A complex fracture network transformation and evaluation method for deep carbonate geothermal reservoirs

Abstract

The present invention discloses a complex fracture network transformation and evaluation method for a deep carbonate geothermal reservoir, and the present invention relates to the field of geothermal development technology. The method comprises the following steps: determining a drilling position, collecting a core area image at the drilling position using a downhole video instrument, and establishing a three-dimensional finite element model of the fracture network based on seismic detection data and the core area image; extracting geometric shape data of the fracture network from the finite element model, characterizing the performance data of the fracture network through the geometric shape data, and generating a comprehensive evaluation coefficient based on the obtained performance data of the fracture network; based on the generated comprehensive evaluation coefficient, comparing it with a preset fracture network grade threshold, and dividing the fracture network into fracture networks of different grades according to different comparison results, wherein the fracture networks of different grades include primary, secondary and tertiary fracture networks; according to the fracture networks of different grades, adopting different transformation schemes for the fracture networks of different grades to improve the fluidity and connectivity of the fracture network.

Description

Technical Field

[0001] The present invention relates to the technical field of geothermal development, and in particular to a complex fracture network transformation and evaluation method for a deep carbonate geothermal reservoir. Background Art

[0002] Against the backdrop of increasing global energy demand, geothermal energy is gaining increasing attention as a clean, renewable energy source. As an excellent geothermal reservoir, carbonate rock has a complex fracture network structure that significantly affects the flow characteristics of geothermal fluids and the effectiveness of the reservoir. However, traditional geothermal resource assessment and transformation methods often fail to accurately reflect the true characteristics of deep carbonate reservoirs, resulting in many technical challenges during drilling and development.

[0003] Existing technologies usually rely on conventional geological exploration and drilling techniques, for example, obtaining reservoir information through geological mapping, drilling coring, etc. However, these methods are insufficient in the identification and characterization of complex fracture networks, and cannot fully reveal key characteristics such as the geometry, connectivity, and flow efficiency of the fracture network. At the same time, traditional evaluation models often ignore the effects of geometric parameters such as fracture spacing, fracture width, and fracture direction on flow characteristics, resulting in inadequate evaluation of reservoir fluidity and connectivity. Therefore, the lack of effective evaluation and transformation methods often leads to inefficient resource development and the inability to achieve efficient utilization of geothermal resources.

[0004] In addition, as the depth of geothermal reservoirs increases, changes in temperature and pressure conditions also have a significant impact on the flow characteristics of the reservoir. Traditional transformation schemes are specific to a certain level of fracture network and fail to dynamically adjust according to actual temperature and pressure conditions, which may lead to unsatisfactory transformation effects. Therefore, a new method is urgently needed to effectively evaluate and transform fracture networks of different levels.

[0005] In the prior art, the publication number CN115983045A discloses a fractured carbonate reservoir evaluation and transformation method optimization method, including the following steps: step 1, collect and organize various relevant data under the original conditions of the reservoir; step 2, take the initial open-flow rate after transformation as the objective function, and analyze its correlation with various factors affecting the reservoir production capacity; step 3, calculate the Pearson correlation coefficient; step 4, establish a relationship for solving the weight, and calculate the weight of each factor; step 5, input the parameters of each block of the target reservoir; step 6, score the target reservoir and optimize the reservoir transformation method. This method can solve the problem that the existing evaluation method only qualitatively selects reasonable transformation methods based on single-factor indicators and cannot qualitatively and quantitatively evaluate the quality of reservoirs and fractures. However, when calculating the Pearson correlation coefficient, it is assumed that the relationship between variables is linear, which may not fully reflect the complex nonlinear relationship. At the same time, in the scoring process, it may be affected by personal experience and subjective judgment, resulting in inconsistency and reduced reliability of the scoring.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0007] The purpose of the present invention is to provide a complex fracture network transformation and evaluation method for deep carbonate geothermal reservoirs to solve the problems raised in the above background technology.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A complex fracture network transformation and evaluation method for a deep carbonate geothermal reservoir, comprising the following specific steps:

