Rock electrical experiments are essential means of researching the conductive properties of rocks and are fundamental to interpreting resistivity logging. Carbonate rocks have more complex pore structures than sandstone, which results in more complex conductive properties. However, conducting experiments on representative rock samples from carbonate reservoirs is difficult, making it challenging to study the micro factors affecting electrical properties. Therefore, researching the conductive properties of carbonate rocks is difficult. To address this, in this paper, three-dimensional (3D) digital rock models with different porosities are generated, and conductive simulations are carried out on these models using the finite element method (FEM). Firstly, a micro-computed tomography (μ-CT) 3D image of a carbonate rock is obtained. Secondly, mathematical morphology-based methods are used on the μ-CT image to generate cores with varying porosities and fluid distributions. Then, the electrical properties are simulated using the FEM method, and the results are analyzed. The results reveal that the formation factor of the reservoir is mainly influenced by the shape and structure of the pores. The Archie equation is more suitable for carbonate reservoirs with water saturation levels greater than 60%. The wettability of the rock can alter the distribution of fluid in the reservoir space under different water saturation conditions. In pure water-wet rocks, the water phase mainly occupies small pores, while the oil phase occupies larger pores. As a result, compared to pure oil-wet rocks, water-wet rocks have more conductive channels and better conductivity. Therefore, it is important to determine the wettability of the rock when calculating water saturation using the Archie equation. The saturation index value of water-wet carbonate rock is about 2, while that of oil-wet rock is around 3–4. This research lays a foundation for studying the electrical conductivity of carbonate reservoirs using digital rocks.

The coal reservoir in the Ordos Mizhi block is buried at a depth of over 2000 m. This study aims to obtain the characteristics of the coal reservoir in the Mizhi block through various experimental methods and combine the gas-bearing characteristics obtained from on-site desorption experiments to analyze the gas content and logging response characteristics of the study area. On this basis, a reservoir parameter interpretation model for the study area is established. This provides a reference for the exploration and development of coal-rock gas in the Mizhi block. The research results show that: (1) The study area is characterized by the development of the No. 8 coal reservoirs of the Benxi Formation, with a thickness ranging from 2 to 11.6 m, averaging 7.2 m. The thicker coal reservoirs provide favorable conditions for the formation and storage of coal-rock gas. The lithotypes are mainly semi-bright and semi-dark. The coal maceral is dominated by the content of the vitrinite, followed by the inertinite, and the exinite is the least. The degree of metamorphism is high, making it a high-grade coal. In the proximate analysis, the moisture ranges from 0.36 to 1.09%, averaging 0.65%. The ash ranges from 2.34 to 42.17%, averaging 16.57%. The volatile ranges from 9.18 to 15.7%, averaging 11.50%. The fixed carbon ranges from 45.24 to 87.51%, averaging 71.28%. (2) According to the results of scanning electron microscopy (SEM), the coal samples in the Mizhi block have developed fractures and pores. Based on the results of the carbon dioxide adsorption experiment, the micropore adsorption capacity is 7.8728–20.3395 cm³/g, with an average of 15.2621 cm³/g. The pore volume is 0.02492–0.063 cm³/g, with an average of 0.04799 cm³/g. The specific surface area of micropores is 79.514–202.3744 m²/g, with an average of 153.5118 m²/g. The micropore parameters are of great significance for the occurrence of coal-rock gas. Based on the results of the desorption experiment, the gas content of the coal rock samples in the study area is 12.97–33.96 m³/t, with an average of 21.8229 m³/t, which is relatively high. (3) Through the correlation analysis of the logging parameters of the coal reservoir, the main logging response parameters of the reservoir are obtained. Based on the results of the logging sensitivity analysis of the coal reservoir, the interpretation model of the reservoir parameters is constructed and verified. Logging interpretation models for parameters such as industrial components, microscopic components, micropore pore parameters, and gas content are obtained. The interpretation models have interpretation effects on the reservoir parameters in the study area.

