Method and device for predicting temperature field of deep well shaft

Abstract

The application provides a deep well shaft temperature field prediction method and device, the method comprises the following steps: obtaining the initial temperature field, grid type and well data of each grid unit on the two-dimensional temperature profile of the target deep well shaft; determining the initial property data of each grid unit according to the initial temperature field and well data of each grid unit; determining the pre-constructed temperature field prediction model corresponding to each grid unit according to the grid type of each grid unit; determining the predicted temperature field of each grid unit according to the initial property data and the pre-constructed temperature field prediction model corresponding to each grid unit, and combining the predicted temperature fields of the grid units to obtain the temperature field prediction result of the target deep well shaft. The application can improve the accuracy of the deep well shaft temperature field prediction, and further improve the safety of the drilling process.

Description

Technical Field

[0001] This application relates to the field of oil and gas drilling technology, and in particular to a method and apparatus for predicting the temperature field of deep wells. Background Technology

[0002] Currently, well control safety remains a prominent issue for safe and efficient drilling of deep wells. The narrow safety pressure window of the formation encountered in deep wells and the large temperature and pressure variations in the wellbore result in complex fluid properties. The predicted temperature field of the wellbore affects the predicted pressure field. Inaccurate temperature field prediction can increase the difficulty of precise prediction and control of wellbore pressure during drilling, and may induce gas intrusion or even blowout accidents.

[0003] Traditional wellbore temperature field prediction methods assume that drilling fluid and formation properties are constant. This assumption affects the fluid flow characteristics in the wellbore, resulting in low accuracy of wellbore temperature field prediction. Summary of the Invention

[0004] To address the problems in the prior art, this application proposes a method and apparatus for predicting the temperature field of deep well shafts, which can improve the accuracy of temperature field prediction in deep well shafts, thereby improving the safety of the drilling process.

[0005] To address the aforementioned technical problems, this application provides the following technical solution:

[0006] In a first aspect, this application provides a method for predicting the temperature field of a deep well shaft, including:

[0007] Obtain the initial temperature field, grid type, and well data of each grid cell on the two-dimensional temperature profile of the target deep wellbore;

[0008] Based on the initial temperature field and well data of each grid cell, determine the initial property data of that grid cell;

[0009] Based on the grid type of each grid cell, determine the pre-built temperature field prediction model corresponding to that grid cell;

[0010] Based on the initial property data corresponding to each grid cell and the pre-built temperature field prediction model, the predicted temperature field of the grid cell is determined, and the predicted temperature fields of each grid cell are combined to form the temperature field prediction result of the target deep well.

[0011] Furthermore, the method for predicting the temperature field of a deep well shaft also includes:

[0012] Obtain the energy conservation equations corresponding to various grid types;

[0013] The energy conservation equations for each grid type are discretized using the finite difference method to construct the temperature field prediction model for that grid type.

[0014] Further, the step of determining the predicted temperature field of each grid cell based on the initial property data and the pre-built temperature field prediction model, and combining the predicted temperature fields of each grid cell to form the temperature field prediction result of the target deep wellbore, includes:

[0015] Based on the initial property data corresponding to each grid cell and the pre-built temperature field prediction model, the temperature field to be verified for that grid cell is determined.

[0016] Based on the initial temperature field of each grid cell, it is determined whether the verification of the temperature field to be verified of each grid cell has passed. If so, the temperature field to be verified of each grid cell is determined as the predicted temperature field of that grid cell, and the predicted temperature fields of each grid cell are combined to form the temperature field prediction result of the target deep well shaft.

[0017] Furthermore, the method for predicting the temperature field of a deep well shaft also includes:

[0018] If, based on the initial temperature field of each grid cell, it is determined that there is an unverified temperature field to be verified, then an update step is performed. This update step includes: determining the property data of each grid cell based on the temperature field to be verified and well data; and determining the updated temperature field to be verified for each grid cell based on the property data corresponding to each grid cell and the pre-built temperature field prediction model.

[0019] Based on the temperature field to be verified of each grid cell, determine whether the verification of the updated temperature field to be verified of each grid cell is successful. If so, combine the updated temperature fields to be verified of each grid cell to form the temperature field prediction result of the target deep well shaft.

[0020] Furthermore, the method for predicting the temperature field of a deep well shaft also includes:

[0021] If, based on the temperature fields to be verified for each grid cell, it is determined that there are updated temperature fields to be verified that have not passed verification, then the updated temperature fields to be verified are used as the temperature fields to be verified, and the update step is returned to be executed until, based on the temperature fields to be verified for each grid cell, it is determined that the updated temperature fields to be verified for each network cell have passed verification.

[0022] Furthermore, determining the initial property data of each grid cell based on its initial temperature field and well data includes:

[0023] The density of each grid cell is determined based on its initial temperature field, reference temperature, coefficient of thermal expansion, and density at the reference temperature.

[0024] Based on the pre-acquired fitting coefficients and the initial temperature field corresponding to each grid cell, determine the specific heat capacity of that grid cell;

[0025] The thermal conductivity of each grid cell is determined based on its initial temperature field, temperature coefficient, and thermal conductivity at 0℃.

[0026] The well data includes: reference temperature, coefficient of thermal expansion, density at the reference temperature, temperature coefficient, and thermal conductivity at 0°C. The initial property data includes: density, specific heat capacity, and thermal conductivity.

[0027] Furthermore, the two-dimensional temperature profile is a two-dimensional profile formed along the wellbore axis and the radial direction of the target deep well.

[0028] Secondly, this application provides a temperature field prediction device for deep well shafts, comprising:

[0029] The acquisition module is used to acquire the initial temperature field, grid type, and well data of each grid cell on the two-dimensional temperature profile of the target deep well shaft;

[0030] The determination module is used to determine the initial property data of each grid cell based on the initial temperature field and well data.

[0031] The model determination module is used to determine the pre-built temperature field prediction model corresponding to each grid cell based on the grid type of each grid cell;

[0032] The prediction module is used to determine the predicted temperature field of each grid cell based on the initial property data and the pre-built temperature field prediction model, and to combine the predicted temperature fields of each grid cell to form the temperature field prediction result of the target deep well.

[0033] Thirdly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the deep well temperature field prediction method.

