Dry hot rock type geothermal resource prediction method and device, medium and program product

Abstract

The invention discloses a hot dry rock type geothermal resource prediction method and device, a medium and a program product, and the method comprises the steps: firstly carrying out the gravity inversion through the gravity measurement data of a to-be-researched region, and constructing a three-dimensional density model of a hot dry rock body; constructing a hot dry rock volume model according to the geothermal geological data; gridding the hot dry rock volume model to obtain a hot dry rock space grid model; performing geological attribute assignment on each cell in the hot dry rock space grid model according to the geological attribute of the to-be-studied area to obtain a hot dry rock attribute model; performing temperature numerical simulation on each thermal reservoir in the hot dry rock space grid model by using temperature data of the to-be-researched area to obtain a hot dry rock temperature model; and on the basis of the hot dry rock space grid model, the attribute model and the temperature model, the hot dry rock geothermal resource quantity is predicted, and a hot dry rock type geothermal resource quantity prediction result is obtained. According to the embodiment of the invention, the prediction accuracy of the hot dry rock type geothermal resources can be effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geothermal resource reserves evaluation, in particular to a hot dry rock type geothermal resource prediction method, device, medium and program product. BACKGROUND

[0002] Geothermal resources are a clean and environmentally friendly renewable energy source with strong social, economic and environmental advantages. Hot dry rock is a new emerging geothermal energy, generally refers to a high temperature rock mass with a temperature of more than 180℃, a burial depth of thousands of meters, and no fluid or only a small amount of underground fluid (dense impermeable) inside. In theory, the deeper the depth, the higher the geothermal temperature, and any area reaching a certain depth can be called hot dry rock. Hot dry rock type geothermal resources have more development potential and prospect due to their universality and high heat temperature. China is located at the intersection of the Pacific plate geothermal zone and the Mediterranean-Himalayan plate edge geothermal zone. The prospect of hot dry rock resources in China is quite promising. Under the background of the double carbon target, economically efficient development and utilization of hot dry rock is of great significance to China's realization of the double carbon target.

[0003] Hot dry rock geothermal resource potential assessment is the basis for hot dry rock development, and reasonable and accurate estimation of hot dry rock resource quantity can accurately evaluate the hot dry rock reserves in China, which is of great significance to the exploration and development of hot dry rock. At present, whether for the calculation of water-heat type geothermal resources or the calculation of hot dry rock resource quantity, the traditional volume method is used, and the calculation formula is Q = p * C * V (T - T0), wherein p is the rock density, C is the specific heat of rock, V is the rock volume, T is the rock temperature at a specific depth, and T0 is the surface temperature. The volume method calculation formula involves multiple parameters including heat storage volume, heat storage temperature, rock density, rock specific heat and other parameters.

[0004] It can be seen that the existing technology is too rough in determining the distribution and volume of the heat storage, and the heat storage temperature distribution is uneven. The lithology density and specific heat are discrete in determining the distribution within the heat storage. The selection of uniform parameters for the heat storage will affect the accuracy of the calculation results, so the results obtained by the traditional volume method often have great uncertainty.

[0005] Therefore, it is urgent to invent a new hot dry rock type geothermal resource prediction method to solve the problem of low prediction accuracy of the existing method for hot dry rock type geothermal resources. SUMMARY

[0006] Therefore, the embodiments of the present application provide a hot dry rock type geothermal resource prediction method, device, medium and program product, which at least partially solve the problems existing in the prior art.

[0007] Other characteristics and advantages of the present application will become apparent from the following detailed description, or will be learned by practice of the present application.

[0008] To achieve the above object, the embodiment of the present application provides the following technical scheme.

