Radio frequency heating effect prediction method, electronic equipment, storage medium and program product

Abstract

The invention provides a radio frequency heating effect prediction method, electronic equipment, a storage medium and a program product, and relates to the technical field of oil and gas resource development. The method comprises the steps that physical property parameters of a target reservoir are obtained, the physical property parameters comprise electromagnetic property parameters and thermophysical property parameters, and the electromagnetic property parameters at least comprise conductivity and relative dielectric constants; based on a prediction model and the physical property parameters, the heating effect of the target reservoir under the radio frequency heating condition is obtained, and the prediction model is obtained by optimizing a preset heat transfer equation based on the electromagnetic property and the thermophysical property of the reservoir. The method is used for improving the prediction accuracy of the radio frequency heating effect.

Description

Technical Field

[0001] The present application relates to the technical field of oil and gas resource development, and particularly relates to a method for predicting radio frequency heating effect, an electronic device, a storage medium and a program product. Background Art

[0002] As a new downhole heating technology, radio frequency heating technology shows great application potential in the development of unconventional oil and gas resources. This technology emits radio frequency electromagnetic waves into the reservoir, converts electromagnetic loss into heat energy, and thus realizes rapid, uniform and efficient reservoir heating. The radio frequency heating technology has many advantages such as fast heating and large range, and shows great potential in the development of unconventional oil and gas resources such as heavy oil, oil sand and coalbed methane.

[0003] The existing technology mainly focuses on the electromagnetic characteristics of reservoir media and their influence on the radio frequency heating effect. For example, by measuring the imaginary part of the dielectric constant and the loss tangent of different media, the relationship between dielectric characteristics and temperature rise law is established, and it is considered that the dielectric characteristics of the medium can be used to predict its ability to absorb electromagnetic waves. By testing the relative dielectric constant and the temperature rise rate of reservoir media such as rock samples, coal samples, quartz sand and oil samples, it is found that the larger the relative dielectric constant, the stronger the electromagnetic wave absorption ability and the faster the temperature rise rate.

[0004] However, the existing research methods for radio frequency heating effect have the problem of inaccurate evaluation of radio frequency heating effect. Summary of the Invention

[0005] The radio frequency heating effect prediction method, electronic device, storage medium and program product provided by the embodiments of the present application are used to solve the problem that the existing research methods for radio frequency heating effect have inaccurate evaluation of radio frequency heating effect.

[0006] In a first aspect, an embodiment of the present application provides a method for predicting radio frequency heating effect, including:

[0007] Obtain the physical property parameters of the target reservoir, where the physical property parameters include electromagnetic property parameters and thermal property parameters, and among them, the electromagnetic property parameters at least include conductivity and relative dielectric constant;

[0008] Based on the prediction model and the physical property parameters, obtain the heating effect of the target reservoir under radio frequency heating conditions, and the prediction model is obtained by optimizing a preset heat transfer equation based on the electromagnetic characteristics and thermal properties of the reservoir.

[0009] In a possible implementation manner, the method further includes:

[0010] Obtain reservoir data corresponding to multiple historical moments within a historical time period. Among them, the reservoir data corresponding to each historical moment includes: the reservoir medium type, physical property parameters, and electromagnetic source information of the reservoir under radio frequency heating conditions for a reservoir;

[0011] For each reservoir data with the same reservoir medium type, obtain the actual heating effect of the reservoir;

[0012] Determine the electromagnetic heat source according to the electromagnetic property parameters and electromagnetic source information of the reservoir;

[0013] Input the electromagnetic heat source and the thermal physical property parameters in the physical property parameters into a preset heat transfer equation to obtain the predicted heating effect;

[0014] Optimize the preset heat transfer equation according to the actual heating effect and the predicted heating effect to obtain an electrothermal coupling model;

[0015] Construct a prediction chart according to the electrothermal coupling model. The prediction chart is used to indicate the heating rate under different physical property parameters;

[0016] Among them, the prediction model includes an electrothermal coupling model and a prediction chart.

[0017] In a possible implementation manner, determining the electromagnetic heat source according to the electromagnetic property parameters and electromagnetic source information of the reservoir includes:

[0018] Based on Maxwell's equations, the electromagnetic property parameters of the reservoir, and the electromagnetic source information, analyze the propagation characteristics of electromagnetic waves in the reservoir to obtain the spatial distributions of the electric field and magnetic field;

[0019] Based on Poynting's law, the electromagnetic property parameters of the reservoir, and the spatial distributions of the electric field and magnetic field, determine the electromagnetic heat source.

