Ground heat flow joint inversion calculation method, device and system and storage medium

Abstract

The invention discloses an earth heat flow joint inversion calculation method, device and system, and a storage medium. The method comprises the following steps: 1, obtaining a temperature constraint value; 2, obtaining a thermal parameter constraint value; 3, determining value ranges of thermal conductivity and heat generation rate parameters of each layer section; 4, constructing an initial temperature profile; 5, carrying out inversion optimization to obtain an optimal thermal parameter; and 6, calculating and extracting an earth heat flow value. By adopting the technical scheme of the invention, a high-precision and high-reliability earth heat flow value can be obtained under the constraint of a small amount of measured data.

Description

Technical Field

[0001] This invention belongs to the field of geophysical exploration and inversion technology, specifically relating to a method, apparatus, system, and storage medium for joint inversion calculation of geothermal flow. Background Technology

[0002] As a core parameter characterizing the thermal state of the Earth's interior, geothermal flow is a key foundational data for fields such as geothermal resource potential assessment, reconstruction of the thermal evolution history of sedimentary basins, analysis of oil and gas resource potential, and selection of target areas for hot dry rock development. Its calculation accuracy directly determines the scientific validity of relevant research conclusions and the reliability of engineering applications.

[0003] Currently, the mainstream approach to geothermal heat flow mainly relies on the calculation of laboratory thermophysical parameters (thermal conductivity, heat generation rate) from continuous formation temperature profiles obtained through drilling and core samples from the entire well section. However, this technical approach has significant limitations in practical applications, specifically: 1) Prominent ambiguity and high uncertainty in inversion results: Geophysical inversion problems are inherently non-unique, and traditional methods rely solely on limited discrete measured data (such as temperature measurement points spaced tens to hundreds of meters apart, and thermal parameter data from scattered core samples) to construct a thermal parameter profile for the entire well section through linear interpolation or empirical extrapolation. This leads to multiple plausible possibilities for the spatial distribution models of thermal conductivity and heat generation rate. This ambiguity directly transfers to subsequent heat flow calculations, resulting in a large error range in the final estimated heat flow value, making it difficult to meet the needs of high-precision geological research and engineering decision-making.

[0004] 2) High dependence on measured data, high acquisition cost, and limited applicability: Obtaining continuous, high-quality core samples is costly and technically challenging. Furthermore, many wells do not involve full coring, resulting in missing layers and sampling bias. In these "incomplete coring wells," traditional methods, lacking sufficient measured data, can only calculate heat flow by roughly comparing data from adjacent wells or using simplified assumptions such as regional average thermal conductivity. This easily deviates from the actual formation thermal state, significantly weakening the reliability of heat flow estimation.

[0005] 3) Strong model dependency and susceptibility to local optima: Traditional methods often employ linear inversion (such as least squares) or local optimization algorithms to construct thermal parameter models. The computational results of these algorithms are highly dependent on the rationality of the initial model. If the initial model is based on empirical assumptions (such as the assumption that thermal conductivity increases linearly with depth) and does not conform to actual geological laws (such as a sudden drop in thermal conductivity due to abrupt changes in lithology at a certain depth), the algorithm is prone to getting trapped in local optima during the iteration process and will be unable to find the globally optimal combination of thermal parameters.

[0006] 4) Low standardization of processes and poor reproducibility of results: In the current heat flux calculation process, key aspects such as the selection of data parameters, methods for completing missing parameters (such as interpolation function selection), and model calibration strategies rely heavily on the experience and judgment of operators, lacking a unified standardized process. Different researchers process the same well data differently, leading to significantly different heat flux results from the same raw data, affecting the horizontal comparison of research results and the formation of industry consensus.

[0007] In summary, the shortcomings of traditional geothermal heat flow calculation methods in terms of multiple solutions control, data dependence, algorithm optimization capabilities, and process standardization make them difficult to meet the needs of current high-precision geological research and energy exploration engineering. There is an urgent need to develop a new technology solution that can achieve high reliability and high repeatability of geothermal heat flow calculation under the constraint of a small amount of measured data. Summary of the Invention

[0008] To address the problems existing in the prior art, this invention provides a method, device, system, and storage medium for joint inversion calculation of geothermal heat flow, which can obtain high-precision and high-reliability geothermal heat flow values ​​under the constraint of a small amount of measured data.

