Thermal conductivity determination method, device, computer storage medium and electronic equipment

Abstract

The application provides a method and device for determining thermal conductivity, a computer storage medium and an electronic device. A three-dimensional geological model is established based on background information of a target area, a geothermal gradient of each layer system is determined based on drilling temperature measurement data, an initial thermal conductivity of each layer system is determined based on the geothermal gradient and a heat flow parameter, a heat transfer model of each layer system is determined by the three-dimensional geological model, initial particle groups of each layer system are input into the heat transfer model to calculate formation temperature information, and the particle groups are updated based on the formation temperature information and measured formation temperature values, so that the thermal conductivity of each layer system is determined, and the accuracy of determining the thermal conductivity can be improved.

Description

Technical Field

[0001] This application relates to the field of exploration and development technology of microbial carbonate reservoirs, and in particular to methods, apparatus, computer storage media and electronic equipment for determining thermal conductivity. Background Technology

[0002] Thermal conduction is the most important mode of heat transfer in the lithosphere, with 1.4 × 10⁻⁶ tons of heat transferred outward globally via conduction. 21 J / a is 100 times that of volcanic activity, earthquakes, and hydrothermal activity. Accurate rock thermal conductivity data are essential for studying the thermal structure of the lithosphere, interpreting deep Earth dynamics, and reconstructing the Earth's thermal history. Furthermore, rock thermal conductivity has significant practical implications for geothermal energy development and utilization, deep oil and gas resource extraction, and the management of thermal hazards in deep mines and buried tunnels. The rigorous definition of thermal conductivity is given by the mathematical expression of Fourier's law, representing the magnitude of the heat flux density vector within an object under a unit temperature gradient.

[0003] Like other rock physical and mechanical parameters, the ideal method for testing thermal conductivity is in-situ measurement, conducted directly or indirectly in the field. Existing methods for measuring formation thermal conductivity mainly fall into two categories: actual measurements based on borehole sampling and indirect measurements based on thermal response. Borehole sampling-based methods require laboratory measurements using optical scanning, transient heat sources, and laser scattering. However, the confining pressure and water content of the collected samples vary significantly from the underlying conditions, leading to inaccurate results. Furthermore, sampling only yields parameters at a single depth, failing to provide thermal conductivity across the entire depth. Indirect measurements based on thermal response analyze thermal response experimental data to assess the sensitivity of temperature to formation thermophysical parameters, employing statistical analysis methods to perform hierarchical inversion of formation thermal conductivity. However, this method requires a large amount of thermal response test data, takes a long time, and the test error increases significantly with depth, failing to accurately represent the true thermophysical properties of underground rocks. Summary of the Invention

[0004] To address the aforementioned problems, this application provides a method, apparatus, computer storage medium, and electronic device for determining thermal conductivity.

[0005] This application provides a method for determining thermal conductivity, including:

[0006] Obtain background information, borehole temperature measurement data, and measured formation temperature values ​​for the target area;

[0007] A three-dimensional geological model of the target area is established based on the background information, wherein the three-dimensional geological model includes multiple strata;

[0008] The geothermal gradient of each layer is determined based on the borehole temperature measurement data, and the initial thermal conductivity of each layer is determined based on the geothermal gradient and heat flow parameters. The heat transfer model of each layer is determined based on the three-dimensional geological model.

[0009] Each initial particle swarm is determined based on the initial thermal conductivity of each layer. Each initial particle swarm is input into the corresponding heat transfer model for numerical simulation calculation to determine the formation temperature information corresponding to each layer. Based on the formation temperature information and the measured values ​​of the formation temperature corresponding to each layer, each fitness function value is determined.

[0010] Each initial particle swarm is updated to obtain each updated particle swarm, and the fitness function value corresponding to each updated particle swarm is determined. When the fitness function value corresponding to each updated particle swarm is the minimum or each particle swarm reaches the maximum number of iterations, the thermal conductivity corresponding to the minimum fitness function value or the maximum number of iterations is determined as the thermal conductivity of each layer in the target region.

[0011] In some embodiments, the background data includes: geological background data, geothermal geological background data, and hydrogeological background data. The step of establishing a three-dimensional geological model of the target area based on the background data includes:

[0012] Based on the aforementioned geological background data, the lithology, thickness, distribution, and fault distribution of the strata in the target area are determined;

[0013] Based on the aforementioned geothermal geological background data, the geothermal gradient of the reservoir caprock, the temperature of the reservoir, and the extent of the geothermal anomaly zone are determined.

[0014] The relationship between groundwater and heat flow parameters is determined based on the aforementioned hydrogeological background data;

[0015] The three-dimensional geological model is established based on the lithology, thickness, distribution of strata, distribution of faults, geothermal gradient of the reservoir caprock, temperature of the reservoir, range of geothermal anomaly zone, and the relationship between groundwater and heat flow parameters.