[0009] According to a first aspect of the embodiment of the present application, a dry hot rock type geothermal resource prediction method is provided, and the method comprises the following steps:

[0010] Gravity inversion is performed on gravity measurement data of a region to be studied, and a three-dimensional density model corresponding to a dry hot rock body in the region to be studied is constructed according to an inversion result;

[0011] Based on the three-dimensional density model, a dry hot rock volume model corresponding to the region to be studied is constructed according to fault surface data, stratum horizon data, geothermal well data, geological profile data and dry hot rock body boundary data at different depths in geothermal geological data of the region to be studied;

[0012] The dry hot rock volume model is gridded to obtain a corresponding dry hot rock space grid model;

[0013] According to geological properties of the region to be studied, each cell in the dry hot rock space grid model is assigned a geological property to obtain a dry hot rock property model;

[0014] Temperature data of the region to be studied is used to perform temperature numerical simulation on each thermal reservoir in the dry hot rock space grid model, and a dry hot rock temperature model is simulated;

[0015] Based on the dry hot rock space grid model, the dry hot rock property model and the dry hot rock temperature model, the geothermal resource quantity of each cell in each thermal reservoir of the dry hot rock in the region to be studied is predicted to obtain a dry hot rock type geothermal resource quantity prediction result of the region to be studied.

[0016] Further, gravity inversion is performed on gravity measurement data of a region to be studied, and a three-dimensional density model corresponding to a dry hot rock body in the region to be studied is constructed according to an inversion result, which comprises the following steps:

[0017] The range of the region to be studied is determined, and first gravity profile data of the region to be studied is obtained;

[0018] The first gravity profile data is screened to remove abnormal data, and second gravity profile data is obtained;

[0019] Gravity inversion processing is performed on the second gravity profile data to obtain an inversion result;

[0020] According to the inversion result, a three-dimensional density model corresponding to the hot dry rock in the region to be studied is constructed.

[0021] Further, based on the three-dimensional density model, a hot dry rock volume model corresponding to the region to be studied is constructed according to fault surface data, stratum horizon data, geothermal well data, geological profile data in geothermal geological data of the region to be studied, and hot dry rock boundary data at different depths, including:

[0022] The fault surface data in the geological data of the region to be studied is converted into a vectorized three-dimensional format, and the converted three-dimensional fault surface data is imported into a geological modeling software;

[0023] The stratum horizon data in the geological data of the region to be studied is converted into a vectorized three-dimensional format, and the converted three-dimensional stratum horizon data is imported into the geological modeling software in a column-based text format;

[0024] The geothermal well completion and logging report data of the region to be studied is imported into the geological modeling software, and the geothermal well completion and logging report data includes well location, top and bottom depth, stratum boundary depth, porosity, permeability, lithology, thermal conductivity, specific heat and density;

[0025] The interpreted geological profile data of the region to be studied is converted into a point-line format and imported into the geological modeling software;

[0026] The hot dry rock boundary at different depths is vectorized, and the vectorized hot dry rock boundary data is imported into the geological modeling software;

[0027] The geological modeling software is used to construct a hot dry rock volume model corresponding to the region to be studied according to the imported data.

[0028] Further, the geological modeling software is used to construct a hot dry rock volume model corresponding to the region to be studied according to the imported data, including:

[0029] Based on the imported data, a stratum column in the hot dry rock volume model is constructed according to the sequence of stratum deposition and the contact relationship between strata;

[0030] According to the imported three-dimensional fault surface data, the properties of the hot dry rock volume model are defined, a fault network is constructed based on the fault properties, and a fault surface branch relationship is defined;

[0031] Based on the stratum column and the fault network, a layer network of the hot dry rock volume model is generated according to the imported data, and a completed hot dry rock volume model is obtained.

[0032] Further, according to the geological properties of the region to be studied, the geological properties of each cell in the hot dry rock spatial grid model are valued, and a hot dry rock property model is obtained, comprising:

[0033] According to the geological property data of the region to be studied, the geological properties of each cell in the hot dry rock spatial grid model are valued by using sequential Gaussian simulation and Kriging difference method, and a hot dry rock property model is obtained.

[0034] The geological property data includes lithology, specific heat, thermal conductivity, permeability, porosity and density.

[0035] Further, according to the temperature data of the region to be studied, the temperature of each thermal reservoir in the hot dry rock spatial grid model is numerically simulated, and a hot dry rock temperature model is simulated, comprising:

[0036] According to the temperature data of the region to be studied, the temperature of each thermal reservoir in the hot dry rock spatial grid model is numerically simulated, and the simulated temperature of each thermal reservoir is obtained, and the calculation formula is:

[0037]

[0038] Wherein, T(z) is the average temperature of the thermal reservoir, Z is the depth, T0 is the surface temperature, q0 is the surface heat flow value, Z i and K i are the thickness and thermal conductivity of each layer respectively, A0 is the surface heat generation rate, A '

[0039] and Z' are the bottom heat generation rate and the total thickness of the calculation point stratum respectively.