[0009] To achieve the above objectives, the present invention provides the following solution: A method for joint inversion calculation of geothermal heat flow includes: Step 1: Obtain measured formation temperature data from the well; Step 2: Obtain rock samples of typical lithology from several key strata in the well, and obtain the measured values ​​of thermal conductivity and heat generation rate of the samples through laboratory measurements. Step 3: Based on the measured values ​​of burial depth or porosity, thermal conductivity and heat generation rate of the rock samples, establish a linear empirical formula for thermal conductivity and depth and an exponential empirical formula for heat generation rate and depth using regression analysis. Combine the lithological combination characteristics of the strata to determine the range of values ​​for thermal conductivity and heat generation rate parameters for each layer. Step 4: Using the median value of the thermal conductivity and heat generation rate parameters of each layer as the initial parameters, and combining the measured formation temperature data, the initial temperature profile is calculated using a one-dimensional steady-state heat conduction model. Step 5: Based on the initial temperature profile, the differential evolution algorithm is used as the global optimization algorithm to perform inversion optimization on the thermal conductivity and heat generation rate parameters of the uncored section, minimize the root mean square error between the forward modeled temperature profile and the measured formation temperature profile, and obtain the optimal thermal conductivity and heat generation rate. Step 6: Under the one-dimensional steady-state heat conduction framework, based on the optimal thermal conductivity and heat generation rate obtained by inversion calculation, and combined with the measured formation temperature data, calculate the temperature gradient layer by layer, and obtain the final geothermal heat flow value according to Fourier's law.

[0010] The present invention also provides a geothermal flow joint inversion calculation device, comprising: The first processing module is used to acquire measured formation temperature data from the drilling site; The second processing module is used to obtain rock samples of typical lithology in several key formations of the well, and to obtain the measured values ​​of thermal conductivity and heat generation rate of the samples through laboratory measurements. The third processing module is used to establish a linear empirical formula for thermal conductivity and depth and an exponential empirical formula for heat generation and depth based on the measured values ​​of burial depth or porosity, thermal conductivity and heat generation rate of rock samples using regression analysis. It also determines the range of values ​​for thermal conductivity and heat generation rate parameters for each layer by combining the lithological combination characteristics of the strata. The fourth processing module is used to calculate the initial temperature profile by taking the median value of the thermal conductivity and heat generation rate parameters of each layer as the initial parameters, and combining the measured formation temperature data with a one-dimensional steady-state heat conduction model. The fifth processing module is used to perform inverse optimization of the thermal conductivity and heat generation rate parameters of the uncored section based on the initial temperature profile, using the differential evolution algorithm as the global optimization algorithm, minimizing the root mean square error between the forward-modeled temperature profile and the measured formation temperature profile, and obtaining the optimal thermal conductivity and heat generation rate. The sixth processing module is used to calculate the temperature gradient layer by layer based on the optimal thermal conductivity and heat generation rate obtained by inversion calculation under the one-dimensional steady-state heat conduction framework, combined with the measured formation temperature data, and to obtain the final geothermal heat flow value according to Fourier's law.

[0011] The present invention also provides a geothermal flow joint inversion calculation system, comprising: a memory and a processor, wherein the memory stores a computer program executed by the processor, and the computer program executes the steps of the geothermal flow joint inversion calculation method when executed by the processor.

[0012] The present invention also provides a storage medium storing a computer program, which executes the steps of the geothermal flow joint inversion calculation method when running.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention introduces a small amount of measured data from key strata as "hard constraints," combines stratigraphic lithology and geological characteristics of burial depth to construct a range of thermal parameter values, and then uses a global optimization algorithm to search for the optimal solution within a reasonable parameter space, ultimately achieving the following objectives: (1) Reduce the ambiguity of inversion and improve the reliability and accuracy of results: The parameter search space is compressed by using measured temperature data, experimental thermophysical data and geological laws, which effectively controls the non-uniqueness of inversion.

[0014] (2) Reduce reliance on core samples from the entire well section: High-precision heat flow estimates can still be obtained in partial core drilling, which significantly reduces data acquisition costs and broadens the scope of application.

[0015] (3) Achieve automation and standardization of the calculation process: Modular design reduces the dependence on human experience and improves the repeatability of results and calculation efficiency.