[0034] Fourthly, this application provides a computer-readable storage medium storing computer instructions thereon, which, when executed, implement the deep wellbore temperature field prediction method.

[0035] As can be seen from the above technical solution, this application provides a method and apparatus for predicting the temperature field of a deep wellbore. The method includes: acquiring the initial temperature field, grid type, and well data of each of multiple grid cells on a two-dimensional temperature profile of the target deep wellbore; determining the initial property data of each grid cell based on its initial temperature field and well data; determining a pre-constructed temperature field prediction model corresponding to each grid cell based on its grid type; determining the predicted temperature field of each grid cell based on its initial property data and the pre-constructed temperature field prediction model; and combining the predicted temperature fields of each grid cell to form the temperature field prediction result of the target deep wellbore, which can improve... The accuracy of temperature field prediction in deep wells can improve the safety of the drilling process. Specifically, traditional wellbore temperature field prediction methods ignore the changes in drilling fluid properties and formation properties with temperature and pressure. This assumption affects the fluid flow characteristics in the wellbore, and thus the temperature field distribution characteristics in the wellbore and formation. This application provides a deep wellbore temperature field prediction method that considers the temperature and pressure effect. It can effectively determine the dynamic changes in the temperature field of the wellbore and formation during deep well drilling, thereby avoiding the increased difficulty in accurately predicting and controlling wellbore pressure during drilling, which can easily induce gas intrusion or even blowout accidents. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart illustrating the method for predicting the temperature field of a deep well shaft in an embodiment of this application.

[0038] Figure 2 This is a schematic diagram illustrating the relationship between a deep well shaft and grid cells, as exemplified in this application.

[0039] Figure 3 This is a schematic diagram illustrating the positional relationship between a deep well shaft and grid cells, as exemplified in this application.

[0040] Figure 4 This is a flowchart illustrating a method for predicting the temperature field of a deep well shaft according to another embodiment of this application;

[0041] Figure 5 This is a flowchart illustrating steps 201 to 203 of the deep well temperature field prediction method in the embodiments of this application.

[0042] Figure 6This is a flowchart illustrating the temperature field prediction method for deep well shafts in an application example of this application.

[0043] Figure 7 This is a schematic diagram of the structure of the temperature field prediction device for deep well shafts in the embodiments of this application;

[0044] Figure 8 This is a schematic block diagram illustrating the system configuration of an electronic device according to an embodiment of this application. Detailed Implementation

[0045] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0046] In deep drilling, the flow pressure, temperature, and drilling fluid / formation properties within the wellbore are interconnected and mutually influential. Changes in drilling fluid density with temperature and pressure affect the gravitational pressure drop within the wellbore, while changes in drilling fluid rheology with temperature and pressure affect frictional pressure loss, thus influencing wellbore pressure. Changes in formation and drilling fluid properties with temperature and pressure also affect drilling fluid velocity and convective heat transfer coefficient, consequently affecting drilling fluid temperature within the wellbore. Therefore, it is necessary to study methods for predicting the temperature field of deep well drilling wellbore that consider temperature and pressure effects.

[0047] Traditional wellbore temperature field prediction methods ignore the changes in drilling fluid properties (density, rheology, and thermophysical properties) and formation properties (density and thermophysical properties) with temperature and pressure. In other words, they assume that the properties of drilling fluid and formation are constant. This assumption affects the fluid flow characteristics in the wellbore, and thus affects the distribution characteristics of the temperature field in the wellbore and formation.

[0048] Therefore, in order to improve the accuracy of temperature field prediction in deep wells and thus enhance the safety of the drilling process, this application provides a temperature field prediction device for deep wells. This device can be a server or a client device. The client device can include smartphones, tablets, set-top boxes, portable computers, desktop computers, personal digital assistants (PDAs), in-vehicle devices, and smart wearable devices, etc. The smart wearable devices can include smart glasses, smartwatches, and smart bracelets, etc.

[0049] In practical applications, the part of predicting the temperature field of deep well casings can be executed on the server side as described above, or all operations can be completed on the client device. The choice can be made based on the processing power of the client device and the limitations of the user's usage scenario. This application does not impose any limitations on this. If all operations are completed on the client device, the client device may further include a processor.

[0050] The aforementioned client device may have a communication module (i.e., a communication unit) that can communicate with a remote server to achieve data transmission. The server may include a server on the task scheduling center side; in other implementation scenarios, it may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, a server cluster consisting of multiple servers, or a distributed server structure.

[0051] The server and the client device can communicate using any suitable network protocol, including network protocols not yet developed as of the date of this application. Such network protocols may include, for example, TCP / IP, UDP / IP, HTTP, HTTPS, etc. Of course, the network protocols may also include, for example, RPC (Remote Procedure Call Protocol) and REST (Representational State Transfer) protocols used on top of the above protocols.

[0052] The following examples illustrate this in detail.

[0053] To improve the accuracy of temperature field prediction in deep wells and thus enhance drilling safety, this embodiment provides a method for predicting the temperature field of a deep well, where the execution entity is a temperature field prediction device. This device includes, but is not limited to, a server, such as... Figure 1 As shown, this method specifically includes the following:

[0054] Step 100: Obtain the initial temperature field, grid type, and well data of each of the multiple grid cells on the two-dimensional temperature profile of the target deep well shaft.

[0055] Specifically, such as Figure 2 and Figure 3As shown, the two-dimensional temperature profile can be a two-dimensional profile formed along the wellbore axis z and the wellbore radial direction r of the target deep well; multiple grid cells can be obtained by cutting along the wellbore axis and the wellbore radial direction of the target deep well on the two-dimensional temperature profile; the initial temperature field of each grid cell can be preset according to the actual situation; the grid type of the grid cell can include: drilling fluid type inside the drill pipe, drill pipe type, drilling fluid type inside the ring control, production casing type, cement sheath type adjacent to the production casing, technical casing type, cement sheath type adjacent to the technical casing and surface casing, surface casing type, cement sheath type adjacent to the surface casing and formation, and formation type. Figure 2 The arrows in the diagram indicate the direction of drilling fluid flow. The drilling fluid is injected into the drill pipe and flows upward along the annulus from the bottom of the drill pipe until it returns to the wellhead; i represents the i-th grid cell in the radial direction of the wellbore, and j represents the j-th grid cell in the axial direction of the wellbore. The two-dimensional profile can be pre-set to have I×J grid cells according to actual needs; T d T dp T a T ca1 T ce1 T ca2 T ce2 T ca3 T ce3 and T f The grid sequentially represents the drilling fluid layer inside the drill pipe, the drill pipe layer, the drilling fluid layer inside the annulus, the production casing layer, the cement sheath adjacent to the production casing, the technical casing layer, the cement sheath adjacent to the technical casing and the surface casing, the surface casing layer, the cement sheath adjacent to the surface casing and the formation, and the temperature field of the formation. The production casing layer can contain grid cells of production casing type and formation type; the cement sheath layer can contain grid cells of cement sheath type and formation type; the technical casing layer can contain grid cells of technical casing type and formation type; and the surface casing layer can contain grid cells of surface casing type and formation type.

