Construction of complex digital rock physics based on full convolution network

Digital rock physics (DRP) is a paramount technology to improve the economic benefits of oil and gas fields, devise more scientific oil and gas field development plans, and create digital oil and gas fields. Currently, a significant gap is present between DRP theory and practical applications. Conventional digital-core construction focuses only on simple cores, and the recognition and segmentation effect of fractures and pores of complex cores is poor. The identification of rock minerals is inaccurate, which leads to the difference between the digital and actual cores. To promote the application of DRP in developing oil and gas fields, based on the high-precision X-ray computed tomography scanning technology, the U-Net deep learning model of the full convolution neural network is used to segment the pores, fractures, and matrix from the complex rock core with natural fractures innovatively. Simultaneously, the distribution of rock minerals is divided, and the distribution of rock conditions is corrected by X-ray diffraction. A pore–fracture network model is established based on the equivalent radius, which lays the foundation for fluid seepage simulation. Finally, the accuracy of the established a digital core is verified by the porosity measured via nuclear magnetic resonance technology, which is of great significance to the development and application of DRP in oil and gas fields.