[0010] Apply seismic waves to the deep carbonate rock area to be analyzed, obtain seismic detection data, determine the drilling position through the seismic detection data, collect core area images at the drilling position using a downhole video instrument, and establish a three-dimensional finite element model of the fracture network based on the seismic detection data and the core area images;

[0011] Extracting geometric shape data of the fracture network based on the established three-dimensional finite element model of the fracture network, characterizing performance data of the fracture network through the geometric shape data, and generating a comprehensive evaluation coefficient according to the obtained performance data of the fracture network, wherein the geometric shape data of the fracture network includes gap spacing, gap width and gap direction, and the performance data of the fracture network includes connectivity of the fracture network, flow efficiency index and reservoir effectiveness index;

[0012] Based on the generated comprehensive evaluation coefficient, the coefficient is compared with a preset seam network grade threshold, and the seam network is divided into seam networks of different grades according to different comparison results, wherein the seam networks of different grades include primary, secondary and tertiary seam networks;

[0013] According to the different levels of fracture networks, combined with the temperature and pressure in the reservoir, different transformation schemes are adopted for fracture networks of different levels to improve the fluidity and connectivity of the fracture networks.

[0014] Furthermore, seismic waves with a frequency of 10 to 50 Hz are applied to the carbonate rock area to be analyzed, and at least 8-10 receivers are arranged around the carbonate rock area to be analyzed to form a uniform grid, and seismic wave data at different angles are collected. The wave speeds between different receivers are calculated based on the seismic wave data, and the position of the receiver with the largest wave speed is found, and the position of the receiver with the largest wave speed is selected as the drilling position.

[0015] Furthermore, based on the determined drilling position, drilling operations are carried out, and during the drilling process, images of the core area are collected using a downhole video device. After the images are subjected to noise reduction processing, they are fused with the images of the core area to analyze the fracture network characteristics, wherein the specific steps of the fracture network characteristic analysis include: inputting the seismic wave data and the core image into the same data processing platform to ensure that the data format is consistent, using an edge detection algorithm to process the collected images, extracting the texture characteristics of the rock formations, and analyzing the cracks and fracture network characteristics of the core area in combination with the wave velocity distribution in the seismic data to determine their distribution, and establishing a three-dimensional finite element model of the deep carbonate rock area based on the analysis results.

[0016] Furthermore, the performance data of the seam network is characterized by the geometric shape data, wherein the formula for calculating the connectivity of the seam network is:

[0017] ;

[0018] In the formula, represents the connectivity of the seam network, Indicates The length of the gap, Indicates The width of the gap, For the The angle between the gap and the flow direction, is the number of gaps in the seam network, is the index of the gap, where .

[0019] Furthermore, the performance data of the seam network is characterized by the geometric shape data, wherein the flow efficiency index of the seam network is calculated based on the formula:

[0020] ;

[0021] In the formula, represents the flow efficiency index of the seam network, is the flow efficiency, represents the flow cross-sectional area, where the flow efficiency The calculation is based on the formula:

[0022] ;

[0023] In the formula, is the reservoir permeability, is the maximum pressure difference, specifically the maximum pressure difference at different positions on the flow path, is the fluid viscosity, represents the flow path length;

[0024] At the same time, the flow efficiency index is corrected based on the temperature in the seam network. The specific formula is:

[0025] ;

[0026] In the formula, is the modified flow efficiency index, is the influence coefficient of temperature on flow efficiency, Indicates the average temperature in the seam network.