[0020] In a possible implementation manner, constructing a prediction chart according to the electrothermal coupling model includes:

[0021] Solve the electrothermal coupling model to obtain the temperature expression of the reservoir under radio frequency heating conditions and multiple temperature coefficients in the temperature expression;

[0022] Based on the temperature expression, conduct a sensitivity analysis of the temperature of the reservoir to determine the changing trend of each temperature coefficient over time;

[0023] Update the temperature expression according to the changing trend of each temperature coefficient over time to obtain a target temperature expression and target temperature coefficients in the target temperature expression;

[0024] According to the correlation relationship between the target temperature coefficients and the physical property parameters, determine the key parameters that affect the temperature distribution in the reservoir. The key parameters include at least conductivity and relative permittivity;

[0025] Construct a prediction chart based on the key parameters and the target temperature expression.

[0026] In a possible implementation, based on the prediction model and the physical property parameters, obtain the heating effect of the target reservoir under radio frequency heating conditions, including:

[0027] Based on the electrothermal coupling model and the physical property parameters in the prediction model, obtain the heating effect of the target reservoir under radio frequency heating conditions;

[0028] Or,

[0029] Based on the prediction chart in the prediction model and the conductivity and relative permittivity in the physical property parameters, obtain the heating effect of the target reservoir under radio frequency heating conditions.

[0030] In a possible implementation, the method further includes:

[0031] Associate the reservoir medium type of the reservoir with the corresponding electrothermal coupling model and prediction chart to obtain a prediction sub-model;

[0032] Based on the prediction sub-models corresponding to all reservoir medium types, obtain the prediction model;

[0033] Correspondingly,

[0034] Based on the prediction model and the physical property parameters, obtain the heating effect of the target reservoir under radio frequency heating conditions, including:

[0035] Obtain the reservoir medium type of the target reservoir;

[0036] Determine the prediction sub-model corresponding to the reservoir medium type of the target reservoir in the prediction model;

[0037] Based on the prediction sub-model and the physical property parameters, obtain the heating effect of the target reservoir under radio frequency heating conditions.

[0038] In a second aspect, an embodiment of the present application provides a radio frequency heating effect prediction device, including:

[0039] An acquisition module, configured to acquire the physical property parameters of the target reservoir, where the physical property parameters include electromagnetic property parameters and thermophysical property parameters, and among them, the electromagnetic property parameters at least include conductivity and relative permittivity;

[0040] A prediction module, configured to obtain the heating effect of the target reservoir under radio frequency heating conditions based on the prediction model and the physical property parameters, and the prediction model is obtained by optimizing a preset heat transfer equation based on the electromagnetic properties and thermophysical properties of the reservoir.

[0041] In a third aspect, an embodiment of the present application provides an electronic device, including: a memory and a processor;

[0042] The memory stores computer-executable instructions;

[0043] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the first aspect and / or various possible implementation manners of the first aspect as described above.

[0044] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed, they are used to implement the first aspect and / or various possible implementation manners of the first aspect as described above.

[0045] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, and when the computer program is executed, it implements the first aspect and / or various possible implementation manners of the first aspect as described above.

[0046] The radio frequency heating effect prediction method, electronic device, storage medium, and program product provided by the embodiments of the present application obtain physical property parameters of a target reservoir by obtaining physical property parameters of the target reservoir, where the physical property parameters include electromagnetic property parameters and thermophysical property parameters. Among them, the electromagnetic property parameters at least include conductivity and relative permittivity; based on a prediction model and the physical property parameters, the heating effect of the target reservoir under radio frequency heating conditions is obtained. The prediction model is a means obtained by optimizing a preset heat transfer equation based on the electromagnetic properties and thermophysical properties of the reservoir. The prediction model established by comprehensively analyzing the electromagnetic properties and thermophysical properties of the reservoir can more accurately simulate the temperature distribution and heating rate of the reservoir under radio frequency heating conditions, improve the prediction accuracy of the radio frequency heating effect, enable operators to master the heating effect before actual operation, thereby reasonably arranging heating time, power, etc., avoid energy waste, and improve heating efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0048] Figure 1 It is a schematic flow chart of a radio frequency heating effect prediction method provided by the present application;

