Quantitative Characterization of Log Facies and Depositional Model of Mixed Siliciclastic-Carbonate Rocks Based on Fisher Discriminant: A Case Study on Daluhu Oilfield, Bohai Bay Basin
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摘要: 针对渤海湾盆地大芦湖油田西部沙四纯上亚段及沙三下亚段混积岩储层岩性复杂、非均质性强导致的岩相识别困难与沉积规律认识不足，本研究综合利用钻录井、测井及地震资料，在构建等时地层格架的基础上，应用Fisher判别分析进行混积岩测井相定量表征，并综合揭示了混积岩沉积演化规律.结果表明：（1）基于Fisher判别分析，优选自然伽马（GR）、声波时差（AC）、体积密度（DEN）和补偿中子孔隙度（CNL）测井曲线，分序列建立砂‒泥和灰‒泥系统岩性定量识别模型，有效解决了灰岩与砂岩测井响应重叠问题，显著提升了岩性识别精度，砂‒泥系统验证集岩性识别正确率为79.5%，粉砂岩预测精度为95%；灰‒泥系统回判总正确率达84.5%.（2）沙四纯上亚段高位体系域发育浅湖‒半深湖相，岩性为灰质页岩与泥岩互层，受古水深和盐度的控制，有机质呈中西部富集和东南部局部发育的格局，集中在0.8%~1.9%；沙三下亚段低位和湖扩体系域以半深湖相为主，有机质北部和南部富集，TOC含量集中在0.9%~2.3%；沙三下亚段HST发育辫状河三角洲‒半深湖复合沉积体系，盆地边缘发育三角洲前缘粉砂岩相，致有机质保存条件变差，洼陷中心以半深湖相沉积为主，富有机质泥质页岩占优.（3）建立四类岩相垂向和平面组合模式：垂向组合为厚层砂夹薄泥型、厚层泥夹薄砂型、厚层灰质页岩夹薄泥型和厚层泥夹薄灰质页岩型.平面上，混积岩沉积体系西部及西南部陡岸发育辫状河三角洲与近岸水下扇复合体，低位和高位体系域坡折带之下的斜坡带和湖盆中发育滑塌成因浊积岩体，东部及东北部缓坡区发育半深湖和深湖环境中灰质为主的沉积相，泥质页岩和灰质页岩相向湖盆中心迁移.本研究为湖相混积岩测井相预测提供了定量化理论依据，为富含有机质烃源岩预测、细粒储层类型预测、沉积规律以及油气勘探研究提供指导.Abstract: The lithological complexity and strong heterogeneity of the mixed siliciclastic and carbonate reservoirs result in difficulties in lithofacies identification and insufficient understanding of depositional patterns in the upper Shahejie 4 Member and lower Shahejie 3 Member in the western Daluhu oilfield, Bohai Bay basin. Integrating drilling, logging, and seismic data, Fisher discriminant analysis was applied for quantitative characterization of mixed siliciclastic and carbonate lithofacies from well logs, and the depositional evolution of mixed sediments was comprehensively revealed, on the basis of a chronostratigraphic framework. The results are as follows. (1) Based on Fisher discriminant analysis, quantitative lithology identification models for sand-mudstone and limestone-mudstone systems were established within subsequences using optimally selected natural gamma ray (GR), Sonic transit time (AC), density (DEN) and compensated neutron (CNL) logging curves, effectively addressing the problem of overlapping log responses between limestone and sandstone and significantly improving lithology identification accuracy. For the sand-mudstone system, the lithology identification accuracy in the validation set was 79.5%, with a siltstone prediction accuracy of 95%; for the limestone-mudstone system, the overall back-prediction accuracy reached 84.5%. (2) The highstand systems tract (HST) of the upper Shahejie 4 Member is dominated by shallow-lake to semi-deep-lake facies, consisting of interbedded calcareous shale and mudstone. Controlled by paleo-water depth and salinity, organic matter is enriched in the central-western and locally in the southeastern parts, with contents mainly between 0.8%-1.9%. The lowstand and lake-expansion systems tracts of the lower Shahejie 3 Member are dominated by semi-deep-lake facies, with organic matter enriched in the northern and southern areas and TOC content concentrated at 0.9%-2.3%. The HST of the lower Shahejie 3 Member developed