[0056] Specifically, the well data for each grid cell may include: density at a reference temperature, temperature coefficient, thermal conductivity at 0°C, and time step data; the time step data may include: a specified time step, the time interval between the specified time step and adjacent time steps, and the specified time step may be the current time step.

[0057] Step 200: Determine the initial property data of each grid cell based on the initial temperature field and well data.

[0058] Specifically, the initial property data can represent the property data of the grid cell determined based on the initial temperature field and well data of each grid cell.

[0059] Step 300: Determine the pre-built temperature field prediction model corresponding to each grid cell based on the grid type of that grid cell.

[0060] Specifically, there can be a one-to-one correspondence between grid types and pre-built temperature field prediction models.

[0061] Step 400: Based on the initial property data corresponding to each grid cell and the pre-built temperature field prediction model, determine the predicted temperature field of the grid cell, and combine the predicted temperature fields of each grid cell to form the temperature field prediction result of the target deep well.

[0062] Specifically, the predicted temperature field of each grid cell can represent the temperature field determined based on the initial property data corresponding to that grid cell and the pre-built temperature field prediction model.

[0063] Furthermore, by pre-setting the initial temperature field for multiple time steps, the initial property data of each grid cell at each time step can be determined based on the well data of each grid cell and the initial temperature field at each time step. Based on the well data of each grid cell, the corresponding pre-built temperature field prediction model, and the initial property data at each time step, the predicted temperature field of the grid cell at each time step can be determined. The predicted temperature fields of each grid cell at the same time step are combined to form the temperature field prediction result of the target deep well shaft at that time step. Finally, the temperature field prediction result of the target deep well shaft at each time step can be obtained, thereby effectively determining the dynamic change law of the temperature field.

[0064] To further improve the reliability of the temperature field prediction model, and thus improve the accuracy of temperature field prediction by applying a reliable temperature field prediction model, in one embodiment of this application, before step 300, the following steps are included:

[0065] Step 031: Obtain the energy conservation equations corresponding to each of the various grid types.

[0066] Specifically, (i) for the downward flow of drilling fluid in the drill pipe, the temperature of the drilling fluid in the drill pipe depends on the following processes: 1) heat is transported axially with the fluid movement; 2) heat exchange between the drill pipe fluid and the drill pipe wall; based on this, the energy conservation equation corresponding to the type of drilling fluid in the drill pipe can be expressed as:

[0067]

[0068] Where, ρ d This indicates the density of the drilling fluid, in kg / m³. 3 V d The velocity of drilling fluid in the drill pipe is expressed in m / s or r. dp,i Indicates the inner radius of the drill pipe, in meters (m) and diameter (C). p,dThis indicates the specific heat capacity of drilling fluid, in J / kg / ℃, and in T. d This indicates the temperature of the drilling fluid in the drill pipe, in °C, where z is the well depth, in meters or h. d-dp The coefficient of convective heat transfer between the drilling fluid and the drill pipe wall, expressed in W / m³. 2 / ℃, T dp Let t be the drill pipe temperature (°C) and t be the time (s). The drilling fluid temperature in the drill pipe can be considered equivalent to the temperature field of the drilling fluid layer within the drill pipe.

[0069] (II) The temperature of the drill pipe depends on the following processes: 1) Heat is transferred axially via thermal conduction; 2) Heat exchange between the drill pipe wall and the drilling fluid within the drill pipe; 3) Heat exchange between the drill pipe wall and the annular fluid. Therefore, the energy conservation equation corresponding to the drill pipe type can be expressed as:

[0070]

[0071] Where: ρ dp This indicates the density of the drill pipe, in kg / m³. 3 C p,dp This indicates the specific heat capacity of the drill pipe, in J / kg / ℃, r. dp,o Let λ be the radius of the drill pipe outer wall, in meters (m). dp The thermal conductivity of the drill pipe is expressed in W / m / ℃ and h. dp-an This represents the convective heat transfer coefficient between the drilling fluid in the annulus and the drill pipe wall, expressed in W / m³. 2 / ℃, T an The drilling fluid temperature in the annulus is expressed in °C; the drilling fluid temperature in the annulus can be considered equivalent to the temperature field of the drilling fluid layer within the annulus.

[0072] (III) For upward flow of drilling fluid within the annulus, the temperature of the drilling fluid within the annulus depends on the following processes: 1) heat transport along the axial direction with the fluid movement; 2) heat exchange between the drilling fluid within the annulus and the drill pipe wall; 3) heat exchange between the drilling fluid within the annulus and the production casing (casing section) or formation (open hole section). Therefore, the energy conservation equation corresponding to the type of drilling fluid within the annulus can be expressed as:

[0073]

[0074] Where, r w V is the inner radius of the wellbore, in meters. an The drilling fluid velocity in the annulus is expressed in m / s and h. w The convective heat transfer coefficient between the drilling fluid in the annulus and the inner wall of the wellbore, in W / m³. 2 / ℃, T ca1 The production casing wall temperature is ℃; the production casing wall temperature can be equated to the temperature field of the production casing layer.