[0027] Furthermore, the formula for calculating the reservoir effectiveness index of the fracture network is:

[0028] ;

[0029] In the formula, is the reservoir effectiveness index of the fracture network, where is the maximum temperature in the reservoir, is the minimum temperature in the reservoir, represents the thermal conductivity coefficient of the reservoir;

[0030] The formula for generating the comprehensive evaluation coefficient based on the obtained performance data of the seam network is:

[0031] ;

[0032] In the formula, is the comprehensive evaluation coefficient, , and The connectivity of the fracture network is , Modified Flow Efficiency Index and reservoir effectiveness index The weight coefficient of and , and Both are greater than 0.

[0033] Furthermore, based on the generated comprehensive evaluation coefficient, the logic for dividing the fracture network into fracture networks of different grades is as follows:

[0034] when , mark the seam network as a third-level seam network;

[0035] when , mark the seam network as a secondary seam network;

[0036] when , mark the seam network as a primary seam network;

[0037] In the formula, Indicates the preset seam network level threshold.

[0038] Furthermore, different transformation plans are adopted for different levels of seam networks, and the specific plans adopted for different levels of seam networks are:

[0039] For the primary fracture network, a fluid injection strategy is adopted, and high permeability fluid CO is selected. 2 Injection is performed to control CO according to the temperature and pressure in the reservoir. 2 The injection rate of CO 2 The injection rate is based on the formula:

[0040] ;

[0041] In the formula, represents the adjusted injection flow rate, Indicates the current injection flow. is the adjustment coefficient, and They represent the target pressure of the fracture network in the reservoir and the current pressure of the fracture network in the reservoir, respectively. Indicates the current temperature of the fracture network within the reservoir;

[0042] For the secondary fracture network, chemical reagents were introduced for acidification treatment, and a fluid injection strategy was adopted, selecting water and CO 2 The mixed fluid is injected, and the injection rate control method is the same as the primary seam network control CO 2 The injection rate is the same;

[0043] For the third-level fracture network, the fracture network fracturing construction technology is used to transform the fracture network.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] First, through precise seismic wave application and detection, high-resolution seismic data can be obtained, and then the drilling location can be effectively determined, laying the foundation for subsequent core sampling and fracture network structure modeling. Secondly, based on the acquired core area images and seismic detection data, the constructed three-dimensional finite element model can accurately reflect the geometric shape and performance characteristics of the fracture network, making the comprehensive evaluation of the fracture network more scientific and reasonable. The generated comprehensive evaluation coefficient combines the geometric characteristics and flow performance of the fracture network, providing a quantitative basis for the division of fracture networks of different levels. It not only improves the understanding of fracture network connectivity, flow efficiency index and reservoir effectiveness index, but also enables the transformation schemes for fracture networks of different levels to be dynamically adjusted according to the temperature and pressure conditions in the reservoir, thereby achieving a more efficient transformation effect. Through the refined management of the fracture network, the fluidity and connectivity of geothermal fluids can be effectively improved, thereby improving the development efficiency and economic benefits of geothermal resources in deep carbonate reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic diagram of the overall method flow of the present invention. DETAILED DESCRIPTION

[0047] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0048] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0049] Example:

[0050] See also Figure 1 , the present invention provides a technical solution:

[0051] A complex fracture network transformation and evaluation method for a deep carbonate geothermal reservoir, comprising the following specific steps:

[0052] Step 1: Apply seismic waves to the deep carbonate rock area to be analyzed, obtain seismic detection data, determine the drilling position through the seismic detection data, collect core area images at the drilling position using a downhole video instrument, and establish a three-dimensional finite element model of the fracture network based on the seismic detection data and the core area images;

[0053] Apply seismic waves with a frequency of 10-50 Hz to the deep carbonate rock area to be analyzed, and arrange at least 8-10 receivers around the carbonate rock area to be analyzed to form a uniform grid, collect seismic wave data at different angles, calculate the wave speed between different receivers based on the seismic wave data, find the location of the receiver with the largest wave speed, and select the location of the receiver with the largest wave speed as the drilling location. By analyzing the propagation characteristics of seismic waves in the stratum, the geological structure of the target area can be clarified, and by analyzing seismic reflection waves and refraction waves, the underground structure, such as faults, folds and crack distribution, can be understood to help determine the best drilling location. At the same time, seismic waves can provide information about the geometry of cracks, such as the direction, width and connectivity of the cracks.