[0049] Figure 2 It is a schematic flow chart of verification between the electromagnetic properties of a reservoir and the actual heating effect of a heating-up test provided by the present application;

[0050] Figure 3 It is a schematic structural diagram of a prediction chart provided by the present application;

[0051] Figure 4Schematic diagram of the specific process of a radio frequency heating effect prediction method provided by this application;

[0052] Figure 5 Schematic diagram of the structure of a radio frequency heating effect prediction device provided by this application;

[0053] Figure 6 Schematic diagram of the structure of an electronic device provided by this application.

[0054] Through the above-mentioned drawings, specific embodiments of this application have been shown, and there will be more detailed descriptions hereinafter. These drawings and textual descriptions are not intended to limit the scope of the concept of this application in any way, but to illustrate the concept of this application to those skilled in the art by referring to specific embodiments. Specific Embodiments

[0055] Here, exemplary embodiments will be described in detail, and their examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. On the contrary, they are merely examples of devices and methods consistent with some aspects of this application as detailed in the appended claims.

[0056] In the prior art, research mainly focuses on the electromagnetic properties of reservoir media and their influence on the radio frequency heating effect. For example, by measuring the imaginary part of the dielectric constant and the loss tangent of different media, the relationship between dielectric properties and the temperature rise law has been established, and it is considered that the dielectric properties of the medium can be used to predict its ability to absorb electromagnetic waves. This technology tests the relative dielectric constant and the heating rate of reservoir media such as rock samples, coal samples, quartz sand, and oil samples, and finds that the greater the relative dielectric constant, the stronger the electromagnetic wave absorption ability and the faster the heating rate. However, this method only relies on a single parameter of the relative dielectric constant and does not comprehensively consider other key factors such as conductivity, thermal conductivity, and specific heat capacity, resulting in certain errors in the prediction results.

[0057] Although there are also methods in the prior art to study the influence of the dielectric properties of substances on electromagnetic heating, especially the influence of water content and bulk density on dielectric properties, mainly by experimentally analyzing the influence law of water content and bulk density on the dielectric properties of substances; thereby analyzing the differential heating effects at each stage during the electromagnetic heating process of different substances, this method does not consider the dynamic influence of frequency and temperature on the dielectric constant and conductivity, restricting its comprehensive evaluation of the electromagnetic heating effect.

[0058] To solve the above problems, embodiments of the present application provide a method for predicting radio frequency heating effect, an electronic device, a storage medium, and a program product. By obtaining the physical property parameters of the target reservoir, including electromagnetic property parameters (such as conductivity and relative permittivity) and thermophysical property parameters, and based on the constructed prediction model based on the optimized heat transfer equation, the temperature change and heating effect of the reservoir under radio frequency heating conditions are determined. Among them, considering that different electromagnetic properties and thermophysical properties will make the energy conversion and heat transfer processes of the reservoir during radio frequency heating different, the prediction model can more accurately simulate the temperature distribution and heating rate of the reservoir under radio frequency heating conditions by comprehensively analyzing and modeling the electromagnetic properties and thermophysical properties of the reservoir, making the predicted heating effect more accurate.

[0059] The following uses specific embodiments to detail the technical solutions of the present application and how the technical solutions of the present application solve the above technical problems. These several specific embodiments below can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the drawings.

[0060] The execution subject of the radio frequency heating effect prediction method provided by the embodiments of the present application can be a server. Among them, the server can be devices such as mobile phones, computers, and tablets. The embodiments of the present application do not make special restrictions on the implementation manner of the execution subject, as long as the execution subject can obtain the physical property parameters of the target reservoir, and the physical property parameters include electromagnetic property parameters and thermophysical property parameters, where the electromagnetic property parameters at least include conductivity and relative permittivity; based on the prediction model and the physical property parameters, the heating effect of the target reservoir under radio frequency heating conditions is obtained, and the prediction model is obtained by optimizing the preset heat transfer equation based on the electromagnetic properties and thermophysical properties of the reservoir.

[0061] It should be noted that the radio frequency heating effect prediction method provided by the embodiments of the present application can be applied to various reservoir types, such as oil reservoirs, geothermal reservoirs, etc., and has wide applicability, and no special restrictions are made here.

