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.

High temperatures in ultra-deep buried-hill wells of the Bohai Bay Basin and rapid wellbore temperature recovery after circulation shutdown restrict the available operating time of logging tools. To quantitatively determine the safe logging window, a transient wellbore–formation heat transfer model was developed to simulate the complete circulation cooling and shut-in recovery process. Formation thermal heterogeneity was characterized using lithology, porosity, and shale volume fraction, and the governing equations were solved using a hybrid explicit–implicit finite volume method with refined radial grids near the wellbore and stage-dependent time stepping. The safe logging window was determined according to the allowable operating temperature of logging tools. Field validation in Well M15 yielded a mean absolute error and root mean square error of 0.75 °C and 0.87 °C, respectively, while the relative temperature prediction errors for another 12 wells were all below 5%. Benchmark tests demonstrated that the proposed method reduced computational time by approximately 69% compared with the uniform small-time-step scheme. Under the investigated conditions, increasing the circulation rate from 10 L/s to 30 L/s reduced the bottom-hole temperature from approximately 120 °C to 65 °C, whereas the cooling benefit gradually diminished at higher rates. The safe logging window exhibited clear dependence on measurement depth and tool temperature limits; at 5274.87 m in Well M15, the predicted windows were 3.32 h and 17.78 h for temperature limits of 150 °C and 170 °C, respectively. Sensitivity analysis indicated that the geothermal gradient was the dominant factor affecting the safe window, while thermal conductivity and bedding anisotropy also influenced the prediction results. The proposed model provides a quantitative basis for optimizing circulation cooling strategies and logging timing in ultra-deep buried-hill wells and offers an effective approach for safe logging window evaluation under high-temperature complex geological conditions.