Heat transfer data random inversion method based on fracture network

Abstract

The invention relates to the technical field of fracture network research, and discloses a heat transfer data random inversion method based on a fracture network, comprising the following steps: step 1, acquiring fracture network data; 2, fracture network data are input into the heat transfer numerical simulation model to simulate a temperature breakthrough curve (BTCs) between injection wells and production wells; 3, an efficient curve fitting algorithm is adopted, BTCs generated through simulation are compared with measurement data of an actual site, and the adaptation degree of different fracture network models is quantified; and step 4, sorting the generated fracture network data according to the fitting result, and selecting the fracture network configuration which is most matched with the actual data as an inversion result. Compared with a traditional method which often depends on repeated operation of a numerical model with high calculation cost and is difficult to rapidly explore an optimal solution in a complex fracture field, the method has high efficiency and accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of fracture network research, and in particular to a random inversion method of heat transfer data based on fracture networks. Background Art

[0002] The geometry and connectivity of fracture networks in enhanced geothermal systems (EGS) have a key impact on heat transfer efficiency and fluid flow. Characterizing fracture networks through heat transfer data inversion has become an important research method. Heat transfer data are usually recorded in the form of temperature breakthrough curves (BTCs) between injection and production wells. These data can reflect the heat transfer behavior of fluids passing through fracture networks and provide an important basis for the inversion of fracture network characteristics. However, due to the high nonlinearity, heterogeneity and complex geometric topology of fracture networks, their accurate characterization still faces many technical difficulties.

[0003] Existing technologies have significant deficiencies in fracture network inversion. First, fracture network inversion is a nonlinear and ill-conditioned problem. Single temperature data is not sufficient to fully constrain the geometric parameters of the fractures, which can easily lead to non-unique solutions or uncertainty in the inversion results. Secondly, in the characterization of fracture geometry, existing methods often ignore key factors such as the directionality, opening, length and connectivity of the fractures, and the generated models lack physical reality. In addition, the computational cost of numerical simulation is high, especially in complex fracture networks. The process of solving transient fluid flow and heat transfer equations is time-consuming and labor-intensive. The simulation model needs to be run thousands of times during the inversion, which is extremely inefficient. Finally, the current inversion method is difficult to adapt to the geological characteristics of different geothermal sites, such as the main direction, distribution density and connectivity of the fractures.

[0004] In view of the above shortcomings, this study explores a more efficient and accurate fracture network characterization scheme by developing a data-driven inversion method. This method can not only reduce the computational cost, but also enhance the adaptability to different geological characteristics, thus providing new theoretical support and practical path for geothermal energy development. Summary of the invention

[0005] In order to solve the technical problems raised in the background technology, the present invention provides an efficient fracture network characterization method based on heat transfer data inversion.

[0006] The present invention is implemented by the following technical solution: a random inversion method of heat transfer data based on a fracture network, comprising the following steps:

[0007] Step 1: Obtain fracture network data;

[0008] Step 2: Input the fracture network data into the heat transfer numerical simulation model to simulate the temperature breakthrough curves (BTCs) between the injection and production wells;

[0009] Step 3: Using an efficient curve fitting algorithm, the simulated BTCs are compared with the measured data of the actual site to quantify the degree of fit of different fracture network models;

[0010] Step 4: Sort the generated fracture network data according to the fitting results, and select the fracture network configuration that best matches the actual data as the inversion result.

[0011] Building a reference dataset

[0012] Several reference fracture networks are established, and numerical simulations of fluid-heat transfer processes are performed to obtain high-resolution temperature breakthrough curves (BTCs) as benchmark data for subsequent inversion analysis. These reference data sets are derived from actual measurement data or highly reliable synthetic simulation data to ensure that they are representative of the target site.

[0013] The specific steps of step 3 are as follows: Matching the simulation curve with the reference data

[0014] The simulated BTCs corresponding to each generated fracture network are compared with the reference BTCs, and the root mean square error (RMSE) is used as a metric for the matching error to quantify the differences between the two sets of data at key monitoring points. The calculation of RMSE covers the overall shape of the temperature curve and the change trend at key nodes to ensure the comprehensiveness and accuracy of the evaluation results.

