Abstract:

The morphology of natural micro-fractures in shale reservoirs is a key factor controlling their fluid flow capacity and mechanical stability, while the microscopic distribution of minerals significantly influences the development characteristics of local micro-fractures. Accurately extracting the geometry of micro-fractures and establishing its relationship with mineral types and spatial distribution are essential for a deeper understanding of wellbore instability mechanisms in shale formations. However, due to the strong heterogeneity of the shale matrix, conventional threshold-based segmentation methods struggle to precisely distinguish micro-fractures from mineral boundaries, leading to considerable uncertainty in the extraction of fracture morphological parameters. To address this issue, this study proposes a TS-LSTM fracture extraction method based on scanning electron microscopy (SEM) images, which combines threshold segmentation with a long short-term memory neural network to achieve high-precision segmentation and completion of micro-fractures. Using the extracted fracture morphologies, the width and tortuosity of the fractures are quantitatively characterized. To quantify the mineral distribution around the fractures, different distances outward from the fracture boundaries are defined, and the area percentage of a specific mineral within each distance zone is designated as the threshold mineral percentage content. On this basis, correlation analysis is applied to investigate the statistical relationships between the local content of three major minerals-quartz, albite, and illite-and fracture width and tortuosity. The results show that the TS-LSTM fracture extraction method can effectively extract micro-fracture regions from complex shale SEM images, with strong completion capability particularly for discontinuous fractures. Using the threshold mineral percentage content at different distances, the mineral distribution around fractures can be quantitatively described. Illite content exhibits a negative correlation with fracture width and a strong positive correlation with tortuosity, indicating that fractures in illite-rich zones are narrower and more tortuous. Quartz content is positively correlated with fracture width and overall negatively correlated with tortuosity, which favors the formation of wider and straighter fractures. However, in local areas with dense quartz grains, fractures may propagate around the grains, leading to increased local tortuosity near quartz. Although albite content shows a certain positive correlation with fracture width, its relationship with tortuosity is more complex. In summary, the type and spatial distribution of minerals collectively shape the complex propagation paths of fractures. This study establishes, through an intelligent approach, the relationship between minerals and micro-fracture morphology, providing a new pathway for developing micro-scale models of wellbore stability in shale formations.

Key words: shale micro-fracture, TS-LSTM, mineral type, image segmentation, fracture tortuosity, wellbore stability, intelligent recognition

摘要：

页岩储层中天然微裂缝的形态是控制其渗流能力与力学稳定性的关键因素，而微观矿物分布会显著影响局部微裂缝的发育特征。准确提取微裂缝的几何形貌，并建立其与矿物类型及空间分布之间的关系，是深入理解页岩井壁失稳机理的重要基础。然而，页岩基质具有强烈的非均质性，传统阈值分割方法难以精确区分微裂缝与矿物边界，导致裂缝形态参数的提取存在较大不确定性。针对这一问题，本研究基于扫描电镜(SEM)图像，提出一种结合阈值分割与长短期记忆神经网络的TS-LSTM裂缝提取方法，实现微裂缝的高精度分割与补全。基于提取的裂缝形态，定量表征了裂缝的宽度与迂曲度。为量化裂缝周围矿物分布特征，以裂缝边界为基准向外扩展不同距离，定义该区域内某种矿物所占面积百分比为阈值矿物百分比含量。在此基础上，采用相关性分析方法，探究了石英、钠长石和伊利石3种主要矿物局部含量与裂缝宽度及迂曲度之间的统计关系。结果表明：TS-LSTM裂缝提取方法能够有效从复杂页岩SEM图像中提取微裂缝区域，尤其对不连续裂缝具有良好的补全能力。利用不同距离下的阈值矿物百分比含量，可以有效定量描述裂缝周围的矿物分布情况。伊利石含量与裂缝宽度存在负相关关系，与迂曲度呈强正相关，表明富伊利石区域裂缝更窄、更曲折；而石英含量与裂缝宽度呈正相关，与迂曲度整体呈负相关，即有助于形成更宽、更平直的裂缝。但在局部石英颗粒密集区域，裂缝可能绕颗粒扩展，导致该局部位置的迂曲度有所增大。钠长石虽然与裂缝宽度之间存在一定正相关关系，但与迂曲度之间关系较为复杂。综合而言，矿物类型及其空间分布共同塑造了裂缝的复杂扩展路径。本研究通过智能方法建立矿物—微裂缝形态的关系，为建立页岩地层井壁稳定性的微观模型提供了一种新途径。

关键词: 页岩微裂缝, TS-LSTM, 矿物类型, 图像分割, 裂缝迂曲度, 井壁稳定, 智能识别

CLC Number: 

• TE122

• P618.13