[0016] In some embodiments, updating each initial particle swarm to obtain each updated particle swarm includes:

[0017] Cross-variation of each initial thermal conductivity;

[0018] The positions and velocities of each initial particle swarm are updated based on the first calculation formula to obtain the updated particle swarm, wherein the first calculation formula is:

[0019] v i =ω×v i +c1×rand()×(pbset i -x i)+c2×rand()×(gbest i -x i );

[0020] Among them, v i The velocity of the particle is given by `rand()`, which is a random number between (0,1). i The current position of the particle is given by c1 and c2, where c1 and c2 are acceleration factors, ω is the inertia factor, and pbest is the value of pbest. i For local optimal solutions, gbest i This is the globally optimal solution.

[0021] In some embodiments, determining the heat transfer model for each layer based on the three-dimensional geological model includes:

[0022] Computational fluid dynamics is used to generate a computational grid from the three-dimensional geological model that has the same size as the geological blocks in the target region.

[0023] The boundary conditions for heat transfer in the target region are loaded for each computational grid, and the physical property conditions of each computational network are set. Based on the initial thermal conductivity of each layer, the heat transfer model of each layer is constructed.

[0024] In some embodiments, determining each fitness function value based on the formation temperature information and measured formation temperature values ​​corresponding to each stratum includes:

[0025] The fitness function values ​​are calculated based on the second formula, whereby the second formula is:

[0026]

[0027] Among them, T ij,meas Let T be the measured value of the formation temperature at the j-th location of the i-th measuring point. ij,cal This refers to the formation temperature information at the j-th location of the i-th measuring point.

[0028] In some embodiments, determining the initial thermal conductivity of each layer based on the geothermal gradient and heat flow parameters includes:

[0029] The initial thermal conductivity of each layer is calculated using a third formula, wherein the third formula is:

[0030]

[0031] Where, λ i is the thermal conductivity of the i-th layer; q is the heat flux parameter; ▽T i Let be the geothermal gradient of the i-th stratum.

[0032] This application provides an apparatus for determining thermal conductivity, comprising:

[0033] The first acquisition module is used to acquire background information, borehole temperature measurement data and measured formation temperature values ​​of the target area.

[0034] A module is established to create a three-dimensional geological model of the target area based on the background data, wherein the three-dimensional geological model includes multiple strata;

[0035] The first determining module is used to determine the geothermal gradient of each layer based on the borehole temperature measurement data, determine the initial thermal conductivity of each layer based on the geothermal gradient and heat flow parameters, and determine the heat transfer model of each layer based on the three-dimensional geological model.

[0036] The second determination module is used to determine each initial particle swarm based on the initial thermal conductivity of each layer, input each initial particle swarm into the corresponding heat transfer model for numerical simulation calculation to determine the formation temperature information corresponding to each layer, and determine each fitness function value based on the formation temperature information corresponding to each layer and the measured formation temperature value corresponding to each layer.

[0037] The third determining module is used to update each initial particle group to obtain each updated particle group, and determine the fitness function value corresponding to each updated particle group; when the fitness function value corresponding to each updated particle group is the minimum or each particle group reaches the maximum number of iterations, the thermal conductivity corresponding to the minimum fitness function value or the maximum number of iterations is determined as the thermal conductivity of each layer in the target region.

[0038] In some embodiments, the background data includes: geological background data, geothermal geological background data, and hydrogeological background data; the establishment module includes:

[0039] The first determining unit is used to determine the lithology, thickness, distribution, and fault distribution of the strata in the target area based on the geological background data.

[0040] The second determining unit is used to determine the geothermal gradient of the geothermal reservoir caprock, the temperature of the geothermal reservoir, and the range of the geothermal anomaly zone based on the geothermal geological background data.

[0041] The third determining unit is used to determine the relationship between groundwater and heat flow parameters based on the hydrogeological background data;

[0042] A unit is established to build the three-dimensional geological model based on the lithology, thickness, distribution of strata, distribution of faults, geothermal gradient of the reservoir caprock, temperature of the reservoir, range of geothermal anomaly zone, and the relationship between groundwater and heat flow parameters.

[0043] This application provides an electronic device, including a memory and a processor. The memory stores a computer program, which, when executed by the processor, performs the method for determining thermal conductivity described in any of the above-mentioned embodiments.

[0044] This application provides a computer storage medium storing a computer program that can be executed by one or more processors and can be used to implement the method for determining thermal conductivity described in any of the above claims.

[0045] This application provides a method, apparatus, computer storage medium, and electronic device for determining thermal conductivity. It establishes a three-dimensional geological model based on background data of the target area, determines the geothermal gradient of each stratum based on borehole temperature measurement data, and determines the initial thermal conductivity of each stratum based on the geothermal gradient and heat flow parameters. The three-dimensional geological model determines the heat transfer model for each stratum. Then, the initial particle swarm of each stratum is input into the heat transfer model to calculate formation temperature information, and the particle swarm is updated based on the formation temperature information and measured formation temperature values, thereby determining the thermal conductivity of each stratum. This method improves the accuracy of thermal conductivity determination. Attached Figure Description

[0046] The present application will be described in more detail below based on embodiments and with reference to the accompanying drawings.