[0015] Optionally, the step 4 is specifically as follows: The operation of the crack network is preferably as follows:

[0016] Based on the matching results, the fracture network with the smallest error is selected as the inversion result. Specifically, the top 100 fracture networks with the lowest RMSE values ​​are selected, representing the set of fracture geometric features that best conform to the reference thermal behavior. This method can not only reduce the uncertainty of a single solution, but also further explore the equivalence and possible geometric variability of the fracture network through statistical analysis.

[0017] Optionally, step 5 is also included to perform diversity analysis and parameter optimization

[0018] The optimal fracture network set is further analyzed to explore the variation range of geometric parameters such as fracture length, azimuth, position and their influence on BTCs matching. By analyzing the diversity statistics of different fracture geometric characteristics, we can deeply understand the control mechanism of fracture network on heat transfer process and provide a theoretical basis for subsequent parameter optimization.

[0019] Optionally, the fracture network data may be obtained by a fracture network generation method, which specifically comprises the following steps:

[0020] Step 1.1: Parameter setting and initialization in MATLAB;

[0021] Step 1.2: Generate a fracture network;

[0022] Step 1.3, read the fracture network data generated in step 1.2, store the data in the three-dimensional matrix DataDFN_CNN, and save it in the format of DataDFN_CNN.mat;

[0023] The operation of step 1.2 is as follows:

[0024] Step a, generating crack length;

[0025] Step b, generating crack angle;

[0026] Step c: random generation of crack positions;

[0027] Step d: shadow area detection and removal;

[0028] Step e, crack intersection requirements;

[0029] Step f, judging the connectivity of the fracture network;

[0030] Step g: Save the finally generated fracture network data in a matrix format of size Nfrac*4, where: Nfrac: represents the number of fractures generated, and each row represents a fracture; 4: represents the four key attributes of each fracture.

[0031] Optionally, the numerical model simulation is constructed by the following method;

[0032] Physical model establishment; fluid flow is based on Darcy's law, cubic law and hydraulic coupling between fractures and matrix to ensure dynamic equilibrium between fluids in different regions; heat transfer is based on convection-conduction coupling equation and heat exchange model between fractures and matrix;

[0033] Numerical discretization and solution methods; discretization methods include using the finite element method (FEM) to discretize the fluid and heat transfer equations, using a time-stepping scheme; the solution process solves the transient flow and heat transfer equations by configuring a stable iterative algorithm, such as a step-by-step method or a nonlinear solver, and optimizes the computational efficiency of large-scale numerical models through parallel computing and appropriate convergence criteria;

[0034] Set model parameters and boundary conditions;

[0035] A two-dimensional heat reservoir model is established to simulate the fracture network and matrix area in the geothermal system. The typical size of the domain is 200m×200m. The wellbore areas on both sides are set, which are injection wells and production wells respectively. The wellbore areas are given high permeability and high porosity.

[0036] Boundary conditions:

[0037] The left injection well was set with constant hydraulic head and low temperature (20°C) conditions to simulate the cold water injection process.

[0038] The right production well was set with constant hydraulic head and heat outflow conditions to simulate hot water collection.

[0039] The upper and lower boundaries are set as adiabatic boundaries to avoid heat exchange with the external environment.

[0040] The initial condition was set to a uniform temperature distribution of 100 °C for both the matrix and the fracture network.

[0041] Material Properties:

[0042] Fracture area: high conductivity and low porosity, emphasizing that fractures are the main channels for fluids.

[0043] Matrix area: low conductivity and high porosity, ensuring that thermal conduction is the main mode of heat transfer;

[0044] Thermophysical parameters such as thermal conductivity, specific heat capacity and density are set according to typical thermal reservoir lithology parameters.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] The fracture network generation algorithm of the present invention generates a large number of fracture networks that meet geometric constraints. Subsequently, based on the fluid-heat transfer model of porous media and fracture coupling, the heat transfer process of each fracture network is numerically simulated to generate temperature breakthrough curves (BTCs) between injection and production wells. Then, by fitting the BTCs generated by simulation with the actual observation data, the fitness of each fracture network is quantitatively evaluated, and finally the fracture configuration that best matches the actual site is screened out.

