Abstract: The location of fracture modification in shale reservoirs is an important factor affecting the fracturing effect. Due to the unique characteristics of the shale grain layer, accurate identification of the grain layer type in the formation is of great importance in selecting the location of reservoir reforming. Recognizing the type of grain layer in optical thin section images is a difficult task in current research. Optical thin section has more accurate lithology delineation than well logging data, and larger scale longitudinal continuous lithology change pattern than ordinary thin section samples, providing centimeter-level reservoir information, which can accurately delineate the type of grain layer and select the sweet spot area for fracturing engineering. The study mainly applies deep neural networks to classify and detect different types of grain layers based on longitudinally continuous large optical thin section data. The results show that the method can continuously and accurately identify three types of grain layers in the longitudinal direction, and can accurately identify the grain layers for the preferred purpose of fracturing and reforming, with an accuracy of up to 70% for the fine sand grain layers in the preferred fracturing and reforming area in a single-well section, and an accuracy of up to 75% for the fine sand grain layers in the preferred fracturing and reforming area in a multi-well section. Through comparative experiments, increasing the amount of data can effectively improve the accuracy of the model.