[0040] According to the simulated temperature of each thermal reservoir, the hot dry rock temperature model is constructed, and the thermal reservoir is the grid layer of the hot dry rock spatial grid model.

[0041] Further, the geothermal resource quantity of each cell in each thermal reservoir of the hot dry rock in the region to be studied is predicted, and the hot dry rock type geothermal resource quantity prediction result of the region to be studied is obtained, comprising:

[0042] The geothermal resource quantity of each cell in each thermal reservoir of the hot dry rock in the region to be studied is predicted, and the hot dry rock type geothermal resource quantity prediction result of the region to be studied is obtained, and the calculation formula is: , C i,j =C ri,j ·ρr i,j ·(1-φ i,j );

[0043] wherein i is the layer number of the hot reservoir, j is the cell number in the hot reservoir, m is the total number of rock layers, n is the total number of hot reservoir layer cells, Q r is the dry hot rock type geothermal resource quantity prediction result of the region to be studied, Q i,j is the geothermal resource quantity prediction result corresponding to the jth cell of the ith hot reservoir, C i,j is the average specific heat capacity of the rock corresponding to the jth cell of the ith hot reservoir, A i,j is the area of the jth cell of the ith hot reservoir, H i,j is the average thickness of the hot reservoir corresponding to the jth cell of the ith hot reservoir, T i,j is the average temperature corresponding to the jth cell of the ith hot reservoir, T0 is the reference temperature, Cr i,j is the specific heat capacity of the rock corresponding to the jth cell of the ith hot reservoir, pr i,j is the rock density corresponding to the jth cell of the ith hot reservoir, φ i,j is the rock porosity corresponding to the jth cell of the ith hot reservoir.

[0044] According to a second aspect of the embodiments of the present application, there is provided a dry hot rock type geothermal resource prediction device, the device comprising: a processor and a memory;

[0045] The memory is configured to store one or more program instructions;

[0046] The processor is configured to execute the one or more program instructions to perform the steps of the dry hot rock type geothermal resource prediction method according to any one of the preceding aspects.

[0047] According to a third aspect of the embodiments of the present application, there is provided a computer readable storage medium having stored thereon a computer program, the computer program, when executed by a processor, implementing the steps of the dry hot rock type geothermal resource prediction method according to any one of the preceding aspects.

[0048] According to a fourth aspect of the embodiments of the present application, there is provided a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to implement the steps of the dry hot rock type geothermal resource prediction method according to any one of the preceding aspects.

[0049] The application discloses a hot dry rock type geothermal resource prediction method, equipment, a medium and a program product, and relates to the field of geothermal resource prediction. BRIEF DESCRIPTION OF DRAWINGS

[0050] In order to make the technical personnel in the art better understand the present application scheme, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the scope of protection of the present application.

[0051] Figure 1 A flowchart of a hot dry rock type geothermal resource prediction method provided by an embodiment of the present application;

[0052] Figure 2 A hot dry rock body three-dimensional inversion horizontal section effect schematic diagram of a hot dry rock type geothermal resource prediction method provided by an embodiment of the present application;

[0053] Figure 3 A hot dry rock body model schematic diagram one of a hot dry rock type geothermal resource prediction method provided by an embodiment of the present application;

[0054] Figure 4 A hot dry rock body model schematic diagram two of a hot dry rock type geothermal resource prediction method provided by an embodiment of the present application. DETAILED DESCRIPTION

[0055] In order to make the technical personnel in the art better understand the present application scheme, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the scope of protection of the present application.

[0056] It should be noted that the terms "first", "second", and the like in the description and claims of the application and above drawings are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the use of such terms is interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of orderly implementation in sequences other than those illustrated or otherwise described herein. Moreover, the terms "comprise", "have" and any variations thereof are intended to cover a non-exclusive inclusion, for example, a process, method, system, product or apparatus that comprises a list of steps or units is not necessarily limited to those steps or units that are expressly listed, but can include additional steps or units not expressly listed or inherent to such process, method, product or apparatus.

[0057] Figure 1 Fig. 1 shows a flow chart of a hot dry rock type geothermal resource prediction method according to an embodiment of the application.