[0075] (iv) The temperature of the production casing depends on the following processes: 1) Heat is transferred axially via thermal conduction; 2) Heat exchange between the production casing and the annular fluid; 3) Heat is transferred radially between the production casing and the cement ring via thermal conduction. Figure 1 The fourth radial layer in the process. Therefore, the energy conservation equation corresponding to the production casing type can be expressed as:

[0076]

[0077] Where, ρ ca1 This indicates the density of the production casing, in kg / m³. 3 C p,ca1 This indicates the specific heat capacity of the production casing, in J / kg / ℃, r. ca1,o Indicates the outer radius of the production casing, m, λ ca1 The thermal conductivity of the production casing is expressed in W / m / ℃, r. ca2,i T represents the inner radius of the technical casing, in meters (m). ce1 λ represents the temperature of the cement ring connected to the production sleeve, in °C. The temperature of the cement ring connected to the production sleeve can be considered equivalent to the temperature field of the cement ring layer adjacent to the production sleeve. ca1-ce1 This represents the combined thermal conductivity between the production sleeve and the cement ring, expressed in W / m / ℃. This combined thermal conductivity can be expressed as:

[0078]

[0079] Where: λ ce1 This represents the thermal conductivity of the cement ring connected to the production sleeve, in W / m / ℃.

[0080] (v) The energy conservation equation corresponding to the type of cement sheath adjacent to the production casing can be expressed as:

[0081]

[0082] Where, ρ ce1 This indicates the density of cement rings, in kg / m³. 3 C p,ce1 The specific heat capacity of cement is expressed in J / kg / ℃, λ. ce1 The thermal conductivity of the cement ring is expressed in W / m / ℃, λ. ce1-ca2 This represents the overall thermal conductivity between the cement ring and the technical sleeve, in W / m / ℃. This overall thermal conductivity can be expressed as:

[0083]

[0084] Where, λ ca2 This indicates the thermal conductivity of the technical sleeve, expressed in W / m / ℃.

[0085] (vi) The temperature of a formation depends on the following processes: 1) Heat is transferred axially via thermal conduction; 2) Heat is transferred radially between two adjacent components via thermal conduction. Therefore, the energy conservation equation corresponding to a formation type can be expressed as:

[0086]

[0087] Where: r represents the radial distance, m, λ f The thermal conductivity of the formation is expressed in W / m / ℃ and T. f Represents formation temperature, °C, C p,f ρ represents the specific heat capacity of the formation, J / kg / ℃. f This indicates the density of the formation, in kg / m³. 3 The formation temperature can be equivalent to the temperature field of the aforementioned formation.

[0088] For the technical casing, the cement sheath adjacent to the technical casing and the surface casing, the surface casing, and the cement sheath adjacent to the surface casing and the formation, their respective temperature control equations are similar to those for the production casing type, and will not be elaborated here.

[0089] Step 032: Apply the finite difference method to discretize the energy conservation equations corresponding to each grid type, and construct the temperature field prediction model corresponding to that grid type.

[0090] Specifically, the temperature field prediction model can represent the energy conservation equation after discrete processing; the values ​​of all parameters in the temperature field prediction model, except for density, specific heat capacity and thermal conductivity, can be obtained in advance and substituted into the model.

[0091] For example, (i) the energy conservation equation corresponding to the type of drilling fluid in the drill pipe is solved using the finite difference method, resulting in the discrete form of the energy conservation equation for the drilling fluid in the drill pipe, i.e., the temperature field prediction model corresponding to the type of drilling fluid in the drill pipe is as follows:

[0092]

[0093] The coefficients are represented as follows:

[0094]

[0095] Where: Δz represents the grid cell length, m; Δt represents the time interval between the current time step and the previous time step, s; This represents the drilling fluid temperature field inside the drill pipe at the current time step and the j-th grid, in °C. This represents the drilling fluid temperature field inside the drill pipe at the current time step and the (j-1)th grid, in °C. This represents the drilling fluid temperature field inside the drill pipe at the (j+1)th grid point at the current time step, in °C. This represents the drill pipe temperature field at the current time step and the j-th grid, in °C. denoted as the drilling fluid temperature field inside the drill pipe at the j-th grid point in the previous time step, in °C.

[0096] (II) The energy conservation equation corresponding to the drill pipe type is solved using the finite difference method, resulting in the discrete form of the drill pipe energy conservation equation. The temperature field prediction model corresponding to the drill pipe type is as follows:

[0097]

[0098] The coefficients are represented as follows:

[0099]

[0100] in: This represents the drill pipe temperature field at the (j-1)th grid point at time step 1, in °C. This represents the drill pipe temperature field at the (j+1)th grid point at time step 1, in °C. This represents the drilling fluid temperature field within the annulus at the current time step and the j-th grid, in °C. Let represent the drill pipe temperature field at the j-th grid point in the previous time step, in °C.

[0101] The energy conservation equation for drilling fluid in the annulus is solved using the finite difference method, yielding the discrete form of the equation as follows:

[0102]

[0103] The coefficients are represented as follows:

[0104]

[0105] in: This represents the annular drilling fluid temperature field at the (j-1)th grid point at the current time step, in °C. This represents the annular drilling fluid temperature field at the (j+1)th grid point at the current time step, in °C. This represents the production casing temperature field at the current time step and the j-th grid, in °C. Let represent the annular drilling fluid temperature field at the j-th grid point in the previous time step, in °C.

[0106] (III) The energy conservation equation corresponding to the production casing type is solved using the finite difference method, yielding the discrete form of the energy conservation equation for the production casing. The temperature field prediction model corresponding to the production casing type is as follows:

[0107]

[0108] The coefficients are represented as follows:

[0109]

[0110] in: This represents the production casing temperature field at the (j-1)th grid point in the current time step, expressed in °C. This represents the production casing temperature field at the (j+1)th grid point in the current time step, expressed in °C. This represents the temperature field of the cement ring at the current time step and the j-th grid, in °C. This represents the production sleeve temperature field at the j-th grid point in the previous time step, in °C.

[0111] (iv) The energy conservation equation corresponding to the formation is solved using the finite difference method, yielding the discrete form of the formation energy conservation equation, i.e., the temperature field prediction model corresponding to the formation is as follows:

[0112]

[0113] The coefficients are represented as follows:

[0114]

[0115] in: This represents the formation temperature field at the current time step, layer i, and grid j, in °C. This represents the formation temperature field at the current time step, layer i, and grid (j-1), in °C. This represents the formation temperature field at the current time step, layer i, and grid (j+1), in °C. This represents the formation temperature field at the current time step, layer i-1, and grid j, in °C. This represents the formation temperature field at the current time step, layer (i+1), and grid j, in °C. Let represent the formation temperature field at the previous time step, layer i, and grid j, in °C.