[0054] Based on the determined drilling position, drilling operations are carried out. During the drilling process, images of the core area are collected using a downhole video device. After the images are subjected to noise reduction processing, they are fused with the images of the core area to analyze the characteristics of the fracture network. The specific steps of the fracture network characteristic analysis include: inputting the seismic wave data and the core image into the same data processing platform to ensure that the data format is consistent; using an edge detection algorithm to process the collected images, such as using the Sobel operator to extract edge features. Using the Sobel operator to extract edge features is a mature conventional technical means and is not described in detail here; extracting the texture characteristics of the rock formation; combining the wave velocity distribution in the seismic data, analyzing the cracks and fracture network characteristics in the core area, and determining their distribution; based on the analysis results, using computer-aided design software (such as SolidWorks, AutoCAD, etc.) or professional finite element software (such as COMSOL Multiphysics, ANSYS, etc.) to create a three-dimensional geometric model of the fracture network; based on the actual geological conditions, accurately reflecting the geometric shape, size and spatial distribution of the fracture network, and establishing a three-dimensional finite element model of the deep carbonate rock area.

[0055] Step 2: extracting the geometric shape data of the fracture network based on the established three-dimensional finite element model of the fracture network, characterizing the performance data of the fracture network through the geometric shape data, and generating a comprehensive evaluation coefficient according to the obtained performance data of the fracture network, wherein the geometric shape data of the fracture network includes the gap spacing, the gap width and the gap direction, and the performance data of the fracture network includes the connectivity of the fracture network, the flow efficiency index and the reservoir effectiveness index;

[0056] The performance data of the seam network is characterized by the geometric shape data, and the formula for calculating the connectivity of the seam network is:

[0057] ;

[0058] In the formula, represents the connectivity of the seam network, Indicates The length of the gap, Indicates The width of the gap, For the The angle between the gap and the flow direction, is the number of gaps in the seam network, is the index of the gap, where ;

[0059] The flow direction generally refers to the main direction of the fluid flowing in the fracture network. The method for obtaining the flow direction includes: performing fluid dynamics simulation (such as CFD) on the three-dimensional finite element model of the fracture network to obtain the velocity vector field of the flow field, and extracting the flow direction therefrom; or in the actual site, measuring the flow velocity and direction of the fluid by equipment such as pressure sensors and flow meters to obtain specific data on the flow direction.

[0060] The performance data of the seam network is characterized by the geometric shape data, and the formula for calculating the flow efficiency index of the seam network is:

[0061] ;

[0062] In the formula, represents the flow efficiency index of the seam network, is the flow efficiency, represents the flow cross-sectional area, where the flow efficiency The calculation is based on the formula:

[0063] ;

[0064] In the formula, is the reservoir permeability, is the maximum pressure difference, specifically the maximum pressure difference at different positions on the flow path, is the fluid viscosity, represents the flow path length;

[0065] At the same time, the flow efficiency index is corrected based on the temperature in the seam network. The specific formula is:

[0066] ;

[0067] In the formula, is the modified flow efficiency index, is the influence coefficient of temperature on flow efficiency, Represents the average temperature in the seam network. The influence coefficient of temperature on flow efficiency The effect of temperature on flow efficiency can be determined by selecting a suitable regression method (such as linear regression, polynomial regression), etc., and then the least squares method or other optimization algorithms can be used to estimate the parameters and test the goodness of fit. The influence coefficient of the temperature parameter can be obtained through the results of regression analysis.