[0047] The core of the present invention lies in its efficiency and accuracy. Compared with the traditional method, which often relies on repeatedly running the numerical model with high computational cost and is difficult to quickly explore the optimal solution in the complex fracture field, the present invention has the following improvements:

[0048] 1. Significantly improve inversion efficiency: Realize rapid simulation and inversion of large-scale fracture networks, providing feasibility for the design and optimization of enhanced geothermal systems.

[0049] 2. Support multi-scenario applications: It is suitable for the characterization and design optimization of fracture networks in actual geothermal sites, and can also be extended to the analysis of fluid and heat transfer processes in other energy systems.

[0050] 3. Reduce computing costs: Reduce dependence on high-performance computing equipment and significantly reduce computing time, providing support for economic analysis in geothermal development.

[0051] Therefore, the present invention not only breaks through the efficiency bottleneck of fracture network characterization, but also provides a new idea for the performance improvement and optimized design of geothermal systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A sample diagram of fracture network data produced by the present invention;

[0053] Figure 2 A dynamic distribution diagram of the temperature field output by the numerical simulation of the present invention;

[0054] Figure 3 It is the temperature breakthrough curve diagram of each monitoring point;

[0055] Figure 4 A comparison diagram of fracture network inversion using the characterization method proposed in the present invention;

[0056] Figure 5 This is a flow chart of the inversion method proposed in the present invention. DETAILED DESCRIPTION

[0057] The present invention is further described below in conjunction with the accompanying drawings and specific implementation methods. It should be noted that, under the premise of no conflict, the various embodiments or technical features described below can be arbitrarily combined to form a new embodiment.

[0058] Embodiment 1:

[0059] Reference Figure 1-Figure 5 , a random inversion method for heat transfer data based on fracture networks is proposed in this scheme, which includes the following steps:

[0060] Step 1: Obtain fracture network data;

[0061] Step 2: Input the fracture network data into the heat transfer numerical simulation model to simulate the temperature breakthrough curves (BTCs) between the injection and production wells;

[0062] Step 3: Using an efficient curve fitting algorithm, the simulated BTCs are compared with the measured data of the actual site to quantify the degree of fit of different fracture network models;

[0063] Step 4: Sort the generated fracture network data according to the fitting results, and select the fracture network configuration that best matches the actual data as the inversion result.

[0064] Building a reference dataset

[0065] Several reference fracture networks are established, and numerical simulations of fluid-heat transfer processes are performed to obtain high-resolution temperature breakthrough curves (BTCs) as benchmark data for subsequent inversion analysis. These reference data sets are derived from actual measurement data or highly reliable synthetic simulation data to ensure that they are representative of the target site.

[0066] The specific steps of step 3 are as follows: Matching the simulation curve with the reference data

[0067] The simulated BTCs corresponding to each generated fracture network are compared with the reference BTCs, and the root mean square error (RMSE) is used as a metric for the matching error to quantify the differences between the two sets of data at key monitoring points. The calculation of RMSE covers the overall shape of the temperature curve and the change trend at key nodes to ensure the comprehensiveness and accuracy of the evaluation results.

[0068] Optionally, the step 4 is specifically as follows: The operation of the crack network is preferably as follows:

[0069] Based on the matching results, the fracture network with the smallest error is selected as the inversion result. Specifically, the top 100 fracture networks with the lowest RMSE values ​​are selected, representing the set of fracture geometric features that best conform to the reference thermal behavior. This method can not only reduce the uncertainty of a single solution, but also further explore the equivalence and possible geometric variability of the fracture network through statistical analysis.

[0070] Optionally, step 5 is also included to perform diversity analysis and parameter optimization

[0071] The optimal fracture network set is further analyzed to explore the variation range of geometric parameters such as fracture length, azimuth, position and their influence on BTCs matching. By analyzing the diversity statistics of different fracture geometric characteristics, we can deeply understand the control mechanism of fracture network on heat transfer process and provide a theoretical basis for subsequent parameter optimization.