[0062] Figure 1 It is a schematic flowchart of a radio frequency heating effect prediction method provided by the present application. The execution subject of this method can be a server or other servers storing the radio frequency heating effect prediction method. No special restrictions are made here in this embodiment. For example Figure 1 as shown, this method may include:

[0063] S101. Obtain the physical property parameters of the target reservoir, where the physical property parameters include electromagnetic property parameters and thermophysical property parameters, and among them, the electromagnetic property parameters at least include conductivity and relative permittivity.

[0064] Among them, the target reservoir is the reservoir for which the radio frequency heating effect needs to be predicted, and it may store resources such as oil and natural gas. The electromagnetic property parameters can be used to describe the behavior of the reservoir medium in the electromagnetic field, and can include conductivity, relative permittivity, permeability, etc. The thermophysical property parameters can be used to describe the heat conduction and heat storage capabilities of the reservoir medium, and can include thermal conductivity, specific heat capacity, thermal diffusivity, etc. These parameters can be obtained through laboratory measurements, on-site measurements, or calculations based on measurement data.

[0065] Exemplarily, a vector network analyzer (PNA-X) combined with the relative permittivity resonator method was used to measure the permittivity and loss factor of each reservoir (which can be reservoirs of the same reservoir medium type or different reservoir medium types) at different frequencies (such as 10 MHz to 915 MHz); at the same time, an insulation resistance tester (RK2683AN) was used to measure the conductivity of each material at different temperatures (such as 20°C to 200°C) in accordance with the conductivity standard GB / T 1410-2006. A thermal conductivity meter was used to test the thermal diffusivity of different reservoir media at different temperatures (such as 20°C to 200°C).

[0066] S102. Based on the prediction model and the physical property parameters, obtain the heating effect of the target reservoir under radio frequency heating conditions. The prediction model is obtained by optimizing the preset heat transfer equation based on the electromagnetic properties and thermophysical properties of the reservoir.

[0067] Among them, the prediction model can be a mathematical model based on the electromagnetic properties and thermophysical property parameters of the reservoir. By optimizing the preset heat transfer equation, the prediction model can more accurately simulate the temperature distribution and heating rate of the reservoir under radio frequency heating conditions. Further, the physical property parameters of the target reservoir can be input into the model to simulate the radio frequency heating process and obtain the heating effect. Among them, the heating effect can include the temperature distribution and heating rate of the reservoir at different times and spatial positions output by the model, as well as information such as the heating efficiency and energy consumption calculated based on this.

[0068] The preset heat transfer equation can be a reservoir temperature distribution model established based on the energy transfer in the electromagnetic heating process to obtain the heat transfer equation in the reservoir.

[0069] The radio frequency heating effect prediction method provided by the embodiments of the present application can more accurately simulate the temperature distribution and heating rate of the reservoir under radio frequency heating conditions through a prediction model established by comprehensively analyzing the electromagnetic characteristics and thermal physical properties of the reservoir, improve the prediction accuracy of the radio frequency heating effect, enable operators to master the heating effect before actual operation, and thus reasonably arrange the heating time, power, etc. to avoid energy waste. At the same time, by optimizing the prediction model to improve the prediction accuracy, it can perform customized analysis for different reservoir conditions, thereby optimizing the radio frequency heating strategy, reducing energy consumption, improving the heating efficiency, and maximizing resource recovery. In addition, more accurate prediction of the heating effect can also identify potential risks, such as overheating or uneven heating, and thus take appropriate preventive measures to ensure the safe and stable progress of the entire heating process.

[0070] Based on the above embodiments, the method may further include: obtaining reservoir data corresponding to multiple historical moments within a historical time period, where the reservoir data corresponding to each historical moment includes: the reservoir medium type, physical property parameters, and electromagnetic source information of the reservoir under radio frequency heating conditions corresponding to a reservoir; for each reservoir data with the same reservoir medium type, obtaining the actual heating effect of the reservoir; determining the electromagnetic heat source according to the electromagnetic characteristic parameters and electromagnetic source information of the reservoir; inputting the electromagnetic heat source and the thermal physical property parameters in the physical property parameters into a preset heat transfer equation to obtain the predicted heating effect; optimizing the preset heat transfer equation according to the actual heating effect and the predicted heating effect to obtain an electrothermal coupling model; constructing a prediction chart based on the electrothermal coupling model, and the prediction chart is used to indicate the heating rate under different physical property parameters; where the prediction model includes the electrothermal coupling model and the prediction chart.

