A method, apparatus, equipment and medium for determining the temperature distribution of heavy oil reservoirs

Abstract

This invention relates to the field of electromagnetic in-situ thermal recovery (EMR) optimization technology for heavy oil, and discloses a method, apparatus, equipment, and medium for determining the temperature distribution of heavy oil reservoirs. This invention establishes a corresponding one-dimensional radial heat conduction mathematical model for heavy oil reservoirs using EMR. Multiple dimensionless variables are defined based on the one-dimensional radial heat conduction mathematical model. The one-dimensional radial heat conduction mathematical model is then converted into a first dimensionless mathematical model based on these multiple dimensionless variables. This first dimensionless mathematical model includes a dimensionless radial distance variable. The dimensionless radial distance variable in the first dimensionless mathematical model is logarithmically transformed to obtain a second dimensionless mathematical model. Dimensional solutions are then performed based on the second dimensionless mathematical model to obtain the temperature distribution of the first heavy oil reservoir. This invention can effectively determine the temperature distribution of heavy oil reservoirs using EMR.

Description

Technical Field

[0001] This invention relates to the field of electromagnetic in-situ thermal recovery of heavy oil optimization technology, and in particular to a method, apparatus, equipment and medium for determining the temperature distribution of heavy oil reservoirs. Background Technology

[0002] Electromagnetic in-situ thermal recovery of heavy oil offers advantages such as rapid heating rate, significant production enhancement, and environmental friendliness, making it one of the main methods for unconventional oil and gas extraction. It can be used not only for heavy oil and oil sands resource development but also holds promise for playing a crucial role in in-situ shale oil conversion and in-situ phase change extraction of natural gas hydrates. Furthermore, it opens up possibilities for the application of new energy power sources such as wind and solar power.

[0003] Downhole electromagnetic heating is a complex process involving numerous physical phenomena, requiring interdisciplinary research. Numerical calculations describing the entire heating process mainly focus on predicting the temperature distribution of heavy oil reservoirs. However, related technologies cannot effectively determine the temperature distribution of heavy oil reservoirs. Summary of the Invention

[0004] This invention provides a method, apparatus, equipment, and medium for determining the temperature distribution of heavy oil reservoirs, which can effectively determine the temperature distribution of heavy oil reservoirs.

[0005] In a first aspect, the present invention provides a method for determining the temperature distribution of a heavy oil reservoir, comprising:

[0006] A corresponding one-dimensional radial heat conduction mathematical model is established for heavy oil reservoirs that are extracted using electromagnetic in-situ thermal recovery.

[0007] Based on the one-dimensional radial heat conduction mathematical model, multiple dimensionless variables are defined;

[0008] The one-dimensional radial heat conduction mathematical model is transformed into a first dimensionless mathematical model based on the multiple dimensionless variables. The first dimensionless mathematical model includes a dimensionless radial distance variable.

[0009] Logarithmize the dimensionless radial distance variable in the first dimensionless mathematical model to obtain the second dimensionless mathematical model;

[0010] The temperature distribution of the first heavy oil reservoir is obtained by solving the dimensional problem based on the second dimensionless mathematical model.

[0011] Optionally, the establishment of a corresponding one-dimensional radial heat conduction mathematical model for heavy oil reservoirs using electromagnetic in-situ thermal recovery includes:

[0012] A corresponding cylindrical model is established based on the heavy oil reservoir; the cylindrical model includes a wellbore module located at the center, an electromagnetic heater module located in the wellbore module, and reservoir modules symmetrically distributed radially along the wellbore module;

[0013] Based on the Lambert-Beer electromagnetic heating law, the heat conduction equation, and the cylindrical model, a one-dimensional radial heat conduction mathematical model is established.

[0014] Optionally, the one-dimensional radial heat conduction mathematical model includes radial distance variables, time variables, temperature rise distribution function, electromagnetic power absorption coefficient, wellbore radius variables, and reservoir radius variables;

[0015] The one-dimensional radial heat conduction mathematical model defines multiple dimensionless variables, including:

[0016] The radial distance variable, time variable, temperature rise distribution function, electromagnetic power absorption coefficient, wellbore radius variable, and reservoir radius variable in the one-dimensional radial heat conduction mathematical model are dimensionless to obtain dimensionless radial distance variable, dimensionless time variable, dimensionless temperature rise distribution function, dimensionless electromagnetic power absorption coefficient, dimensionless wellbore radius variable, and dimensionless reservoir radius variable, which are then used as the multiple dimensionless variables.

[0017] Optionally, the step of logarithmizing the dimensionless radial distance variable in the first dimensionless mathematical model to obtain the second dimensionless mathematical model includes:

[0018] Solve the first dimensionless mathematical model to obtain a dimensionless steady-state analytical solution;

[0019] Based on the multiple dimensionless variables, the dimensionless steady-state analytical solution is transformed into a dimensional one to obtain the preliminary temperature distribution of the heavy oil reservoir.

[0020] Based on the preliminary temperature distribution of the heavy oil reservoir, it is determined that the temperature gradient near the wellbore module is greater than a preset threshold, and that the heat is concentrated near the wellbore module.

[0021] To improve the accuracy of the radial temperature distribution curve near the well module, the dimensionless radial distance variable in the first dimensionless mathematical model is logarithmically transformed to obtain the second dimensionless mathematical model.

[0022] Optionally, after obtaining the temperature distribution of the first heavy oil reservoir by performing a dimensional solution based on the second dimensionless mathematical model, the method further includes:

[0023] The temperature distribution of the first heavy oil reservoir is compared with the temperature distribution of the preliminary heavy oil reservoir to determine the degree of agreement between the temperature distribution of the first heavy oil reservoir and the temperature distribution of the preliminary heavy oil reservoir.

[0024] The accuracy of the temperature distribution in the first heavy oil reservoir is determined based on the degree of agreement.