[0072] Optionally, the numerical model simulation is constructed by the following method;

[0073] Physical model establishment; fluid flow is based on Darcy's law, cubic law and hydraulic coupling between fractures and matrix to ensure dynamic equilibrium between fluids in different regions; heat transfer is based on convection-conduction coupling equation and heat exchange model between fractures and matrix;

[0074] Numerical discretization and solution methods; discretization methods include using the finite element method (FEM) to discretize the fluid and heat transfer equations, using a time-stepping scheme; the solution process solves the transient flow and heat transfer equations by configuring a stable iterative algorithm, such as a step-by-step method or a nonlinear solver, and optimizes the computational efficiency of large-scale numerical models through parallel computing and appropriate convergence criteria;

[0075] Set model parameters and boundary conditions;

[0076] A two-dimensional heat reservoir model is established to simulate the fracture network and matrix area in the geothermal system. The typical size of the domain is 200m×200m. The wellbore areas on both sides are set, which are injection wells and production wells respectively. The wellbore areas are given high permeability and high porosity.

[0077] Boundary conditions:

[0078] The left injection well was set with constant hydraulic head and low temperature (20°C) conditions to simulate the cold water injection process.

[0079] The right production well was set with constant hydraulic head and heat outflow conditions to simulate hot water collection.

[0080] The upper and lower boundaries are set as adiabatic boundaries to avoid heat exchange with the external environment.

[0081] The initial condition was set to a uniform temperature distribution of 100 °C for both the matrix and the fracture network.

[0082] Material Properties:

[0083] Fracture area: high conductivity and low porosity, emphasizing that fractures are the main channels for fluids.

[0084] Matrix area: low conductivity and high porosity, ensuring that thermal conduction is the main mode of heat transfer;

[0085] Thermophysical parameters such as thermal conductivity, specific heat capacity and density are set according to typical thermal reservoir lithology parameters.

[0086] Compared with the prior art, the present invention has the following beneficial effects:

[0087] The fracture network generation algorithm of the present invention generates a large number of fracture networks that meet geometric constraints. Subsequently, based on the fluid-heat transfer model of porous media and fracture coupling, the heat transfer process of each fracture network is numerically simulated to generate temperature breakthrough curves (BTCs) between injection and production wells. Then, by fitting the BTCs generated by simulation with the actual observation data, the fitness of each fracture network is quantitatively evaluated, and finally the fracture configuration that best matches the actual site is screened out.

[0088] Embodiment 2: In this solution, the fracture network data can be obtained by a fracture network generation method, which specifically includes the following steps:

[0089] Step 1.1: Parameter setting and initialization in MATLAB.

[0090] Step 1.2: Generate a fracture network;

[0091] Step 1.3, read the fracture network data generated in step 1.2, store the data in the three-dimensional matrix DataDFN_CNN, and save it in the format of DataDFN_CNN.mat;

[0092] The operation of step 1.2 is as follows:

[0093] Step a, generating crack length;

[0094] Step b, generating crack angle;

[0095] Step c: random generation of crack positions;

[0096] Step d: shadow area detection and removal;

[0097] Step e, crack intersection requirements;

[0098] Step f, judging the connectivity of the fracture network;

[0099] Step g: Save the finally generated fracture network data in a matrix format of size Nfrac*4, where: Nfrac: represents the number of fractures generated, and each row represents a fracture; 4: represents the four key attributes of each fracture.

[0100] Specifically, the parameter setting and initialization operations are as follows:

[0101] Number of fractures: The number of fractures in each fracture network is randomly generated. The range of the number of fractures can be set according to the requirements, for example, between [14,20].

[0102] Crack angle: Set the crack direction parameters angle1 = -10 and angle2 = 65, representing the two main crack directions. std = 2 is the standard deviation of the crack angle.

[0103] Spatial dimensions: Define the spatial dimensions of the fracture network, xL=200, yL=200, and set the minimum fracture length lmin=50 and the maximum length lbig=140.

[0104] The operation of generating a fracture network includes the following steps.

[0105] Crack length generation

[0106] The crack length is generated based on the statistical model of fractal geometry, and the specific steps are as follows:

[0107] Parameter setting: Set the range between the minimum value lminl and the maximum value lbig of the crack length. The length of each crack is randomly generated within this range.

[0108] Distribution generation: The fractional dimension D and the power a are used to control the distribution of the crack length. The probability distribution of the crack length is calculated using the following formula.

