Deep reservoirs are in a high-pressure and high-temperature (HPHT) environment, while the experimental conditions for rock electrical properties that meet the deep reservoir conditions are harsh and costly. Although digital rock technology can simulate the electrical properties of rocks, it is limited to electrical simulation studies under normal temperature and pressure conditions (NPT), which limits their ability to capture the electrical characteristics of deep hydrocarbon reservoirs. This limitation affects the accuracy of saturation prediction based on resistivity logging. To simulate the rock electrical properties under HPHT conditions, we proposed a low-cost and high-efficiency HPHT digital rock electrical simulation workflow. Firstly, samples from deep formations were CT-scanned and used to construct multi-component digital rocks that reflect the real microstructure of the samples. Then, mathematical morphology was used to simulate the overburden correction under high-pressure conditions, and the changes in the conductivity of formation water and clay minerals at different temperatures were used to simulate the conductivity changes of rock components under high-temperature conditions. To carry out the electrical simulation of digital rock in deep reservoirs, a numerical simulation condition for HPHT in deep layers was established, and the finite element method (FEM) was used. Finally, based on the equivalent changes in the conductivity of different components, the effects of clay minerals and formation water under HPHT conditions on rock electrical properties were studied and applied to predict the water saturation based on well logging data. We found that considering the influence of temperature, salinity, and clay type, the saturation index (n) of the rock depends on the ratio of the clay conductivity to the formation water conductivity. The larger the ratio is, the smaller the value of n. In addition, the average relative error between the predicted water saturation under HPHT conditions and the sealed coring analysis was 6.8%, which proved the accuracy of the proposed method. Overall, this method can effectively simulate the pressure and temperature environment of deep formations, reveal the electrical conductivity mechanisms of rocks under formation pressure and temperature conditions, and has promising prospects for the study of rock physical properties and reservoir evaluation in deep formations.

The rock coring of the reservoir in the Bohai A field is difficult. The cores of the target section in the study area are loose, making it difficult to accurately measure the core-bound water saturation. The purpose of this research was to develop and validate a method for calculating a reservoir core-bound water saturation ratio using the cast thin section. First, pepper noise denoising and image enhancement were performed on the thin section by median filtering and gamma variation. Based on this, the enhanced sheet images were thresholded for segmentation by the two-dimensional OTSU algorithm, which automatically picked up the thin section pore-specific parameters. Then, the thin section image was equivalent to a capillary cross-section, while the thin film water fused to the pore surface was observed as bound water. For hydrophilic rocks with a strong homogeneity, the area of thin film water in the pore space of the sheet was divided by the total area of the pore space, which produced the bound water saturation. Next, the theoretical relationship between the film water thickness and the critical pore throat radius was derived based on the Young–Laplace equation. The bound water saturation of the rock was calculated by combining the pore perimeter and the area that was automatically picked up from the thin film for a given critical pore throat radius of the rock. Finally, 22 images of thin sections of sparse sandstone from the coring well section of the study area were image processed using the new method proposed in this paper, and the bound water saturation was calculated. The calculated results were compared with 22 NMR-bound water saturations and 11 semi-permeable baffle plate-bound water saturations in the same layer section. The results showed that the bound water saturation values calculated by the three methods produced consistent trends with absolute errors within 5%. The calculated results confirm the reliability of the method proposed in this paper. This method can effectively avoid the problem of the inaccurate results of core experiments due to the easy damage of sparse sandstone and provides a new idea for the accurate determination of the bound water saturation of sparse sandstone.

Permeability prediction of deep metamorphic rock reservoirs in the southwestern Bohai Bay Basin poses an enormous challenge due to the strong heterogeneity. Fractures widely develop in such reservoirs, yet their contributions to permeability were neglected in traditional prediction models. To develop an effective model to predict permeability, parameters related to fracture needed to be taken into account. In this study, taking the Archaeozoic Formation in BZ 19–6 Region—a typical deep metamorphic rock reservoir in the southwestern Bohai Bay Basin—as an example, the porosity frequency spectra were first extracted from electrical imaging logging, and the correlations between the shape of porosity frequency spectrum and rock pore structure were analyzed. Afterwards, two parameters, which were defined as the logarithmic mean (φ_gm) and standard deviation between two golden section points (φ_gsr), were extracted to reflect the main peak position and wide porosity frequency spectrum, and a novel permeability prediction model was established. After the target formations were classified into two types according to the differences in pore types and pore–fracture configuration relationships, the model coefficients were calibrated. Consecutive permeability curves were derived from the proposed model in the intervals where porosity frequency spectra were obtained. Comparisons of predicted permeabilities from the proposed model, traditional method and core-measured results showed that the proposed model yielded far more reliable results, with an average relative error of only 11.12% between the predicted and core-measured permeabilities. In contrast, the average relative error of the traditional method reached 36.10%. The proposed model contributed significantly to the characterization and effectiveness evaluation of fractured deep metamorphic rock reservoirs.

Deep metamorphic rock reservoir permeability prediction faced great challenge due to strong heterogeneity. Fractures were widely developed, but the traditional models did not involve into the contribution of fractures. To establish an effective model to predict permeability, parameters related to fracture needed to be taken into account. In this study, taking the Archaeozoic Formation in BZ 19-6 Region - a typical deep metamorphic rock reservoir and located in southwestern Bohai Bay Basin - as an example, the porosity frequency spectra were first extracted from electrical imaging logging, and relationships between the shape of porosity frequency spectrum and rock pore structure were analyzed. Two parameters, which were defined as the logarithmic mean (φgm) and standard deviation between two golden section points (φgsr), were used to reflect the main position and wide of porosity frequency spectrum, and a novel model to predict permeability was established. After the target formations were classified into two types based on the difference of pore type and pore-fracture configuration relationship, the involved model coefficients were calibrated. Consecutive permeability curves were derived from our raised model in the intervals with which porosity frequency spectra were acquired. Comparisons of predicting permeabilities with core-derived results illustrated that this proposed model was much reasonable, and the resolution of predicted permeability curve was high. Except for the deep metamorphic rock reservoirs, the estimated model could be widely used in other types of formations (e.g., volcanic rock reservoirs, carbonate rock reservoirs) where secondary pores were developed.