[0025] Optionally, after obtaining the temperature distribution of the first heavy oil reservoir, the method further includes:

[0026] The parameter values ​​of key parameters in the one-dimensional radial heat conduction mathematical model are adjusted to obtain multiple second heavy oil reservoir temperature distributions that vary with the parameter values ​​of the key parameters; wherein, the key parameters include at least one of heating time, electromagnetic heater frequency, and electromagnetic heater power;

[0027] Based on the temperature distribution of the first heavy oil reservoir, the temperature distribution of the multiple second heavy oil reservoirs, and the parameter values ​​of the key parameters, the variation pattern of the temperature distribution of the heavy oil reservoir under the condition that the parameter values ​​change is statistically analyzed.

[0028] Optionally, after statistically analyzing the variation of the temperature distribution in the heavy oil reservoir under varying parameter values, the method further includes:

[0029] The true experimental scale is determined based on the aforementioned multiple dimensionless variables;

[0030] Based on the actual experimental scale, an electromagnetic heating experiment was designed for the heavy oil reservoir.

[0031] Based on the electromagnetic heating experiment, the accuracy of the temperature distribution of the first heavy oil reservoir, the temperature distribution of the second heavy oil reservoir, and the variation law were verified.

[0032] In a second aspect, the present invention provides an apparatus for determining the temperature distribution of a heavy oil reservoir, comprising:

[0033] A unit is established to build a corresponding one-dimensional radial heat conduction mathematical model for heavy oil reservoirs that are extracted using electromagnetic in-situ thermal recovery.

[0034] A definition unit is used to define multiple dimensionless variables according to the one-dimensional radial heat conduction mathematical model;

[0035] The conversion unit is used to convert the one-dimensional radial heat conduction mathematical model into a first dimensionless mathematical model based on the plurality of dimensionless variables, wherein the first dimensionless mathematical model includes a dimensionless radial distance variable.

[0036] The logarithmic unit is used to logarithmize the dimensionless radial distance variable in the first dimensionless mathematical model to obtain the second dimensionless mathematical model.

[0037] The solution unit is used to perform dimensional solutions based on the second dimensionless mathematical model to obtain the temperature distribution of the first heavy oil reservoir.

[0038] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the method for determining the temperature distribution of heavy oil reservoirs as described in the first aspect or any corresponding embodiment.

[0039] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the method for determining the temperature distribution of a heavy oil reservoir as described in the first aspect or any corresponding embodiment thereof.

[0040] The present invention provides a method, apparatus, equipment, and medium for determining the temperature distribution of heavy oil reservoirs. Based on heavy oil reservoirs using electromagnetic in-situ thermal recovery (EMR), a corresponding one-dimensional radial heat conduction mathematical model is established. Multiple dimensionless variables are defined according to the one-dimensional radial heat conduction mathematical model. The one-dimensional radial heat conduction mathematical model is then converted into a first dimensionless mathematical model based on these multiple dimensionless variables. This first dimensionless mathematical model includes a dimensionless radial distance variable. The dimensionless radial distance variable in the first dimensionless mathematical model is logarithmically transformed to obtain a second dimensionless mathematical model. Dimensional solutions are then performed based on the second dimensionless mathematical model to obtain the temperature distribution of the first heavy oil reservoir. This invention can effectively determine the temperature distribution of heavy oil reservoirs using EMR. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in this invention 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 some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0042] Figure 1 A flowchart of a method for determining the temperature distribution of a heavy oil reservoir provided in an embodiment of the present invention;

[0043] Figure 2 This invention provides a physical model of a heavy oil reservoir for oil recovery using electromagnetic in-situ thermal recovery.

[0044] Figure 3 An example diagram of the temperature distribution curve of a heavy oil reservoir provided in an embodiment of the present invention;

[0045] Figure 4 This is an example diagram of the temperature sensor distribution in an electromagnetic heating experiment provided by an embodiment of the present invention;

[0046] Figure 5 This is a schematic diagram of a device for determining the temperature distribution of a heavy oil reservoir, provided in an embodiment of the present invention.

[0047] Figure 6 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0049] The following is combined with Figures 1-4 The present invention describes a method for determining the temperature distribution of heavy oil reservoirs.

[0050] like Figure 1 As shown in the figure, this embodiment proposes a first method for determining the temperature distribution of heavy oil reservoirs, which may include the following steps:

[0051] S101. Based on the heavy oil reservoir using electromagnetic in-situ thermal recovery, establish a corresponding one-dimensional radial heat conduction mathematical model.

[0052] Among them, heavy oil reservoirs can be reservoirs that are extracted using electromagnetic in-situ thermal recovery methods.

[0053] Optionally, step S101 may include:

[0054] A corresponding cylindrical model is established based on the heavy oil reservoir. The cylindrical model includes a wellbore module located at the center, an electromagnetic heater module located in the wellbore module, and reservoir modules symmetrically distributed radially along the wellbore module.

[0055] Based on the Lambert-Beer electromagnetic heating law, the heat conduction equation, and the cylinder model, a one-dimensional radial heat conduction mathematical model is established.

[0056] Specifically, such as Figure 2As shown, this embodiment can establish a physical model, namely a cylindrical model, for electromagnetic in-situ thermal recovery of heavy oil. In this physical model, the heavy oil reservoir is symmetrically distributed radially along the wellbore, and the electromagnetic heater is located at the wellbore location. This physical model considers the heat conduction of the heavy oil reservoir and the electromagnetic waves radiated by the electromagnetic heater. Based on this physical model, this embodiment can establish a one-dimensional radial heat conduction mathematical model for electromagnetic in-situ thermal recovery of heavy oil using the Lambert-Beer electromagnetic heating law and the heat conduction equation. This mathematical model considers heat conduction and electromagnetic heating, and couples the temperature field and the electromagnetic field.

[0057] The one-dimensional radial heat conduction mathematical model can include:

[0058] ;

[0059] .

[0060] Initial conditions: hour, .

[0061] External boundary conditions: hour, .

[0062] Inner boundary conditions: hour, .