[0071] In this step, the historical time period is the time interval before determining the heating effect in the target reservoir by executing the radio frequency heating effect prediction method. The historical moment can be each specific time point within the historical time period used to obtain and record the corresponding reservoir data.

[0072] The reservoir medium type can be used to indicate the reservoir medium corresponding to the reservoir, such as a rock sample reservoir, a coal sample reservoir, an oil sample reservoir, a quartz sand reservoir, etc. Different types of reservoir media have differences in physical properties and heat and mass transfer characteristics.

[0073] The electromagnetic source information can refer to the parameters used to describe the electromagnetic source during the radio frequency heating process, such as the initial conditions of the electric field and magnetic field, frequency, power, etc. These parameters affect the distribution of the electromagnetic field in the reservoir and the input of electromagnetic energy, and thus affect the heating effect.

[0074] The actual heating effect can be the temperature change actually achieved in the reservoir during the actual radio frequency heating process, which can be obtained through on-site measurement or monitoring data, or through physical simulation experiments of radio frequency heating. For example, a downhole radio frequency heating experimental system can be used to conduct heating-up tests (i.e., physical simulation experiments of radio frequency heating) of different reservoir media under fixed electromagnetic source information (frequency and power), and the maximum temperature and heating rate of different reservoir media within the same time can be measured respectively.

[0075] The electromagnetic heat source can refer to the electromagnetic term in the preset heat transfer equation, which is used to indicate the heat generated by the action of the electromagnetic field on the reservoir. The intensity and distribution of the electromagnetic heat source mainly depend on factors such as electromagnetic source information, the electromagnetic characteristics of the reservoir (such as conductivity and permittivity), and the geometric shape of the reservoir. By reasonably designing the electromagnetic field parameters and configurations, the heating process can be effectively controlled to achieve uniform and efficient heat transfer.

[0076] The electro-thermal coupling model is a model obtained by introducing the electromagnetic heat source and thermal physical properties parameters into the preset heat transfer equation and optimizing it according to the actual heating effect and the predicted heating effect. It comprehensively considers the interaction between the electromagnetic and heat transfer processes. The method for optimizing the preset heat transfer equation can be: after verification between the actual heating effect and the predicted heating effect, if the error is mainly reflected in the temperature rising speed or the maximum temperature, more accurate thermal physical property data can be found through experiments or literature, and the parameter values in the model can be updated, such as adjusting the thermal conductivity and specific heat capacity; if the error is mainly reflected in the electromagnetic heating efficiency, it may be necessary to re-evaluate the intensity of the electromagnetic heat source, such as recalculating or adjusting the permittivity and conductivity.

[0077] The prediction chart can be a visualization tool constructed based on the electro-thermal coupling model. It can display the heating rate of the reservoir under different physical property parameters in the form of a chart, which is convenient for intuitively predicting and analyzing the heating effect. Moreover, it can be updated with new reservoir data to ensure the accuracy and timeliness of the prediction.

[0078] In one example, based on the energy transfer during the electromagnetic heating process, a reservoir temperature distribution model is established, and thus the preset heat transfer equation in the reservoir is defined as:

[0079] ;

[0080] Among them, is the reservoir temperature, K; is the time, s; is the thermal diffusivity, which is determined according to the thermal physical property parameters (such as thermal conductivity) and the effective volume heat capacity or directly measured by a thermal conductivity meter; is the radial distance with the origin at the wellbore center; Indicates the power transmitted to the reservoir, in kW, determined based on electromagnetic source information and electromagnetic properties (such as dielectric constant and loss factor); is the reservoir thickness, in m; is the effective volume heat capacity of the reservoir, in J / m³·K, determined based on thermophysical parameters (such as specific heat capacity and density).