Electrical imaging logging images play a crucial role in petroleum exploration; however, in practical applications, blank strips frequently appear due to instrument malfunctions or data transmission failures, severely compromising geological interpretation and hydrocarbon evaluation. Existing image inpainting methods have limited adaptability to blank strips at different depth scales and exhibit blurred high-resolution geological textures. To address these issues, this paper proposes a blank strip filling method that integrates Arbitrary Kernel Convolution (AKConv) with the Aggregated Contextual-Transformations Generative Adversarial Network (AOT-GAN). Specifically, the adaptive sampling mechanism of AKConv is incorporated into the generator network of AOT-GAN, enabling the model—to effectively capture long-range contextual information and adaptively handle blank strips of varying scales and shapes through multi-scale feature fusion. Experimental results on real oilfield datasets demonstrate that the proposed method achieves significant improvements in PSNR, SSIM, and MAE, exhibiting superior structural preservation and texture sharpness—especially in restoring deep and large-scale blank strips. Furthermore, visual comparisons confirm the method’s superior performance in recovering key geological features, such as bedding continuity and fracture structures, thus providing an effective approach for electrical imaging logging image restoration.

With the gradual decline in conventional oil and gas production growth, unconventional natural gas has become a strategic alternative for hydrocarbon supply. Coal-rock gas (CRG) represents a deep unconventional gas resource with huge potential. Major exploration breakthroughs of CRG have been achieved in China, while systematic research targeting CRG as an independent gas reservoir is still lacking internationally. After effective commercial development, CRG serves as an important supplementary energy source for the domestic natural gas supply. Existing logging evaluation methods exhibit notable deficiencies, as porosity is typically estimated by fitting well logging data or proximate analysis data, resulting in limited accuracy. To address the lack of a dedicated logging volumetric model, ambiguous coal-matrix framework parameters, and substantial porosity calculation errors in deep CRG reservoirs, this study investigates the medium–high rank No. 8 coal seam of the Benxi Formation in the central-eastern Ordos Basin. From an oil and gas reservoir logging evaluation perspective, multi-scale experiments were conducted to systematically characterize the material composition and microscopic characteristics of the coal rock. From the perspective of oil and gas reservoir logging evaluation, a three-component logging volumetric model, consisting of a coal matrix, inorganic minerals, and pore fluids, was constructed, and the corresponding coal-matrix framework parameters were calibrated. The results demonstrate that coal rock is an organic–inorganic composite system, with organic macerals dominated by vitrinite (averaging 59.1%) and inertinite (27.1%). The sum of fixed carbon and volatiles exhibits strong correlations with total organic carbon (TOC) and micro-CT-derived coal-matrix content, yielding determination coefficients of 0.99 and 0.95, respectively, which validates the reliability of the multi-scale quantitative composition characterization. The coal-matrix framework parameters are non-constant: density ranges from 1.08 to 1.56 g·cm⁻³, acoustic slowness from 281 to 425 μs·m⁻¹, and compensated neutron from 39% to 79%. Borehole enlargement severely affects compensated density and neutron logs but has negligible interference with acoustic slowness. Notably, inertinite content shows a significant negative correlation with the acoustic-slowness framework response (R² = 0.80), indicating that structurally dense inertinite is a key intrinsic factor controlling the elastic response of the coal matrix. For porosity evaluation, a dual-variable framework model is proposed. The core novelty of this method is that it simultaneously incorporates variations in inorganic mineral content and differences in inertinite proportion within organic components as dynamic framework constraints, breaking through the limitation of the conventional constant-matrix assumption. The acoustic-slowness-based model achieves an average relative error of merely 7.1%, effectively resolving the large errors inherent in conventional fitting methods. The dedicated coal-rock logging evaluation system established in this study overcomes the limitations of fixed framework models, offers a scientific basis for fine-scale interpretation and resource assessment of deep CRG reservoirs, and provides a valuable reference for evaluating analogous reservoirs.