基于XGBoost算法的复杂碳酸盐岩岩性测井识别

doi:10.12108/yxyqc.20200410

岩性油气藏 ›› 2020, Vol. 32 ›› Issue (4): 98–106.doi: 10.12108/yxyqc.20200410

基于XGBoost算法的复杂碳酸盐岩岩性测井识别

孙予舒^1,2, 黄芸³, 梁婷^1,2, 季汉成^1,2, 向鹏飞^1,2, 徐新蓉^1,2

1. 中国石油大学(北京)地球科学学院, 北京 102249;
2. 中国石油大学(北京)油气资源与探测国家重点实验室, 北京 102249;
3. 中国石油华北油田分公司勘探开发研究院, 河北任丘 062550

收稿日期:2019-09-12 修回日期:2019-11-29 出版日期:2020-08-01 发布日期:2020-06-16
通讯作者: 季汉成(1966-),男,博士,教授,主要从事沉积学、储层地质学、石油地质学方面的教学和研究工作。Email:jihancheng@vip.sina.com。 E-mail:jihancheng@vip.sina.com
作者简介:孙予舒(1994-),男,中国石油大学(北京)在读硕士研究生,研究方向为机器学习、沉积学及储层地质学。地址:(102249)北京市昌平区府学路18号。Email:sunyushu1022@163.com
基金资助:
中国石油天然气股份有限公司重大科技专项“华北油田持续有效稳产勘探开发关键技术研究与应用”（编号：2017E-15）和“冀中凹陷下古生界潜山及内幕优势储层成因、演化及分布特征研究”（编号：HBYT-YJY-2018-JS-274）联合资助

Identification of complex carbonate lithology by logging based on XGBoost algorithm

SUN Yushu^1,2, HUANG Yun³, LIANG Ting^1,2, JI Hancheng^1,2, XIANG Pengfei^1,2, XU Xinrong^1,2

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

摘要/Abstract

摘要： 碳酸盐岩储层在形成过程中受到多种因素的影响，储层岩性复杂多样，基于测井资料对碳酸盐岩岩性识别具有重要意义。为了解决传统的测井岩性识别方法和机器学习方法对于复杂碳酸盐岩岩性识别准确率不高的问题，以廊固凹陷北部奥陶系碳酸盐岩为例，将XGBoost算法应用于复杂碳酸盐岩岩性识别，并将模型的性能与决策树C4.5算法和支持向量机算法进行对比。结果表明，采用的XGBoost算法的岩性识别模型对研究区碳酸盐岩岩性识别的准确率达到了88.18%，相较于决策树C4.5算法和支持向量机算法准确率均提高了10%左右，且由于XGBoost算法采用多线程和分布式计算的方法，使得训练时间大大缩短。基于XGBoost算法建立的岩性识别模型能够有效地识别复杂碳酸盐岩岩性，为复杂碳酸盐岩岩性的测井识别提供了新的思路。

关键词: XGBoost算法, 机器学习, 碳酸盐岩, 岩性识别, 测井解释

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

中图分类号:

P618.13

孙予舒, 黄芸, 梁婷, 季汉成, 向鹏飞, 徐新蓉. 基于XGBoost算法的复杂碳酸盐岩岩性测井识别[J]. 岩性油气藏, 2020, 32(4): 98–106.

SUN Yushu, HUANG Yun, LIANG Ting, JI Hancheng, XIANG Pengfei, XU Xinrong. Identification of complex carbonate lithology by logging based on XGBoost algorithm[J]. Lithologic Reservoirs, 2020, 32(4): 98–106.

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基于XGBoost算法的复杂碳酸盐岩岩性测井识别

Identification of complex carbonate lithology by logging based on XGBoost algorithm

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