Geothermal caprock evaluation modeling method
By constructing a single-factor and structure-mechanics-integrity coupled model, and combining physical properties and structural data, the inconsistency problem in the evaluation of geothermal cap was solved, resulting in more stable and reliable evaluation results, and improving the model's cross-regional applicability and prediction accuracy.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- INST OF GEOMECHANICS
- Filing Date
- 2026-03-16
- Publication Date
- 2026-05-29
AI Technical Summary
In existing technologies, the evaluation methods for geothermal cap layers lack a unified mathematical and physical framework, which cannot effectively integrate factors with different physical meanings and dimensions, resulting in inconsistent evaluation results and difficulty in cross-regional comparisons. Furthermore, the lack of quantitative coupling of fault density, brittleness index, and rock plasticity self-healing ability makes it impossible to accurately assess leakage risk and self-healing effect. Relying on expert experience, the model prediction results are unstable.
A single-factor evaluation model and a structure-mechanism-integrity coupled model were constructed. By combining physical property data and structural data, parameters were calibrated using regression and Bayesian methods. A standardized scoring function and parameter framework were established, incorporating the quantification of fault density, brittleness index, and plastic self-healing ability. The model was calibrated using historical observation data.
It improves the comparability and portability of caprock evaluation results, realizes a comprehensive and objective evaluation of caprock integrity and crack risk, reduces the model's dependence on subjective experience, and improves the stability and reliability of predictions.
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Figure CN122113237A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of geothermal resource exploration technology, and in particular relates to a method for evaluating and modeling geothermal caprocks. Background Technology
[0002] In the field of geothermal resource exploration, accurate evaluation of the sealing and insulation performance of the geothermal system caprock is crucial for ensuring efficient resource utilization and engineering safety. Current technologies for caprock evaluation typically rely on isolated analysis of single factors or empirical judgments. Specifically, common practices include empirical classification of lithology into "good," "medium," and "poor," but this method ignores the variations in sealing performance within the same lithology caused by differences in diagenesis and physical properties; using fixed thickness thresholds to judge caprock effectiveness fails to fully consider the synergistic effect between thickness and lateral continuity of the formation; and while continuous physical parameters such as permeability and thermal conductivity are graded and scored, the grading standards lack unified physical mechanisms and statistical basis, leading to highly subjective scoring results. Furthermore, for key structural and mechanical factors controlling caprock integrity, such as fault development, rock brittleness, and geostress fields, existing methods are mostly limited to qualitative descriptions and have not yet been able to incorporate these factors into a quantifiable, unified model for comprehensive evaluation.
[0003] These methods have revealed a series of significant shortcomings in practical applications. First, the scoring models established by different research institutions or projects for factors such as lithology, thickness, and permeability vary in form and parameters, making it extremely difficult to compare evaluation results across regions and basins, hindering the formation of a unified regional understanding and impeding the dissemination of experience and the transfer of models. Second, due to the lack of a quantitative coupled model integrating fault density, brittleness index, effective stress, and rock plasticity self-healing capacity, engineers find it difficult to accurately assess leakage risks near fault zones and cannot quantitatively characterize the positive effects of crack self-healing in highly plastic caprocks under long-term geological conditions, resulting in blind spots in caprock integrity assessment. Finally, the key parameters and thresholds in existing scoring models are highly dependent on the personal experience of experts, failing to systematically utilize historical engineering data, such as measured breakthrough pressures, records of past fluid leakage events, and comprehensive sealing levels, to calibrate and optimize the models, leading to insufficient reliability of model predictions and inadequate applicability in specific geological regions.
[0004] The core of solving the above problems lies in how to integrate factors with different physical meanings and dimensions under a unified mathematical and physical framework, and establish a deep integration evaluation system that can integrate prior knowledge and dynamically calibrate using actual observation data. Summary of the Invention
[0005] To address the aforementioned technical problems, this invention proposes a geothermal cap layer evaluation and modeling method to solve the problems existing in the prior art.
[0006] Firstly, to achieve the above objectives, the present invention provides a method for evaluating and modeling geothermal cap layers, comprising the following steps: Obtain physical and structural data of the geothermal cap layer; Based on the aforementioned physical property data, a single-factor evaluation model is constructed that includes evaluation factors for lithology, thickness, permeability, and thermal conductivity. Based on the aforementioned construction data, a construction-mechanics-integrity coupled model is constructed, which includes basic construction score, crack risk index, and plastic self-healing factor. Using historical closed-loop observation data, the parameters in the single-factor evaluation model and the structure-mechanics-integrity coupling model are calibrated.
