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        1. [1]江凱,王守東,胡永靜,等.基于Boosting Tree算法的測井巖性識別模型[J].測井技術,2018,42(04):395-400.[doi:10.16489/j.issn.1004-1338.2018.04.005]
           JIANG Kai,WANG Shoudong,HU Yongjing,et al.Lithology Identification Model by Well Logging Based on Boosting Tree Algorithm[J].WELL LOGGING TECHNOLOGY,2018,42(04):395-400.[doi:10.16489/j.issn.1004-1338.2018.04.005]
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          基于Boosting Tree算法的測井巖性識別模型()
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          《測井技術》[ISSN:1004-1338/CN:61-1223/TE]

          卷:
          第42卷
          期數:
          2018年04期
          頁碼:
          395-400
          欄目:
          處理解釋
          出版日期:
          2018-09-05

          文章信息/Info

          Title:
          Lithology Identification Model by Well Logging Based on Boosting Tree Algorithm
          文章編號:
          1004-1338(2018)04-0395-06
          作者:
          江凱123 王守東123 胡永靜4 浦世照4 段航123 王政文1
          1.中國石油大學(北京)地球物理與信息工程學院, 北京 102249; 2.油氣資源與探測國家重點 實驗室, 北京 102249; 3.海洋石油勘探國家工程實驗室, 北京 102249; 4.中國石油新疆油田 公司勘探開發研究院, 新疆 克拉瑪依 834000
          Author(s):
          JIANG Kai123 WANG Shoudong123 HU Yongjing 4 PU Shizhao4 DUAN Hang123 WANG Zhengwen1
          1.College of Geophysics and Information Engineering, China University of Petroleum(Beijing), Beijing 102249, China; 2. State Key Laboratory of Petroleum Resources and Prospecting, Beijing 102249, China; 3. National Engineering Laboratory for Offshore Oil Exploitation, Beijing 102249, China; 4. Exploration and Development Research Institute, PetroChina Xinjiang Oilfield Company, Karamay, Xinjiang 834000, China
          關鍵詞:
          測井解釋 巖性識別 人工智能 機器學習 Boosting Tree
          Keywords:
          Keywords: log interpretation lithology identification artificial intelligence machine learning Boosting Tree
          分類號:
          P631.84
          DOI:
          10.16489/j.issn.1004-1338.2018.04.005
          文獻標志碼:
          A
          摘要:
          使用Boosting Tree算法,以錄井資料和測井資料為基礎,優選出自然伽馬、自然電位、沖洗帶電阻率、侵入帶電阻率、原狀地層電阻率、密度、補償中子、聲波時差8個對巖性敏感度較高的測井屬性,建立巖性識別模型。使用該方法對瑪北油田巖石類型齊全的6號井的目的層巖性進行識別,正確率達到89.1%,優于決策樹、支持向量機(SVM)等傳統的機器學習方法。使用Boosting Tree算法對巖性進行識別也為測井解釋提供了新的思路。
          Abstract:
          Abstract: Using the Boosting Tree algorithm, and based on mud logging data and wireline logging data, the logging with high lithology sensitivity, including natural gamma, natural potential, flushed zone resistivity, invaded zone resistivity, undisturbed formation resistivity, density, compensated neutron logging and AC logging, are selected to establish lithology identification model. The developed method is used to identify the lithology of target zone of Well No.6 with complete rock types in Mabei Oilfield, and the correct rate reaches 89.1%, which is better than traditional machine learning methods such as decision tree and support vector machine(SVM). The identification of lithology using the Boosting Tree algorithm provides a new idea for logging interpretation.

