 2023, 35(4): 79-89.
[14] WANG Jiangong, LI Jiangtao, LI Xiang, GAO Yanfang, ZHANG Ping, SUN Xiujian, BAI Yadong, ZUO Mingtao. Differences and controlling factors of lithofacies assemblages of Cenozoic lacustrine microbial carbonate rocks in western Qaidam Basin [J]. Lithologic Reservoirs, 2023, 35(3): 1-17.
[15] SONG Xingguo, CHEN Shi, YANG Minghui, XIE Zhou, KANG Pengfei, LI Ting, CHEN Jiuzhou, PENG Zijun. Development characteristics of FⅠ16 fault in Fuman oilfield of Tarim Basin and its influence on oil and gas distribution [J]. Lithologic Reservoirs, 2023, 35(3): 99-109.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] . [J]. Lithologic Reservoirs, 2022, 34(2): 0 .
[2] LI Zaiguang,LI Lin. Automatic mapping based on well data[J]. Lithologic Reservoirs, 2007, 19(2): 84 -89 .
[3] CHENG Yuhong,GUO Yanru,ZHENG Ximing,FANG Naizhen,MA Yuhu. The interpretation method and application effect determined by multiple seismic and logging factors[J]. Lithologic Reservoirs, 2007, 19(2): 97 -101 .
[4] LIU Juntian,JIN Zhenjia,LI Zaiguang,TAN Xinping,GUO Lin,WANG Bo,LIU Yuxiang. Controlling factors for lithologic hydrocarbon reservoirs and petroleum prospecting target in Xiaocaohu area , Taibei Sag[J]. Lithologic Reservoirs, 2007, 19(3): 44 -47 .
[5] SHANG Changliang, FU Shouxian. Application of 3D seismic survey in loess tableland[J]. Lithologic Reservoirs, 2007, 19(3): 106 -110 .
[6] WANG Changyong, ZHENG Rongcai, WANG Jianguo, CAO Shaofang, Xiao Mingguo. Sedimentary characteristics and evolution of Badaowan Formation of Lower Jurassic in northwest margin of Junggar Basin[J]. Lithologic Reservoirs, 2008, 20(2): 37 -42 .
[7] WANG Ke1 LIU Xianyang, ZHAO Weiwei, SONG Jianghai, SHI Zhenfeng, XIANG Hui. Char acter istics and geological significance of seismites of Paleogene in Yangxin Subsag of J iyang Depr ession[J]. Lithologic Reservoirs, 2008, 20(2): 54 -59 .
[8] SUN Hongbin, ZHANG Fenglian. Structural-sedimentary evolution char acter istics of Paleogene in Liaohe Depr ession[J]. Lithologic Reservoirs, 2008, 20(2): 60 -65 .
[9] LI Chuanliang. Can uplift r esult in abnormal high pr essur e in formation?[J]. Lithologic Reservoirs, 2008, 20(2): 124 -126 .
[10] WEI Qinlian，ZHENG Rongcai，XIAO Ling，MA Guofu，DOU Shijie，TIAN Baozhong. Study on horizontal heterogeneity in Serie Inferiere of Triassic in 438b block , Algeria[J]. Lithologic Reservoirs, 2009, 21(2): 24 -28 .
TRENDMD:

Copyright © 2018 Lithologic Reservoirs

Support by Beijing Magtech support@magtech.com.cn