[0068] Reservoir permeability Permeability can be directly determined by core experiments (such as the constant flow method or the instantaneous flow method), or in the absence of experimental conditions, geological literature can be consulted to obtain permeability data for the study area or similar rock formations;

[0069] The flow cross-sectional area The specific steps include: selecting the solver and calculation method in the finite element software to simulate the flow; running the simulation to calculate the flow of the fluid in the fracture network according to the fluid dynamics equations (such as the Navier-Stokes equations); after the simulation is completed, the post-processing function of the software is used to analyze the flow path. On the flow path, the flow cross-sectional area can be extracted. ;

[0070] Maximum pressure difference , specifically, the maximum pressure difference at different locations on the flow path. Generally, computational fluid dynamics software (CFD) is used to simulate and predict the pressure distribution on the flow path to obtain the maximum pressure difference;

[0071] Fluid viscosity , a viscometer can be used to measure the fluid and obtain the viscosity value of the fluid at a specific temperature and pressure. Commonly used viscometers include rotational viscometers and capillary viscometers;

[0072] The formula for calculating the reservoir effectiveness index of the fracture network is:

[0073] ;

[0074] In the formula, is the reservoir effectiveness index of the fracture network, where is the maximum temperature in the reservoir, is the minimum temperature in the reservoir, It indicates that the thermal conductivity coefficient of the reservoir can be set according to expert experience and existing data;

[0075] The formula for generating the comprehensive evaluation coefficient based on the obtained performance data of the seam network is:

[0076] ;

[0077] In the formula, is the comprehensive evaluation coefficient, , and The connectivity of the fracture network is , Modified Flow Efficiency Index and reservoir effectiveness index The weight coefficient of and , and Both are greater than 0.

[0078] The connectivity of a fracture network refers to the degree of interconnection between the gaps, which affects the flow path and flow rate of the fluid in the fracture network. Good connectivity means that the fluid can flow efficiently through the gaps and reduce flow resistance. The flow efficiency index measures the efficiency of the fluid passing through the fracture network under specific conditions. The evaluation of flow efficiency helps to select the best fracture network configuration in the engineering design stage and improve the overall flow performance. The reservoir effectiveness index reflects the distribution and transfer of thermal energy in the reservoir, especially in the process of thermal recovery (such as geothermal energy, oil and gas thermal recovery). Reservoirs with a high reservoir effectiveness index can usually transfer heat to the fluid faster and increase the recoverability of the fluid. Changes in the reservoir effectiveness index may reflect changes in the fluidity of the reservoir. A higher reservoir effectiveness index may indicate good flow channels and connectivity. Therefore, the reservoir effectiveness index is proportional to the comprehensive evaluation coefficient. Similarly, the connectivity and flow efficiency index of the fracture network are both proportional to the comprehensive evaluation coefficient, that is, the connectivity of the fracture network. , flow efficiency index and reservoir effectiveness index The larger it is, the higher the corresponding comprehensive evaluation coefficient value is.

[0079] At the same time, since the connectivity of the fracture network has the greatest impact on the performance evaluation of the fracture network, the flow efficiency index and the reservoir effectiveness index have relatively little impact on the performance evaluation of the fracture network compared with the connectivity of the fracture network, so .

[0080] Step 3: Based on the generated comprehensive evaluation coefficient, the coefficient is compared with a preset seam network grade threshold, and the seam network is divided into seam networks of different grades according to different comparison results, wherein the seam networks of different grades include primary, secondary and tertiary seam networks;

[0081] Based on the generated comprehensive evaluation coefficient, the logic for dividing the fracture network into different levels of fracture networks is as follows:

[0082] when , marking the fracture network as a third-level fracture network; indicating that the fluidity and connectivity of the fracture network are poor and need to be transformed;

[0083] when , marking the fracture network as a secondary fracture network; indicating that the fluidity and connectivity of the fracture network are average and need to be modified

[0084] when , marking the fracture network as a primary fracture network; indicating that the fluidity and connectivity of the fracture network are good and can be modified to further improve the performance of the fracture network.

[0085] In the formula, Indicates the preset seam network level threshold. The seam network level threshold can be set based on expert experience and actual environmental conditions.