[0016] (4) Enhance the global optimization capability of the algorithm: The differential evolution algorithm is adopted, which is not sensitive to the initial model, avoids getting trapped in local optima, and is more suitable for inversion problems of complex layered media. Attached Figure Description

[0017] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of the geothermal heat flow joint inversion calculation method according to an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] Example 1 like Figure 1 As shown, this invention provides a method for joint inversion calculation of geothermal flow, comprising: Step 1: Obtain temperature constraint value Obtain drilling-measured formation temperature data This data is used as the temperature constraint value in the inversion calculation process. Measured formation temperature data reflects the true temperature distribution of the formation and is the core reference for subsequent temperature profile calculations and inversion optimization. It is necessary to ensure that the data acquisition process conforms to steady-state temperature measurement specifications to avoid temperature data deviations caused by factors such as drilling fluid circulation and formation disturbance. Step 1 provides a reliable temperature field constraint basis for the entire inversion process.

[0022] Step 2: Obtain thermal parameter constraint values Rock samples of typical lithologies (the main rock types with the highest volume percentage and best representing the overall petrological and physical properties of the key strata, such as sandstone in a large sandstone layer or mudstone in a thick mudstone layer) were obtained from several key strata during drilling (such as thick single-lithological sections, strata with significant lithological changes, and important geological interfaces). The measured values ​​of the thermal conductivity of the samples were obtained through standardized laboratory measurement procedures. ) and measured value of heat generation rate ( These two types of data are used as constraint values ​​for thermal conductivity and heat generation rate in the inversion calculation, respectively. Laboratory measurements must strictly follow the testing standards for thermophysical parameters to ensure the accuracy of thermal conductivity and heat generation rate data. Step 2 provides "hard data" for key strata, which is the basis for constructing empirical relationships and constraining inversion parameters.

[0023] Step 3: Determine the range of values ​​for the thermal conductivity and heat generation rate parameters for each layer. Based on a limited number of measured points, the core of constructing a reasonable range for the variation of thermal parameters throughout the well section is to establish empirical formulas and determine the range of parameter values; specifically, this includes: 3.1 Establishing an empirical conversion formula Based on the burial depth (or porosity), measured thermal conductivity, and measured heat generation rate of the rock samples obtained in step 2, regression analysis using the least squares method is employed to establish an empirical formula. The samples used in the regression analysis are the key stratigraphic sample data obtained in step 2. For thermal conductivity, establish its relationship with depth z Linear empirical formula: ; in, Let be the predicted thermal conductivity at depth z, in W / (m·K); z is the depth, in meters (m). The thermal conductivity-depth gradient is obtained through linear regression, with units of W / (m²·K). A positive value indicates that the thermal conductivity increases with increasing depth. For the surface ( z =0) The thermal conductivity estimate is obtained through linear regression, and the unit is W / (m·K).

[0024] For the heat generation rate, an empirical formula is established for its exponential relationship with depth (z): ; in, For depth z The predicted heat generation rate at the location is expressed in μW / m³; z is the depth, expressed in meters (m). For the surface ( z=0) The estimated heat generation rate is obtained through regression analysis, and the unit is μW / m³; D The attenuation depth is the characteristic depth of the heat generation rate attenuation, obtained through regression analysis, and its unit is meters (m).

[0025] To facilitate regression, the formula is modified. Taking the natural logarithm of both sides, we can transform it into a linear form. By analyzing data points Perform linear regression to obtain and And thus obtain and .

[0026] 3.2 Calculation parameter value range Based on the lithological assemblage characteristics of the strata (such as the distribution of different lithologies like sandstone, mudstone, and limestone), the range of values ​​for thermal conductivity and heat generation rate parameters for each stratum is calculated using the following formula: First, calculate the coefficient of variation: , in, The coefficient of variation; The basic coefficient of variation (preset based on regional geological background and lithology, with a value range of 0.1-0.3). This is a lithology correction factor (determined based on the stability of thermal parameters of different lithologies; for example, limestone has high thermal parameter stability, so the correction factor is 0.8-1.0, while mudstone has large fluctuations in thermal parameters, so the correction factor is 1.0-1.2). The depth correction factor (as the burial depth increases, the degree of ground compaction increases, and the stability of thermal parameters is enhanced; the correction factor value gradually decreases from 1.2 to 0.8 as the depth increases).

[0027] Thermal conductivity range: , in, This is the minimum value of thermal conductivity; This represents the maximum value of thermal conductivity; The reference values ​​for thermal conductivity of each segment are calculated based on the empirical conversion formula for thermal conductivity established in step 3 (K = K (Depth)).