[0007] Optionally, the process of constructing the single-factor evaluation model includes: establishing a lithological sealing scoring model; establishing a thickness scoring model; establishing a permeability scoring model; and establishing a thermal conductivity scoring model.
[0008] Optionally, the construction process of the construction-mechanics-integrity coupled model includes: Basic structural score is calculated based on fault density; Crack risk indicators are calculated based on the brittleness index and effective stress. The plastic self-healing factor is calculated based on the brittleness index and the crack risk index; The structural-mechanical-integrity score is calculated based on the basic structural score, the crack risk index, and the plastic self-healing factor.
[0009] Optionally, the data calibration process includes: The parameters in the thickness scoring model and the permeability scoring model were fitted and calibrated using regression methods; The parameters in the lithological sealing scoring model and the thermal conductivity scoring model were calibrated and standardized using the Bayesian method. The parameters in the construction-mechanics-integrity coupling model are jointly calibrated using regression or Bayesian methods.
[0010] Optionally, the process of establishing a lithological sealing scoring model includes weighted summation of the scores for permeability, porosity, and clay content.
[0011] Optionally, the thickness scoring model can be established using the Sigmoid function.
[0012] Optionally, the penetration rate scoring model can be established using a logarithmic scaling function with the reference penetration rate as the median.
[0013] Optionally, the calculation process of the crack risk index includes limiting the product of the brittleness index and the normalized effective stress value to a preset range.
[0014] Secondly, the present invention also provides a computer terminal device, comprising: One or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the steps of the geothermal cap layer evaluation and modeling method in the first aspect described above.
[0015] Thirdly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements the steps of the geothermal cap layer evaluation and modeling method in the first aspect described above.
[0016] Compared with the prior art, the present invention has the following advantages and technical effects: This invention provides a geothermal caprock evaluation modeling method. By constructing a unified single-factor evaluation model system, it provides standardized scoring functions and parameter frameworks for key factors such as lithology, thickness, permeability, and thermal conductivity, significantly improving the comparability of caprock evaluation results across different regions and the model's portability. Simultaneously, this invention establishes a coupled tectonic-mechanical-integrity model, incorporating fault density, brittleness index, effective stress, and plastic self-healing capacity into a unified mathematical framework for coupled quantification, achieving a more comprehensive and objective evaluation of caprock integrity and fracture risk. Furthermore, by introducing regression and Bayesian calibration procedures based on historical breakthrough pressure, leakage events, and other observational data, key parameters in the model can be objectively calibrated, effectively reducing the model's reliance on subjective experience, thereby improving the stability and predictive reliability of caprock evaluation results. Attached Figure Description
[0017] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart illustrating a geothermal cap layer evaluation and modeling method according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the single-factor evaluation model structure according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the construction-mechanics-integrity coupling model of an embodiment of the present invention. Detailed Implementation
[0018] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.
[0019] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.
[0020] like Figure 1 As shown, this embodiment provides a geothermal cap layer evaluation modeling method, including: Obtain physical and structural data of the geothermal cap layer; Based on the aforementioned physical property data, a single-factor evaluation model is constructed that includes evaluation factors for lithology, thickness, permeability, and thermal conductivity. Based on the aforementioned construction data, a construction-mechanics-integrity coupled model is constructed, which includes basic construction score, crack risk index, and plastic self-healing factor. Using historical closed-loop observation data, the parameters in the single-factor evaluation model and the structure-mechanics-integrity coupling model are calibrated.
[0021] Furthermore, the process of constructing the single-factor evaluation model includes: establishing a lithological sealing scoring model; establishing a thickness scoring model; establishing a permeability scoring model; and establishing a thermal conductivity scoring model.
[0022] Furthermore, the construction process of the coupled structure-mechanics-integrity model includes: Basic structural score is calculated based on fault density; Crack risk indicators are calculated based on the brittleness index and effective stress. The plastic self-healing factor is calculated based on the brittleness index and the crack risk index; The structural-mechanical-integrity score is calculated based on the basic structural score, the crack risk index, and the plastic self-healing factor.
[0023] Furthermore, the data calibration process includes: The parameters in the thickness scoring model and the permeability scoring model were fitted and calibrated using regression methods; The parameters in the lithological sealing scoring model and the thermal conductivity scoring model were calibrated and standardized using the Bayesian method. The parameters in the construction-mechanics-integrity coupling model are jointly calibrated using regression or Bayesian methods.