[0081] Figure 2 This is a schematic flow diagram for verifying the relationship between the electromagnetic properties of a reservoir and the actual heating effect in a heating experiment provided by this application, as Figure 2 shown. By testing the thermoelectric property parameters of the reservoir medium, the relative dielectric constant, conductivity, and thermal conductivity are obtained. Based on these parameters, the predicted heating effect of the model can be determined. Then, based on the maximum temperature and heating rate obtained from the physical simulation experiment of radio frequency heating, the actual predicted effect is determined. Through the mutual verification and comparison between the actual heating effect and the predicted heating effect, the parameters in the heat transfer equation can be gradually adjusted. For example, the value of the electromagnetic term can be adjusted to better match the propagation characteristics of electromagnetic waves, or the value of the heat transfer term can be adjusted to improve the simulation of heat conduction. It should be noted that the adjusted model needs to be verified under different experimental conditions to ensure its accuracy and robustness in various situations.

[0082] By obtaining reservoir data within a historical time period, analyzing the actual heating effect and the predicted heating effect, optimizing the preset heat transfer equation to obtain an electrothermal coupling model, and constructing a prediction chart, the electrothermal coupling process of the reservoir during radio frequency heating can be more accurately characterized, making the prediction of the reservoir heating effect more accurate and reliable, and providing strong support for the refined design of the radio frequency heating extraction plan.

[0083] Based on the above embodiments, the method for determining the electromagnetic heat source according to the electromagnetic property parameters and electromagnetic source information of the reservoir may include: analyzing the propagation characteristics of electromagnetic waves in the reservoir based on Maxwell's equations, the electromagnetic property parameters of the reservoir, and the electromagnetic source information to obtain the spatial distribution of the electric field and magnetic field; determining the electromagnetic heat source based on Poynting's law, the electromagnetic property parameters of the reservoir, and the spatial distribution of the electric field and magnetic field.

[0084] In this step, Maxwell's equations are a set of partial differential equations that describe the relationship between the electric field, magnetic field, charge density, and current density, reflecting the basic properties of the electric field and magnetic field. By inputting the electromagnetic property parameters of the reservoir (such as relative dielectric constant, magnetic permeability, and conductivity, etc.), electromagnetic source information (such as the initial conditions of the electric field and magnetic field, frequency, and power, etc.), as well as the geometric shape and boundary conditions of the reservoir, the propagation and loss of electromagnetic waves in the reservoir medium are solved to obtain the spatial distribution and time evolution of the electric field and magnetic field in the reservoir medium, such as heating power, frequency, and time.

[0085] Poynting's law represents the law of conservation of electromagnetic field energy. Through this law, the transmission and conversion of electromagnetic energy can be calculated, and then the electromagnetic heat source can be determined. Further, by inputting the electromagnetic characteristic parameters of the reservoir and the spatial distributions of the electric and magnetic fields, the energy flux density is calculated using Poynting's law, and then the electromagnetic heat source is obtained based on the energy flux density.

[0086] Through the analysis method based on Maxwell's equations and Poynting's law, the propagation characteristics of electromagnetic waves in the reservoir can be accurately simulated, and the distribution of the electromagnetic heat source can be determined for evaluating and optimizing the radio frequency heating process. Compared with other simple estimation methods, it provides more detailed and accurate information on the distribution of the electromagnetic heat source, enabling a more reliable heat source data basis for subsequent construction of the electro-thermal coupling model and prediction of the reservoir heating effect, and thus improving the accuracy of the simulation and prediction of the entire radio frequency heating extraction process.

[0087] Based on the above embodiments, the method for constructing a prediction chart according to the electro-thermal coupling model may include: solving the electro-thermal coupling model to obtain the temperature expression of the reservoir under radio frequency heating conditions and multiple temperature coefficients in the temperature expression; based on the temperature expression, performing a sensitivity analysis on the temperature of the reservoir to determine the changing trend of each temperature coefficient over time; according to the changing trend of each temperature coefficient over time, updating the temperature expression to obtain the target temperature expression and the target temperature coefficients in the target temperature expression; according to the correlation relationship between the target temperature coefficients and the physical characteristic parameters, determining the key parameters that affect the temperature distribution in the reservoir, and the key parameters at least include conductivity and relative permittivity; constructing a prediction chart according to the key parameters and the target temperature expression.

[0088] Among them, the temperature expression can be a mathematical function, which is the result obtained by solving the electro-thermal coupling model and is used to describe how the reservoir temperature changes with time, spatial position, and various physical parameters under radio frequency heating conditions. Through this expression, the dynamic change process of the reservoir temperature can be quantitatively analyzed.