[0028] Range of heat generation rate: ,

[0029] in, This represents the minimum heat generation rate. This represents the maximum heat generation rate. To convert the empirical formula for heat generation rate established in step 3.1 The calculated baseline values ​​for the heat generation rate of each section; The minimum threshold value for heat generation rate; This is set as the maximum threshold for heat generation rate to avoid excessively large parameter ranges due to abnormal data.

[0030] Step 3, by combining measured data with geological patterns, defines a physically reasonable parameter search space for subsequent inversion optimization, which is key to reducing ambiguity.

[0031] Step 4: Construct the initial temperature profile The initial parameters are the median value of the thermal conductivity and heat generation rate parameters for each layer, as determined in step 3. Initial heat generation rate Combining the measured formation temperature data obtained in step 1, a forward modeling calculation was performed using the one-dimensional steady-state heat conduction equation to obtain the initial temperature profile. .

[0032] The expression for the one-dimensional steady-state heat conduction equation is: ; in, z For depth, K ( z () is a function of thermal conductivity as a function of depth. T For temperature, A ( z ) is a function of heat generation rate as a function of depth.

[0033] With the near-surface isothermal layer temperature T( The upper boundary condition is given by , and the solution is obtained using finite difference or layer-by-layer integration methods. For example, for the j-th layer (j=1,2,3,..., j is a positive integer), its bottom temperature is... Based on the bottom temperature of the previous layer Calculation (i.e., By T( The calculation is obtained, and so on. ): ; in, Let j be the thickness of the j-th layer; , The initial values ​​for the thermal conductivity and heat generation rate of the j-th layer are the median values ​​within the range taken in step 3. Top Boundary Heat flux value (positive downwards, unit mW / m²).

[0034] In fixed , Under these conditions, temperature observation { } represents a constraint; where, For the first i Vertical depth of each measured temperature point ( i =1,2,3,..., i (positive integers) In depth The measured formation temperature was optimized using least squares fitting. : ; Based on the obtained The initial temperature profile was obtained. .

[0035] Step 5: Inversion optimization to obtain optimal thermal parameters Initial temperature profile This will serve as a reference benchmark for step 5, inversion and evaluation: During the inversion iteration process, after each adjustment of the thermal conductivity and heat generation rate parameters of the uncore segment, the forward temperature profile will be recalculated based on the adjusted parameters. And compared it with measured formation temperature data and initial temperature profile. By comparing the parameters, we can determine whether the adjustment direction is reasonable. Simultaneously, we further optimize the parameters based on the objective function (the root mean square error between calculated and measured temperatures) to ensure that the inversion process always revolves around temperature distribution trends that conform to geological patterns. This initial temperature profile provides a reliable starting point for subsequent inversion optimization that aligns with geological patterns and measured temperatures, reducing the algorithm's sensitivity to the initial model.

[0036] Differential evolution (DE) algorithm was used as the global optimization algorithm to inversely optimize the thermal conductivity and heat generation rate parameters of the uncored section in order to obtain the thermophysical parameters that best match the measured temperature. Differential evolution algorithm was chosen because it has a strong global search capability, is insensitive to initial values, and can effectively handle nonlinear and multi-extremum problems.

[0037] 5.1 Algorithm Parameter Settings Population size (NP): Based on the complexity of the inversion problem and the dimension of the parameter space, it is set to 5-10 times the number of parameters to be inverted. This ratio has been verified to achieve a good balance between search efficiency and global convergence.

[0038] Variation factor (F): Typically takes values ​​in the range of [0.5, 1.0].

[0039] Crossover probability (CR): Typically takes values ​​in the range of [0.7, 0.9].

[0040] Mutation strategy: "DE / rand / 1 / bin" or its variants can be used.

[0041] 5.2 Inversion Process Parameter vector: This represents the thermal conductivity of each uncored segment. and heat generation rate The concatenation forms a parameter vector, representing a possible solution (individual).

[0042] Search boundary: The thermal conductivity of each segment determined in step 3 ( ) and heat generation rate ( The range of values ​​for ) is set as the search boundary of the optimization algorithm.

[0043] Population initialization: The first individual uses the baseline parameter vector from step 4 ( , The remaining individuals are randomly generated within their respective boundaries, forming the initial population.