[0047] Figure 1 A schematic diagram illustrating the implementation process of a method for determining thermal conductivity provided in an embodiment of this application;

[0048] Figure 2 A schematic diagram illustrating the implementation process of another method for determining thermal conductivity provided in this application embodiment;

[0049] Figure 3 A schematic diagram of a device for determining thermal conductivity provided in an embodiment of this application;

[0050] Figure 4 This is a schematic diagram of the composition structure of the electronic device provided in the embodiments of this application.

[0051] In the accompanying drawings, the same parts are referred to by the same reference numerals, and the drawings are not drawn to scale. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0053] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0054] If the application documents contain similar descriptions such as "first, second, third", the following explanation shall be added: In the following description, the terms "first, second, third" are used only to distinguish similar objects and do not represent a specific order of objects. It is understood that "first, second, third" may be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0056] To address the problems existing in related technologies, this application provides a method for determining thermal conductivity, which is applied to electronic devices such as computers and mobile terminals. The function implemented by the thermal conductivity determination method provided in this application can be achieved by the processor of the electronic device calling program code, wherein the program code can be stored in a computer storage medium.

[0057] Example 1

[0058] This application provides a method for determining thermal conductivity. Figure 1 This is a schematic diagram illustrating the implementation process of a method for determining thermal conductivity provided in an embodiment of this application, as shown below. Figure 1 As shown, it includes:

[0059] Step S101: Obtain background information, borehole temperature measurement data, and measured formation temperature values ​​for the target area.

[0060] In this application embodiment, the background information includes geological background information, geothermal geological background information, and hydrogeological background information.

[0061] Step S102: Establish a three-dimensional geological model of the target area based on the background information, wherein the three-dimensional geological model includes multiple strata.

[0062] In this embodiment of the application, the lithology, thickness, distribution, and fault distribution of the strata in the target area can be determined based on the geological background data; the geothermal gradient of the reservoir caprock, the temperature of the reservoir, and the range of the geothermal anomaly zone can be determined based on the geothermal geological background data; the relationship between groundwater and heat flow parameters can be determined based on the hydrogeological background data; and the three-dimensional geological model can be established based on the lithology, thickness, distribution, and fault distribution of the strata, the geothermal gradient of the reservoir caprock, the temperature of the reservoir, the range of the geothermal anomaly zone, and the relationship between groundwater and heat flow parameters.

[0063] In this embodiment of the application, the establishment of the three-dimensional geological model based on the lithology, thickness, distribution of strata and the distribution of faults, the geothermal gradient of the geothermal reservoir caprock, the temperature of the geothermal reservoir, the range of the geothermal anomaly zone, and the relationship between groundwater and heat flow parameters includes: constructing parametric surfaces of each interface based on interpolation data of strata interfaces and faults, and establishing a three-dimensional geological model of the target area through a series of geometric operations such as mutual cutting and deletion between strata interfaces, faults and model entities.

[0064] In this embodiment of the application, the three-dimensional geological model includes: strata and faults.

[0065] Step S103: Determine the geothermal gradient of each layer based on the borehole temperature measurement data, determine the initial thermal conductivity of each layer based on the geothermal gradient and heat flow parameters, and determine the heat transfer model of each layer based on the three-dimensional geological model.

[0066] In this embodiment of the application, the initial thermal conductivity of each layer can be calculated using a third calculation formula, wherein the third calculation formula is shown in the following formula (1):

[0067]

[0068] Where, λ i is the thermal conductivity of the i-th layer; q is the heat flux parameter; ▽T i Let be the geothermal gradient of the i-th stratum.

[0069] In this embodiment of the application, computational fluid dynamics can be used to generate a computational grid with the same size as the geological blocks in the target area from the three-dimensional geological model; the boundary conditions for heat transfer in the target area are loaded onto each computational grid, and the physical property conditions of each computational network are set; and the heat transfer model of each layer is constructed based on the initial thermal conductivity of each layer.

[0070] In this embodiment of the application, when constructing the heat transfer model of each layer, the heat transfer model of each layer can be constructed based on the energy conservation and mass balance equations.

[0071] In this embodiment of the application, when there is groundwater flow in the stratum, its mass balance equation can be expressed as equation (2):

[0072]

[0073] in, ρ represents the formation porosity. w Q is the density of water. m Let u be the mass source and u be the Darcy velocity field. u can be expressed as equation (3):

[0074]

[0075] Where k is the rock permeability, μ w Let be the dynamic viscosity of water, P be the water pressure, g be the acceleration due to gravity, and z be the vertical coordinate (positive upwards).

[0076] Energy conservation in the formation can be expressed by the convective heat transfer equation as equation (4):

[0077]

[0078] Among them, C p,w Let ρ be the specific heat capacity of water, and Q be the heat source. p ) eff For effective volumetric heat capacity, (ρC) p ) eff It can be expressed as equation (5):

[0079] (ρC p ) eff =(1-φ)ρ r C p,r +φρ w C p,w (5);

[0080] Where, ρ r C is the density of the rock. p,r k represents the specific heat capacity of the rock. eff For effective thermal conductivity, k eff It can be expressed as equation (6):

[0081] k eff =(1-φ)k r +φk w (6);

[0082] Based on equations (4), (5) and (6), the heat transfer model of the formation can be obtained.