[0058] As shown in Fig. 1, the hot dry rock type geothermal resource prediction method according to an embodiment of the application can include steps S100, S200, S300, S400, S500 and S600. Figure 1

[0059] In step S100, gravity inversion is performed using gravity measurement data of a region to be studied, and a three-dimensional density model corresponding to a hot dry rock body in the region to be studied is constructed according to the inversion result.

[0060] Specifically, the above steps include:

[0061] First, the range of the region to be studied is determined, first gravity profile data of the region to be studied is obtained, the first gravity profile data is screened and analyzed for geological characteristics, abnormal data is removed, and second gravity profile data is obtained; automatic gravity inversion of the two-dimensional profile and human-computer interaction forward calculation are performed using the above second gravity profile data, and the maximum depth of the low boundary is determined; then a three-dimensional initial model (polygonal column, including 31 horizontal sections) is constructed, three-dimensional constrained automatic inversion and final model forward calculation are performed based on the three-dimensional initial model according to the horizontal plane element method, and a three-dimensional density model corresponding to the hot dry rock body in the region to be studied is obtained according to the inversion result.

[0062] Figure 2 Fig. 3 shows a three-dimensional inversion horizontal section effect schematic diagram of a hot dry rock body (Hunan Weishan rock body)

[0063] In the above steps, the embodiments of the application use geophysical software to interpret the granite morphology at different depths.

[0064] ​Then, in step S200, based on the three-dimensional density model, a dry hot rock volume model corresponding to the region to be studied is constructed according to fault surface data, stratum horizon data, geothermal well data, geological profile data and dry hot rock body boundary data at different depths in geothermal geological data of the region to be studied.

[0065] Specifically, the above steps include a data importing step and a modeling constructing step.

[0066] The data importing step includes:

[0067] In the data preparation stage, fault surface information in the geological data is extracted in MapGIS, the fault surface data is converted into a vectorized three-dimensional point or line format, and the converted three-dimensional fault surface data is imported into the geological modeling software GOCAD.

[0068] In the data preparation stage, fault surface information in the geological data is extracted in MapGIS, the fault surface data is converted into a vectorized three-dimensional point or line format, and the converted three-dimensional fault surface data is imported into the geological modeling software GOCAD.

[0069] The geothermal well completion and logging reports of the region to be studied are collected, and the geothermal well completion and logging report data is imported into the geological modeling software, wherein the geothermal well completion and logging report data includes well location, top and bottom depth, stratum boundary depth, porosity, permeability, lithology, thermal conductivity, specific heat and density.

[0070] The interpreted data of the geological profile of the region to be studied obtained by two-dimensional seismic and high-power time-frequency electromagnetic method is converted into a point-line format and imported into the geological modeling software GOCAD.

[0071] The dry hot rock body boundaries at different depths are vectorized using geographic information data processing software, the vectorized shape format dry hot rock boundary data is imported into the geological modeling software GOCAD, and a closed rock body is generated using a layer surface generation tool.

[0072] A three-dimensional geological model of the hot reservoir in the study area is established using the study area geothermal geological map, hot reservoir depth map, geological profile map, drilling basic data and logging data, a corresponding block model is established in the Workflow of the GOCAD software, and finally they are integrated to form a three-dimensional geological model of the dry hot rock in the study area.

[0073] Through the established three-dimensional geological model, the grid data of the hot reservoir is exported, the hot reservoir depth, thickness and the like are read through the grid data, and the hot reservoir volume is calculated.

[0074] The modeling constructing step includes:

[0075] Construct stratigraphic columns. Based on the data imported into GOCAD, the stratigraphic columns in the hot dry rock volume model are constructed according to the sequence of deposition of each stratum and the contact relationship between strata. The stratigraphic columns are the basis for GOCAD geological modeling. The rationality of stratigraphic column construction is related to the success or failure of structural modeling.

[0076] Define the fault properties. Based on the imported 3D fault surface data, define the fault surface of the hot dry rock volume model as a normal fault, reverse fault, or strike-slip fault.

[0077] Construct a fault plane network based on the fault properties and define the fault plane branch relationship, and make local fine-tuning on unreasonable areas of the fault plane.

