Fracture Classification-Grading Prediction Technology and Application in Carbonate Reservoir Rocks: A Case Study from Tahe Oilfield, Tarim Basin
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摘要: 碳酸盐岩储层作为全球油气资源的重要载体，其内部溶洞和断裂系统的发育特征直接影响油气的储集与运移能力.针对深层-超深层碳酸盐岩储层中多尺度断裂预测的难题，研究以塔里木盆地塔河油田奥陶系碳酸盐岩为例，提出了一种基于地震波场特征分析的分类分级断裂预测技术.通过三维正演模拟揭示了规模缝洞体对常规断裂预测属性（如相干、最大似然）的干扰机制，发现缝洞体边界的“串珠状”反射异常会导致断裂假连通和归位偏差.基于断裂的尺度与溶蚀特征差异，将研究区断裂系统划分为大尺度破碎-溶蚀断裂（> 20 m）、中尺度弱-未溶蚀断裂（10~20 m）和小尺度裂缝（< 10 m），并分别开发了针对性的预测方法：针对大尺度断裂，提出基于梯度结构张量薄化的断裂归位技术，有效克服溶洞异常边界的干扰；针对中尺度断裂，结合AFE相干加强属性与U⁃Net深度学习算法，显著提升了断裂纵向连续性；针对小尺度裂缝，利用Likelihood属性与构造导向滤波实现弱反射信号的精准提取.进一步通过深度前馈神经网络（deep feedforward neural network，DFNN）融合多尺度断裂属性及钻井漏失数据，构建了井控多属性融合模型.应用结果表明，该技术体系在塔河油田复杂缝洞区实现了断裂系统的全尺度刻画，大尺度走滑断裂呈NNE⁃NNW向共轭分布，中尺度断裂形成花状构造，小尺度裂缝密集发育于断裂东侧主动盘.本研究为深层碳酸盐岩储层断裂预测提供了新的技术思路，对同类油气藏的勘探开发具有重要参考价值.Abstract: Carbonate reservoirs are important carriers of global oil and gas resources. The development characteristics of their internal karst caves and fault systems directly affect the storage and migration capacity of oil and gas. In order to solve the problem of multi-scale fault prediction in deep and ultra-deep carbonate reservoirs, this study takes the Ordovician carbonate rocks in the Tahe oilfield in the Tarim basin as an example and proposes a classification and grading fault prediction technology based on seismic wave field characteristic analysis. The interference mechanism of large-scale fracture-cavity bodies on conventional fault prediction attributes (such as coherence and maximum likelihood) is revealed through three-dimensional forward simulation, and it is found that the "beaded" reflection anomaly at the boundary of the fracture-cavity body will lead to false connectivity of faults and relocation deviation. Based on the differences in the scale and dissolution characteristics of the faults, the fault system in the study area is divided into large-scale broken-dissolution faults (> 20 m), medium-scale weak-undissolved faults (10-20 m) and small-scale fractures (< 10 m), and targeted prediction methods are developed for each of them: for large-scale faults, a fracture retrieval technology based on gradient structural tensor thinning is proposed to effectively overcome the interference of abnormal boundaries of karst caves; for medium-scale faults, the longitudinal continuity of the faults is significantly improved by combining AFE coherent enhancement attributes with U-Net deep learning algorithm; for small-scale fractures, the Likelihood attribute and structural guidance filtering are used to accurately extract weak reflection signals. Further, a well control multi-attribute fusion model is constructed by fusing multi-scale fault attributes and drilling loss data through deep feed forward neural network (DFNN). The application results show that this technology system has achieved full-scale characterization of the fault system in the complex fracture-cave area of Tahe oilfield. Large-scale strike-slip faults are distributed in a conjugate NNE-NNW direction, medium-scale faults form flower-like structures, and small-scale fractures are densely developed on the active disk (east side) of the fault. This study provides a new technical approach for the prediction of deep carbonate reservoir faults and has important reference value for the exploration and development of similar oil and gas reservoirs.