[0083] In this embodiment of the application, the fluid flow process in the fault is expressed by Darcy's law in tangential form as equation (7):

[0084]

[0085] Where, q fr k is the volumetric flow rate in the fault. fr d represents the permeability of the fault. fr The aperture of the fault, ▽ T This is the gradient operator on the fault section.

[0086] The continuity equation in a fault is expressed as equation (8):

[0087]

[0088] Energy conservation in a fault can be expressed as equation (9):

[0089]

[0090] The heat transfer model in the fault is then determined based on equations (8) and (9).

[0091] Step S104: Determine each initial particle swarm based on the initial thermal conductivity of each layer, input each initial particle swarm into the corresponding heat transfer model for numerical simulation calculation to determine the formation temperature information corresponding to each layer, and determine each fitness function value based on the formation temperature information corresponding to each layer and the measured formation temperature value corresponding to each layer.

[0092] In this embodiment of the application, the initial thermal conductivity of each layer can be initialized to determine each initial particle swarm.

[0093] In this embodiment of the application, when the layer corresponding to the initial particle is a fault, the heat transfer model in the fault determined by equations (8) and (9) is input to calculate the formation temperature information. When the layer corresponding to the initial particle group is a formation, the heat transfer model of the formation can be obtained by inputting equations (4), (5) and (6) to calculate the formation temperature information.

[0094] In this embodiment of the application, each fitness function value can be calculated based on the second calculation formula, wherein the second calculation formula is as shown in the following formula (10):

[0095]

[0096] Among them, T ij,meas Let T be the measured value of the formation temperature at the j-th location of the i-th measuring point. ij,cal Let f be the formation temperature information at the j-th location of the i-th measuring point, and f be the fitness function value.

[0097] Step S105: Update each initial particle swarm to obtain each updated particle swarm, and determine the fitness function value corresponding to each updated particle swarm; when the fitness function value corresponding to each updated particle swarm is the minimum or each particle swarm reaches the maximum number of iterations, determine the thermal conductivity corresponding to the minimum fitness function value or the maximum number of iterations as the thermal conductivity of each layer in the target region.

[0098] In this embodiment of the application, updating each initial particle group to obtain each updated particle group may include: performing crossover and mutation on each initial thermal conductivity; and updating the position and velocity of each initial particle group based on a first calculation formula to obtain the updated particle group, wherein the first calculation formula is as shown in the following formula (11):

[0099] v i =ω×v i +c1×rand()×(pbset i -x i )+c2×rand()×(gbest i -x i (11)

[0100] Among them, v i The velocity of the particle is given by `rand()`, which is a random number between (0,1). i The current position of the particle is given by c1 and c2, where c1 and c2 are acceleration factors, ω is the inertia factor, and pbest is the value of pbest. i For local optimal solutions, gbest i The global optimal solution

[0101] After determining the updated particle swarm, the updated particle swarm is input into the heat transfer model to determine the corresponding formation temperature information. Then, the fitness function values ​​can be calculated based on formula (10).

[0102] In this embodiment of the application, the updated particle swarm may be updated multiple times.

[0103] In this embodiment of the application, when the fitness function value corresponding to each updated particle swarm is the minimum or each particle swarm reaches the maximum number of iterations, the thermal conductivity corresponding to the minimum fitness function value or the maximum number of iterations is determined as the thermal conductivity of each layer in the target region.

[0104] This application provides a method for determining thermal conductivity. A three-dimensional geological model is established based on background data of the target area. The geothermal gradient of each stratum is determined based on borehole temperature measurement data. The initial thermal conductivity of each stratum is determined based on the geothermal gradient and heat flow parameters. The three-dimensional geological model determines the heat transfer model for each stratum. Then, the initial particle swarm of each stratum is input into the heat transfer model to calculate formation temperature information. The particle swarm is updated based on the formation temperature information and measured formation temperature values, thereby determining the thermal conductivity of each stratum. This method improves the accuracy of thermal conductivity determination.

[0105] Example 2

[0106] Based on the foregoing embodiments, this application further provides a method for determining thermal conductivity. Figure 2 This is a schematic diagram illustrating the implementation process of a method for determining thermal conductivity provided in an embodiment of this application, as shown below. Figure 2 As shown, the method includes:

[0107] Step S201: Establish a geological model of the study area (same as the three-dimensional geological model in the above embodiment).

[0108] Establishing a geological model of the study area involves the following steps: obtaining background data on the study area, including geological background data, geothermal geological background data, and hydrogeological background data; determining the lithology, thickness, distribution of each stratum and the distribution of faults in the study area by analyzing the geological background data; determining the geothermal gradient of the reservoir caprock, the temperature of the reservoir, and the extent of the geothermal anomaly zone by analyzing the geothermal geological background data; determining the interaction between groundwater and geothermal fluids by analyzing the hydrogeological data, and analyzing the source, storage, migration, and discharge conditions of the geothermal fluids; constructing parametric surfaces of each interface based on interpolated data of stratigraphic interfaces and faults, and establishing a three-dimensional geological model of the study area through a series of geometric operations such as mutual cutting and deletion between stratigraphic interfaces, faults, and model entities.