[0078] Construct stratigraphic layers. Based on the stratigraphic column and fault network, the imported seismic reflection surface data, well stratification data, stratigraphic erosion lines, stratigraphic non-depositional boundaries, etc. are limited by faults and generated according to the rules defined by the stratigraphic column to form a layer network of the hot dry rock volume model, thus obtaining the completed hot dry rock volume model.

[0079] Next, in step S300 , the hot dry rock volume model is meshed to obtain a corresponding hot dry rock spatial mesh model.

[0080] Specifically, the above steps include:

[0081] The 3D Reservoir Grid Builder module is used to grid the hot dry rock volume model to obtain the corresponding hot dry rock spatial grid model.

[0082] Figure 3 This is a schematic diagram of the spatial grid model of dry hot rock mass only. Figure 4 Schematic diagram of the spatial grid model for placing hot dry rock in the area to be studied.

[0083] In step S400, geological attributes are assigned to each cell in the hot dry rock spatial grid model according to the geological attributes of the area to be studied, so as to obtain a hot dry rock attribute model.

[0084] Specifically, the above steps include:

[0085] Using methods such as sequential Gaussian simulation and Kriging difference, geological attributes are assigned to each cell in the hot dry rock spatial grid model in a certain order (the order is chosen because some attributes have a certain correlation with lithology, etc.) according to the geological attribute data of the study area to obtain the hot dry rock attribute model. The above geological attribute data include lithology, specific heat, thermal conductivity, permeability, porosity and density.

[0086] Then, in step S500, temperature numerical simulation is performed on each thermal reservoir in the hot dry rock spatial grid model using the temperature data of the region to be studied, and a hot dry rock temperature model is simulated.

[0087] Specifically, the above steps include:

[0088] The built hot dry rock spatial grid model is imported into a numerical simulation software to establish a hot dry rock temperature model, and the calculation formula is: wherein T(z) is the average temperature of the thermal reservoir, Z is the depth of the calculated grid, T0 is the surface temperature, q0 is the surface heat flow value (mW / m 2 ), Z i and K i are the thickness (m) and thermal conductivity (W / m·k) of each layer, respectively, and A0 is the surface heat generation rate (μW / m 3 ), A' and Z' are the bottom heat generation rate (μW / m 3 ) and the total thickness of the stratum at the calculation point (m), respectively.

[0089] The grid layers at different depths of the hot dry rock spatial grid model are taken as each thermal reservoir, and a hot dry rock temperature model is constructed according to the simulated temperature of each thermal reservoir.

[0090] Finally, in step S600, based on the hot dry rock spatial grid model, the hot dry rock attribute model and the hot dry rock temperature model, the geothermal resource quantity of each cell in each thermal reservoir of the hot dry rock in the region to be studied is predicted, and the hot dry rock type geothermal resource quantity prediction result of the region to be studied is obtained.

[0091] The thermal reservoir method is a traditional method for calculating the quantity of geothermal resources, which is simple and widely used. Generally, the product of the volume of the thermal reservoir and the temperature and the assumed unit volume heat capacity is used to obtain an approximation, and the specific formula is Q r =A·H·C·(T r -T0), C=C r ·ρ r ·(1-φ)+ρ w ·C w ·φ, wherein Q r is the heat stored in the thermal reservoir (J), A is the area of the evaluation area (m 2 ), H is the thickness of the thermal reservoir (m), C is the average volumetric specific heat capacity of the thermal reservoir rock and geothermal fluid (J / m 3 ·℃), T r is the temperature of the thermal reservoir (℃), T0 is the reference temperature (℃), ρ r is the density of the thermal reservoir rock (kg / m 3 ), C r is the specific heat of the thermal reservoir rock (J / kg·℃), and φ is the porosity of the thermal reservoir rock. wis the density of the geothermal fluid (kg / m 3 is the specific heat of the geothermal fluid (J / kg·℃). w is the density of the geothermal fluid (kg / m

[0092] It can be known from the above formula that, when calculating the heat of the rock mass, the product of the volume of the rock solid, the average temperature, the rock specific heat and the density value is added to the product of the average temperature and the water specific heat of the pore volume in the geothermal reservoir. However, when the thickness of the geothermal reservoir changes greatly, the temperature, the rock specific heat and other geothermal reservoir parameters are anisotropic, and the average value is directly taken for calculation, the calculation result will have a large deviation.