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图 2 三维数值缝洞模型及能量、相干、似然属性平面图
a.不同尺度缝洞体模型；b.不同尺度缝洞体模型与能量叠合；c.三维模型的相干属性；d.三维模型相干、似然属性<
Fig. 2. 3D numerical fracture⁃cavity model and planar maps of energy, coherence, and likelihood attributes
图 3 常规相干属性与张量薄化剖面及平面对比
a.常规相干断裂检测与原始剖面叠合图；b.张量薄化断裂检测与原始剖面叠合图；c.常规相干断裂检测平面图；d.张量薄化断裂检测平面图
Fig. 3. Comparison of conventional coherence attributes and tensor thinning profiles/planar maps
图 5 AFE及机器学习的断裂检测效果剖面对比
a.断裂检测剖面（黑色）；b.基于机器学习的断裂预测剖面（红色、蓝色）
Fig. 5. Comparison of fracture detection results between AFE and machine learning in cross⁃sections
图 6 小尺度裂缝空间展布预测效果
Fig. 6. Prediction results of small⁃scale fracture spatial distribution
图 7 多属性融合数据体.
a.方差体；b.AFE相干加强属性体；c.张量薄化体；d.U⁃Net架构下深度学习体；e.深度前馈神经网络多属性分类分级融合体
Fig. 7. Multi⁃attribute fusion data volume
图 8 典型断裂深度偏移剖面及分类分级预测、精细相干与原始地震剖面叠合图
a.典型断裂深度偏移剖面；b.分类分级预测与原始地震剖面叠合图；c.精细相干与原始地震剖面叠合图
Fig. 8. Depth⁃migrated profiles of typical fractures with superimposed maps of classification/prediction, refined coherence, and original seismic sections.
图 9 分类分级预测与精细相干效果平面对比
a.精细相干（黑色）+能量（红黄绿）；b.分类分级断裂（黑色）+能量（红、绿色）
Fig. 9. Planar comparison of classification/grading prediction and refined coherence effects
图 10 研究区不同层面大-中尺度断裂几何样式分布特征与断层发育空间模式
a~d.红色线段代表大尺度断裂，黄、绿、蓝色分别代表不同层位中尺度断裂，灰色代表小尺度断裂
Fig. 10. Distribution characteristics of large⁃to⁃medium⁃scale fracture geometric patterns and spatial development models of faults at different horizons in the study area
图 11 塔河地区大尺度、中尺度分级分类断裂识别代表性走滑构造样式
暗色为识别出的大尺度断裂，浅灰色代表识别中的中尺度断裂.a.正花状构造；b.负花状构造；c.直立状构造
Fig. 11. Representative strike⁃slip structural styles of large⁃scale and medium⁃scale classified fractures identified in the Tahe area
图 12 研究区分级分类地震属性融合识别断裂和裂隙不同平面分布特征
a.T80界面；b.T78界面；c.T76界面解释；d. T74界面
Fig. 12. Planar distribution characteristics of fractures and fissures identified through hierarchical classification and seismic attribute fusion in the study area
图 13 不同尺度裂缝地震识别效果展示
b.表示图c的位置；平面c和剖面d断裂东侧裂隙数量明显高于西侧
Fig. 13. Demonstration of different scale fracture seismic identification results
表 1 塔河油田碳酸盐岩断裂分类分级预测技术
Table 1. Classification and grading prediction technology of carbonate fractures in Tahe oilfield
断裂类型 断距尺度 典型剖面 波场特征 预测技术 大尺度
破碎-溶蚀断裂大于20 m 
宽度40 m、100 m杂乱强反射 张量薄化 中尺度弱-未溶蚀断裂 10~20 m 
断距20 m、15 m、10 m同相轴错断、扰动 AFE+机器学习预测 小尺度弱-未溶蚀断裂-裂缝 小于10 m 
叠前各向速度、能量差异和高灵敏度弱信号增强 叠前各向异性预测和
Likelihood属性
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