[0116] To further improve the accuracy of temperature field predictions, see [link to relevant documentation]. Figure 4 In one embodiment of this application, step 400 includes:

[0117] Step 401: Determine the temperature field to be verified for each grid cell based on the initial property data and the pre-built temperature field prediction model corresponding to each grid cell.

[0118] Step 402: Based on the initial temperature field of each grid cell, determine whether the verification of the temperature field to be verified of each grid cell has passed. If so, proceed to step 403.

[0119] Step 403: Determine the temperature field to be verified for each network unit as the predicted temperature field of that network unit, and combine the predicted temperature fields of each network unit to form the temperature field prediction result of the target deep well shaft.

[0120] Specifically, the absolute value of the difference between the initial temperature field and the temperature field to be verified for each grid cell can be obtained. It can be determined whether the maximum value of the absolute value of the difference between each grid cell is less than or equal to the temperature field difference threshold. If so, it is determined that the temperature field to be verified for each grid cell has passed the verification; otherwise, it is determined that there is a temperature field to be verified that has not passed the verification.

[0121] To further improve the accuracy of temperature field predictions, see [link to relevant documentation]. Figure 4 In one embodiment of this application, the method further includes the following after step 402:

[0122] Step 404: If, based on the initial temperature field of each grid cell, it is determined that there is an unverified temperature field to be verified, then an update step is executed. This update step includes: determining the property data of each grid cell based on the temperature field to be verified and well data; and determining the updated temperature field to be verified of each grid cell based on the property data corresponding to each grid cell and the pre-built temperature field prediction model.

[0123] Step 405: Based on the temperature field to be verified of each grid cell, determine whether the verification of the updated temperature field to be verified of each grid cell has passed. If so, proceed to step 406.

[0124] Step 406: Combine the updated temperature fields to be verified of each network unit to form the temperature field prediction result of the target deep well shaft.

[0125] Specifically, the updated temperature field to be verified for each network cell can be determined as the predicted temperature field for that network cell, and the predicted temperature fields of each network cell can be combined to form the temperature field prediction result of the target deep well shaft. The absolute value of the difference between the temperature field to be verified and the updated temperature field to be verified for each grid cell can be obtained, and it can be determined whether the maximum value of the absolute value of the difference between each grid cell is less than or equal to the temperature field difference threshold. If so, it is determined that the updated temperature field to be verified for each network cell has passed the verification; otherwise, it is determined that there is an updated temperature field to be verified that has not passed the verification.

[0126] To further improve the accuracy of temperature field predictions, see [link to relevant documentation]. Figure 4 In one embodiment of this application, the method further includes the following after step 405:

[0127] Step 407: If, based on the temperature fields to be verified of each grid cell, it is determined that there are updated temperature fields to be verified that have not passed verification, then the updated temperature fields to be verified are used as the temperature fields to be verified, and the update step is returned to be executed until, based on the temperature fields to be verified of each grid cell, it is determined that the updated temperature fields to be verified of each network cell have passed verification.

[0128] Specifically, after the temperature field to be verified is verified after the update of each network unit, the updated temperature fields to be verified of each network unit can be used to form the temperature field prediction result of the target deep well shaft.

[0129] To improve the accuracy of property data, variations in drilling fluid and formation with temperature are considered; see [reference needed]. Figure 5 In one embodiment of this application, step 200 includes:

[0130] Step 201: Determine the density of each grid cell based on its initial temperature field, reference temperature, coefficient of thermal expansion, and density at the reference temperature.

[0131] Specifically, the density of the grid cells can be determined using the following formula:

[0132] ρ=ρ0-αρ0(T-T0)

[0133] Where ρ is density, kg / m³ 3 ρ0 is the density at the reference temperature, kg / m³ 3 α is the coefficient of thermal expansion, in °C. -1 T represents the initial temperature field of the grid cell, in °C, and T0 represents the reference temperature, in °C. The reference temperature, coefficient of thermal expansion, and reference density can be preset according to actual conditions.

[0134] Step 202: Determine the specific heat capacity of each grid cell based on the pre-acquired fitting coefficients and the initial temperature field corresponding to each grid cell.

[0135] Specifically, the specific heat capacity of a grid cell can be determined using the following formula:

[0136] C p = a + bT + cT -2

[0137] Among them, C p , where is the specific heat capacity, J / kg / ℃, a, b, and c are fitting coefficients, and T is the initial temperature field of the grid cell, ℃. The fitting coefficients can be obtained by fitting the initial temperature field of each grid cell and its corresponding actual specific heat capacity in advance.

[0138] Step 203: Determine the thermal conductivity of each grid cell based on its initial temperature field, temperature coefficient, and thermal conductivity at 0°C. The well data includes: reference temperature, coefficient of thermal expansion, density at the reference temperature, temperature coefficient, and thermal conductivity at 0°C. The initial property data includes: density, specific heat capacity, and thermal conductivity.

[0139] Specifically, the temperature coefficient and the thermal conductivity at 0°C are only related to the inherent properties of the material (solid and liquid), i.e., the mesh type of the mesh element, which can be preset according to the actual situation; the thermal conductivity corresponding to the mesh element can be determined according to the following formula:

[0140] λ=λ0(1+βT)

[0141] Where λ is the thermal conductivity (W / m / ℃), λ0 is the thermal conductivity at 0℃ (W / m / ℃), and β is the temperature coefficient (℃). -1 T represents the initial temperature field of the grid cell, in °C; similarly, the property data of the grid cell can be obtained.

[0142] For further explanation of this plan, please refer to Figure 6 This application provides an application example of a method for predicting the temperature field of a deep well shaft, as described in detail below:

[0143] Step 1: Obtain the construction parameter values, wellbore parameter values, and drilling fluid property parameter values ​​in the drilling project.

[0144] Specifically, the construction parameter values ​​can be drilling fluid discharge rate, and the wellbore parameter values ​​can include: well depth, wellbore size, number of casing layers, casing depth, density, specific heat capacity and thermal conductivity of casing and cement sheath, and drilling fluid property parameter values ​​can include: drilling fluid density, drilling fluid viscosity, drilling fluid specific heat capacity and drilling fluid thermal conductivity, etc.