[0044] Fixed constraints: During the optimization process, the measured thermal conductivity and heat generation rate of the core layers taken in step 2 remain unchanged as fixed constraints to ensure that the known information is not destroyed.

[0045] Objective function construction: The objective function is to minimize the root mean square error (RMSE) between the forward-modeled temperature profile and the measured formation temperature profile. The RMSE calculation formula is as follows: ; in, For the first i Forward modeling of temperature at each depth point For the first i Measured temperature at each depth point n This represents the number of measured temperature data points.

[0046] Population Iterative Evolution: The algorithm continuously evolves the population through mutation, crossover, and selection operations. Iteration terminates when the maximum number of iterations (e.g., 80 generations) is reached or the RMSE improvement is less than the tolerance (e.g., 1e-5) for several consecutive generations (e.g., 20 generations). The output is the optimal thermal conductivity that minimizes the objective function. and heat generation rate

[0047] Step 5 searches for the optimal solution within a strictly constrained space through global optimization, ensuring the reliability of the results and avoiding local optima.

[0048] Step 6: Calculate and extract the geothermal heat flow value Based on the optimal thermal conductivity obtained from step 5 and heat generation rate Within the framework of one-dimensional steady-state heat conduction, combined with the measured formation temperature data from step 1... Numerical differentiation on the layer yields Then, according to Fourier's law... The heat conduction and heat flux profile is obtained. Among them, in Under the coordinate convention that downwards is positive, depth heat flux at the point This indicates heat flow pointing towards the Earth's surface. Reference depth will be used. (or at the surface) As the final surface heat flow value, geothermal heat flow plane maps can be drawn based on geothermal heat flow values ​​from multiple wells using spatial interpolation methods (such as Kriging interpolation), visually presenting the regional heat flow distribution characteristics.

[0049] Step 6 uses high-precision thermal property parameters optimized by inversion to finally extract reliable geothermal flow values, providing key data support for geological research and engineering decision-making.

[0050] This invention has the following characteristics: 1) Reduce the ambiguity of inversion and improve the reliability and accuracy of results: This invention introduces a small amount of core measured data (thermal conductivity, heat generation rate, temperature) from key strata as hard constraints, and combines lithology and depth to construct the parameter value range, thereby limiting the adjustment space of inversion parameters from the source. This effectively reduces the ambiguity of the inversion problem, makes the calculated geothermal heat flow value closer to the real thermal state of the strata, and significantly improves the reliability and accuracy of the results.

[0051] 2) Achieving process automation and standardization, reducing reliance on experience, and improving efficiency and repeatability: The system designed in this invention adopts a modular architecture, standardizing data input, verification, constraint construction, inversion optimization, and result output, achieving fully automated processing from data to results. Operators do not need complex inversion algorithm experience; they only need to input measured data according to the specifications to complete the calculation. This not only reduces reliance on human experience but also avoids errors caused by manual operation, significantly improving calculation efficiency and the repeatability of results.

[0052] 3) Enhancing algorithm robustness and global optimization capability, avoiding local optima: This invention employs global optimization algorithms such as differential evolution and combines geological characteristics (lithology, depth) to constrain the parameter search range. Compared to traditional linear inversion or Bayesian methods, it effectively solves the problems of sensitivity to the initial model and susceptibility to local optima. The algorithm can globally search for the optimal solution within a reasonable parameter space, enhancing its robustness and global optimization capability, ensuring stable and accurate calculation results even under complex geological conditions.

[0053] Example 2: The present invention also provides a geothermal flow joint inversion calculation device, comprising: The first processing module is used to acquire measured formation temperature data from the drilling site; The second processing module is used to obtain rock samples of typical lithology in several key formations of the well, and to obtain the measured values ​​of thermal conductivity and heat generation rate of the samples through laboratory measurements. The third processing module is used to establish a linear empirical formula for thermal conductivity and depth and an exponential empirical formula for heat generation and depth based on the measured values ​​of burial depth or porosity, thermal conductivity and heat generation rate of rock samples using regression analysis. It also determines the range of values ​​for thermal conductivity and heat generation rate parameters for each layer by combining the lithological combination characteristics of the strata. The fourth processing module is used to calculate the initial temperature profile by taking the median value of the thermal conductivity and heat generation rate parameters of each layer as the initial parameters, and combining the measured formation temperature data with a one-dimensional steady-state heat conduction model. The fifth processing module is used to perform inverse optimization of the thermal conductivity and heat generation rate parameters of the uncored section based on the initial temperature profile, using the differential evolution algorithm as the global optimization algorithm, minimizing the root mean square error between the forward-modeled temperature profile and the measured formation temperature profile, and obtaining the optimal thermal conductivity and heat generation rate. The sixth processing module is used to calculate the temperature gradient layer by layer based on the optimal thermal conductivity and heat generation rate obtained by inversion calculation under the one-dimensional steady-state heat conduction framework, combined with the measured formation temperature data, and to obtain the final geothermal heat flow value according to Fourier's law.

