Optimization of TOC well logging prediction models and their application to source rock evaluation in the Shanan Sag of Bohai Sea
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摘要:
针对渤海海域沙南凹陷油气勘探程度低，烃源岩样品取芯少，连续性差，烃源岩研究薄弱及勘探潜力争议较大等问题，基于沙南凹陷内6口单井的测井资料及泥岩样品实测TOC数据，分别应用多元线性回归法和改进的ΔlgR法分层段建立沙南凹陷东三段、沙一二段、沙三段烃源岩TOC定量预测模型，对比优选后发现改进的ΔlgR法和多元线性回归法中的四参数线性回归法可有效地预测沙南凹陷单井湖相烃源岩TOC纵向分布，并以改进的ΔlgR法最优；选取改进的ΔlgR法预测TOC曲线对两口代表性单井烃源岩进行评价，结果表明沙三段具有烃源岩占比地层厚度高、TOC高、好—优质烃源岩厚度大等特点，为凹陷主力烃源岩系，沙南凹陷勘探前景广阔。
Abstract:Oil and gas exploration degree of the Shanan Sag of the Bohai Basin is relatively low due to lack of drilling cores and source rock samples. TOC logging prediction models for source rocks are established in this paper for the Members of E3d3、E3s1+2、E2s3 by the multiple linear regression method and the improved ΔlgR method based on the logging data and limited measured TOC data of the mudstone samples collected from six single wells. After comparison and optimization, it is found that both the improved ΔlgR method and the four- parameter linear regression method are effective to predict the TOC vertical distribution pattern of the lacustrine source rocks in single wells in the Sag and the improved ΔlgR method is obviously more efficient. Therefore, predicted TOC curve of improved ΔlgR is selected to evaluate the source rocks for two representative single wells. The result shows that the E2s3 is characterized by high ratio of source rocks, high TOC content and large thickness of high-quality source rocks. It is the main source rock of the study area and the Shanan sag must have good prospect for oil and gas exploration. The research results are helpful to the evaluation of source rocks in the Shanan sag and will provide solid scientific basis for further understanding of oil and gas exploration potential in the study area.
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Key words:
- source rocks /
- organic carbon content /
- logging evaluation /
- Shanan Sag
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表 1 沙南凹陷烃源岩TOC测井多元线性回归法定量预测模型优选
Table 1. Quantitative TOC logging prediction models by multivariate linear regression method for source rocks in Shanan Sag
层位 测井参数 TOC定量预测模型 相关系数 东三段 单参数 声波时差（AC） TOC=0.063AC−4.328 0.63 电阻率（RT） TOC=0.003RT+2.222 0.016 密度（DEN） TOC=−6.56DEN+18.309 0.502 中子（CNL） TOC=0.105CNL−0.953 0.465 双参数 AC, CNL TOC=0.076AC−0.037CNL−4.604 0.636 三参数 AC, RT, CNL TOC=0.09AC+0.046RT−0.052CNL−5.756 0.682 四参数 AC, RT, CNL, DEN TOC=(0.214AC+0.091RT−0.183CNL−5.928)/DEN−2.414 0.695 沙一二段 单参数 声波时差（AC） TOC=0.161AC−13.484 0.816 电阻率（RT） TOC=0.726RT−0.297 0.611 密度（DEN） TOC=−8.487DEN+23.28 0.38 中子（CNL） TOC=0.082CNL+0.136 0.23 双参数 AC, RT TOC=0.134AC+0.381RT−12.294 0.862 三参数 AC, RT, CNL TOC=0.141AC+0.373RT−0.031CNL−12.069 0.864 四参数 AC, RT, CNL, DEN TOC=(0.335AC+0.942RT−0.067CNL−36.258)/DEN+2.992 0.865 沙三段 单参数 声波时差（AC） TOC=0.084AC−5.363 0.685 电阻率（RT） TOC=0.154RT+1.484 0.42 密度（DEN） TOC=−17.185DEN+45.894 0.686 中子（CNL） TOC=0.162CNL−1.458 0.625 双参数 AC, CNL TOC=0.06AC+0.068CNL−4.806 0.708 三参数 AC, RT, CNL TOC=0.101AC+0.198RT−0.028CNL−7.653 0.845 四参数 AC, RT, CNL, DEN TOC=(0.243AC+0.489RT−0.068CNL−20.204)/DEN+0.77 0.847 
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表 2 沙南凹陷烃源岩TOC测井ΔlgR法定量预测模型
Table 2. Quantitative TOC logging prediction models by ΔlgR method of source rocks in Shanan Sag
层位 TOC定量预测模型 相关系数 东三段 TOC=（0.206AC+3.795lgRT−17.645）/DEN 0.767 沙一二段 TOC=（0.325AC+6.94lgRT−30.115）/DEN 0.835 沙三段 TOC=（0.215AC+7.648lgRT−19.825）/DEN 0.877
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表 3 沙南凹陷烃源岩实测TOC值与测井计算TOC值误差对比分析
Table 3. Comparison of errors between measured TOC from coresand predicted TOC by different means
预测方法 层位 平均TOC/% 平均绝对误差* 平均相对误差**/% 数据点/个 实测 测井计算 四参数线性回归法 东三段 2.232 2.221 0.560 25.1 21 沙一二段 2.599 2.606 0.898 34.6 13 沙三段 2.535 2.519 0.579 22.8 21 改进的ΔlgR法 东三段 2.232 2.234 0.516 23.1 21 沙一二段 2.599 2.580 1.048 40.3 13 沙三段 2.535 2.545 0.493 19.4 21 *平均绝对误差= 
/样品数；
**平均相对误差=平均绝对误差/平均实测TOC。
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表 4 CFD23-1-1井、CFD16-3-1井烃源岩评价结果统计
Table 4. Statistical table of source rock evaluation results of Well CFD23-1-1 and Well CFD16-3-1
井号 层位 不同级别烃源岩厚度/m 地层厚度/m 烃源岩厚度占地层厚度百分比/% 差 中 好 优 合计 CFD23-1-1 东三段 41 78 20 67 206 255 80.78 沙一二段 0 12 20.5 86.5 119 133 89.47 沙三段 0 12 76 168 256 283 90.46 CFD16-3-1 东三段 0 97.5 45 0 142.5 296 48.14 沙一二段 12 37 0 0 49 170 28.82 沙三段 49 59 148 96.5 352.5 551.5 63.92
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