Method characterizing shale oil reservoirs in Jimsar based on quantitative digital analysis of cuttings

For the reservoirs bearing shale oil, due to their abnormal tightness, there are many problems, such as high coring cost and long core testing period, in characterizing these reservoirs with traditional methods. Furthermore, it is also insufficient for laboratory measurement methods to finely characterize the shale reservoir space and microscopic pore structure. To this end, a method characterizing shale oil reservoirs based on quantitative digital analysis of cuttings is proposed. Based on deep learning, the scanning electron microscope (SEM) images of cuttings were recognized and segmented to characterize the nano-scale pore structure, and then combined with the J-function analysis method, the minimum movable pore radius of shale oil reservoirs was quantitatively analyzed to assist the efficient development of shale oil reservoirs. The study selects the cutting sample from Well Ji 174, a typical well in the Lucaogou shale oil reservoir in Jimsar, as the research object, and gets the lower limit of the pore size that can allow oil movement, in addition, the nuclear magnetic logging results also verified the effectiveness of the method.