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.

We present a regularized inversion method for three-dimensional (3D) magnetotelluric (MT) data with axial anisotropic conductivities based on the edge-based finite element (FE) method. The Gauss–Newton (GN) approach is used to minimize the inversion objective function, including data misfit and regularization penalties, considering both structural complexity and anisotropic penalties. The most time-intensive task in the 3D MT inversion process is solving the large sparse system of linear equations. To speed up the inversion calculation, a hybrid direct–iterative solver combined with a block-diagonal preconditioner that has not yet been applied in anisotropic inversion is developed to accelerate the solutions for the sparse linear system resulting from forward modeling and sensitivity computations. In each GN iteration, a preconditioned conjugate gradient (PCG) method is adopted to overcome the difficulty of the sensitivity matrix storage for the anisotropic scene and obtain a model update without explicitly calculating and storing the sensitivity matrix. Before the inversion test, we use a model to demonstrate that the hybrid solver is computationally beneficial in terms of memory usage and time spent as compared to the direct solver. The good convergence properties and efficiency of the GN–PCG inversion scheme are demonstrated by two synthetic models and USArray data. The proposed inversion scheme can be an important supplement to existing anisotropic inversion algorithms and provide technical support for MT data interpretation.

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.

Accurate predictions of earth’s interior thermal conductivity are essential for interpreting the mechanisms involved in geothermal systems and delineating geothermal resource targets. Existing thermal conductivity prediction methods, which typically involve measurements of rock samples and predictions based on geophysical well logging, cannot accurately predict regional thermal conductivity distributions. We develop a 3D thermal conductivity prediction based on temperature field estimation. First, the finite-element method is used for temperature forward modeling. Subsequently, the inexact Gauss-Newton method is implemented in the prediction algorithm. Synthetic studies indicate that the method can recover the true model well. The subsurface temperature distribution of the Xiong’an New Area is predicted using the coefficient correction method of the optimal temperature. Next, we obtain the 3D distribution of thermal conductivity of deep formation in the Xiong’an New Area with the predicted temperature field. The distribution of terrestrial heat flow also is estimated. In addition, the law of thermal accumulation of strata at different depths is analyzed, and the internal relation among resistivity, temperature, and thermal conductivity is studied. Thus, the 3D thermal conductivity prediction method provides the theoretical basis for interpreting mechanisms for the origin of geothermal systems and heat accumulation patterns. It could have significant utility for the analysis of geothermal fields and the sustainable development and use of geothermal resources.

The magnetotelluric (MT) method is highly advantageous in geothermal resource exploration because of its deep probing capability, low cost, and sensitivity to key geothermal system elements, such as heat sources, reservoirs, and caprocks. To address the challenge of balancing the MT inversion resolution and computational efficiency, we develop a Gauss-Newton optimization inversion algorithm based on the solution space dimensionality reduction (SSDR) technique. This algorithm uses SSDR to establish algebraic mapping relationships between the edges of grid cells, effectively reducing the degrees of freedom in the linear equation system required for forward and pseudoforward modeling, thereby saving computational costs. In addition, during the inversion process, a multiple right-hand direct-iterative hybrid solver is used to solve the dimensionally reduced linear equations, further enhancing the computational efficiency of the inversion. We then validate and analyze the accuracy and efficiency of the forward and inversion algorithms using various synthetic models. Finally, we apply this inversion strategy to field MT data from the Dabie Mountain geothermal area in eastern China. On the basis of the inversion results, we then analyze the thermal genesis model of the region, providing technical support for the exploration and development of deep geothermal resources.

Accurate calculation of the permeability of tight sandstone gas reservoirs has been a challenge, due to the enhanced effect of pore structure. Reservoir permeability with the same porosity and different pore structure often varies greatly. The permeability estimated by the traditional core sample regression analysis method has low accuracy, and the nuclear magnetic resonance (NMR) logging method is affected by the hydrocarbon of the reservoir. In this paper, the defined parameter can effectively quantify the difference of pore structure. Based on regression analysis of core measurement data, the model with optimal factor parameters of permeability calculation is established. This method combines the advantages of empirical models and pore structure models in calculating permeability. The results show that the method can effectively improve the accuracy of permeability. It has been successfully applied to the tight sandstone gas reservoir of He3 member in Hangjinqi area, Ordos Basin, China. Compared with other permeability theoretical models, it provides a more accurate and practical method for calculating permeability.