[0109] Step S202: Determine the initial thermal conductivity of the formation.

[0110] Based on the geothermal fluid flow characteristics, recharge and discharge conditions of the study area, the seepage and temperature boundary conditions of the three-dimensional geological model are determined; based on the strata division and borehole temperature measurement data, the geothermal gradient of each stratum is calculated; using the lithology, geothermal gradient and geothermal heat flow parameters of each stratum, the initial thermal conductivity of the strata is determined.

[0111] The method for estimating thermal conductivity using geothermal gradient and geothermal heat flow is as follows:

[0112]

[0113] Where, λ iq is the thermal conductivity of the i-th layer, W / (K*m); q is the geothermal heat flux, W / m 2 ;▽T i Let be the geothermal gradient of the i-th layer, in K / m.

[0114] Step S203: Simulate and calculate the geothermal field of the study area.

[0115] Using computational fluid dynamics, a three-dimensional geological model of the study area was established, a computational grid with the same size as the actual geological block was generated, boundary conditions with the same heat transfer conditions as the study area were applied, physical property conditions corresponding to the study area were set, and the initial thermal conductivity values ​​of each layer obtained in step two were substituted to establish a multi-field coupled numerical model to simulate and solve the temperature field of the study area.

[0116] In this embodiment of the application, when constructing the heat transfer model of each layer, the heat transfer model of each layer can be constructed based on the energy conservation and mass balance equations.

[0117] In this embodiment of the application, when there is groundwater flow in the stratum, its mass balance equation can be expressed as equation (2):

[0118]

[0119] in, ρ represents the formation porosity. w Q is the density of water. m Let u be the mass source and u be the Darcy velocity field. u can be expressed as equation (3):

[0120]

[0121] Where k is the rock permeability, μ w Let be the dynamic viscosity of water, P be the water pressure, g be the acceleration due to gravity, and z be the vertical coordinate (positive upwards).

[0122] Energy conservation in the formation can be expressed by the convective heat transfer equation as equation (4):

[0123]

[0124] Among them, C p,w Let ρ be the specific heat capacity of water, and Q be the heat source. p ) eff For effective volumetric heat capacity, (ρC) p ) eff It can be expressed as equation (5):

[0125] (ρC p ) eff =(1-φ)ρ r C p,r +φρ wC p,w (5);

[0126] Where, ρ r C is the density of the rock. p,r k represents the specific heat capacity of the rock. eff For effective thermal conductivity, k eff It can be expressed as equation (6):

[0127] k eff =(1-φ)k r +φk w (6);

[0128] Based on equations (4), (5) and (6), the heat transfer model of the formation can be obtained.

[0129] In this embodiment of the application, the fluid flow process in the fault is expressed by Darcy's law in tangential form as equation (7):

[0130]

[0131] Where, q fr k is the volumetric flow rate in the fault. fr d represents the permeability of the fault. fr The aperture of the fault, ▽ T This is the gradient operator on the fault section.

[0132] The continuity equation in a fault is expressed as equation (8):

[0133]

[0134] Energy conservation in a fault can be expressed as equation (9):

[0135]

[0136] The heat transfer model in the fault is then determined based on equations (8) and (9).

[0137] Step S204: Use a single-objective optimization particle swarm optimization algorithm to determine the thermal conductivity of different strata.

[0138] The particle swarm is initialized based on the initial thermal conductivity obtained in step 202.

[0139] The initial thermal conductivity particle swarm is introduced into the established numerical model of the study area to calculate the temperature distribution of the formation, determine the applicability function, and compare the simulated values ​​with the measured values. The applicability function determined in this invention is Equation (10):

[0140]

[0141] Among them, T ij,measT is the measured temperature at the j-th location of the i-th measuring point. ij,cal The temperature at the j-th position of the i-th measuring point is calculated based on the heat transfer model.

[0142] Based on the principle of minimum applicability function, the particle swarm is cross-crossed and mutated to regenerate an updated thermal conductivity population. The method for updating the particle swarm's velocity and position is shown in equation (11):

[0143] v i =ω×v i +c1×rand()×(pbset i -x i )+c2×rand()×(gbest i -x i (11);

[0144] Among them, v i The velocity of the particle is given by `rand()`, which is a random number between (0,1). i Let c1 and c2 be the current position of the particle, c1 and c2 be the acceleration factors, and ω be the inertia factor, which is non-negative. A larger ω value indicates stronger global optimization ability and weaker local optimization ability. This invention uses a linear decreasing weight strategy to calculate the inertia factor to obtain better optimization results. i For local optimal solutions, gbest i This is the globally optimal solution.

[0145] The newly generated particle swarm is then fed back into the numerical model to calculate the formation temperature curve, which is then compared with the measured values. This process is repeated with another round of selection, crossover, and mutation to generate new optimized particle swarms until the minimum applicability function converges. The final optimized particle swarm, representing the predicted in-situ formation thermal conductivity, is then output.

