Intelligent Reconstruction of Digital Shale and Its Application in the Analysis of Conductivity Mechanism

Shale oil as a critical unconventional oil and gas resource, the reservoirs exhibit complex microstructures and compositional heterogeneity, which significantly influence their electrical conductivity. This characteristic in turn poses unique challenges for the accurate identification of well logging and the precise calculation of oil saturation based on electrical characteristics in shale oil reservoirs. Traditional methods such as x-ray computed tomography (x-ray CT) and focused ion beam-scanning electron microscopy (FIB-SEM) are limited by the terms of resolution and experimental cost, which restrict their applicability in studying the conductivity mechanisms of shale reservoirs. However, with the continuous development of technologies such as machine learning and artificial intelligence, combing with high-resolution imaging techniques, offering a promising solution to effectively address the aforementioned challenges. This study combined mineralogy analysis processing system (MAPS) and generative adversarial networks (GANs) technology to establish a digital shale intelligent reconstruction methodology. This approach enabled precise characterization of the content and spatial distribution of key conductive components such as pyrite, clay minerals, and pores. Furthermore, the component content were dynamically adjusted using mathematical morphology algorithms, and then the influence mechanism on electrical properties was quantitatively evaluated through finite element simulation. The research findings indicate that when the porosity ranges between 1.3% and 8.0%, the resistivity can be reduced by 73.4%, with a permeability threshold observed at the porosity of 5.0%, which significantly improves the connectivity. When the clay mineral content reaches 14.77%, the resistivity decreases by 32.5% due to the surface conduction effect. When the pyrite content is between 2.6% and 3.8%, a conductive network is formed, resulting in a one-order-of-magnitude reduction in resistivity. However, when the content of nonconductive minerals such as quartz account for 62.0%, a dilution effect occurs, leading to an 11.25% increase in resistivity. In addition, this study quantified the Archie formula parameters for shale oil reservoirs. The cementation exponent in the horizontal and vertical directions were determined to be 1.44 and 1.56, respectively, reflecting the influence of the pore structure anisotropy and the saturation index on the additional conductivity of clay in shale oil reservoirs. These findings have established a comprehensive analytical framework for understanding the multimineral competitive conductivity mechanisms of shale reservoir, thereby providing a crucial methodological basis for the evaluation of unconventional reservoirs and the identification of sweet spots.