[0093] In order to improve the calculation accuracy, in view of the fact that the geological conditions of each geothermal reservoir in the research area are quite different, the thickness of the geothermal reservoir is large, and the geothermal reservoir parameters are quite different in the horizontal and vertical directions, the embodiment of the present application improves the above method, and the above step S600 specifically includes:

[0094] Firstly, the area and the average thickness of each geothermal reservoir cell are derived from the dry hot rock spatial grid model, the specific heat, the density and the porosity of each geothermal reservoir cell are derived from the dry hot rock attribute model, and the temperature of each geothermal reservoir is derived from the dry hot rock temperature model.

[0095] Using the derived data, the geothermal resource quantity of each cell in each geothermal reservoir of the dry hot rock in the research area is predicted, the dry hot rock type geothermal resource quantity prediction result of the research area is obtained, and the calculation formula is: C i,j =C ri,j ·ρr i,j ·(1-φ i,j ).

[0096] Wherein, i is the layer number of the geothermal reservoir, j is the cell number in the geothermal reservoir, m is the total number of rock layers, n is the total number of geothermal reservoir layer cells, Q r is the dry hot rock type geothermal resource quantity prediction result of the research area, Q i,j is the geothermal resource quantity prediction result corresponding to the jth cell of the ith geothermal reservoir, C i,j is the average specific heat capacity of the rock corresponding to the jth cell of the ith geothermal reservoir, A i,j is the area of the jth cell of the ith geothermal reservoir, H i,j is the average thickness of the geothermal reservoir corresponding to the jth cell of the ith geothermal reservoir, T i,j is the average temperature corresponding to the jth cell of the ith geothermal reservoir, T0 is the reference temperature, Cr i,j is the specific heat capacity of the rock corresponding to the jth cell of the ith geothermal reservoir, ρr i,j is the density of the rock corresponding to the jth cell of the ith geothermal reservoir, and φ i,jThe rock porosity corresponding to the jth cell of the ith thermal reservoir.

[0097] In addition, the embodiment of the present application further provides a device, comprising: a processor and a memory; the memory is used for storing one or more program instructions; and the processor is used for running the one or more program instructions to execute the steps of the dry hot rock type geothermal resource prediction method.

[0098] In addition, the embodiment of the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the steps of the dry hot rock type geothermal resource prediction method.

[0099] In addition, the embodiment of the present application further provides a computer program product, which comprises computer program instructions, and the computer program instructions are executed by a processor to realize the steps of the dry hot rock type geothermal resource prediction method.

[0100] The accuracy of the dry hot rock resource quantity calculation needs to accurately determine the volume of the deep dry hot rock first. The rock is continuously distributed in a certain scale space, and the metamorphic rock or crystalline rock mass, especially the intermediate-acid granite mass, is the main medium of the dry hot rock.

[0101] In view of this, the dry hot rock type geothermal resource prediction method, device, medium and program product provided by the embodiment of the present application are used for the dry hot rock of the granite mass with the same lithology rock composition, combine the geophysical exploration technology and the three-dimensional modeling technology, detect the spatial distribution of the deep granite mass dry hot rock through the gravity and seismic measurement and other geophysical technologies, establish the three-dimensional model of the dry hot rock by using the three-dimensional modeling technology, establish the grid model of the dry hot rock, accurately determine the volume of the dry hot rock, import the established dry hot rock geological model grid into the numerical simulation software, establish the attribute and temperature model, accurately read the attribute and temperature of each thermal reservoir cell, and finally calculate the dry hot rock mass resource quantity by using the gridding calculation formula, so that the prediction accuracy of the dry hot rock type geothermal resource is effectively improved.