[0146] This application provides a method for determining thermal conductivity. A three-dimensional geological model is established based on background data of the target area. The geothermal gradient of each stratum is determined based on borehole temperature measurement data. The initial thermal conductivity of each stratum is determined based on the geothermal gradient and heat flow parameters. The three-dimensional geological model determines the heat transfer model for each stratum. Then, the initial particle swarm of each stratum is input into the heat transfer model to calculate formation temperature information. The particle swarm is updated based on the formation temperature information and measured formation temperature values, thereby determining the thermal conductivity of each stratum. This method improves the accuracy of thermal conductivity determination.

[0147] Example 3

[0148] Based on the foregoing embodiments, this application provides a specific example of another method for determining thermal conductivity, taking the calculation of thermal conductivity of six strata in a certain area as an example. First, background data of the study area is obtained, including geological background data, geothermal geological background data, and hydrogeological background data, and a three-dimensional geological model of the study area is established based on the relevant background data. Subsequently, based on the geothermal fluid flow characteristics, recharge and discharge conditions of the study area, the seepage and temperature boundary conditions of the three-dimensional geological model are analyzed and determined. A numerical model is established based on the mass conservation and energy conservation equations. Next, based on the stratigraphic division, geothermal flow value, and geothermal gradient, the initial thermal conductivity of each stratum is calculated using the third calculation formula. The fitness function is determined as Equation (10), and the pbest and gbest of the particle swarm are updated.

[0149] Wherein, the acceleration factor c1=c2=2, and the inertia factor adopts a linear decreasing weight strategy, as shown in equation (12):

[0150]

[0151] Among them, G k To determine the maximum number of iterations, we take 10 in this example, ω ini Let ω be the initial inertia weight, which is 0.9 in this example. end The inertia weight is set to 0.4 in this example when iterating to the maximum number of generations. The optimal thermal conductivity is retrieved by repeating the process. The result is output when the result converges or the maximum number of iterations is reached; this is considered the optimal thermal conductivity value. As the iteration progresses, the calculated temperature depth curve continuously approaches the measured temperature depth curve. The final thermal conductivity values ​​for each layer are shown in Table 1.

[0152] Table 1 Calculation results of formation thermal conductivity

[0153]

[0154] This invention discloses a method for determining thermal conductivity, used to calculate the in-situ thermal conductivity of rocks with different depths and lithologies in a study area. The invention first obtains geological, geothermal, and hydrogeological background data for the study area, establishes a three-dimensional geological model of the area, and determines the model's boundary and initial conditions. Then, a series of estimated formation thermal conductivity values ​​are set and substituted into the geological model. Numerical simulation is used to simulate the geothermal field. The measured geothermal temperature curves from each borehole are compared with the simulated curves. By modifying the formation thermal conductivity values ​​and using a single-objective particle swarm optimization algorithm, the simulated curves are made to match the measured curves, thus obtaining the in-situ thermal conductivity values ​​of the formation. This calculation method can accurately and reliably calculate the in-situ thermal conductivity of formations, and its calculation accuracy is not affected by formation depth and lithology, making it widely applicable and highly efficient.

[0155] Example 4

[0156] Based on the foregoing embodiments, this application provides a device for determining thermal conductivity. The various modules and units included in the device can be implemented by a processor in a computer device; of course, they can also be implemented by specific logic circuits. In the implementation process, the processor can be a central processing unit (CPU), a microprocessor unit (MPU), a digital signal processor (DSP), or a field programmable gate array (FPGA), etc.

[0157] This application provides a device for determining thermal conductivity. Figure 3 A schematic diagram illustrating the determination of thermal conductivity provided in an embodiment of this application is shown below. Figure 3 As shown, the thermal conductivity determining device 300 includes:

[0158] The first acquisition module 301 is used to acquire background information, borehole temperature measurement data and measured formation temperature of the target area;

[0159] Module 302 is used to establish a three-dimensional geological model of the target area based on the background data, wherein the three-dimensional geological model includes multiple strata;

[0160] The first determining module 303 is used to determine the geothermal gradient of each layer based on the borehole temperature measurement data, and to determine the initial thermal conductivity of each layer based on the geothermal gradient and heat flow parameters, and to determine the heat transfer model of each layer based on the three-dimensional geological model.

[0161] The second determining module 304 is used to determine each initial particle swarm based on the initial thermal conductivity of each layer, input each initial particle swarm into the corresponding heat transfer model for numerical simulation calculation to determine the formation temperature information corresponding to each layer, and determine each fitness function value based on the formation temperature information corresponding to each layer and the measured formation temperature value corresponding to each layer.

[0162] The third determining module 305 is used to update each initial particle group to obtain each updated particle group and determine the fitness function value corresponding to each updated particle group; when the fitness function value corresponding to each updated particle group is the minimum or each particle group reaches the maximum number of iterations, the thermal conductivity corresponding to the minimum fitness function value or the maximum number of iterations is determined as the thermal conductivity of each layer in the target region.

[0163] In some embodiments, the background data includes: geological background data, geothermal geological background data, and hydrogeological background data; the establishment module includes:

[0164] The first determining unit is used to determine the lithology, thickness, distribution, and fault distribution of the strata in the target area based on the geological background data.

