基于支持向量机(SVM)的不同咸化程度烃源岩总有机碳含量预测模型

初勇志; 刘成林; 太万雪; 阳宏

doi:10.11781/sysydz202204739

基于支持向量机(SVM)的不同咸化程度烃源岩总有机碳含量预测模型

doi: 10.11781/sysydz202204739

初勇志^{1, 2,},
刘成林^{1, 2, ,},
太万雪^{1, 2},
阳宏^{1, 2}

1.
中国石油大学地球科学学院, 北京 102249
2.
中国石油大学(北京) 地球科学学院, 北京 102249

基金项目:

国家自然科学基金面上项目“咸化湖盆条件下盐类对地层超压的作用机制研究” 41872127

详细信息

作者简介:
初勇志(2000—), 男, 从事油气成藏和资源评价研究。E-mail: chuyongzhi18@163.com

通讯作者:
刘成林(1970—), 男, 博士, 教授, 博士生导师, 从事油气资源评价与非常规油气地质研究。E-mail: liucl@cup.edu.cn

中图分类号: TE122.1
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- 被引次数: 0
出版历程
- 收稿日期: 2021-12-12
- 修回日期: 2022-07-01
- 刊出日期: 2022-07-28

Prediction model of TOC contents in source rocks with different salinity degrees based on Support Vector Machine (SVM)

CHU Yongzhi^{1, 2
,},
LIU Chenglin^{1, 2
, ,},
TAI Wanxue^{1, 2},
YANG Hong^{1, 2}

1.
State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing 102249, China
2.
College of Geosciences, China University of Petroleum (Beijing), Beijing 102249, China

摘要

摘要: 总有机碳含量(TOC)作为评价烃源岩有机质丰度的重要参数，其精确预测对油气勘探开发具有重要意义。目前总有机碳含量预测以ΔlogR方法、多元回归分析等基于统计分析的方法为主，存在泛化能力弱、主观性强等问题。机器学习方法的引入，可有效解决这类非稳定性、非线性、高复杂性的问题，但当下的研究仍停留在方法的比较与选取层面，没有对优良模型进行深入分析并检验其适用性。采用应用效果更好的支持向量机模型进行总有机碳含量预测，选取渤海湾盆地渤中凹陷古近系东营组淡水湖相和柴达木盆地西部狮子沟地区古近系咸化湖相烃源岩作为研究对象，对模型的效果进行检验与对比。通过相关性和XGBoost特征重要性分析，选定声波时差(DT)、体积密度(DEN)、自然电位(SP)、自然伽马(GR)、深度等作为输入层，以总有机碳含量作为输出层，确立SVM烃源岩总有机碳含量预测模型。研究结果表明，模型在应用至差异较大的沉积环境时具有很强的泛化能力以适应不同地区的地质特征；测井曲线对于烃源岩有机质丰度的敏感性由于沉积环境存在差异而有所区别。该模型在渤海湾盆地淡水湖相区域的应用中相关性更高，误差更小。
- 总有机碳含量预测 /
- 测井资料 /
- 支持向量机 /
- 渤海湾盆地 /
- 柴达木盆地
Abstract: The total organic carbon (TOC) content is an important parameter for the evaluation of abundance of organic matter in source rocks, and its predicting accuracy is of great significance to oil and gas exploration and development. At present, TOC prediction is mainly based on statistical analysis methods such as ΔlogR method and multiple regression analysis, problems such as weak generalization ability and strong subjectivity exist. The introduction of machine learning methods can effectively solve these problems of instability, nonlinearity, and high complexity. However, current research remains at the level of method comparison and selection with no indepth analysis of good models and their applicability. In this paper, a Support Vector Machine (SVM) model with better application effects was used to predict TOC contents of source rocks with different salinity degrees. As source rocks of freshwater lacustrine facies, the Paleogene Dongying Formation in the Bozhong Sag of Bohai Bay Basin and Paleogene source rocks in the Shizigou area of the western Qaidam Basin as saline lacustrine facies source rocks were selected to test and compare the effectiveness of the model. Through correlation analysis and XGBoost feature importance analysis, the logging sonic differential time (DT), volume density (DEN), spontaneous potential (SP), Gamma ray (GR) and depth were selected as the input layer, while the TOC was used as the output layer to establish a TOC prediction model based on SVM. Results show a strong generalization ability when applied to different sedimentary environments. It can adapt to the geological characteristics of different regions. The sensitivity of logging curves to the abundance of organic matter in source rocks varies in different sedimentary environments, which makes the model more relevant when applying to the fresh water lacustrine facies area in the Bohai Bay Basin.
- TOC prediction /
- logging data /
- Support Vector Machine /
- Bohai Bay Basin /
- Qaidam Basin

HTML全文

图 1 渤海湾盆地渤中凹陷构造位置及新生界地层划分

据文献[11]，有修改。

Figure 1. Tectonic location and stratigraphic division of Cenozoic, Bozhong Sag, Bohai Bay Basin

下载: 全尺寸图片幻灯片

图 2 柴达木盆地狮子沟背斜构造位置及新生界地层分布

据文献[21], 有修改。

Figure 2. Distribution of Cenozioc in Shizigou anticline and its tectonic location, Qaidam Basin

下载: 全尺寸图片幻灯片

图 3 渤海湾盆地渤中凹陷和柴达木盆地狮子沟地区实测总有机碳含量与测井参数相关图

Figure 3. Measured TOC contents and logging parameters of samples from Bozhong Sag of Bohai Bay Basin and Shizigou area of Qaidam Basin

下载: 全尺寸图片幻灯片

图 4 渤海湾盆地渤中凹陷和柴达木盆地狮子沟地区总有机碳含量实测和预测数据相关性

Figure 4. Correlations of measured and predicted TOC contents of samples from Bozhong Sag of Bohai Bay Basin and Shizigou area of Qaidam Basin

下载: 全尺寸图片幻灯片

图 5 渤海湾盆地渤中凹陷烃源岩总有机碳含量预测模型BZ13-2-5井单井对比

Figure 5. Single well comparison of source rock TOC prediction model in Bozhong Sag, Bohai Bay Basin

下载: 全尺寸图片幻灯片

表 1 渤海湾盆地渤中凹陷烃源岩样品不同核函数预测效果对比

Table 1. Comparison of forecasting effects of different core functions applied on source rock samples from Bozhong Sag, Bohai Bay Basin

预测效果线性核函数多项式核函数高斯核函数

R² 0.95 0.53 0.92

MSE 0.04 0.41 0.06

MAE 0.17 0.46 0.21

下载: 导出CSV

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