[0145] Step 2: Perform two-dimensional meshing along the axis and the radial direction of the wellbore.

[0146] Step 3: Given the initial temperature field T of each grid cell in the wellbore and formation. old The initial temperature field T of each grid cell can be... old Temperature field that makes up the wellbore-formation system.

[0147] Step 4: Based on the temperature field of the wellbore-formation system, update the property data of drilling fluid, drill pipe, formation and multiple casings, including production casing, technical casing and surface casing.

[0148] Specifically, property data may include: density, specific heat capacity, and thermal conductivity.

[0149] The density of drilling fluid can be determined based on the density-temperature equation, the temperature field of drilling fluid, the coefficient of thermal expansion, the reference temperature, and the density of drilling fluid at the reference temperature. Similarly, the density of drill pipe can be determined based on the density-temperature equation, the temperature field of drill pipe, the coefficient of thermal expansion, the reference temperature, and the density of drill pipe at the reference temperature. The density of formation can also be determined based on the density-temperature equation, the temperature field of formation, the coefficient of thermal expansion, the reference temperature, and the density of formation at the reference temperature. Finally, the density of casing can be determined based on the density-temperature equation, the temperature field of casing, the coefficient of thermal expansion, the reference temperature, and the density of formation at the reference temperature.

[0150] The specific heat capacity of drilling fluid can be determined based on the equation of specific heat capacity changing with temperature and the temperature field of drilling fluid; the specific heat capacity of drill pipe can be determined based on the equation of specific heat capacity changing with temperature and the temperature field of drill pipe; the specific heat capacity of formation can be determined based on the equation of specific heat capacity changing with temperature and the temperature field of formation; and the specific heat capacity of casing can be determined based on the equation of specific heat capacity changing with temperature and the temperature field of casing.

[0151] The thermal conductivity of drilling fluid can be determined based on the equation of thermal conductivity versus temperature, the temperature field of drilling fluid, and the thermal conductivity and temperature coefficient at 0℃; the thermal conductivity of drill pipe can be determined based on the equation of thermal conductivity versus temperature, the temperature field of drill pipe, and the thermal conductivity and temperature coefficient at 0℃; the thermal conductivity of formation can be determined based on the equation of thermal conductivity versus temperature, the temperature field of formation, and the thermal conductivity and temperature coefficient at 0℃; the thermal conductivity of casing can be determined based on the equation of thermal conductivity versus temperature, the temperature field of casing, and the thermal conductivity and temperature coefficient at 0℃.

[0152] Step 5: Calculate the temperature field T of each grid cell starting from the drill pipe inlet. new .

[0153] Specifically, the wellbore-formation energy conservation equation can include: the energy conservation equation corresponding to the drilling fluid type in the drill pipe, the energy conservation equation corresponding to the drill pipe type, the energy conservation equation corresponding to the drilling fluid type in the annulus, the energy conservation equation corresponding to the production casing type, the energy conservation equation corresponding to the cement sheath type adjacent to the production casing, the energy conservation equation corresponding to the cement sheath type of the technical casing, the energy conservation equation corresponding to the cement pipe type adjacent to the technical casing and the surface casing, the energy conservation equation corresponding to the surface casing type, the energy conservation equation corresponding to the cement sheath type adjacent to the surface casing and the formation, and the energy conservation equation corresponding to the formation type.

[0154] The wellbore-formation energy conservation equation is solved using the finite difference method. First, the entire wellbore-formation system is discretized; the discretization results are as follows: Figure 2 As shown.

[0155] Step 6: Determine |T old -T new | Is the temperature difference threshold err greater than the threshold value? If yes, return to step 3; otherwise, end the prediction at the specified time step.

[0156] Specifically, calculate the initial temperature field T old With temperature field T new If the error between them is greater than the temperature field difference threshold, then the temperature field T will be... new Assign values ​​to the initial temperature field, repeat step 3 until the error is less than the temperature field difference threshold; set the initial temperature field for the next time step, and perform the temperature field calculation for the next time step.

[0157] From a software perspective, to improve the accuracy of temperature field prediction in deep wells and thus enhance drilling safety, this application provides an embodiment of a deep well temperature field prediction device for implementing all or part of the aforementioned deep well temperature field prediction method. See [link to relevant documentation]. Figure 7 The temperature field prediction device for the deep well shaft specifically includes the following components:

[0158] The acquisition module 10 is used to acquire the initial temperature field, grid type and well data of each of the multiple grid cells on the two-dimensional temperature profile of the target deep well shaft;

[0159] The determination module 20 is used to determine the initial property data of each grid cell based on the initial temperature field and well data of each grid cell;

[0160] Model module 30 is used to determine the pre-built temperature field prediction model corresponding to each grid cell based on the grid type of each grid cell.

[0161] The prediction module 40 is used to determine the predicted temperature field of each grid cell based on the initial property data and the pre-built temperature field prediction model, and to combine the predicted temperature fields of each grid cell into the temperature field prediction result of the target deep well.

[0162] The embodiments of the deep well temperature field prediction device provided in this specification can be used to execute the processing flow of the embodiments of the deep well temperature field prediction method described above. Its functions will not be repeated here, but can be referred to the detailed description of the embodiments of the deep well temperature field prediction method described above.

[0163] As described above, the temperature field prediction method and apparatus for deep wells provided in this application can improve the accuracy of temperature field prediction for deep wells, thereby improving the safety of the drilling process. Specifically, traditional wellbore temperature field prediction methods ignore the changes in drilling fluid properties and formation properties with temperature and pressure. This assumption affects the fluid flow characteristics in the wellbore, and thus affects the temperature field distribution characteristics in the wellbore and formation. This application provides a deep wellbore temperature field prediction method that considers the temperature and pressure effect, which can effectively determine the dynamic change law of temperature field in the wellbore and formation during deep well drilling. This can avoid the increased difficulty in precise prediction and control of wellbore pressure during drilling, which can easily induce gas intrusion or even blowout accidents.