[0086] Step 4: According to the different levels of fracture networks, combined with the temperature and pressure in the reservoir, different transformation schemes are adopted for fracture networks of different levels to improve the fluidity and connectivity of the fracture networks.

[0087] Different transformation plans are adopted for different levels of seam networks. The specific plans adopted for different levels of seam networks are as follows:

[0088] For the primary fracture network, a fluid injection strategy is adopted, and high permeability fluid CO is selected. 2 Injection is performed to control CO according to the temperature and pressure in the reservoir. 2 The injection rate;

[0089] For the secondary fracture network, chemical reagents were introduced for acidification treatment, and a fluid injection strategy was adopted, selecting water and CO 2 The mixed fluid is injected;

[0090] For the third-level fracture network, the fracture network fracturing construction technology is used to transform the fracture network.

[0091] Among them, control CO 2 The injection rate is based on the formula:

[0092] ;

[0093] In the formula, represents the adjusted injection flow rate, Indicates the current injection flow. is the adjustment coefficient, and They represent the target pressure of the fracture network in the reservoir and the current pressure of the fracture network in the reservoir, respectively. Indicates the current temperature of the fracture network in the reservoir. Controlling the flow rate can improve the fluidity and connectivity of the fracture network without causing any danger. Too much flow rate may lead to the risk of fracture network bursting, while too little flow rate may lead to weaker transformation effect of the fracture network.

[0094] Chemical reagents are introduced for acidification treatment, where hydrochloric acid is selected as the chemical reagent. According to the reservoir characteristics, appropriate acid and additives are selected and the acid solution is prepared in proportion. Usually the concentration of hydrochloric acid is between 15% and 28%, depending on the reaction requirements of the reservoir.

[0095] For the transformation plan of the secondary seam network, water and CO 2 The mixed fluid was injected, where the fluid mixing ratio was set to 70% water and 30% CO 2 ; Control of injection rate and control of CO 2 The injection rate is the same;

[0096] For the transformation plan of the tertiary fracture network, the fracture network is transformed by using the fracture network fracturing construction process. New cracks and new flow channels are formed by fracturing to form a new fracture network together, thereby improving the fluidity and connectivity of the fracture network. The fracture network fracturing construction process is a common technical means, and the specific operation steps are not repeated here.

[0097] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0098] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0099] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0100] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A complex fracture network transformation and evaluation method for deep carbonate geothermal reservoirs, characterized in that: The specific steps include: Apply seismic waves to the deep carbonate rock area to be analyzed, obtain seismic detection data, determine the drilling position through the seismic detection data, collect core area images at the drilling position using a downhole video instrument, and establish a three-dimensional finite element model of the fracture network based on the seismic detection data and the core area images; Extracting geometric shape data of the fracture network based on the established three-dimensional finite element model of the fracture network, characterizing performance data of the fracture network through the geometric shape data, and generating a comprehensive evaluation coefficient according to the obtained performance data of the fracture network, wherein the geometric shape data of the fracture network includes gap spacing, gap width and gap direction, and the performance data of the fracture network includes connectivity of the fracture network, flow efficiency index and reservoir effectiveness index; The performance data of the seam network is characterized by the geometric shape data, and the formula for calculating the connectivity of the seam network is: In the formula, represents the connectivity of the seam network, Indicates The length of the gap, Indicates The width of the gap, For the The angle between the gap and the flow direction, is the number of gaps in the seam network, is the index of the gap, where ; The performance data of the seam network is characterized by the geometric shape data, and the formula for calculating the flow efficiency index of the seam network is: In the formula, represents the flow efficiency index of the seam network, is the flow efficiency, represents the flow cross-sectional area, where the flow efficiency The calculation is based on the formula: In the formula, is the reservoir permeability, is the maximum pressure difference, specifically the maximum pressure difference at different positions on the flow path, is the fluid viscosity, represents the flow path length; At the same time, the flow efficiency index is corrected based on the temperature in the seam network. The specific formula is: In the formula, is the modified flow efficiency index, is the influence coefficient of temperature on flow efficiency, Indicates the average temperature in the seam network; The formula for calculating the reservoir effectiveness index of the fracture network is: In the formula, is the reservoir effectiveness index of the fracture network, where is the maximum temperature in the reservoir, is the minimum temperature in the reservoir, represents the thermal conductivity coefficient of the reservoir; The formula for generating the comprehensive evaluation coefficient based on the obtained performance data of the seam network is: In the formula, is the comprehensive evaluation coefficient, , and The connectivity of the fracture network is , Modified Flow Efficiency Index and reservoir effectiveness index The weight coefficient of and , and All are greater than 0; Based on the generated comprehensive evaluation coefficient, the coefficient is compared with a preset seam network grade threshold, and the seam network is divided into seam networks of different grades according to different comparison results, wherein the seam networks of different grades include primary, secondary and tertiary seam networks; Based on the generated comprehensive evaluation coefficient, the logic for dividing the fracture network into different levels of fracture networks is as follows: when , mark the seam network as a third-level seam network; when , mark the seam network as a secondary seam network; when , mark the seam network as a primary seam network; In the formula, Indicates the preset seam network level threshold; According to the different levels of fracture networks, combined with the temperature and pressure in the reservoir, different transformation schemes are adopted for fracture networks of different levels to improve the fluidity and connectivity of the fracture networks.

