油气藏评价与开发 >
2023 , Vol. 13 >Issue 4: 525 - 536
DOI: https://doi.org/10.13809/j.cnki.cn32-1825/te.2023.04.015
基于机器学习的鄂尔多斯盆地陇东地区长7段岩相测井识别方法
收稿日期: 2022-06-20
网络出版日期: 2023-09-01
基金资助
中国石油天然气集团有限公司科学研究与技术开发项目“测井核心装备与软件平台研制”(2021DJ3903)
Well-log lithofacies classification based on machine learning for Chang-7 member in Longdong area of Ordos Basin
Received date: 2022-06-20
Online published: 2023-09-01
岩相分析是储层评价的基础，受取心数量和成本的影响，针对未取心井利用测井资料开展岩相识别工作至关重要。根据岩心薄片鉴定结果，并结合成像测井资料将鄂尔多斯盆地陇东地区长7段岩相划分为6类。在岩心标定的基础上，对各类岩相的测井响应特征进行总结，建立该研究区基于常规测井曲线的岩相识别模式，结合机器学习算法开展岩相的自动识别。由于传统的分类算法受岩相样本不均衡的影响较大，对比多种不均衡数据分类算法在该地区的应用效果发现，集成学习Bagging算法通过组合多个基分类器，极大地改善了各类岩相的分类性能，并将该地区岩相的整体识别精度提升了20 %。据地区应用效果显示，单井识别精度可达84.33 %，具有较好的适用性。
谌丽 , 王才志 , 宁从前 , 刘英明 , 王浩 . 基于机器学习的鄂尔多斯盆地陇东地区长7段岩相测井识别方法[J]. 油气藏评价与开发, 2023 , 13(4) : 525 -536 . DOI: 10.13809/j.cnki.cn32-1825/te.2023.04.015
Lithofacies analysis serves as the foundation for reservoir evaluation. However, due to the limited coring quantity and cost constraints, identifying lithofacies using logging data for uncored wells becomes crucial. In the Longdong area of the Ordos Basin, the lithofacies of the Chang-7 member have been classified into six types dependent on core identification results and imaging logging data. Based on core calibration, the logging response characteristics of different lithofacies were summarized, leading to the establishment of the lithofacies recognition mode using conventional logging curve. To achieve automatic lithofacies recognition in the study area, machine learning algorithms were employed. The traditional classification algorithms were affected significantly by the unbalanced sample. After comparing the application effects of different unbalanced data classification algorithm in the region, it’s found that bagging algorithm of ensemble learning notably improved the classification performance of all lithofacies by combining multiple classifiers. As a result, the overall lithofacies identification precision of this region has been improved by 20 %. According to the regional application results, the identification accuracy of single well can reach 84.33 %, demonstrating its practical applicability and effectiveness.
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