[0102] In an embodiment of the present application, the processor can be an integrated circuit chip, which has the processing capability of signals. The processor can be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component. The methods, steps and logical block diagrams disclosed in the embodiments of the present application can be implemented or executed by the processor. The general purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the methods disclosed in conjunction with the embodiments of the present application can be directly embodied in hardware code of the processor, or a combination of hardware and software modules in the processor. The software module can be located in a random access memory, a flash memory, a read only memory, a programmable read only memory or an electrically erasable programmable memory, a register, or other mature storage medium in the art. The processor reads information in the storage medium and combines the hardware to complete the steps of the above methods. The storage medium can be a memory, for example, can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. The non-volatile memory can be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically EPROM (EEPROM), or a flash memory. The volatile memory can be a Random Access Memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM can be used, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous Dynamic RAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), Synchl ink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM).The storage media described in the embodiments of the present application are intended to include, but are not limited to, these and any other suitable type of memory. It will be appreciated by those skilled in the art that the functions described in the above-mentioned one or more examples can be implemented in combination with hardware and software. When the software is applied, the corresponding functions can be stored in a computer readable medium or transmitted as one or more instructions or codes on the computer readable medium. The computer readable medium includes a computer storage medium and a communication medium, wherein the communication medium includes any medium that facilitates the transfer of computer programs from one place to another. The storage medium can be any available medium that can be accessed by a general or special purpose computer. Although the present application has been described in detail above with general description and specific embodiments, some modifications or improvements can be made to the present application on the basis of the present application, which is obvious to those skilled in the art. Therefore, these modifications or improvements made without departing from the spirit of the present application fall within the scope of the present application.

[0103] The above description is only the preferred embodiment of the present application, and does not limit the present application in any form. Those skilled in the art can make some simple modifications, equivalent changes or modifications to the above disclosed technical content, which fall within the protection scope of the present application.

Claims

1. A method for predicting hot dry rock geothermal resources, characterized in that: The method comprises: Performing gravity inversion using gravity measurement data of the area to be studied, and constructing a three-dimensional density model corresponding to the hot dry rock mass in the area to be studied based on the inversion results; Based on the three-dimensional density model, a hot dry rock volume model corresponding to the area to be studied is constructed according to the fault surface data, stratigraphic layer data, geothermal well data, geological profile data and boundary data of hot dry rock masses at different burial depths in the geothermal geological data of the area to be studied; Meshing the hot dry rock volume model to obtain a corresponding hot dry rock spatial mesh model; Assigning geological attributes to each cell in the hot dry rock spatial grid model according to the geological attributes of the area to be studied, to obtain a hot dry rock attribute model; Using the temperature data of the area to be studied, numerically simulate the temperature of each thermal reservoir in the hot dry rock spatial grid model to obtain a hot dry rock temperature model; Based on the hot dry rock spatial grid model, the hot dry rock property model and the hot dry rock temperature model, the geothermal resource quantity of each cell in each thermal reservoir of the hot dry rock in the area to be studied is predicted to obtain the prediction result of the hot dry rock type geothermal resource quantity in the area to be studied.

2. A method for predicting hot dry rock geothermal resources according to claim 1, characterized in that: Gravity inversion is performed using gravity measurement data of the area to be studied, and a three-dimensional density model corresponding to the hot dry rock mass in the area to be studied is constructed based on the inversion results, including: Determine the scope of the area to be studied, and obtain first gravity profile data of the area to be studied; screening the first gravity profile data, removing abnormal data, and obtaining second gravity profile data; performing gravity inversion processing using the second gravity profile data to obtain an inversion result; According to the inversion results, a three-dimensional density model corresponding to the hot dry rock mass in the area to be studied is constructed.

3. The method for predicting hot dry rock geothermal resources according to claim 1, characterized in that: Based on the three-dimensional density model, according to the fault surface data, stratigraphic data, geothermal well data, geological profile data and boundary data of hot dry rock masses at different burial depths in the geothermal geological data of the area to be studied, a hot dry rock volume model corresponding to the area to be studied is constructed, including: Converting the fault surface data in the geological data of the area to be studied into a vectorized three-dimensional format, and importing the converted three-dimensional fault surface data into geological modeling software; Converting stratigraphic data in the geological data of the area to be studied into a vectorized three-dimensional format, and importing the converted three-dimensional stratigraphic data into the geological modeling software in a column-based text format; Importing the geothermal well completion and logging report data of the area to be studied into the geological modeling software, wherein the geothermal well completion and logging report data includes well location, top and bottom burial depth, stratum boundary burial depth, porosity, permeability, lithology, thermal conductivity, specific heat, and density; Converting the interpreted geological profile data of the area to be studied into a point-line format and importing it into the geological modeling software; Vectorizing the boundaries of hot dry rock masses at different burial depths, and importing the vectorized hot dry rock boundary data into the geological modeling software; The geological modeling software is used to construct a hot dry rock volume model corresponding to the area to be studied based on the imported data.

