Deep Geothermal Temperature Prediction via Resistivity-Pressure Coupling
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Solution Overview
Problem
Current methods for predicting deep geothermal fields, such as those based on empirical formulas, are inaccurate due to rough estimates of thermophysical parameters and lack of spatial variability, leading to significant errors in temperature distribution predictions.
Innovation Solution
A method and device that utilize a temperature-pressure-resistivity coupling constraint to predict deep geothermal fields by acquiring and normalizing resistivity, temperature, and pressure data from boreholes, performing fine inversion of electromagnetic and gravity data, and constructing a constraint equation to accurately characterize the relationship between resistivity and temperature at different depths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If borehole temperature logging is used for direct measurement, then temperature characteristics can be obtained, but the high cost and small amount of interpolation data lead to considerable errors
Solution Approach 1:
The patent introduces resistivity as an intermediary parameter to indirectly infer temperature distribution. Instead of directly measuring temperature everywhere through expensive borehole logging, the method uses readily available resistivity data from electromagnetic surveys as a mediator that correlates with temperature, allowing spatial interpolation without additional direct temperature measurements.
Solution Approach 2:
The patent replaces the mechanical/physical borehole logging system with a geophysical electromagnetic survey system. By substituting direct thermal measurement with electromagnetic resistivity measurement, the method achieves broader spatial coverage at lower cost, though requiring a calibration relationship between resistivity and temperature.
2Measurement precision
If geochemical methods are used for indirect calculation, then temperature distribution range can be predicted, but regional temperature distribution and depth matching cannot be estimated
Solution Approach 1:
The patent transitions from one-dimensional borehole temperature profiles to three-dimensional regional temperature distribution by incorporating spatially extensive resistivity survey data. The electromagnetic surveys provide coverage across the entire study area, allowing interpolation and extrapolation to generate a full 3D temperature model rather than isolated wellbore profiles.
3Productivity
If geophysical methods with fixed empirical formulas are used, then prediction can be performed, but the assumption of fixed parameters under any geological environment leads to large errors
Solution Approach 1:
The patent makes the prediction system dynamic by allowing the resistivity-temperature relationship to vary spatially and with depth. Instead of applying a single fixed empirical formula, the method calibrates the relationship locally using available borehole data and adjusts parameters according to regional geology and depth, making the system adaptive to different geological conditions.
Solution Approach 2:
The patent applies different resistivity-temperature relationship parameters to different regions and depth zones. By dividing the study area into zones with distinct geological characteristics and calibrating the empirical relationship locally in each zone using available borehole data, the method accounts for spatial variability in rock properties, fluid content, and thermal regime.
4Ease of manufacture
If thermophysical parameters are roughly estimated for temperature model establishment, then model construction can proceed, but large errors result in the predicted temperature field
Solution Approach 1:
The patent incorporates feedback by using available borehole temperature and resistivity measurements to calibrate and validate the empirical relationship. The model parameters are adjusted based on the observed correlation between measured resistivity and temperature at borehole locations, creating a self-correcting system that improves accuracy while maintaining the simplicity of empirical modeling.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach achieves an accuracy of 85.51% in predicting deep geothermal field temperatures, providing a practical and effective method for geothermal resource evaluation and monitoring.
Implementation Method 1
deduces an accurate relationship characterization between a normalized resistivity, a temperature, and a pressure in different formations (at different depths) in an underground space through a borehole logging resistivity-overburden pressure-temperature data combination
Data Source
AI summary
A method for predicting a deep geothermal field based on a temperature, pressure, and resistivity coupling constraint includes: equally dividing logging resistivity-overburden pressure-temperature data of m boreholes in a study area into N formations on a depth basis, and normalizing resistivity in each formation; deducing an accurate relationship characterization between a normalized resistivity, a temperature and a pressure in different formations of different formations; inverting an electromagnetic data volume in the study area to obtain a resistivity distribution characteristic, equally dividing an inverted resistivity into M formations, and normalizing the inverted resistivity into a normalized inverted resistivity; inverting a density distribution based on gravity observation data, and converting the density distribution into an overburden pressure; and calculating a distribution characteristic of a deep underground temperature field based on the accurate relationship characterization in different formations, the normalized inverted resistivity, and the overburden pressure.