[0063] in, The radial distance is the independent variable, in meters. Time is the independent variable, and the unit is seconds. In terms of radial distance With time Let be the temperature rise distribution function of the independent variable, in degrees Celsius; In terms of radial distance With time is the temperature distribution function of the independent variable, in degrees Celsius; This represents the initial temperature of the reservoir, in degrees Celsius. The density of the reservoir is expressed in kilograms per cubic meter. is the specific heat capacity of the reservoir, expressed in J / (kg·℃); The thermal conductivity of the reservoir is expressed in W / (m·℃). The electromagnetic power absorption coefficient of the reservoir is expressed in units of 1 / m. The radius of the well shaft is in meters. The reservoir radius is expressed in meters. The length of the electromagnetic heater is in meters. The power of the electromagnetic heater is expressed in watts. ρ is the dielectric constant of the reservoir, expressed in F / m; The electrical conductivity of the reservoir is expressed in S / m. This refers to the operating frequency of the electromagnetic heater, measured in Hz. The magnetic permeability of the reservoir is expressed in H / m. The density, specific heat capacity, and thermal conductivity of the reservoir are determined by a weighted average of the relevant parameters of the rock formation and the fluid. The inner boundary of this mathematical model is adiabatic, and the outer boundary is isothermal.

[0064] S102. Define multiple dimensionless variables based on the one-dimensional radial heat conduction mathematical model.

[0065] Specifically, in this embodiment, the variables in the one-dimensional radial heat conduction mathematical model can be dimensionless to obtain multiple dimensionless variables.

[0066] Optionally, the one-dimensional radial heat conduction mathematical model includes radial distance variables, time variables, temperature rise distribution function, electromagnetic power absorption coefficient, wellbore radius variables, and reservoir radius variables. In this case, step S102 may include:

[0067] The radial distance variable, time variable, temperature rise distribution function, electromagnetic power absorption coefficient, wellbore radius variable, and reservoir radius variable in the one-dimensional radial heat conduction mathematical model were dimensionless to obtain dimensionless radial distance variable, dimensionless time variable, dimensionless temperature rise distribution function, dimensionless electromagnetic power absorption coefficient, dimensionless wellbore radius variable, and dimensionless reservoir radius variable, which were then used as multiple dimensionless variables.

[0068] Among them, the radial distance variable, time variable, wellbore radius variable, and reservoir radius variable are the independent variables radial distance, independent variable time, wellbore radius, and reservoir radius mentioned in the one-dimensional radial heat conduction mathematical model, respectively.

[0069] Specifically, in this embodiment, the following dimensionless variables can be defined:

[0070] , , , , , .

[0071] in, The radial distance is dimensionless. For dimensionless time, For dimensionless radial distance With dimensionless time Let be the dimensionless temperature rise distribution function of the independent variable. The electromagnetic power absorption coefficient of the dimensionless reservoir is... The radius of the dimensionless wellbore. The reservoir radius is dimensionless. The density of the reservoir is expressed in kg / m³. 3 ; is the specific heat capacity of the reservoir, expressed in J / (kg·℃); The thermal conductivity of the reservoir is expressed in W / (m·℃). The length of the electromagnetic heater is in meters. This refers to the power of the electromagnetic heater, measured in watts.

[0072] in, , , , , and These are, respectively, dimensionless radial distance variable, dimensionless time variable, dimensionless temperature rise distribution function, dimensionless electromagnetic power absorption coefficient, dimensionless wellbore radius variable, and dimensionless reservoir radius variable.

[0073] S103. Based on multiple dimensionless variables, the one-dimensional radial heat conduction mathematical model is transformed into a first dimensionless mathematical model, which includes a dimensionless radial distance variable.

[0074] Specifically, in this embodiment, the aforementioned multiple dimensionless variables can be introduced into the one-dimensional radial heat conduction mathematical model to obtain an equivalent dimensionless mathematical model, making the solution of the mathematical model more concise:

[0075] .

[0076] Initial conditions: hour ;

[0077] External boundary conditions: hour ;

[0078] Inner boundary conditions: hour .

[0079] S104. Logarithmize the dimensionless radial distance variable in the first dimensionless mathematical model to obtain the second dimensionless mathematical model.

[0080] Optionally, step S104 may include:

[0081] Solving the first dimensionless mathematical model yields a dimensionless steady-state analytical solution;

[0082] Based on multiple dimensionless variables, the dimensionless steady-state analytical solution is transformed into a dimensionless form to obtain the preliminary temperature distribution of the heavy oil reservoir.

[0083] Based on the preliminary temperature distribution of the heavy oil reservoir, it was determined that the temperature gradient near the wellbore module was greater than the preset threshold, and that the heat was concentrated near the wellbore module.

[0084] To improve the accuracy of the radial temperature distribution curve near the well module, the dimensionless radial distance variable in the first dimensionless mathematical model is logarithmically transformed to obtain the second dimensionless mathematical model.

[0085] Specifically, this embodiment can solve the first dimensionless mathematical model to obtain a dimensionless steady-state analytical solution:

[0086] .

[0087] Specifically, this embodiment can transform the obtained dimensionless steady-state analytical solution into a dimensional steady-state analytical solution, i.e., the preliminary temperature distribution of the heavy oil reservoir, based on the defined multiple dimensionless variables mentioned above:

[0088] .

[0089] Specifically, this embodiment can plot a temperature distribution curve corresponding to the preliminary temperature distribution of the heavy oil reservoir, and based on this temperature distribution curve, determine that the temperature gradient is large near the wellbore. Given that the heat is mainly concentrated near the wellbore, in order to accurately obtain the radial temperature distribution curve near the wellbore during dimensionless solution, the dimensionless radial distance variable in the first dimensionless mathematical model is first logarithmically transformed.

[0090] .

[0091] In this embodiment, after logarithmizing the dimensionless radial distance variable of the first dimensionless mathematical model, the second dimensionless mathematical model can be obtained:

[0092] .

[0093] Initial conditions: hour ;

[0094] External boundary conditions: hour ;

[0095] Inner boundary conditions: hour .

[0096] S105. Based on the second dimensionless mathematical model, a dimensional solution is performed to obtain the temperature distribution of the first heavy oil reservoir.

[0097] Specifically, this embodiment can use fully implicit differencing, which has higher stability and efficiency than explicit differencing, to solve the second dimensionless mathematical model. The differencing scheme is as follows:

[0098] .

