For the successful discovery and development of tight sand gas reserves, it is necessary to locate sand with certain features. These features must largely include a significant accumulation of hydrocarbons, rock physics models, and mechanical properties. However, the effective representation of such reservoir properties using applicable parameters is challenging due to the complicated heterogeneous structural characteristics of hydrocarbon sand. Rock physics modeling of sandstone reservoirs from the Lower Goru Basin gas fields represents the link between reservoir parameters and seismic properties. Rock physics diagnostic models have been utilized to describe the reservoir sands of two wells inside this Middle Indus Basin, including contact cement, constant cement, and friable sand. The results showed that sorting the grain and coating cement on the grain’s surface both affected the cementation process. According to the models, the cementation levels in the reservoir sands of the two wells ranged from 2% to more than 6%. The rock physics models established in the study would improve the understanding of characteristics for the relatively high V_p/V_s unconsolidated reservoir sands under study. Integrating rock physics models would improve the prediction of reservoir properties from the elastic properties estimated from seismic data. The velocity–porosity and elastic moduli-porosity patterns for the reservoir zones of the two wells are distinct. To generate a rock physics template (RPT) for the Lower Goru sand from the Early Cretaceous period, an approach based on fluid replacement modeling has been chosen. The ratio of P-wave velocity to S-wave velocity (V_p/V_s) and the P-impedance template can detect cap shale, brine sand, and gas-saturated sand with varying water saturation and porosity from wells in the Rehmat and Miano gas fields, both of which have the same shallow marine depositional characteristics. Conventional neutron-density cross-plot analysis matches up quite well with this RPT’s expected detection of water and gas sands.

Accurate estimation of the earth’s interior temperature is extremely important for studying fundamental scientific and applied geothermal problems. Existing temperature estimation methods cannot provide reliable accuracy in the cross-borehole space and beyond the borehole’s depth; however, resistivity could overcome this difficulty as a temperature-dependent proxy parameter. At present, this approach is based on the use of purely empirical formulas, whose validity is unjustifiably postulated to be invariant with respect to geologic settings. We develop an electromagnetic (EM) geothermometer based on the coefficient correction method of the optimal temperature (CCMOT). This geothermometer can accurately determine the relationship between the normalized resistivity and temperature in an underground space based on resistivity-temperature logging data and EM data; therefore, a visualized temperature distribution can be calculated. The CCMOT is applied to the subsurface temperature prediction in the Xiong’an New Area, with an accuracy of 86.69%–97.25%. Sensitivity analysis of the key variables of the CCMOT reveal that the CCMOT has relatively little dependence on the number of constraining boreholes and the optimization of the subdivision spacing of the logging data can significantly improve temperature prediction accuracy. The CCMOT can be used to determine the distribution of the heat structure of the reservoir and to interpret the geothermal field. In addition, the CCMOT is of great significance to the evaluation, scientific development, and sustainable use of geothermal resources.

The application of the Cole-Cole model within time-domain induced polarization (TDIP) forward field modeling shows that the model parameters can characterize time-varying states of the TDIP field and support observed data analysis. The Cole-Cole model contains real and imaginary parts, and it requires a frequency-to-time conversion for TDIP forward modeling. However, the TDIP field is usually expressed by a real number, and its intuitive time-varying states field intensity increases with charging time. Therefore, the forward model should be constructed in a simpler form. We have aimed to develop a forward model using mathematical functions not based on physical principles. The Weibull (WB) growth model, which is primarily used to describe the time-varying curve features in regression analysis, is introduced into the basic algorithm of the TDIP forward model. Subsequently, a forward expression of the TDIP effect is established. Based on the time-varying shape and scale parameters, this expression describes the time-varying rate and relaxation states of the TDIP fields. Furthermore, based on the extensively used conjugate gradient optimization, an apparent WB parameter scheme is initiated to calculate the spectral parameters that represent the relaxation and time-varying rate obtained from the multi-time-channel TDIP data. Finally, this scheme is applied to interpret the different simulated and actual TDIP data. The results demonstrate that the WB growth model can be used for the TDIP forward model without involving physical principles, the model parameters without specific physical significance can be used to represent the time-varying states of TDIP fields, and apparent WB parameters can be used to discern different TDIP observed data. The setting of the TDIP forward model and model parameters can actually be more flexible and diverse, so as to obtain simpler forward expressions and ensure a highly efficient inverse solution.

Temperature is one of the most prominent factors affecting production operations, predicting the accurate wellbore-formation temperature in a water-flooding production well is of great importance for multiple applications. In this paper, an improved coupled model of oil–water two-phase fluid flow and heat transfer was developed to investigate the transient temperature behavior for a producing well with multiple pay zones. Firstly, a novel method was derived to simulate the water saturation and the water breakthrough time (WBT) for tubing, which are key monitoring parameters in the process of water flooding. Then, we incorporated water saturation and an equation set for immiscible displacement to calculate the seepage velocity and the pressure of the two-phase fluid in the pay zones. Next, the upward seepage velocity of the tubing fluid change with depth was focused on, and the proper coupled initial and boundary conditions are presented at the interfaces, therewith the implicit finite difference method was used to compute the transient temperature with the input of the seepage characteristics for the reservoirs. Meanwhile, the validity of the proposed model has been verified by the typical model. Finally, a sensitivity analysis delineated that the production rate and the production time had a significant impact on the tubing fluid temperature. The overburden was hotter with a lower volumetric heat capacity or a higher thermal conductivity. In addition, the sensitivity of the porosity and the irreducible water saturation to formation temperature was significantly different before and after the WBT. The coupled model presented herein helps to advance the transient seepage characteristics analysis of pay zones, the precise temperature prediction is very useful for reservoir characterization and production analysis purposes and provides insight for designing the exploitation scheme in deep reservoirs and geothermal resources.