[0024] Furthermore, the process of establishing a lithological sealing scoring model includes weighted summation of the scores for permeability, porosity, and clay content.
[0025] Furthermore, the thickness scoring model is established using the Sigmoid function.
[0026] Furthermore, the penetration rate scoring model is established using a logarithmic scaling function with the reference penetration rate as the median.
[0027] Furthermore, the calculation process of the crack risk index includes limiting the product of the brittleness index and the normalized effective stress value to a preset range.
[0028] In this embodiment, a computer terminal device is provided, including: One or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the steps of the above-described geothermal cap layer evaluation and modeling method.
[0029] In this embodiment, a computer-readable storage medium is also provided, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the above-described geothermal cap layer evaluation and modeling method.
[0030] Example 1: Establishment of a Single-Factor Evaluation Model like Figure 1 and Figure 2 As shown, this embodiment first constructs a single-factor scoring function for four key elements: lithology, thickness, permeability, and thermal conductivity. The lithological sealing scoring model, under the condition of measured physical properties, will sub-score permeability. Porosity score Clay content sub-score Mapped to respectively The intervals are then summed according to their weights to obtain: ,in The preferred values are 0.5, 0.3, and 0.2. In the absence of measured physical properties, typical scores for different lithologies (mudstone, shale, argillaceous limestone, dense sandstone, etc.) are given based on regional experience and used as Bayesian priors.
[0031] The thickness scoring model uses the Sigmoid function, expressed as: .in, This is the actual thickness of the capping layer. The characteristic thickness is used to characterize the thickness that makes... The critical effective thickness; This is the slope control parameter for the Sigmoid curve, used to control the transition width from a low value to a high value in the thickness score; The base of the natural logarithm. As H approaches the characteristic thickness... When the score rapidly jumps from a low value to a high value, it reflects the physical fact that the closure is significantly enhanced after the critical thickness is exceeded; a three-segment linear function approximation can also be used for areas with less data.
[0032] The penetration rate scoring model uses a logarithmic scaling function to reference the penetration rate. (Preferred value) ~ m 2 As the median point, it is represented as: ; in, This refers to the permeability obtained through actual measurement or interpretation. As a reference penetration rate threshold, the preferred value is [value to be filled in]. ~ m 2 ; This is the penetration sensitivity coefficient, used to control the degree of influence of different orders of magnitude of penetration on the score. (Penetration rate below...) The time score rises rapidly (approaching 1), exceeding... The time score drops rapidly (approaching 0), The score is exactly 0.5. (Exponential parameter) The preferred value is 1.0 to 3.0, which is used to control the sensitivity of the impact of different orders of magnitude of penetration rate on the score.
[0033] The thermal conductivity scoring model uses a Gaussian function, expressed as: .in, This is the measured thermal conductivity. For optimal thermal conductivity, it is preferably located between 1.5 and 2.5 W / (m·K); This is a thermal conductivity tolerance parameter, used to characterize the width of the score decrease when the thermal conductivity deviates from the optimal range. The optimal range is defined as approximately 1.5–2.5 W / (m·K). Width parameter The preferred thermal conductivity range is 0.5–1.0 W / (m·K). The physical basis for this optimal range is as follows: excessively high thermal conductivity (>3.0 W / (m·K), such as in quartz sandstone) accelerates the loss of reservoir heat to the overlying strata, reducing the quality of geothermal resources and the economic viability of development; while excessively low thermal conductivity (<1.0 W / (m·K), such as in dry shale), although beneficial for heat preservation, may lead to excessively large temperature gradients within the caprock, causing thermal stress concentration and the risk of thermally induced cracking. The thermal conductivity of typical caprock lithologies such as mudstone and argillaceous limestone mostly falls within the range of 1.5–2.5 W / (m·K), balancing heat preservation performance and thermal stress safety.
[0034] The above four single-factor models all adopt the same approach. , , and This represents the scores for four single-factor categories, which serve as the basis for subsequent structural-mechanical-integrity analysis and comprehensive evaluation.
[0035] Example 2: Construction-Mechanics-Integrity Coupled Model like Figure 3 As shown, this embodiment constructs a coupled construction-mechanics-integrity model based on single-factor construction parameters: Fault density was calculated using fault interpretation results and statistical analysis. And introduce characteristic fault density The basic construction score uses a stretching exponential decay function: ; in, To evaluate the fault density in the region; For reference fault density; The shape parameter (preferably 1.0 to 2.0) is used. When the fault density is much less than the threshold, the basic structure score is close to 1. When the fault density exceeds the threshold, the score rapidly decays and approaches 0.