a braided-river delta-semi-deep lake composite depositional system, with delta-front siltstone facies developed along the basin margins, resulting in poorer organic matter preservation, whereas the sag center was dominated by semi-deep lacustrine deposits, with organic-rich argillaceous shale being predominant. (3) Four types of vertical lithofacies association models were established: thick-bedded sandstone interbedded with thin mudstone, thick-bedded mudstone interbedded with thin sandstone, thick-bedded calcareous shale interbedded with thin mudstone, and thick-bedded mudstone interbedded with thin calcareous shale. In plan view, the mixed siliciclastic-carbonate depositional system is characterized by braided-river delta and nearshore subaqueous fan complexes along the western and southwestern steep margins. Slump-induced turbidite bodies developed in the slope zones and lake basin below the slope-break belts of the lowstand and highst and systems tracts. In the eastern and northeastern gentle-slope areas, calcareous-dominated sedimentary facies developed in semi-deep to deep lacustrine environments, while muddy shale and calcareous shale facies shifted toward the basin center. This study provides a quantitative theoretical basis for lacustrine mixed lithofacies prediction from well logs, offering guidance for organic-rich source rock and reservoir prediction, depositional pattern analysis, and oil and gas exploration.
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图 1 研究区渤海湾盆地大芦湖油田西部位置示意(Wang et al., 2017; Lu et al., 2020; 林中凯等, 2023)
Fig. 1. Schematic diagram of location of study area-western Daluhu oilfield in the Bohai Bay basin (Wang et al., 2017; Lu et al., 2020; Lin et al., 2023)
图 2 关键层序界面测井-地震响应特征
a. FJF179井综合柱状图; b. FJF179井地震剖面响应
Fig. 2. Logging-seismic response characteristics of key sequence boundaries
图 3 依据FJF-Y1井建立TOC预测模型
a.沙三下亚段测井幅度差与实测TOC值的关系；b. 沙四纯上亚段测井幅度差与实测TOC值的关系；c. 沙三下亚段和沙四纯上亚段实测和预测TOC测井综合柱状图
Fig. 3. TOC prediction model established according to well FJF-Y1
图 4 依据矿物组分岩相划分方案
Fig. 4. Ternary diagram of lithofacies classification based on mineral components
图 5 五类岩相与不同测井曲线关系交会图
Fig. 5. Crossplot of the relationship between 5 lithofacies and different logging curves
图 8 GQF-X184井和ZLG-X86井Fisher判别模型预测岩性与岩心岩性对比
Fig. 8. Comparison between lithologies predicted by the Fisher discriminant model and core-derived lithologies in wells GQF-X184 and ZLG-X86
图 9 岩相连井剖面
a. GQF-X190-GQF172-ZLG-X86-ZLG92;b. ZLG-X98-ZLG88-ZLG898-ZLG897
Fig. 9. Lithofacies correlation profile
图 11 研究区各体系域沉积相展布模式
a. 沙四纯上亚段HST岩相分布; b. 沙三下亚段LST+EST岩相分布; c. 沙三下亚段HST岩相分布
Fig. 11. Spatial distribution patterns of depositional facies in the study area by systems tract
表 1 砂‒泥系统训练集各岩性回判准确率统计
Table 1. Statistics of the accuracy of lithological inversion in sand-mud system training set
分区 岩性 泥质页岩 粉（细）砂岩 总计 项目 计数 概率(%) 计数 概率(%) 计数 概率(%) 正确 86 97.7 31 96.9 117 97.5 错误 2 2.3 1 3.1 3 2.5 总计 88 100 32 100 120 100 
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