[0165] The second determining unit is used to determine the geothermal gradient of the geothermal reservoir caprock, the temperature of the geothermal reservoir, and the range of the geothermal anomaly zone based on the geothermal geological background data.

[0166] The third determining unit is used to determine the relationship between groundwater and heat flow parameters based on the hydrogeological background data;

[0167] A unit is established to build the three-dimensional geological model based on the lithology, thickness, distribution of strata, distribution of faults, geothermal gradient of the reservoir caprock, temperature of the reservoir, range of geothermal anomaly zone, and the relationship between groundwater and heat flow parameters.

[0168] In some embodiments, the third determining module includes:

[0169] Cross-processing unit, used to cross-process and vary the initial thermal conductivity;

[0170] An update unit is used to update the position and velocity of each initial particle group based on a first calculation formula to obtain an updated particle group, wherein the first calculation formula is shown in the following formula (11):

[0171] v i =ω×v i +c1×rand()×(pbset i -x i )+c2×rand()×(gbest i -x i (11)

[0172] Among them, v i The velocity of the particle is given by `rand()`, which is a random number between (0,1). i The current position of the particle is given by c1 and c2, where c1 and c2 are acceleration factors, ω is the inertia factor, and pbest is the value of pbest. i For local optimal solutions, gbest i This is the globally optimal solution.

[0173] In some embodiments, the first determining module includes:

[0174] A generation unit is used to generate a computational grid with the same size as the geological blocks in the target region from the three-dimensional geological model using computational fluid dynamics methods.

[0175] The building unit is used to load the boundary conditions for heat transfer in the target region onto each computing grid, set the physical property conditions of each computing network, and build the heat transfer model of each layer based on the initial thermal conductivity of each layer.

[0176] In some embodiments, the second determining module is specifically used for:

[0177] The fitness function values ​​are calculated based on the second formula, which is shown in equation (10) below:

[0178]

[0179] Among them, T ij,meas Let T be the measured value of the formation temperature at the j-th location of the i-th measuring point. ij,cal This refers to the formation temperature information at the j-th location of the i-th measuring point.

[0180] In some embodiments, the first determining module is configured to:

[0181] The initial thermal conductivity of each layer is calculated using a third formula, which is shown in equation (1) below:

[0182]

[0183] Where, λ i is the thermal conductivity of the i-th layer; q is the heat flux parameter; ▽T i Let be the geothermal gradient of the i-th stratum.

[0184] It should be noted that, in the embodiments of this application, if the above-described method for determining thermal conductivity is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), magnetic disks, or optical disks. Thus, the embodiments of this application are not limited to any specific hardware and software combination.

[0185] Accordingly, this application provides a computer storage medium storing a computer program thereon, characterized in that the computer program, when executed by a processor, implements the steps in the method for determining thermal conductivity provided in the above embodiments.

[0186] Example 5

[0187] This application provides an electronic device; Figure 4 This is a schematic diagram of the composition structure of the electronic device provided in the embodiments of this application, such as... Figure 4 As shown, the electronic device 700 includes: a processor 701, at least one communication bus 702, a user interface 703, at least one external communication interface 704, and a memory 705. The communication bus 702 is configured to enable communication between these components. The user interface 703 may include a display screen, and the external communication interface 704 may include standard wired and wireless interfaces. The processor 701 is configured to execute a program stored in the memory for determining thermal conductivity, to implement the steps in the thermal conductivity determination method provided in the above embodiment.

[0188] The descriptions of the above embodiments of the electronic devices and storage media are similar to those of the above method embodiments, and have similar beneficial effects. For technical details not disclosed in the embodiments of the electronic devices and storage media of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0189] It should be noted that the descriptions of the storage medium and device embodiments above are similar to the descriptions of the method embodiments above, and have similar beneficial effects. For technical details not disclosed in the storage medium and device embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0190] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above-described processes do not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. The sequence numbers of the above-described embodiments are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0191] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0192] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0193] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0194] In addition, each functional unit in the various embodiments of this application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0195] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as mobile storage devices, read-only memory (ROM), magnetic disks, or optical disks.