[0036] The total stress is obtained from tectonic stress field simulation or regional geostress inversion results. Combined with pore pressure Calculate Biot's effective stress .in, This is the Biot coefficient, used to reflect the degree of pore pressure transmission to skeleton stress, and its value is usually between 0 and 1. Effective stress; This is the reference stress.
[0037] The crack risk index is expressed as follows: ; in, It is the brittleness index; As a reference stress (preferably 20–50 MPa), through Cut off the indicator at Within the range. When this value is close to 1, it indicates that the caprock is approaching the critical state of rupture.
[0038] For highly plastic rock formations, a self-healing factor is introduced based on research on creep healing in salt rocks and mudstones. : ; in, The plasticity sensitivity index is preferably set between 1.5 and 3.0. The crack influence coefficient is preferably set between 0.3 and 0.6. This factor describes the characteristic that the lower the brittleness of the rock strata, the stronger its self-healing ability. When the brittleness index is low ( )hour, and Therefore This demonstrates the strong self-healing ability of highly ductile rock strata; when hour, The self-healing ability disappears. Plasticity sensitivity index. The preferred value is 1.5 to 3.0, and the crack influence coefficient is... The preferred value is 0.3 to 0.6.
[0039] The basic construction score is finally obtained through a product. Crack risk compensation item and self-repair factors The overall score is calculated based on the construction, mechanics, and integrity aspects. ,in The crack risk weighting coefficient is preferably set to a value of 0.2 to 0.5, which provides input to the SM subsystem in the main invention.
[0040] In obtaining the scores for each individual factor ( , , , ) and Construction-Mechanics-Integrity Score Then, the final comprehensive closure score is obtained through weighted linear fusion: ,in Each weight ; Default reference value can be taken In practical applications, the model can be re-optimized based on the geological characteristics of the target area using regional calibration data. This comprehensive expression is used to describe the application method of the established model system and does not serve as an additional limitation on the scope of the main method steps.
[0041] Example 3: Parameter Calibration Process In areas with historical breakthrough pressure, leakage event records, and caprock sealing levels, this embodiment uses the following steps for parameter calibration: The relationship between thickness and closure was statistically analyzed, and regression methods were used to fit the optimal feature thickness and the slope of the Sigmoid or piecewise function. A reference permeability was fitted by the relationship between permeability and breakthrough pressure or leakage frequency. With index ; Typical scores and best thermal conductivity in lithology and thermal conductivity rating A Bayesian prior is constructed, and the posterior distribution is updated using regional engineering examples as samples. Specifically, given a physical-empirical form (such as lithology category-typical score pairs, thermal conductivity-score Gaussian curves), each parameter can be treated as a random variable. The "evaluation level-physical property parameter" pairs of historical caprock samples are used as observation data. The posterior mean and confidence interval are obtained through maximum a posteriori estimation or Markov chain Monte Carlo method, so that the updated scoring function is more consistent with the statistical characteristics of the target area. In the coupled model of construction-mechanics-integrity , , , , Parameters such as fault density, brittleness index, effective stress, and leakage records are calibrated using a sample set and regression and / or Bayesian methods to enable the model to exhibit high discriminative and predictive capabilities in existing cases. Within the Bayesian framework, the prior distribution of these parameters can be set as a relaxed normal or log-normal distribution. Likelihood functions are constructed using observations such as "whether leakage occurs" and "the magnitude of the breakthrough pressure." After obtaining the posterior distribution of the parameters, their mean or high posterior density intervals are used to represent the applicable value range in the region.
[0042] Taking thickness scoring function calibration as an example, the following objective function can be constructed: ; in, For the first The thickness of each historical cap layer sample The observed score is mapped based on the closedness level of the sample (e.g., "excellent, good, average, poor"). For the thickness scoring function's parameter vector, a set of parameters with optimal fit on the sample set is obtained by minimizing the sum of squared residuals. Similarly, for the permeability scoring function... Regression calibration can also be performed by minimizing the objective function of "the error between the model's predicted score and its historical closure performance".
[0043] In the Bayesian calibration process, a unified approach can be adopted. In the form of, For parameters The prior distribution, For observation data, Let be the likelihood function. The posterior distribution is used as the basis for analysis. By analyzing the mean, variance, and confidence interval of the posterior distribution, quantitative basis can be provided for the selection of parameters for single-factor models and construction-mechanism-integrity coupling models in different regions.
