Stochastic Fracture Simulation Based on Three-Dimensional Laser Scan Technology: a Case Study of Kuqa River Outcrop in Kuqa Depression
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摘要: 库车坳陷是塔里木盆地重要的天然气勘探开发目标区，其大北-克深地区含气层系下白垩统埋深超过7 500 m，岩性致密孔隙度平均为4.8%，但仍然获得高产工业气流，岩层内所发育的裂缝成为天然气高产的核心要素. 因此利用合理技术对裂缝数据进行有效表达是明确深层致密砂岩裂缝发育特征及空间分布规律的关键，是有效开发裂缝型气藏前提. 利用三维激光扫描能够获得大数据的优势，运用点数据拼接、去噪、切割处理方法，直接识别点云数据中的有效裂缝信息，并结合野外剖面实测获得的裂缝数据对所识别的裂缝信息进行修正处理，建立目标区域裂缝信息地质知识库. 确定目标区域裂缝发育的空间范围，裂缝密度发育规律，裂缝方位分布状态，为后期随机模拟确定约束条件. 在三维激光扫描点云处理数据的约束下，应用随机模拟技术的fisher函数能够较好的表现裂缝发育区域单条裂缝之间方位偏转现象，体现自然裂缝的随机发育特征. 利用裂缝开度信息，应用Oda计算方法对对每条裂缝的渗流能力进行了计算分析，定量确定裂缝发育区对围岩流体渗流影响的大小.库车坳陷库车河剖面计算结果显示：右侧裂缝簇导致的渗流带宽度可达3.1 m，延伸长度平均为2.6 m；中间区域所展现裂缝簇渗流带宽度1.2 m，延伸长度平均为1.4 m；左侧裂缝簇渗流带宽度约0.9 m，延伸长度仅仅为0.33 m左右，这与野外数据吻合. 与人工对比显示：数据处理面积是人工测量的25倍，所消费的时间仅仅是人工处理的1/10，大大提高了工作效率.由此可见裂缝簇内裂缝密集程度越高，对流体渗流影响就越大，流体在裂缝簇渗流扩展的宽度，影响的范围就越广，较好的表达了库车深层裂缝致密气藏的特征. 说明基于激光扫描技术的裂缝随机模拟技术能够真实的表达裂缝三维空间展布状态，以及对流体渗流的贡献.Abstract: The Kuqa Depression is an important natural gas exploration target area in the Tarim Basin. Although the gas-bearing layer from the lower Cretaceous in the Dabei-Keshen area is buried deeper than 7500 m, and the average lithologic tight porosity is 4.8%, the area is still getting high-yield industrial gas stream. Fractures developed in rock formations have become the key factor for high natural gas production. Therefore, using reasonable techniques to effectively express fractured atais the key to clarifying the development characteristics and spatial distribution off ractures in deep tight sandstone, and is the premise for the effective development off ractured gas reservoirs. The advantages of big data can be obtained by using 3D laser scanning. Using point data splicing, denoising, and cutting methods, directly identifying the effective fracture information in the point cloud data, and analyzing the identified fracture information in combination with the fracture data obtained from the field profile measurement are incredibly useful to establish a geological knowledge base of fracture information in the target area. Identifying the spatial extent of fracture development in the target area, the development law of fracture density, and the azimuthal distribution of fractures helps to determine the constraints for the later random simulation. Under the constraints of 3D laser scanning point cloud processing data, the fisher function using stochastic simulation technology can better represent the azimuthal deflection phenomenon between single fractures in the fracture development area, reflecting the random development characteristics of natural fractures. Using the fracture opening information, the Oda calculation method is used to calculate and analyze the seepage capacity of each fracture, and the influence of the fracture development area on the surrounding rock fluid seepage is quantitatively determined. The calculation results of the Kuqa River section in the Kuqa Depression show that the vadose zone caused by the fracture cluster on the right side can reach 3.1m in width and 2.6m in extension; The vadose zone of the fracture cluster in the middle area is 1.2m wide and the extension length is 1.4 m; The width of the fracture cluster vadose zone on the left is about 0.9m, and its extension length is only about 0.33m.Itisconsistentwithfielddata. It can be seen that the higher the density of fractures in the fracture cluster, the greater the impact on fluid vadose zone. The wider the width of the fluid vadose zone expansion in the fracture cluster, the scope of influence wider. This better expresses the characteristics of deep fractured tight gas reservoirs in Kuqa. It shows that the stochastic simulation technology of fractures based on laser scanning technology can truly express the three-dimensional spatial distribution of fractures and the contribution of luidflow through porous medium.
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图 3 库车坳陷裂缝发育状态
a. 露头剪切裂缝（穿砾）；b.岩心剪切裂缝（博孜104）；c.成像测井裂缝（大北202）
Fig. 3. Features of fractures in Kuqa Depression
图 10 库车河扫描剖面及效果图
a野外实际剖面；b扫描点位数据
Fig. 10. Scanning section and renderings of Kuqa River
图 12 点云数据拼接（下）与剖面特征（上）对比
Fig. 12. Comparison of point cloud datas stitching (bottom) and profile features (top)
图 14 库车河剖面点云数据切割处理的结果
Fig. 14. Processing results of point cloud datas of Kuqariver profile
图 15 库车河剖面三维激光扫描点云数据所提取裂缝空间展布
a. 裂缝空间分布；b. 结构面中裂缝的位置
Fig. 15. Spatial distribution of fractures extracted from 3D laser scanning point cloud data in Kuqariver profile
图 16 Fisher分布不同k值所表示裂缝簇内裂缝离散度
Fig. 16. Fracture dispersion within fracture cluster represented by different k values of Fisher distribution
图 17 三维激光扫描数据确定性约束下随机模拟的库车河剖面裂缝（白色为裂缝）
Fig. 17. Fractures in the Kuqa river profile under stochastic simulation under the deterministic constraints of 3D laser scanning data (white is cracks)
图 18 随机模拟库车河剖面不同裂缝簇所能影响的渗流宽度
Fig. 18. The seepage width affected by different fracture clusters in the stochastic simulation of Kuqariver profile
图 19 实测裂缝与模拟裂缝对比
a. 实测裂缝；b. 模拟裂缝；c. 裂缝方位极对比
Fig. 19. The comparative analysis of measured fractures and simulated fractures
表 1 模拟结果与剖面实测数据对比
Table 1. Comparison between simulation results and measured datas of profile
左侧（平均） 右侧（平均） 密度(m/m2) 长度(m) 方位(°) 面积
（m2）时间
（h）密度(m/m2) 长度(m) 方位(°) 面积
（m2）时间
（h）剖面实测 0.130 0.330 334 4 4 1.32 2.53 341 4 8 模拟计算 0.154 0.308 337 100 0.5 1.22 2.61 338 140 0.8 误差 15% 7% 0.9% - - 8% 3.06% 0.8% - - 
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