[0196] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a controller to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0197] The above description is merely an embodiment of this application, but the scope of protection of this application 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 this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for determining thermal conductivity, characterized in that, include: Acquire background information, borehole temperature measurement data, and measured formation temperature values ​​for the target area; the background information includes: geological background information, geothermal geological background information, and hydrogeological background information; A three-dimensional geological model of the target area is established based on the background data, wherein the three-dimensional geological model includes multiple strata; the establishment of the three-dimensional geological model of the target area based on the background data includes: determining the lithology, thickness, distribution, and fault distribution of the strata in the target area based on the geological background data; determining the geothermal gradient of the reservoir caprock, the temperature of the reservoir, and the range of the geothermal anomaly zone based on the geothermal geological background data; determining the relationship between groundwater and heat flow parameters based on the hydrogeological background data; and establishing the three-dimensional geological model based on the lithology, thickness, distribution, and fault distribution of the strata, the geothermal gradient of the reservoir caprock, the temperature of the reservoir, the range of the geothermal anomaly zone, and the relationship between groundwater and heat flow parameters. Based on the borehole temperature measurement data, the geothermal gradient of each layer is determined, and the initial thermal conductivity of each layer is determined based on the geothermal gradient and heat flow parameters. A heat transfer model for each layer is then determined based on the three-dimensional geological model. The determination of the heat transfer model for each layer based on the three-dimensional geological model includes: using computational fluid dynamics to generate a computational grid with the same size as the geological blocks in the target area; loading boundary conditions for heat transfer in the target area onto each computational grid; setting the physical property conditions for each computational grid; and constructing a heat transfer model for each layer based on the initial thermal conductivity of each layer. Each initial particle swarm is determined based on the initial thermal conductivity of each layer. Each initial particle swarm is input into the corresponding heat transfer model for numerical simulation calculation to determine the formation temperature information corresponding to each layer. Based on the formation temperature information and the measured values ​​of the formation temperature corresponding to each layer, each fitness function value is determined. Each initial particle swarm is updated to obtain each updated particle swarm, and the fitness function value corresponding to each updated particle swarm is determined. When the fitness function value corresponding to each updated particle swarm is the minimum or each particle swarm reaches the maximum number of iterations, the thermal conductivity corresponding to the minimum fitness function value or the maximum number of iterations is determined as the thermal conductivity of each layer in the target region.

2. The method according to claim 1, characterized in that, The process of updating each initial particle swarm to obtain each updated particle swarm includes: Cross-variation of each initial thermal conductivity; The updated particle swarm is obtained by updating the positions and velocities of each initial particle group based on the first calculation formula, wherein the first calculation formula is: ； in, v i The velocity of the particle is given by `rand()`, which returns a random number between 0 and 1. x i This is the particle's current position. c 1 and c 2 is the acceleration factor, and ω is the inertia factor. pbest i This is a locally optimal solution. gbest i This is the globally optimal solution.

3. The method according to claim 1, characterized in that, The determination of each fitness function value based on the formation temperature information and measured values ​​of each formation layer includes: The fitness function values ​​are calculated based on the second formula, which is: ； in, T ij,meas Let be the measured value of the formation temperature at the j-th location of the i-th measuring point. T ij,cal This refers to the formation temperature information at the j-th location of the i-th measuring point. This is the fitness function value.

4. The method according to claim 1, characterized in that, The determination of the initial thermal conductivity of each layer based on the geothermal gradient and heat flow parameters includes: The initial thermal conductivity of each layer is calculated using a third formula, wherein the third formula is: ； in, λ i Let be the thermal conductivity of the i-th layer; q For heat flux parameters; ▽ T i Let be the geothermal gradient of the i-th stratum.

5. A device for determining thermal conductivity, characterized in that, include: The first acquisition module is used to acquire background information, borehole temperature measurement data, and measured formation temperature values ​​of the target area; the background information includes: geological background information, geothermal geological background information, and hydrogeological background information; A modeling module is used to establish a three-dimensional geological model of the target area based on the background data, wherein the three-dimensional geological model includes multiple strata; the modeling module includes: a first determining unit, used to determine the lithology, thickness, distribution, and fault distribution of the strata in the target area based on the geological background data; a second determining unit, used to determine the geothermal gradient of the reservoir caprock, the temperature of the reservoir, and the range of the geothermal anomaly zone based on the geothermal geological background data; a third determining unit, used to determine the relationship between groundwater and heat flow parameters based on the hydrogeological background data; and a modeling unit, used to establish the three-dimensional geological model based on the lithology, thickness, distribution, and fault distribution of the strata, the geothermal gradient of the reservoir caprock, the temperature of the reservoir, the range of the geothermal anomaly zone, and the relationship between groundwater and heat flow parameters. The first determining module is used to determine the geothermal gradient of each layer based on the borehole temperature measurement data, and to determine the initial thermal conductivity of each layer based on the geothermal gradient and heat flow parameters, and to determine the heat transfer model of each layer based on the three-dimensional geological model; the first determining module includes: a generation unit, used to generate a computational grid with the same size as the geological blocks in the target area from the three-dimensional geological model using computational fluid dynamics methods; and a construction unit, used to load the boundary conditions of heat transfer in the target area onto each computational grid, set the physical property conditions of each computational network, and construct the heat transfer model of each layer based on the initial thermal conductivity of each layer; The second determination module is used to determine each initial particle swarm based on the initial thermal conductivity of each layer, input each initial particle swarm into the corresponding heat transfer model for numerical simulation calculation to determine the formation temperature information corresponding to each layer, and determine each fitness function value based on the formation temperature information corresponding to each layer and the measured formation temperature value corresponding to each layer. The third determining module is used to update each initial particle group to obtain each updated particle group, and determine the fitness function value corresponding to each updated particle group; when the fitness function value corresponding to each updated particle group is the minimum or each particle group reaches the maximum number of iterations, the thermal conductivity corresponding to the minimum fitness function value or the maximum number of iterations is determined as the thermal conductivity of each layer in the target region.

6. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, performs the method for determining thermal conductivity as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer program stored in the computer-readable storage medium is executable by one or more processors and can be used to implement the method for determining thermal conductivity as described in any one of claims 1 to 4.

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