[0099] Initial conditions: ;

[0100] External boundary conditions: ;

[0101] Inner boundary conditions: .

[0102] Subsequently, this embodiment can transform the obtained dimensionless numerical solution into a dimensional numerical solution, that is, complete the dimensional solution, obtain the temperature distribution of the first heavy oil reservoir, and also obtain the radial temperature distribution curve and heating range of the electromagnetic in-situ thermal recovery heavy oil corresponding to any heating time.

[0103] It should be noted that the one-dimensional radial heat conduction mathematical model for electromagnetic in-situ thermal recovery of heavy oil established in this embodiment is based on the Lambert-Beer electromagnetic heating law and the heat conduction equation, and simultaneously couples the temperature field and the electromagnetic field. This model can concisely and accurately describe the radial temperature distribution during electromagnetic in-situ thermal recovery of heavy oil. This invention defines dimensionless variables based on the mathematical model. Introducing dimensionless variables into the mathematical model simplifies its expression and expands its applicability, while also facilitating the subsequent determination of the steady-state and numerical solutions.

[0104] In this embodiment, when solving the numerical solution of the dimensionless mathematical model, the logarithm of the radial distance is introduced to address the large temperature gradient near the wellbore. This can accurately simulate the temperature changes near the wellbore and rewrite the governing equations of the mathematical model into a simpler form. The rewritten mathematical model is solved using fully implicit finite difference, which provides higher stability and efficiency in the numerical solution process.

[0105] The method for determining the temperature distribution of heavy oil reservoirs proposed in this embodiment establishes a corresponding one-dimensional radial heat conduction mathematical model based on heavy oil reservoirs using electromagnetic in-situ thermal recovery. Multiple dimensionless variables are defined according to this model. The one-dimensional radial heat conduction mathematical model is then transformed into a first dimensionless mathematical model, which includes a dimensionless radial distance variable. The dimensionless radial distance variable in the first dimensionless mathematical model is logarithmically transformed to obtain a second dimensionless mathematical model. Dimensional solutions are then performed based on the second dimensionless mathematical model to obtain the temperature distribution of the first heavy oil reservoir. This embodiment can effectively determine the temperature distribution of heavy oil reservoirs using electromagnetic in-situ thermal recovery.

[0106] It should be noted that, according to the inventors' research, the downhole electromagnetic heating process is complex, involving numerous physical phenomena and requiring interdisciplinary research. Numerical calculations describing the entire heating process mainly focus on predicting temperature distribution and oil production. For solving the reservoir temperature distribution, the established mathematical model needs to consider the coupling of the electromagnetic and temperature fields. Based on electromagnetic and heat transfer theories, an instantaneous temperature distribution prediction model for electromagnetically heated uniformly conductive media can be derived. This model assumes that the heavy oil reservoir is radially symmetrically distributed. Research results show that radiation heating is a feasible method for enhancing production, and the power absorption coefficient is an important parameter affecting the radiation heating effect. However, this parameter is significantly affected by frequency and reservoir properties. Therefore, numerical simulations and experiments are needed to explore the influence of frequency and reservoir properties on temperature distribution.

[0107] based on Figure 1 This embodiment proposes a second method for determining the temperature distribution of heavy oil reservoirs. This method, after step S105, may further include:

[0108] The temperature distribution of the first heavy oil reservoir is compared with that of the preliminary heavy oil reservoir to determine the degree of agreement between the two temperature distributions.

[0109] The accuracy of the temperature distribution in the first heavy oil reservoir is determined based on the degree of agreement.

[0110] Specifically, in this embodiment, a sufficiently large electromagnetic heating time can be obtained for the dimensionless value corresponding to the second dimensionless mathematical model. The temperature distribution curve corresponding to the temperature distribution of the first heavy oil reservoir can be used to fit the temperature distribution curve corresponding to the preliminary heavy oil reservoir temperature distribution to verify the correctness of the numerical solution.

[0111] Optionally, after step S105 above, the method may further include:

[0112] The parameter values ​​of key parameters in the one-dimensional radial heat conduction mathematical model are adjusted to obtain multiple temperature distributions of the second heavy oil reservoir that vary with the parameter values ​​of the key parameters; wherein the key parameters include at least one of heating time, electromagnetic heater frequency, and electromagnetic heater power;

[0113] Based on the temperature distribution of the first heavy oil reservoir, the temperature distribution of multiple second heavy oil reservoirs, and the parameter values ​​of key parameters, the variation law of the temperature distribution of heavy oil reservoirs under the condition of changing parameter values ​​is statistically analyzed.

[0114] Specifically, in this embodiment, the heating time in the one-dimensional radial heat conduction mathematical model can be adjusted. Electromagnetic heater frequency Electromagnetic heater power By analyzing the values ​​of parameters, we obtained the radial temperature distribution curve of electromagnetic in-situ thermal recovery of heavy oil and the variation law of the heating range with these parameters.

[0115] Optionally, after analyzing the variation of heavy oil reservoir temperature distribution under varying parameter values, this method may further include:

[0116] Determine the true experimental scale based on multiple dimensionless variables;

[0117] Design an electromagnetic heating experiment for heavy oil reservoirs based on real-world experimental scale;

[0118] Based on electromagnetic heating experiments, the accuracy of the temperature distribution and variation law of the first heavy oil reservoir and the second heavy oil reservoir was verified.

[0119] Specifically, this embodiment can design an electromagnetic heating experiment based on the established cylindrical model and the multiple dimensionless variables and similarity criteria defined above to verify the correctness of the obtained temperature distribution curve and temperature distribution variation law. The corresponding experimental scale is determined using dimensionless variables according to the actual oilfield scale.

[0120] , , , ;

[0121] .