2. A complex fracture network transformation and evaluation method for deep carbonate geothermal reservoirs according to claim 1, characterized in that: Seismic waves with a frequency of 10 to 50 Hz are applied to the carbonate rock area to be analyzed. At the same time, at least 8-10 receivers are arranged around the carbonate rock area to be analyzed to form a uniform grid, and seismic wave data at different angles are collected. The wave speeds between different receivers are calculated based on the seismic wave data, and the location of the receiver with the largest wave speed is found. The location of the receiver with the largest wave speed is selected as the drilling location.

3. A complex fracture network transformation and evaluation method for deep carbonate geothermal reservoirs according to claim 2, characterized in that: Based on the determined drilling position, drilling operations are carried out, and during the drilling process, images of the core area are collected using a downhole video device. After the images are subjected to noise reduction processing, they are fused with the images of the core area to analyze the fracture network characteristics. The specific steps of the fracture network characteristic analysis include: inputting the seismic wave data and the core image into the same data processing platform to ensure that the data format is consistent, using the edge detection algorithm to process the collected images, extracting the texture characteristics of the rock formation, and combining the wave velocity distribution in the seismic data to analyze the cracks and fracture network characteristics of the core area and determine their distribution. Based on the analysis results, a three-dimensional finite element model of the deep carbonate rock area is established.

4. A complex fracture network transformation and evaluation method for deep carbonate geothermal reservoirs according to claim 1, characterized in that: Different transformation plans are adopted for different levels of seam networks. The specific plans adopted for different levels of seam networks are as follows: For the primary fracture network, a fluid injection strategy is adopted, and high permeability fluid CO2 is selected for injection. The injection rate of CO2 is controlled according to the temperature and pressure in the reservoir. The formula for controlling the injection rate of CO2 is: In the formula, represents the adjusted injection flow rate, Indicates the current injection flow. is the adjustment coefficient, and They represent the target pressure of the fracture network in the reservoir and the current pressure of the fracture network in the reservoir, respectively. Indicates the current temperature of the fracture network within the reservoir; For the secondary fracture network, chemical reagents are introduced for acidification treatment, and a fluid injection strategy is adopted, in which a mixed fluid of water and CO2 is selected for injection. The injection rate is controlled in the same way as the injection rate of CO2 in the primary fracture network. For the third-level fracture network, the fracture network fracturing construction technology is used to transform the fracture network.

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