[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[J].测井技术,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算法的测井岩性识别模型()
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《测井技术》[ISSN:1004-1338/CN:61-1223/TE]

卷:
第42卷
期数:
2018年04期
页码:
395-400
栏目:
处理解释
出版日期:
2018-09-05

文章信息/Info

Title:
Lithology Identification Model by Well Logging Based on Boosting Tree Algorithm
文章编号:
1004-1338(2018)04-0395-06
作者:
江凯123 王守东123 胡永静4 浦世照4 段航123 王政文1
1.中国石油大学(北京)地球物理与信息工程学院, 北京 102249; 2.油气资源与探测国家重点 实验室, 北京 102249; 3.海洋石油勘探国家工程实验室, 北京 102249; 4.中国石油新疆油田 公司勘探开发研究院, 新疆 克拉玛依 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
关键词:
测井解释 岩性识别 人工智能 机器学习 Boosting Tree
Keywords:
Keywords: log interpretation lithology identification artificial intelligence machine learning Boosting Tree
分类号:
P631.84
DOI:
10.16489/j.issn.1004-1338.2018.04.005
文献标志码:
A
摘要:
使用Boosting Tree算法,以录井资料和测井资料为基础,优选出自然伽马、自然电位、冲洗带电阻率、侵入带电阻率、原状地层电阻率、密度、补偿中子、声波时差8个对岩性敏感度较高的测井属性,建立岩性识别模型。使用该方法对玛北油田岩石类型齐全的6号井的目的层岩性进行识别,正确率达到89.1%,优于决策树、支持向量机(SVM)等传统的机器学习方法。使用Boosting Tree算法对岩性进行识别也为测井解释提供了新的思路。
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.

参考文献/References:

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备注/Memo

备注/Memo:
基金项目: 国家科技重大专项(2016ZX05024-001-004)资助 第一作者: 江凯,男,1993年生,研究方向为地震及测井解释的智能化方法。E-mail:tftyjk@hotmail.com 通讯作者: 王守东,男,1967年生,教授、博士生导师,从事地震资料数字处理方法研究。E-mail:ctlab@cup.edu.cn
更新日期/Last Update: 2018-09-05