[0122] in, The independent variable is radial distance, representing the experimental scale, in meters. The heating time for the experiment is in seconds. Let be the temperature rise distribution function of the experiment, in °C; The electromagnetic power absorption coefficient in the experiment is expressed in units of 1 / m. The density of the experimental material is expressed in kg / m³.3 ; The specific heat capacity of the experimental material is expressed in J / (kg·℃). The thermal conductivity of the experimental material is expressed in W / (m·℃). The radius of the protective shell of the experimental electromagnetic heater is in meters. The radius of the experimental material container is in meters; The length of the experimental electromagnetic heating device is in meters. The power of the experimental electromagnetic heating equipment is expressed in watts. Radial distance, the independent variable at the oilfield scale, is expressed in meters. Heating time at the oilfield scale, in seconds; This is the temperature rise distribution function for the oil field, in °C. The electromagnetic power absorption coefficient of the oil field is expressed in units of 1 / m. The density of the reservoir is expressed in kg / m³. 3 ; is the specific heat capacity of the reservoir, expressed in J / (kg·℃); The thermal conductivity of the reservoir is expressed in W / (m·℃). The radius of the protective casing for the oilfield electromagnetic heater is in meters. The reservoir radius is expressed in meters. The length of the electromagnetic heating equipment in the oilfield is in meters. This refers to the operating power of the electromagnetic heating equipment in the oilfield, measured in watts.

[0123] It should be noted that the materials and equipment used in the electromagnetic heating experiment may include a material container, an electromagnetic generator, a lid for sealing the material container and equipped with temperature sensors, and the material itself. To obtain more accurate temperature changes near the heating center, the temperature sensor measurement points are evenly distributed according to the logarithm of the radial distance. To prevent the influence of too many temperature sensors in the same row on the heat conduction of the material, the measurement points are distributed at different angles. The experimental procedure is as follows: the electromagnetic heating device is vertically fixed at the center of the cylindrical material container; sand, water, and oil are mixed evenly according to the corresponding proportion of the actual reservoir and placed in the cylindrical material container to simulate the actual reservoir environment; the lid equipped with temperature sensors is fastened to the top of the material container to prevent electromagnetic wave leakage; the electromagnetic generator is started to heat the material at a constant power for a certain period of time and the temperature data changes on the temperature sensors at each measurement point are recorded; based on the temperature data recorded by the temperature sensors, a curve showing the temperature change of the same measurement point over time and a radial temperature distribution curve are plotted.

[0124] Specifically, this embodiment can employ the controlled variable method, by changing the heating time in the experiment. The frequency of the electromagnetic heater in the experiment The power of the electromagnetic heater in the experiment Multiple electromagnetic heating experiments were conducted using parameters such as [parameter 1], and the effects of these parameters on the radial temperature distribution curve and heating range of electromagnetic in-situ thermal recovery of heavy oil were studied.

[0125] In this embodiment, the frequency of the electromagnetic heating device is studied. In the experiment on the effect of electromagnetic heating on the radial temperature distribution curve, an equivalent relationship between the change in material conductivity and the change in electromagnetic heating frequency can be established based on the formula for the electromagnetic power absorption coefficient. In the experiment, only the ion concentration of water in the material needs to be changed to achieve a change in the material's conductivity, which can be equivalent to different electromagnetic heating frequencies. Temperature distribution curves at different frequencies can then be measured. This equivalent relationship is established based on the expression for the electromagnetic power absorption coefficient.

[0126] ;

[0127] ;

[0128] .

[0129] in, The dielectric constant of the experimental material is expressed in F / m. The conductivity of the experimental material before the frequency change is expressed in S / m. The operating frequency of the experimental electromagnetic heater before the frequency change is expressed in Hz. The assumed operating frequency of the experimental electromagnetic heater after the frequency change is given, in Hz. To ensure the experimental electromagnetic heater operates at a frequency of [frequency value missing] In the case of frequency becoming The conductivity of the equivalent experimental material, in units of S / m.

[0130] It should also be noted that, in order to study the temperature distribution of heavy oil in electromagnetic in-situ thermal recovery and the factors affecting the temperature distribution, the physical model of this embodiment assumes that the heavy oil reservoir is radially symmetrically distributed. A mathematical model is established based on the one-dimensional heat conduction equation and the Lambert-Beer electromagnetic heating law. Dimensionless variables are defined according to the mathematical model. After introducing the dimensionless variables into the model, a dimensionless mathematical model is obtained, and a steady-state solution is obtained. The logarithm of the radial distance is introduced into the dimensionless mathematical model to establish a new dimensionless mathematical model, and a numerical solution is obtained using fully implicit finite difference. The consistency is verified using the steady-state solution. Based on the numerical solution after introducing dimensionless variables, the radial temperature distribution of the heavy oil in electromagnetic in-situ thermal recovery and the variation law of the heating range with these parameters are obtained by changing parameters such as heating time, reservoir conductivity, electromagnetic heater frequency, and electromagnetic heater power involved in the mathematical model. Electromagnetic heating experiments are designed based on the physical model to verify the correctness of the obtained temperature distribution curve and temperature distribution variation law. The defined dimensionless variables and similarity criteria are used to simulate the actual oilfield scale on an experimental scale. The experimental setup consisted of a material container, an electromagnetic generator, a lid for sealing the container and equipped with a temperature sensor, and the material itself. The temperature sensor's measurement points were evenly distributed at different angles according to the logarithm of the radial distance. Multiple sets of electromagnetic heating experiments were conducted using the controlled variable method. Based on the expression for the electromagnetic power absorption coefficient, changing the conductivity of the fluid in the material was equivalent to changing the frequency of the electromagnetic heater.

[0131] In this embodiment, when designing and verifying the electromagnetic heating experiment of the temperature curve of the numerical simulation, the requirement for the size of the electromagnetic heating experiment can be reduced based on the dimensionless variable. The large-scale actual oilfield can be simulated by using a smaller-scale experiment. This method is more in line with the research on temperature distribution and the corresponding power and frequency optimization technical requirements in the development technology of electromagnetic in-situ thermal recovery of heavy oil reservoirs.

[0132] In this embodiment of the electromagnetic heating experiment, the temperature sensors are not distributed uniformly along the radial direction as in the traditional method. Instead, to address the situation where heat is concentrated near the electromagnetic heater, causing drastic temperature changes in the vicinity of the heater, the temperature sensors are evenly distributed at different angles according to the logarithm of the radial distance, which can better measure the temperature distribution near the electromagnetic heater.