[0109] N fra (L) = c·L -a

[0110] Where L is the length of the crack, a is the power that controls the crack length distribution, and c is the normalization constant. The length generated in this way follows a power law distribution, which can simulate the distribution of cracks of different lengths in nature.

[0111] Random length generation: Generate the length of each crack through the cumulative distribution function (CDF). The crack length is obtained by random number sampling to ensure the diversity of the crack network.

[0112] Crack angle generation; the crack angles are generated based on a normal distribution, with random deviations from two main directions:

[0113] Main angles: Set two main directions, θ1 = -10° and θ2 = 65°.

[0114] Random deviation: The actual angle of each fracture is generated using a normal distribution with standard deviation σ = 2°. Some fractures are randomly deviated from the main direction to simulate the direction variation in the fracture network.

[0115] Angle normalization: Ensure that the generated angle is between 0° and 180° to avoid angles that are too large or too small.

[0116] Random generation of crack locations;

[0117] Random center point generation: The center point of each crack is randomly generated in the set domain [0, xL]×[0, yL]. The position follows a uniform distribution.

[0118] Specific region generation: For the first few cracks, a specific y-direction distribution can be set so that their centers are located in a specific region (e.g., near the center of the domain) to simulate a more orderly crack distribution.

[0119] Shadow area detection and removal;

[0120] After generating the cracks, it is necessary to detect whether the newly generated cracks invade the shadow area of ​​the existing cracks:

[0121] Shadow zone generation: A shadow zone is generated for each rift, usually a certain width (w, 2 unit length) around the rift, which represents the influence range of the rift.

[0122] Shadow area detection: Use the geometric intersection algorithm to detect whether the newly generated cracks overlap with the existing crack shadow areas. If overlap occurs, it means that the new crack generation position is not appropriate and the center position or angle needs to be regenerated.

[0123] Fissure intersection requirements;

[0124] Connectivity of the fracture network is achieved by enforcing that new fractures intersect with existing fractures:

[0125] Forced intersection: Each newly generated fracture is required to intersect with at least one existing fracture. This process is achieved by detecting the geometric intersection point of two fractures.

[0126] Geometric intersection detection: Use the geometric intersection algorithm to detect whether two cracks intersect. If the intersection requirement is not met, the algorithm will regenerate the center or angle of the crack until the intersection condition is met.

[0127] Determination of the connectivity of the fracture network;

[0128] The resulting fracture network must be connected from the left to the right of the domain:

[0129] Left-right connectivity detection: After the fracture network is generated, the algorithm will check whether there is a connected path from the left side of the domain x=0x=0x=0 to the right side x=xLx=xLx=xL. Only when the fracture network meets this connectivity will it be considered a qualified fracture network.

[0130] Connectivity determination: If the fracture network does not meet the connectivity requirements, the generation process will restart until a connected fracture network is generated.

[0131] The generated fracture network data will eventually be saved in a matrix format of size Nfrac*4.

[0132] Nfrac: represents the number of fractures generated, each row represents a fracture. 4: represents the four key attributes of each fracture, specifically: x1: the x-coordinate of the starting point of the fracture. y1: the y-coordinate of the starting point of the fracture. x2: the x-coordinate of the ending point of the fracture. y2: the y-coordinate of the ending point of the fracture. A total of 9,000 such fracture network samples were generated as training data.

[0133] The following table shows an example. Figure 1 shown.

[0134]

[0135]

[0136] The above-mentioned embodiments are only preferred embodiments of the present invention and cannot be used to limit the scope of protection of the present invention. Any non-substantial changes and substitutions made by technicians in this field on the basis of the present invention shall fall within the scope of protection required by the present invention.

Claims

1. A random inversion method for heat transfer data based on fracture networks, characterized in that: The steps include: Step 1: Obtain fracture network data; Step 2: Input the fracture network data into the heat transfer numerical simulation model to simulate the temperature breakthrough curve between the injection and production wells; Step 3: Using an efficient curve fitting algorithm, the simulated temperature breakthrough curve is compared with the actual site measurement data to quantify the degree of fit of different fracture network models; Step 4: Sort the generated fracture network data according to the fitting results, and select the fracture network configuration that best matches the actual data as the inversion result.

