Generalized Δlog R model with spontaneous potential and its application in predicting total organ carbon content
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摘要: 为精准预测吐哈盆地中二叠统桃东沟群烃源岩总有机碳含量，基于研究区内多口钻井测井参数与有机质丰度之间的关系，对广义Δlog R模型进行拓展，提出了一种考虑自然电位广义Δlog R模型。研究发现：研究区内烃源岩与电阻率密度呈弱正相关，与自然电位、自然伽马和密度呈弱负相关，与中子相关性不太明显。通过与其他方法对比，优选出考虑自然电位的广义Δlog R模型的相关系数(R2)为0.804 2，平均误差率为15.58 %，是预测区内中二叠统桃东沟群烃源岩总有机碳含量的最佳方法。该成果将为研究区烃源岩测井评价等工作提供依据。Abstract: In order to accurately predict the total organic carbon content of Middle Permian taodonggou group source rocks in Turpan Hami basin, based on the analysis of the relationship between logging parameters of multiple wells and organic matter abundance in the study area, the generalized Δlog R model is extended, that is, a generalized Δlog R model with spontaneous potential is proposed. It is found that the source rock in the study area has a weak positive correlation with resistivity density, a weak negative correlation with spontaneous potential, spontaneous gamma and density, and little correlation with neutrons. Compared with other methods, the correlation coefficient (R2) of the generalized Δlog R model with spontaneous potential is 0.804 2 and the average error rate is 15.58 %, which is the best method to predict the total organic carbon content of the source rocks of the Middle Permian Taodonggou group in the area. The results provide a basis for logging evaluation of source rocks in the study area.
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Key words:
- Turpan Hami basin /
- source rock /
- Δlog R model /
- Taodonggou group
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图 1 吐哈盆地中二叠统地层沉积环境、构造分区及收集钻遇烃源岩钻井位置(修编自参考文献[22])
Figure 1. Sedimentary environment, structural division and drilling location of hydrocarbon source rocks encountered in the Middle Permian in Turpan Hami basin
图 2 研究区内烃源岩TOC与常规测井参数的关系
Figure 2. Relationship between TOC of source rocks and conventional logging parameters in the study area
图 3 传统Δlog R法与优化Δlog R法预测结果
Figure 3. Prediction results of traditional Δlog R method and optimized Δlog R method
图 4 三种广义Δlog R法的模型预测结果分析
Figure 4. Model prediction results of three generalized Δlog R methods
图 5 多元回归参数法预测结果分析
Figure 5. Prediction results of multiple regression parameter method
图 6 南湖1井桃东沟群烃源岩TOC含量测井预测剖面
Figure 6. TOC logging prediction profile of source rocks of Taodonggou group in Nanhu 1 well
表 1 研究区内钻井的ρ基、DT基与Ro值
Table 1. ρ基, Ro and DT基 value of drilling wells in the study area
井名 ρ基/(Ω·m) DT基/(μs·ft-1) Ro/% 南湖1井 17.438 71.803 0.62 艾参1井 54.054 75.832 0.68 鲁南1井 2.166 72.894 0.51 鲁东5井 13.830 73.817 0.52 玉东1井 12.506 69.075 0.57 
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表 2 三种广义Δlog R法的模型、相关系数及误差率
Table 2. Models, correlation coefficients and error rates of three generalized Δlog R methods
广义Δlog R法 模型 相关系数 平均误差率/% 参考文献[10] TOC=(-23.120 7lg GR+46.083 3)×Δlog R+1.388 2 0.278 9 27.38 考虑DEN TOC=(-12.191 6lg GR+4.656 8DEN+13.584 7)×Δlog R+0.979 2 0.517 3 23.58 考虑SP TOC=(-16.954 8lg GR+3.097 4DEN-0.103 3SP+30.228 8)×Δlog R+2.296 4 0.804 2 15.58
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表 3 现有预测TOC含量的成熟方法对比
Table 3. Comparison and optimization of existing mature methods for predicting TOC
方法 相关系数 平均误差率/% 传统Δlog R法 0.306 4 42.61 优化Δlog R法 0.426 6 39.21 广义Δlog R法 0.278 9 27.38 考虑DEN的Δlog R法 0.513 5 23.58 考虑SP的Δlog R法 0.804 2 15.58 多元回归参数法 0.523 7 19.83
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