[0133] In this embodiment of the electromagnetic heating experiment, only the ion concentration of water in the material needs to be changed to achieve a change in the material's conductivity. Based on the formula for the electromagnetic power absorption coefficient, an equivalent relationship can be established between changes in material conductivity and changes in electromagnetic heating frequency. Therefore, changing the material's conductivity can be equivalent to different electromagnetic heating frequencies, allowing for the measurement of temperature distribution curves at different frequencies.

[0134] This embodiment can optimize the design of power, frequency and reservoir parameters, and experimentally distribute temperature sensors at different angles according to the logarithmic radial distance to solve the problem of drastic temperature changes near the heater. It can also establish an equivalent relationship between the material conductivity and the change of electromagnetic heating frequency to explore the influence of frequency on the temperature of heavy oil in electromagnetic in-situ thermal recovery.

[0135] The method for determining the temperature distribution of heavy oil reservoirs proposed in this embodiment can verify the accuracy of the temperature distribution of the first heavy oil reservoir by fitting the temperature distribution curve, and can also statistically analyze the changes in the temperature distribution of heavy oil reservoirs with the changes in the parameter values ​​of key parameters. Furthermore, it can design electromagnetic heating experiments to verify the accuracy of the temperature distribution of heavy oil reservoirs, thereby improving the accuracy of determining the temperature distribution of heavy oil reservoirs.

[0136] To better illustrate the execution process of the above method, this embodiment presents Example 1 below.

[0137] Example 1, establish as follows Figure 2 The physical model shown corresponds to a heavy oil reservoir where electromagnetic in-situ thermal recovery is employed. This model assumes the reservoir is homogeneous and isotropic, with radially symmetrical distribution. The electromagnetic heater is 12.5 m long, the wellbore radius is 0.125 m, the reservoir radius is 100 m, and the reservoir density is 1610 kg / m³. 3 The specific heat capacity of the reservoir is 1283 J / (kg·℃); the thermal conductivity of the reservoir is 4.98 W / (m·℃); the electromagnetic heating power is 1000 kW; the electrical conductivity of the reservoir is 0.01 S / m; the magnetic permeability of the reservoir is 4π × 10⁻⁶. -7 H / m; the dielectric constant of the reservoir is 1.2 × 8.85 × 10⁻⁶. 11 F / m; operating frequency of the electromagnetic heater, 6.8kHz. A mathematical model is established based on the one-dimensional heat conduction equation and the Lambert-Beer electromagnetic heating law. Dimensionless variables are defined according to the mathematical model. Introducing these dimensionless variables into the model yields a dimensionless mathematical model, which is then solved to obtain a dimensionless steady-state solution. Introducing the dimensionless variables into the steady-state solution yields the temperature distribution curve and heating range when the electromagnetic in-situ thermal recovery system for heavy oil reaches steady state. The logarithm of the radial distance is introduced into the dimensionless mathematical model to establish a new dimensionless mathematical model. A fully implicit difference solution is used to obtain a dimensionless numerical solution, and the consistency is verified using the steady-state solution. Introducing the dimensionless variables into the numerical solution yields the radial temperature distribution curve and heating range of the electromagnetic in-situ thermal recovery heavy oil for any heating time. Taking a one-month heating period as an example... Figure 3 The radial temperature distribution curve shown is as follows. Figure 3 In this context, t=30day indicates that the heating time is 30 days.

[0138] By changing parameters such as heating time, reservoir conductivity, electromagnetic heater frequency, and electromagnetic heater power involved in the mathematical model, the radial temperature distribution and heating range of heavy oil obtained by electromagnetic in-situ thermal recovery were obtained, along with the variation law of these parameters. An electromagnetic heating experiment was designed based on the physical model to verify the correctness of the obtained temperature distribution curve and temperature distribution variation law. Definite dimensionless variables and similarity criteria were used to simulate the actual oilfield scale on an experimental scale. In the experiment, the temperature sensor measurement points were evenly distributed at different angles, such as 45 degrees, according to the logarithm of the radial distance. Taking 10 measurement points as an example, the distribution of the measurement points is as follows: Figure 4 As shown. The experimental setup consists of a material container, an electromagnetic generator, a lid for sealing the material container and equipped with a temperature sensor, and the material itself. The experiment was conducted as follows: the electromagnetic heating device was vertically fixed at the center of the cylindrical material container; sand, water, and oil were mixed evenly according to the corresponding proportions of the actual reservoir and placed in the cylindrical material container to simulate the actual reservoir environment; the lid equipped with the temperature sensor was fastened to the top of the material container to prevent electromagnetic wave leakage; the electromagnetic generator was started, and the material was heated at a constant power for a certain period of time, and the temperature data changes on the temperature sensors at each measuring point were recorded; based on the temperature data recorded by the temperature sensors, a curve showing the temperature change over time at the same measuring point and a radial temperature distribution curve were plotted. Multiple sets of electromagnetic heating experiments were conducted using the controlled variable method, where, based on the expression for the electromagnetic power absorption coefficient, changing the conductivity of the fluid in the material was equivalent to changing the frequency of the electromagnetic heater.

[0139] like Figure 5 As shown in the figure, this embodiment proposes a device for determining the temperature distribution of heavy oil reservoirs, which may include:

[0140] Unit 101 is established to establish a corresponding one-dimensional radial heat conduction mathematical model for heavy oil reservoirs that are extracted using electromagnetic in-situ thermal recovery.

[0141] Define unit 102, which is used to define multiple dimensionless variables according to a one-dimensional radial heat conduction mathematical model;

[0142] The conversion unit 103 is used to convert a one-dimensional radial heat conduction mathematical model into a first dimensionless mathematical model based on multiple dimensionless variables. The first dimensionless mathematical model includes a dimensionless radial distance variable.

[0143] The logarithmic unit 104 is used to logarithmize the dimensionless radial distance variable in the first dimensionless mathematical model to obtain the second dimensionless mathematical model.