[0089] The temperature coefficient can refer to the coefficients associated with factors such as the electromagnetic characteristic parameters, thermal physical parameters, and heating conditions of the reservoir in the temperature expression. They determine the influence weights of each parameter on the reservoir temperature, and each temperature coefficient represents the contribution degree of a specific factor to the temperature change.

[0090] Sensitivity analysis refers to a research method used to evaluate the magnitude of the impact of small changes in the input parameters (such as the electromagnetic characteristic parameters corresponding to the temperature coefficients) in the model on the output result (reservoir temperature). Through sensitivity analysis, it can be clarified which factors have the most significant impact on the reservoir temperature change.

[0091] The target temperature expression is a new formula obtained by optimizing and correcting the original temperature expression based on the analysis of the changing trend of the temperature coefficient with time in the original temperature expression. The target temperature expression can more accurately reflect the temperature change of the reservoir during the actual heating process.

[0092] The target temperature coefficient refers to the coefficient included in the target temperature expression. After sensitivity analysis and expression update, it can more accurately reflect the relationship between the reservoir temperature and various influencing factors compared with the original temperature coefficient.

[0093] The key parameters refer to the physical property parameters that have a key impact on the reservoir temperature distribution. The change of these parameters will significantly change the temperature distribution of the reservoir during radio frequency heating and is the core factor affecting the heating effect.

[0094] In an example, the temperature expression can satisfy:

[0095] ;

[0096] Wherein, 、 、 are temperature coefficients, is the integration constant, and are functions of time and space; is the initial temperature.

[0097] Furthermore, respectively satisfy:

[0098] ;

[0099] ;

[0100] ;

[0101] Wherein, is the wellbore radius during radio frequency heating; is the reservoir radius.

[0102] Based on the above expressions, by quickly estimating and performing sensitivity analysis on the reservoir temperature, it can be obtained that as time t increases The term related to time will change as follows: .

[0103] From this, it can be inferred that as time increases, The contribution to temperature rise will gradually decrease and finally tend to 0. Similarly, as time t increases, will become a constant and no longer change with time. Therefore, when time continuously increases, and All the time terms tend to constants, and the time-dependent parts of these terms can be ignored. The final temperature expression (the target temperature expression) will approach:

[0104] ;

[0105] Furthermore, the target temperature coefficient (which can characterize the heating rate of the reservoir) can be expressed as:

[0106] ;

[0107] where C is the integral term, satisfying ; is the electromagnetic wave attenuation factor, satisfying:

[0108] ;

[0109] where is the angular frequency, in rad / s, , is the frequency, in Hz; is the magnetic permeability; is the conductivity, in S / m; is the relative permittivity.

[0110] From the target temperature coefficient, it can be seen that the factors affecting the reservoir temperature do not directly depend on each other with time. The integral term C itself only depends on the radius and and and other factors, and the integral result is a constant. Therefore, the relationship between temperature change and time is mainly determined by and the integral constant C. Furthermore, the temperature and time dependence is linear, and the magnitude of the heating rate depends on the slope (the target temperature coefficient). By discussing the relationship between the slope and , C, and parameters related to h, etc., the heating rate of the reservoir can be judged.

[0111] Through the analysis of the target temperature coefficient, it can be obtained that and , and h show a linear increasing or decreasing relationship, and there is no need to construct a prediction chart. However, the relationship between and the integral term C is unknown. By analyzing the factors affecting the integral term, it can be known that has a relatively large correlation with the conductivity and the relative permittivity. Therefore, the conductivity and the relative permittivity are taken as key parameters to construct a prediction chart to visually display the heating rate of the reservoir under different key parameters.

[0112] Figure 3A structural schematic diagram of a prediction chart provided for this application is as follows Figure 3 shown. The horizontal axis and the vertical axis respectively represent the relative permittivity and the conductivity; the isotherms are used to divide different types of reservoir media; different colors are used to represent the strength of the heating rate. Among them, red corresponds to a stronger heating rate, and blue corresponds to a weaker heating rate. Exemplarily, when the reservoir medium is a rock sample, the relative permittivity is 4 and the conductivity is 6×10 -5 S / m, the heating rate is close to 0.6500 °C / s.