[0164] From a hardware perspective, in order to improve the accuracy of temperature field prediction in deep well shafts and thus enhance the safety of the drilling process, this application provides an embodiment of an electronic device for implementing all or part of the temperature field prediction method for deep well shafts. The electronic device specifically includes the following components:

[0165] The device comprises a processor, memory, a communications interface, and a bus; wherein the processor, memory, and communications interface communicate with each other via the bus; the communications interface is used to realize information transmission between the deep well temperature field prediction device and related devices such as user terminals; the electronic device can be a desktop computer, tablet computer, or mobile terminal, etc., and this embodiment is not limited to these. In this embodiment, the electronic device can be implemented with reference to the embodiments for implementing the deep well temperature field prediction method and the embodiments for implementing the deep well temperature field prediction device, the contents of which are incorporated herein by reference, and repeated details will not be described again.

[0166] Figure 8 This is a schematic block diagram illustrating the system configuration of the electronic device 9600 according to an embodiment of this application. Figure 8 As shown, the electronic device 9600 may include a central processing unit 9100 and a memory 9140; the memory 9140 is coupled to the central processing unit 9100. It is worth noting that... Figure 8 This is an example; other types of structures can also be used to supplement or replace this structure to achieve telecommunications functions or other functions.

[0167] In one or more embodiments of this application, the temperature field prediction function of the deep well shaft can be integrated into the central processing unit 9100. The central processing unit 9100 can be configured to perform the following control:

[0168] Step 100: Obtain the initial temperature field, grid type, and well data of each of the multiple grid cells on the two-dimensional temperature profile of the target deep wellbore;

[0169] Step 200: Determine the initial property data of each grid cell based on the initial temperature field and well data;

[0170] Step 300: Determine the pre-built temperature field prediction model corresponding to each grid cell based on the grid type of that grid cell;

[0171] Step 400: Based on the initial property data corresponding to each grid cell and the pre-built temperature field prediction model, determine the predicted temperature field of the grid cell, and combine the predicted temperature fields of each grid cell to form the temperature field prediction result of the target deep well.

[0172] As can be seen from the above description, the electronic equipment provided in the embodiments of this application can improve the accuracy of temperature field prediction in deep wells, thereby improving the safety of the drilling process.

[0173] In another embodiment, the temperature field prediction device for the deep well shaft can be configured separately from the central processing unit 9100. For example, the temperature field prediction device for the deep well shaft can be configured as a chip connected to the central processing unit 9100, and the temperature field prediction function of the deep well shaft can be realized through the control of the central processing unit.

[0174] like Figure 8 As shown, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily need to include these components. Figure 8 All components shown; in addition, the electronic device 9600 may also include Figure 8 For components not shown, please refer to existing technologies.

[0175] like Figure 8 As shown, the central processing unit 9100, sometimes also referred to as a controller or operating control, may include a microprocessor or other processor device and / or logic device, which receives inputs and controls the operation of various components of the electronic device 9600.

[0176] The memory 9140 may be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It may store the aforementioned failure-related information, and also store a program for executing that information. The central processing unit 9100 may execute the program stored in the memory 9140 to perform information storage or processing, etc.

[0177] Input unit 9120 provides input to central processing unit 9100. Input unit 9120 may be, for example, a keypad or touch input device. Power supply 9170 provides power to electronic device 9600. Display 9160 displays images and text. Display may be, for example, an LCD display, but is not limited thereto.

[0178] The memory 9140 can be a solid-state memory, such as a read-only memory (ROM), random access memory (RAM), a SIM card, etc. It can also be a memory that retains information even when power is off, can be selectively erased, and contains more data; examples of this type of memory are sometimes referred to as EPROMs. The memory 9140 can also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142 for storing application programs and function programs or processes for executing the operation of the electronic device 9600 via the central processing unit 9100.

[0179] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various drivers for the electronic device's communication functions and / or for performing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0180] The communication module 9110 is a transmitter / receiver 9110 that transmits and receives signals via the antenna 9111. The communication module (transmitter / receiver) 9110 is coupled to the central processing unit 9100 to provide input signals and receive output signals, which can be the same as in a conventional mobile communication terminal.

[0181] Based on different communication technologies, multiple communication modules 9110 can be configured in the same electronic device, such as cellular network modules, Bluetooth modules, and / or wireless LAN modules. The communication module (transmitter / receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby realizing typical telecommunications functions. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. Additionally, the audio processor 9130 is coupled to a central processing unit 9100, enabling on-device recording via the microphone 9132 and on-device playback of stored sound via the speaker 9131.

[0182] As can be seen from the above description, the electronic equipment provided in the embodiments of this application can improve the accuracy of temperature field prediction in deep wells, thereby improving the safety of the drilling process.

[0183] Embodiments of this application also provide a computer-readable storage medium capable of implementing all steps of the deep well temperature field prediction method in the above embodiments. The computer-readable storage medium stores a computer program that, when executed by a processor, implements all steps of the deep well temperature field prediction method in the above embodiments. For example, when the processor executes the computer program, it implements the following steps:

[0184] Step 100: Obtain the initial temperature field, grid type, and well data of each of the multiple grid cells on the two-dimensional temperature profile of the target deep wellbore;

[0185] Step 200: Determine the initial property data of each grid cell based on the initial temperature field and well data;

[0186] Step 300: Determine the pre-built temperature field prediction model corresponding to each grid cell based on the grid type of that grid cell;

[0187] Step 400: Based on the initial property data corresponding to each grid cell and the pre-built temperature field prediction model, determine the predicted temperature field of the grid cell, and combine the predicted temperature fields of each grid cell to form the temperature field prediction result of the target deep well.

[0188] As can be seen from the above description, the computer-readable storage medium provided in the embodiments of this application can improve the accuracy of temperature field prediction in deep wells, thereby improving the safety of the drilling process.

[0189] The various embodiments of the methods described in this application are presented in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on explaining the differences from other embodiments. Relevant details can be found in the descriptions of the method embodiments.