[0144] Solver 105 is used to perform dimensional solutions based on the second dimensionless mathematical model to obtain the temperature distribution of the first heavy oil reservoir.

[0145] It should be noted that the processing procedures for establishing unit 101, defining unit 102, transforming unit 103, logarithmicizing unit 104, and solving unit 105, as well as their beneficial effects, can be found by referring to [the documentation / references] respectively. Figure 1 Steps S101 to S105 are not described in detail here.

[0146] Optionally, unit 101 is also used for:

[0147] A corresponding cylindrical model is established based on the heavy oil reservoir. The cylindrical model includes a wellbore module located at the center, an electromagnetic heater module located in the wellbore module, and reservoir modules symmetrically distributed radially along the wellbore module.

[0148] Based on the Lambert-Beer electromagnetic heating law, the heat conduction equation, and the cylinder model, a one-dimensional radial heat conduction mathematical model is established.

[0149] Optionally, the one-dimensional radial heat conduction mathematical model includes radial distance variables, time variables, temperature rise distribution function, electromagnetic power absorption coefficient, wellbore radius variables, and reservoir radius variables;

[0150] Unit 102 is also defined for:

[0151] The radial distance variable, time variable, temperature rise distribution function, electromagnetic power absorption coefficient, wellbore radius variable, and reservoir radius variable in the one-dimensional radial heat conduction mathematical model were dimensionless to obtain dimensionless radial distance variable, dimensionless time variable, dimensionless temperature rise distribution function, dimensionless electromagnetic power absorption coefficient, dimensionless wellbore radius variable, and dimensionless reservoir radius variable, which were then used as multiple dimensionless variables.

[0152] Optionally, the logarithmicization unit 104 is also used for:

[0153] Solving the first dimensionless mathematical model yields a dimensionless steady-state analytical solution;

[0154] Based on multiple dimensionless variables, the dimensionless steady-state analytical solution is transformed into a dimensionless form to obtain the preliminary temperature distribution of the heavy oil reservoir.

[0155] Based on the preliminary temperature distribution of the heavy oil reservoir, it was determined that the temperature gradient near the wellbore module was greater than the preset threshold, and that the heat was concentrated near the wellbore module.

[0156] To improve the accuracy of the radial temperature distribution curve near the well module, the dimensionless radial distance variable in the first dimensionless mathematical model is logarithmically transformed to obtain the second dimensionless mathematical model.

[0157] Optionally, the above-mentioned device further includes:

[0158] The comparison unit is used to compare the temperature distribution of the first heavy oil reservoir with the preliminary temperature distribution of the heavy oil reservoir after the dimensional solution is obtained based on the second dimensionless mathematical model, so as to determine the degree of agreement between the temperature distribution of the first heavy oil reservoir and the preliminary temperature distribution of the heavy oil reservoir.

[0159] The first determining unit is used to determine the accuracy of the temperature distribution of the first heavy oil reservoir based on the degree of agreement.

[0160] Optionally, the above-mentioned device further includes:

[0161] The adjustment unit is used to adjust the parameter values ​​of key parameters in the one-dimensional radial heat conduction mathematical model after obtaining the temperature distribution of the first heavy oil reservoir, so as to obtain multiple temperature distributions of the second heavy oil reservoir that vary with the parameter values ​​of the key parameters; wherein, the key parameters include at least one of heating time, electromagnetic heater frequency and electromagnetic heater power;

[0162] The statistical unit is used to statistically analyze the variation pattern of the temperature distribution of heavy oil reservoirs under different parameter values, based on the temperature distribution of the first heavy oil reservoir, the temperature distribution of multiple second heavy oil reservoirs, and the parameter values ​​of key parameters.

[0163] Optionally, the above-mentioned device further includes:

[0164] The second determining unit is used to determine the true experimental scale based on multiple dimensionless variables after statistically analyzing the variation law of the temperature distribution of heavy oil reservoirs under the condition of parameter value changes.

[0165] Design unit, used to design electromagnetic heating experiments for heavy oil reservoirs based on real experimental scale;

[0166] The verification unit is used to verify the accuracy of the temperature distribution and variation law of the first heavy oil reservoir and the second heavy oil reservoir based on electromagnetic heating experiments.

[0167] The device for determining the temperature distribution of heavy oil reservoirs proposed in this embodiment establishes a corresponding one-dimensional radial heat conduction mathematical model based on heavy oil reservoirs using electromagnetic in-situ thermal recovery. Multiple dimensionless variables are defined according to this model. The one-dimensional radial heat conduction mathematical model is then converted into a first dimensionless mathematical model based on these multiple dimensionless variables. This first dimensionless mathematical model includes a dimensionless radial distance variable. The dimensionless radial distance variable in the first dimensionless mathematical model is logarithmically transformed to obtain a second dimensionless mathematical model. Dimensional solutions are then performed based on the second dimensionless mathematical model to obtain the temperature distribution of the first heavy oil reservoir. This embodiment can effectively determine the temperature distribution of heavy oil reservoirs using electromagnetic in-situ thermal recovery.

[0168] The heavy oil reservoir temperature distribution determination device in this embodiment is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0169] This invention also provides a computer device having the above-described features. Figure 5 The apparatus shown is for determining the temperature distribution of heavy oil reservoirs.

[0170] Please see Figure 6 The present invention provides a schematic diagram of the structure of a computer device according to an optional embodiment. The computer device includes one or more processors 10, a memory 20, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are interconnected via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 6 Take a processor 10 as an example.

[0171] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0172] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0173] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function. The data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, which can be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0174] Memory 20 may include volatile memory, such as random access memory. Memory may also include non-volatile memory, such as flash memory, hard disk, or solid-state drive. Memory 20 may also include combinations of the above types of memory.