4. A method for predicting hot dry rock geothermal resources according to claim 3, characterized in that: The hot dry rock volume model corresponding to the area to be studied is constructed using the geological modeling software according to the imported data, including: Based on the imported data, according to the depositional sequence of each stratum and the contact relationship between strata, a stratigraphic column in a hot dry rock volume model is constructed; Defining fault properties of the hot dry rock volume model based on the imported three-dimensional fault surface data, constructing a fault network based on the fault properties, and defining fault surface branching relationships; Based on the stratigraphic column and the fault network, and according to the imported data, a layer network of a hot dry rock volume model is generated to obtain a constructed hot dry rock volume model.

5. The method for predicting hot dry rock geothermal resources according to claim 1, characterized in that: Assigning geological attributes to each cell in the hot dry rock spatial grid model according to the geological attributes of the area to be studied to obtain a hot dry rock attribute model, including: Using sequential Gaussian simulation and Kriging difference method, assigning geological attributes to each cell in the hot dry rock spatial grid model according to the geological attribute data of the area to be studied, to obtain a hot dry rock attribute model; The geological attribute data includes lithology, specific heat, thermal conductivity, permeability, porosity and density.

6. A method for predicting hot dry rock geothermal resources according to claim 1, characterized in that: Using the temperature data of the area to be studied, a temperature numerical simulation is performed on each heat reservoir in the hot dry rock spatial grid model to obtain a hot dry rock temperature model, including: Using the temperature data of the area to be studied, a temperature numerical simulation is performed on each heat reservoir in the hot dry rock spatial grid model to obtain the simulated temperature of each heat reservoir. The calculation formula is: Among them, T(z) is the average temperature of the heat reservoir, Z is the depth, T0 is the surface temperature, q0 is the surface heat flow value, Z i and K i are the thickness and thermal conductivity of each layer, A0 is the surface heat generation rate, A' and Z' are the bottom heat generation rate and the total thickness of the formation at the calculation point, respectively; The hot dry rock temperature model is constructed according to the simulated temperature of each heat reservoir, and the heat reservoir is a grid layer of the hot dry rock spatial grid model.

7. The method for predicting hot dry rock geothermal resources according to claim 1, characterized in that: The geothermal resource amount of each cell in each heat reservoir of the hot dry rock in the area to be studied is predicted to obtain the prediction result of the hot dry rock geothermal resource amount in the area to be studied, including: The geothermal resource amount of each cell in each heat reservoir of the hot dry rock in the area to be studied is predicted to obtain the prediction result of the hot dry rock geothermal resource amount in the area to be studied. The calculation formula is: Where i is the thermal reservoir layer number, j is the cell number in the thermal reservoir, m is the total number of rock layers, n is the total number of cells in the thermal reservoir layer, Q r is the prediction result of hot dry rock geothermal resources in the study area, Q i,j is the geothermal resource prediction result corresponding to the jth cell of the i-th thermal reservoir, C i,j is the average specific heat capacity of the rock corresponding to the jth cell of the i-th heat reservoir, A i,j is the area of ​​the jth unit cell of the i-th heat reservoir, H i,j is the average thickness of the heat reservoir corresponding to the jth cell of the i-th heat reservoir, T i,j is the average temperature corresponding to the jth cell of the i-th thermal reservoir, T0 is the reference temperature, is the rock specific heat capacity corresponding to the jth cell of the i-th heat reservoir, is the rock density corresponding to the jth cell of the i-th thermal reservoir, φ i,j is the rock porosity corresponding to the jth cell of the i-th thermal reservoir.

8. A device for predicting hot dry rock geothermal resources, characterized in that: The device includes: a processor and a memory; The memory is used to store one or more program instructions; The processor is used to run one or more program instructions to execute the steps of a hot dry rock geothermal resource prediction method as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for predicting hot dry rock geothermal resources according to any one of claims 1 to 7 are implemented.

10. A computer program product, characterized in that The computer program product includes computer program instructions, which, when executed by a processor, implement the steps of a method for predicting hot dry rock geothermal resources according to any one of claims 1 to 7.

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