          參考文獻/References:

          [1] SILVA A A, NETO I A L, MISSAGIA R M, et al. Artificial neural networks to support petrographic classification of carbonate-siliciclastic rocks using well logs and textural information [J]. Journal of Applied Geophysics, 2015, 117: 118-125. [2] 趙顯令, 王貴文, 周正龍, 等. 地球物理測井巖性解釋方法綜述 [J]. 地球物理學進展, 2015, 30(3): 1278-1287. [3] 洪有密. 測井原理與綜合解釋 [M]. 東營: 中國石油大學出版社, 2008. [4] 葉濤, 韋阿娟, 鄧輝, 等. 基于常規測井資料的火山巖巖性識別方法研究——以渤海海域中生界為例 [J]. 地球物理學進展, 2017, 32(4): 1842-1848. [5] 匡立春, 孫中春, 歐陽敏, 等. 吉木薩爾凹陷蘆草溝組復雜巖性致密油儲層測井巖性識別 [J]. 測井技術, 2013, 37(6): 638-642. [6] 范宜仁, 黃隆基, 代詩華. 交會圖技術在火山巖巖性與裂縫識別中的應用 [J]. 測井技術, 1999, 23(1): 53-56. [7] 黃布宙, 潘保芝. 松遼盆地北部深層火成巖測井響應特征及巖性劃分 [J]. 石油物探, 2001, 40(3): 42-47. [8] 張瑩, 潘保芝. 多種巖性分類方法在火山巖巖性識別中的應用 [J]. 測井技術, 2011, 35(5): 474-478. [9] 鐘儀華, 李榕. 基于主成分分析的最小二乘支持向量機巖性識別方法 [J]. 測井技術, 2009, 33(5): 425-429. [10] 朱怡翔, 石廣仁. 火山巖巖性的支持向量機識別 [J]. 石油學報, 2013, 34(2): 312-322. [11] 李洪奇, 郭海峰, 郭海敏, 等. 復雜儲層測井評價數據挖掘方法研究 [J]. 石油學報, 2009, 30(4): 542-549. [12] KONATE A A, PAN H, FANG S, et al. Capability of self-organizing map neural network in geophysical Log data classification: case study from the CCSD-MH [J]. Journal of Applied Geophysics, 2015, 118: 37-46. [13] 李洪奇, 譚鋒奇, 許長福, 等. 基于決策樹方法的礫巖油藏巖性識別 [J]. 測井技術, 2010, 34(1): 16-21. [14] 王振洲, 張春雷, 高世臣. 利用決策樹方法識別復雜碳酸鹽巖巖性——以蘇里格氣田蘇東41-33區塊為例 [J]. 油氣地質與采收率, 2017(6): 25-33. [15] FRIEDMAN J, HASTIE T, TIBSHIRANI R. Additive logistic regression: a statistical view of boosting(with discussion and a rejoinder by the authors)[J]. Annals of Statistics, 2000, 28(2): 337-374. [16] 周志華.機器學習 [M].北京:清華大學出版社, 2016. [17] FRIEDMAN J H. Greedy function approximation: a gradient boosting machine [J]. Annals of Statistics, 2001, 29(5): 1189-1232. [18] BISHOP C M. Pattern recognition and machine learning(information science and statistics)[M]. Springer-Verlag New York, Inc, 2006. [19] QUINLAN J R. C4.5: programs for machine learning [M]. Morgan Kaufmann Publishers Inc, 1993. [20] SCHAPIRE R E. A Brief Introduction to Boosting [C]∥ Sixteenth International Joint Conference on Artificial Intelligence. Morgan Kaufmann Publishers Inc, 1999: 1401-1406. [21] 王貴文, 孫中春, 付建偉, 等. 瑪北地區砂礫巖儲集層控制因素及測井評價方法 [J]. 新疆石油地質, 2015, 36(1): 8-13. [22] 張順存, 蔣歡, 張磊, 等. 準噶爾盆地瑪北地區三疊系百口泉組優質儲層成因分析 [J]. 沉積學報, 2014, 32(6): 1171-1180. [23] 鄒妞妞, 史基安, 張大權, 等. 準噶爾盆地西北緣瑪北地區百口泉組扇三角洲沉積模式 [J]. 沉積學報, 2015, 33(3): 607-615.

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          備注/Memo

          備注/Memo:
          基金項目: 國家科技重大專項(2016ZX05024-001-004)資助 第一作者: 江凱,男,1993年生,研究方向為地震及測井解釋的智能化方法。E-mail:[email protected] 通訊作者: 王守東,男,1967年生,教授、博士生導師,從事地震資料數字處理方法研究。E-mail:[email protected]
          更新日期/Last Update: 2018-09-05
          11选五开奖结果