[0175] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0176] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0177] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for determining the temperature distribution of a heavy oil reservoir, characterized in that, include: A corresponding one-dimensional radial heat conduction mathematical model is established for heavy oil reservoirs that are extracted using electromagnetic in-situ thermal recovery. Based on the one-dimensional radial heat conduction mathematical model, multiple dimensionless variables are defined; The one-dimensional radial heat conduction mathematical model is transformed into a first dimensionless mathematical model based on the multiple dimensionless variables. The first dimensionless mathematical model includes a dimensionless radial distance variable. Logarithmize the dimensionless radial distance variable in the first dimensionless mathematical model to obtain the second dimensionless mathematical model; Based on the second dimensionless mathematical model, a dimensional solution is performed to obtain the temperature distribution of the first heavy oil reservoir. The establishment of a corresponding one-dimensional radial heat conduction mathematical model for heavy oil reservoirs using electromagnetic in-situ thermal recovery includes: A corresponding cylindrical model is established based on the heavy oil reservoir; the cylindrical model includes a wellbore module located at the center, an electromagnetic heater module located in the wellbore module, and reservoir modules symmetrically distributed radially along the wellbore module; Based on the Lambert-Beer electromagnetic heating law, the heat conduction equation, and the cylindrical model, a one-dimensional radial heat conduction mathematical model is established. The one-dimensional radial heat conduction mathematical model includes radial distance variables, time variables, temperature rise distribution function, electromagnetic power absorption coefficient, wellbore radius variables, and reservoir radius variables. The one-dimensional radial heat conduction mathematical model defines multiple dimensionless variables, including: The radial distance variable, time variable, temperature rise distribution function, electromagnetic power absorption coefficient, wellbore radius variable, and reservoir radius variable in the one-dimensional radial heat conduction mathematical model are dimensionless to obtain dimensionless radial distance variable, dimensionless time variable, dimensionless temperature rise distribution function, dimensionless electromagnetic power absorption coefficient, dimensionless wellbore radius variable, and dimensionless reservoir radius variable, which are then used as the multiple dimensionless variables. The step of logarithmizing the dimensionless radial distance variable in the first dimensionless mathematical model to obtain the second dimensionless mathematical model includes: Solve the first dimensionless mathematical model to obtain a dimensionless steady-state analytical solution; Based on the multiple dimensionless variables, the dimensionless steady-state analytical solution is transformed into a dimensional one to obtain the preliminary temperature distribution of the heavy oil reservoir. Based on the preliminary temperature distribution of the heavy oil reservoir, it is determined that the temperature gradient near the wellbore module is greater than a preset threshold, and that the heat is concentrated near the wellbore module. To improve the accuracy of the radial temperature distribution curve near the well module, the dimensionless radial distance variable in the first dimensionless mathematical model is logarithmically transformed to obtain the second dimensionless mathematical model.

2. The method according to claim 1, characterized in that, After obtaining the temperature distribution of the first heavy oil reservoir by performing a dimensional solution based on the second dimensionless mathematical model, the method further includes: The temperature distribution of the first heavy oil reservoir is compared with the temperature distribution of the preliminary heavy oil reservoir to determine the degree of agreement between the temperature distribution of the first heavy oil reservoir and the temperature distribution of the preliminary heavy oil reservoir. The accuracy of the temperature distribution in the first heavy oil reservoir is determined based on the degree of agreement.

3. The method according to claim 1, characterized in that, After obtaining the temperature distribution of the first heavy oil reservoir, the method further includes: The parameter values ​​of key parameters in the one-dimensional radial heat conduction mathematical model are adjusted to obtain multiple second heavy oil reservoir temperature distributions that vary with the parameter values ​​of the key parameters; wherein, the key parameters include at least one of heating time, electromagnetic heater frequency, and electromagnetic heater power; Based on the temperature distribution of the first heavy oil reservoir, the temperature distribution of the multiple second heavy oil reservoirs, and the parameter values ​​of the key parameters, the variation pattern of the temperature distribution of the heavy oil reservoir under the condition that the parameter values ​​change is statistically analyzed.

4. The method according to claim 3, characterized in that, After analyzing the variation of the temperature distribution in heavy oil reservoirs under varying parameter values, the method further includes: The true experimental scale is determined based on the aforementioned multiple dimensionless variables; Based on the actual experimental scale, an electromagnetic heating experiment was designed for the heavy oil reservoir. Based on the electromagnetic heating experiment, the accuracy of the temperature distribution of the first heavy oil reservoir, the temperature distribution of the second heavy oil reservoir, and the variation law were verified.

5. A device for determining the temperature distribution of a heavy oil reservoir, characterized in that, The method for determining the temperature distribution of a heavy oil reservoir according to any one of claims 1 to 4, the apparatus comprising: A unit is established to build a corresponding one-dimensional radial heat conduction mathematical model for heavy oil reservoirs that are extracted using electromagnetic in-situ thermal recovery. A definition unit is used to define multiple dimensionless variables according to the one-dimensional radial heat conduction mathematical model; The conversion unit is used to convert the one-dimensional radial heat conduction mathematical model into a first dimensionless mathematical model based on the plurality of dimensionless variables, wherein the first dimensionless mathematical model includes a dimensionless radial distance variable. The logarithmic unit is used to logarithmize the dimensionless radial distance variable in the first dimensionless mathematical model to obtain the second dimensionless mathematical model. The solution unit is used to perform dimensional solutions based on the second dimensionless mathematical model to obtain the temperature distribution of the first heavy oil reservoir. The logarithmic unit is further configured to: Solve the first dimensionless mathematical model to obtain a dimensionless steady-state analytical solution; Based on the multiple dimensionless variables, the dimensionless steady-state analytical solution is transformed into a dimensional one to obtain the preliminary temperature distribution of the heavy oil reservoir. Based on the preliminary temperature distribution of the heavy oil reservoir, it is determined that the temperature gradient near the wellbore module is greater than a preset threshold, and that the heat is concentrated near the wellbore module. To improve the accuracy of the radial temperature distribution curve near the well module, the dimensionless radial distance variable in the first dimensionless mathematical model is logarithmically transformed to obtain the second dimensionless mathematical model.

6. A computer device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the method for determining the temperature distribution of a heavy oil reservoir as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the method for determining the temperature distribution of a heavy oil reservoir as described in any one of claims 1 to 4.

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