[0190] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0191] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0192] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0193] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0194] This application uses specific embodiments to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for predicting the temperature field of a deep well shaft, characterized in that, include: Obtain the initial temperature field, grid type, and well data of each grid cell on the two-dimensional temperature profile of the target deep wellbore; Based on the initial temperature field and well data of each grid cell, determine the initial property data of that grid cell; Based on the grid type of each grid cell, determine the pre-built temperature field prediction model corresponding to that grid cell; Based on the initial property data corresponding to each grid cell and the pre-built temperature field prediction model, the predicted temperature field of the grid cell is determined, and the predicted temperature fields of each grid cell are combined to form the temperature field prediction result of the target deep well shaft. The process of determining the initial property data of each grid cell based on its initial temperature field and well data includes: The density of each grid cell is determined based on its initial temperature field, reference temperature, coefficient of thermal expansion, and density at the reference temperature. Based on the pre-acquired fitting coefficients and the initial temperature field corresponding to each grid cell, determine the specific heat capacity of that grid cell; The thermal conductivity of each grid cell is determined based on its initial temperature field, temperature coefficient, and thermal conductivity at 0℃. The step of determining the density corresponding to each grid cell based on the initial temperature field, reference temperature, coefficient of thermal expansion, and density at the reference temperature includes: Determine the density of the mesh cells using the following formula: in, For density, , Density at reference temperature , The coefficient of thermal expansion is , The initial temperature field of the grid cells. , For reference temperature, ; The step of determining the specific heat capacity of a grid cell based on the pre-acquired fitting coefficients and the initial temperature field corresponding to each grid cell includes: Determine the specific heat capacity of the mesh element using the following formula: in, For specific heat capacity, , , , These are the fitting coefficients. The initial temperature field of the grid cells. ; The step of determining the thermal conductivity of each grid cell based on its initial temperature field, temperature coefficient, and thermal conductivity at 0°C includes: Determine the thermal conductivity of the corresponding mesh element using the following formula: in, Thermal conductivity, , 0 Thermal conductivity at that time , For temperature coefficient, , The initial temperature field of the grid cells. .

2. The method for predicting the temperature field of a deep wellbore according to claim 1, characterized in that, Also includes: Obtain the energy conservation equations corresponding to various grid types; The energy conservation equations for each grid type are discretized using the finite difference method to construct the temperature field prediction model for that grid type.

3. The method for predicting the temperature field of a deep wellbore according to claim 1, characterized in that, The process involves determining the predicted temperature field of each grid cell based on its initial property data and a pre-built temperature field prediction model, and then combining the predicted temperature fields of all grid cells to form the temperature field prediction result for the target deep wellbore. Based on the initial property data corresponding to each grid cell and the pre-built temperature field prediction model, the temperature field to be verified for that grid cell is determined. Based on the initial temperature field of each grid cell, it is determined whether the verification of the temperature field to be verified of each grid cell has passed. If so, the temperature field to be verified of each grid cell is determined as the predicted temperature field of that grid cell, and the predicted temperature fields of each grid cell are combined to form the temperature field prediction result of the target deep well shaft.

4. The method for predicting the temperature field of a deep wellbore according to claim 3, characterized in that, Also includes: If, based on the initial temperature field of each grid cell, it is determined that there is an unverified temperature field to be verified, then an update step is performed. This update step includes: determining the property data of each grid cell based on the temperature field to be verified and the well data. Based on the property data corresponding to each grid cell and the pre-built temperature field prediction model, determine the updated temperature field to be verified for that grid cell; Based on the temperature field to be verified of each grid cell, determine whether the verification of the updated temperature field to be verified of each grid cell is successful. If so, combine the updated temperature fields to be verified of each grid cell to form the temperature field prediction result of the target deep well shaft.

5. The method for predicting the temperature field of a deep wellbore according to claim 4, characterized in that, Also includes: If, based on the temperature fields to be verified for each grid cell, it is determined that there are updated temperature fields to be verified that have not passed verification, then the updated temperature fields to be verified are used as the temperature fields to be verified, and the update step is returned to be executed until, based on the temperature fields to be verified for each grid cell, it is determined that the updated temperature fields to be verified for each network cell have passed verification.

6. The method for predicting the temperature field of a deep wellbore according to claim 1, characterized in that, The two-dimensional temperature profile is a two-dimensional profile formed along the wellbore axis and the radial direction of the target deep well.

7. A temperature field prediction device for deep well shafts, characterized in that, include: The acquisition module is used to acquire the initial temperature field, grid type, and well data of each grid cell on the two-dimensional temperature profile of the target deep well shaft; The determination module is used to determine the initial property data of each grid cell based on the initial temperature field and well data. The model determination module is used to determine the pre-built temperature field prediction model corresponding to each grid cell based on the grid type of each grid cell; The prediction module is used to determine the predicted temperature field of each grid cell based on the initial property data and the pre-built temperature field prediction model, and to combine the predicted temperature fields of each grid cell to form the temperature field prediction result of the target deep well shaft. The process of determining the initial property data of each grid cell based on its initial temperature field and well data includes: The density of each grid cell is determined based on its initial temperature field, reference temperature, coefficient of thermal expansion, and density at the reference temperature. Based on the pre-acquired fitting coefficients and the initial temperature field corresponding to each grid cell, determine the specific heat capacity of that grid cell; The thermal conductivity of each grid cell is determined based on its initial temperature field, temperature coefficient, and thermal conductivity at 0℃. The step of determining the density corresponding to each grid cell based on the initial temperature field, reference temperature, coefficient of thermal expansion, and density at the reference temperature includes: Determine the density of the mesh cells using the following formula: in, For density, , Density at reference temperature , The coefficient of thermal expansion is , The initial temperature field of the grid cells. , For reference temperature, ; The step of determining the specific heat capacity of a grid cell based on the pre-acquired fitting coefficients and the initial temperature field corresponding to each grid cell includes: Determine the specific heat capacity of the mesh element using the following formula: in, For specific heat capacity, , , , These are the fitting coefficients. The initial temperature field of the grid cells. ; The step of determining the thermal conductivity of each grid cell based on its initial temperature field, temperature coefficient, and thermal conductivity at 0°C includes: Determine the thermal conductivity of the corresponding mesh element using the following formula: in, Thermal conductivity, , 0 Thermal conductivity at that time , For temperature coefficient, , The initial temperature field of the grid cells. .

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the temperature field prediction method for deep well shafts according to any one of claims 1 to 6.

9. A computer-readable storage medium storing computer instructions thereon, characterized in that, When the instruction is executed, it implements the deep well temperature field prediction method according to any one of claims 1 to 6.

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