[0054] Example 3: The present invention also provides a geothermal flow joint inversion calculation system, comprising: a memory and a processor, wherein the memory stores a computer program executed by the processor, and the computer program executes the geothermal flow joint inversion calculation method when executed by the processor.

[0055] Example 4: The present invention also provides a storage medium storing a computer program, which executes a geothermal heat flow joint inversion calculation method when running.

[0056] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for joint inversion calculation of geothermal heat flow, characterized in that, include: Step 1: Obtain measured formation temperature data from the well; Step 2: Obtain rock samples of typical lithology from several key strata in the well, and obtain the measured values ​​of thermal conductivity and heat generation rate of the samples through laboratory measurements. Step 3: Based on the measured values ​​of burial depth or porosity, thermal conductivity and heat generation rate of the rock samples, establish a linear empirical formula for thermal conductivity and depth and an exponential empirical formula for heat generation rate and depth using regression analysis. Combine the lithological combination characteristics of the strata to determine the range of values ​​for thermal conductivity and heat generation rate parameters for each layer. Step 4: Using the median value of the thermal conductivity and heat generation rate parameters of each layer as the initial parameters, and combining the measured formation temperature data, the initial temperature profile is calculated using a one-dimensional steady-state heat conduction model. Step 5: Based on the initial temperature profile, the differential evolution algorithm is used as the global optimization algorithm to perform inversion optimization on the thermal conductivity and heat generation rate parameters of the uncored section, minimize the root mean square error between the forward modeled temperature profile and the measured formation temperature profile, and obtain the optimal thermal conductivity and heat generation rate. Step 6: Under the one-dimensional steady-state heat conduction framework, based on the optimal thermal conductivity and heat generation rate obtained by inversion calculation, and combined with the measured formation temperature data, calculate the temperature gradient layer by layer, and obtain the final geothermal heat flow value according to Fourier's law.

2. A joint inversion calculation device for geothermal heat flow, characterized in that, include: The first processing module is used to acquire measured formation temperature data from the drilling site; The second processing module is used to obtain rock samples of typical lithology in several key formations of the well, and to obtain the measured values ​​of thermal conductivity and heat generation rate of the samples through laboratory measurements. The third processing module is used to establish a linear empirical formula for thermal conductivity and depth and an exponential empirical formula for heat generation and depth based on the measured values ​​of burial depth or porosity, thermal conductivity and heat generation rate of rock samples using regression analysis. It also determines the range of values ​​for thermal conductivity and heat generation rate parameters for each layer by combining the lithological combination characteristics of the strata. The fourth processing module is used to calculate the initial temperature profile by taking the median value of the thermal conductivity and heat generation rate parameters of each layer as the initial parameters, and combining the measured formation temperature data with a one-dimensional steady-state heat conduction model. The fifth processing module is used to perform inverse optimization of the thermal conductivity and heat generation rate parameters of the uncored section based on the initial temperature profile, using the differential evolution algorithm as the global optimization algorithm, minimizing the root mean square error between the forward-modeled temperature profile and the measured formation temperature profile, and obtaining the optimal thermal conductivity and heat generation rate. The sixth processing module is used to calculate the temperature gradient layer by layer based on the optimal thermal conductivity and heat generation rate obtained by inversion calculation under the one-dimensional steady-state heat conduction framework, combined with the measured formation temperature data, and to obtain the final geothermal heat flow value according to Fourier's law.

3. A joint inversion calculation system for geothermal heat flow, characterized in that, include: A memory and a processor, wherein the memory stores a computer program executed by the processor, the computer program, when executed by the processor, implements the steps of the geothermal flow joint inversion calculation method as described in claim 1.

4. A storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the steps of the geothermal flow joint inversion calculation method as described in claim 1.

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