2. A method for stochastic inversion of heat transfer data based on fracture networks as claimed in claim 1, characterized in that: The specific steps of step 3 are as follows: Matching the simulation curve with the reference data The simulated BTCs corresponding to each generated fracture network are compared with the reference BTCs, and the root mean square error (RMSE) is used as a metric for the matching error to quantify the differences between the two sets of data at key monitoring points. The calculation of RMSE covers the overall shape of the temperature curve and the change trend at key nodes to ensure the comprehensiveness and accuracy of the evaluation results.

3. A method for stochastic inversion of heat transfer data based on fracture networks as claimed in claim 1, characterized in that: The specific steps of step 4 are as follows: The operation of optimizing the crack network is as follows: Based on the matching results, the fracture network with the smallest error is selected as the inversion result. Specifically, the top 100 fracture networks with the lowest RMSE values ​​are selected, representing the set of fracture geometric features that best conform to the reference thermal behavior. This method can not only reduce the uncertainty of a single solution, but also further explore the equivalence and possible geometric variability of the fracture network through statistical analysis.

4. A method for stochastic inversion of heat transfer data based on fracture networks as claimed in claim 1, characterized in that: It also includes step five, conducting diversity analysis and parameter optimization; The optimal fracture network set is further analyzed to explore the variation range of geometric parameters such as fracture length, azimuth, position and their influence on BTCs matching. By analyzing the diversity statistics of different fracture geometric characteristics, we can deeply understand the control mechanism of fracture network on heat transfer process and provide a theoretical basis for subsequent parameter optimization.

5. The method for stochastic inversion of heat transfer data based on fracture networks according to claim 1, characterized in that: The fracture network data can be obtained by a fracture network generation method, which specifically includes the following steps: Step 1.1: Parameter setting and initialization in MATLAB; Step 1.2: Generate a fracture network; Step 1.3, read the fracture network data generated in step 1.2, store the data in the three-dimensional matrix DataDFN_CNN, and save it in the format of DataDFN_CNN.mat; The operation of step 1.2 is as follows: Step a, generating crack length; Step b, generating crack angle; Step c: random generation of crack positions; Step d: shadow area detection and removal; Step e, crack intersection requirements; Step f, judging the connectivity of the fracture network; Step g: Save the finally generated fracture network data in a matrix format of size Nfrac*4, where: Nfrac: represents the number of fractures generated, and each row represents a fracture; 4: represents the four key attributes of each fracture.

6. A method for stochastic inversion of heat transfer data based on fracture networks as claimed in claim 1, characterized in that: The numerical model simulation is constructed by the following method; Physical model establishment; fluid flow is based on Darcy's law, cubic law and hydraulic coupling between fractures and matrix to ensure dynamic equilibrium between fluids in different regions; heat transfer is based on convection-conduction coupling equation and heat exchange model between fractures and matrix; Numerical discretization and solution methods; discretization methods include using finite element method to discretize fluid and heat transfer equations, using time stepping scheme; the solution process solves transient flow and heat transfer equations by configuring stable iterative algorithms, such as step-by-step iteration method or nonlinear solver, and optimizes the computational efficiency of large-scale numerical models through parallel computing and appropriate convergence criteria; Set model parameters and boundary conditions; A two-dimensional heat reservoir model is established to simulate the fracture network and matrix area in the geothermal system. The typical size of the domain is 200m×200m. The wellbore areas on both sides are set, which are injection wells and production wells respectively. The wellbore areas are given high permeability and high porosity. Boundary conditions: The injection well on the left was set with constant hydraulic head and low temperature conditions to simulate the cold water injection process. The right production well was set with constant hydraulic head and heat outflow conditions to simulate hot water collection. The upper and lower boundaries are set as adiabatic boundaries to avoid heat exchange with the external environment. The initial condition was set to a uniform temperature distribution of 100 °C for both the matrix and the fracture network. Material Properties: Fracture area: high conductivity and low porosity, emphasizing that fractures are the main channels for fluids. Matrix area: low conductivity and high porosity, ensuring that thermal conduction is the main mode of heat transfer; Thermophysical parameters such as thermal conductivity, specific heat capacity and density are set according to typical thermal reservoir lithology parameters.

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