【摘要】 页岩孔隙研究对页岩油甜点预测和储层评价具有重要意义，与常规储层相比，页岩储层的孔隙类型更为多样，孔隙结构更为复杂，纳米尺度的孔隙广泛发育。目前，常规的岩石物理实验在页岩储层参数表征方面遇到困难，难以满足页岩等复杂岩石类型评价的需求。基于多分辨率的数字岩心技术，在数据规则化的基础上，利用高分辨率的数字岩心图像，采用深度学习算法，对页岩储层的孔隙类型进行自动智能识别。该算法识别精度达到0.65(mAP@0.5),极大提升了页岩孔隙类型识别的时效性，为非常规储层孔隙类型的表征提供了新的方法和手段。更多还原

【Abstract】 Shale pore research is important for shale oil dessert prediction and reservoir evaluation. Compared with conventional reservoirs, shale reservoirs have more diverse pore types and more complex pore structures, and nano-scale pores are widely developed. At present, conventional petrophysical experiments encounter difficulties in characterizing the parameters of shale reservoirs, and it is difficult to meet the needs of evaluation of complex rock types such as shales. Based on the multi-resolution digital core technology, the pore type of shale reservoir is automatically and intelligently identified by deep learning algorithm, which is based on the data regularization and using high-resolution digital core images. The recognition accuracy of the algorithm reaches 0.65(mAP@0.5), which greatly improves the timeliness of pore type identification of shale and provides a new method and means for the characterization of pore type of unconventional reservoirs.更多还原