[0044] Example 4: Specific Case of Parameter Calibration Taking the evaluation of the deep geothermal cap layer in a basin as an example, this paper demonstrates the parameter calibration process of the thickness scoring model and the structure-mechanics-integrity coupling model.
[0045] The thickness data and sealing performance of 15 developed caprocks in this basin are shown in Table 1.
[0046] Table 1
[0047] The parameters of the Sigmoid function are fitted using the least squares method: ; Calibration result: Prior value is m, m; posterior value: m, m; Model fit goodness of fit: .
[0048] The structural parameters and leakage records of 10 caprocks in this basin are shown in Table 2.
[0049] Table 2
[0050] The parameters are calibrated using the Bayesian method, and the prior distribution is set as: characteristic fault density. It follows a log-normal distribution ,in The logarithmic mean is... Logarithmic standard deviation; reference stress Follows a normal distribution MPa, where The mean, Standard deviation; plasticity sensitivity index Follows uniform distribution .
[0051] Using the leakage probability as the likelihood function, the posterior distribution was obtained using the Markov Chain Monte Carlo (MCMC) method, as shown in Table 3.
[0052] Table 3
[0053] The calibrated parameters were used to predict the results of five sets of validation samples, as shown in Table 4.
[0054] Table 4
[0055] The verification results show that the calibrated model has a prediction accuracy of 80%, and the biased samples show a conservative prediction trend, which meets the engineering safety requirements.
[0056] This invention provides a geothermal caprock evaluation modeling method. By constructing a unified single-factor evaluation model system, it provides standardized scoring functions and parameter frameworks for key factors such as lithology, thickness, permeability, and thermal conductivity, significantly improving the comparability of caprock evaluation results across different regions and the model's portability. Simultaneously, this invention establishes a coupled tectonic-mechanical-integrity model, incorporating fault density, brittleness index, effective stress, and plastic self-healing capacity into a unified mathematical framework for coupled quantification, achieving a more comprehensive and objective evaluation of caprock integrity and fracture risk. Furthermore, by introducing regression and Bayesian calibration procedures based on historical breakthrough pressure, leakage events, and other observational data, key parameters in the model can be objectively calibrated, effectively reducing the model's reliance on subjective experience, thereby improving the stability and predictive reliability of caprock evaluation results.
[0057] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.
Claims
1. A method for evaluating and modeling geothermal cap layers, characterized in that, Includes the following steps: Obtain physical and structural data of the geothermal cap layer; Based on the aforementioned physical property data, a single-factor evaluation model is constructed that includes evaluation factors for lithology, thickness, permeability, and thermal conductivity. Based on the aforementioned construction data, a construction-mechanics-integrity coupled model is constructed, including basic construction score, crack risk index, and plastic self-healing factor. Using historical closed-loop observation data, the parameters in the single-factor evaluation model and the structure-mechanics-integrity coupling model are calibrated.
2. The method according to claim 1, characterized in that, The process of constructing the single-factor evaluation model includes: establishing a lithological sealing scoring model; establishing a thickness scoring model; establishing a permeability scoring model; and establishing a thermal conductivity scoring model.
3. The method according to claim 1, characterized in that, The construction process of the coupled structure-mechanics-integrity model includes: calculating the basic structural score based on fault density; Crack risk indicators are calculated based on the brittleness index and effective stress. The plastic self-healing factor is calculated based on the brittleness index and the crack risk index; The structural-mechanical-integrity score is calculated based on the basic structural score, the crack risk index, and the plastic self-healing factor.
4. The method according to claim 1, characterized in that, The process of data calibration includes: The parameters in the thickness scoring model and the permeability scoring model were fitted and calibrated using regression methods; The parameters in the lithological sealing scoring model and the thermal conductivity scoring model were calibrated and standardized using the Bayesian method. The parameters in the construction-mechanics-integrity coupling model are jointly calibrated using regression or Bayesian methods.
5. The method according to claim 2, characterized in that, The process of establishing a lithological sealing scoring model includes weighted summation of the scores for permeability, porosity, and clay content.
6. The method according to claim 2, characterized in that, The thickness scoring model is established using the Sigmoid function.
7. The method according to claim 2, characterized in that, The penetration rate scoring model is established using a logarithmic scaling function with the reference penetration rate as the median.
8. The method according to claim 3, characterized in that, The calculation process of the crack risk index includes limiting the product of the brittleness index and the normalized effective stress value to a preset range.
9. A computer terminal device, characterized in that, include: One or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors perform the steps of the method as described in any one of claims 1-8.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1-8.