[0113] By solving the electro-thermal coupling model and performing sensitivity analysis, a highly practical prediction chart is constructed, which can help engineers and researchers deeply understand the temperature change law of the reservoir during radio frequency heating and accurately grasp the influence of key parameters on the temperature distribution. Based on the prediction chart, in actual reservoir exploitation operations, the heating effect under different reservoir conditions can be predicted in advance before radio frequency heating, so as to optimize the radio frequency heating scheme, reasonably adjust the heating parameters, improve the energy utilization efficiency, reduce the exploitation cost, and reduce unnecessary resource waste and environmental pollution.

[0114] Based on the above embodiments, the method for obtaining the heating effect of the target reservoir under radio frequency heating conditions based on the prediction model and physical property parameters may include: obtaining the heating effect of the target reservoir under radio frequency heating conditions based on the electro-thermal coupling model and physical property parameters in the prediction model; or, obtaining the heating effect of the target reservoir under radio frequency heating conditions based on the prediction chart in the prediction model and the conductivity and relative permittivity in the physical property parameters.

[0115] Exemplarily, during radio frequency heating, after accurately measuring the physical property parameters of the target reservoir, such as conductivity, relative permittivity, thermal conductivity, specific heat capacity, etc., input them into the established electro-thermal coupling model, use professional numerical calculation software, such as finite element analysis software, to set the boundary conditions and initial conditions of radio frequency heating, such as the starting time of heating, the power of the electromagnetic source, the heat dissipation situation at the boundary of the heating area, etc., and then solve and calculate the electro-thermal coupling model to simulate the temperature distribution of each position inside the reservoir at different time nodes, so as to obtain the heating effect of the target reservoir changing with time under radio frequency heating conditions.

[0116] In another example, it is also possible to obtain the conductivity and relative permittivity of the target reservoir, find the corresponding curve or data point in the established prediction chart according to the values of these two physical property parameters, and combine the actual required heating time to estimate the heating effect of the target reservoir under radio frequency heating conditions.

[0117] Through these two methods of obtaining the heating effect based on the prediction model, a diverse and efficient approach is provided for the analysis of the radio frequency heating effect of the reservoir. The method based on the electro-thermal coupling model can comprehensively consider various physical characteristic parameters of the reservoir and complex heating conditions. Through accurate numerical calculations, it can meticulously simulate the dynamic change of the internal temperature distribution of the reservoir over time, providing detailed data support for the refined design of the heating scheme. The method based on the prediction chart has the advantages of intuitiveness and convenience. Using the existing empirical data and analysis results, it can quickly estimate the heating effect according to the key physical characteristic parameters, facilitating rapid decision-making in the preliminary planning and initial evaluation.

[0118] Based on the above embodiments, the method may further include: associating the reservoir medium type of the reservoir with the corresponding electro-thermal coupling model and prediction chart to obtain a prediction sub-model; based on the prediction sub-models corresponding to all reservoir medium types, obtaining a prediction model; correspondingly, based on the prediction model and physical characteristic parameters, obtaining the heating effect of the target reservoir under radio frequency heating conditions, including: obtaining the reservoir medium type of the target reservoir; determining the prediction sub-model corresponding to the reservoir medium type of the target reservoir in the prediction model; based on the prediction sub-model and physical characteristic parameters, obtaining the heating effect of the target reservoir under radio frequency heating conditions.

[0119] Further, collect samples of various different reservoir medium types, such as rock sample reservoirs, coal sample reservoirs, oil sample reservoirs, quartz sand reservoirs, etc. For each reservoir medium type, conduct experiments and / or simulation calculations respectively to obtain the relevant data under radio frequency heating, and then establish the corresponding electro-thermal coupling model and prediction chart; then summarize the prediction sub-models of all different reservoir medium types; an indexing and calling mechanism can be set up to quickly locate and call the corresponding prediction sub-model according to the input reservoir medium type.

[0120] By associating the reservoir medium type with the corresponding electro-thermal coupling model and prediction chart to form a prediction sub-model, the unique physical properties and heating response characteristics of different reservoir media can be fully considered. The comprehensive prediction model established on this basis covers various reservoir situations, greatly expanding the applicable range of the model. In practical applications, quickly calling the corresponding prediction sub-model according to the reservoir medium type of the target reservoir to analyze the heating effect avoids the errors caused by using a general model and improves the prediction efficiency and accuracy.

[0121] Figure 4 The specific flow diagram of a radio frequency heating effect prediction method provided by this application is as Figure 4 shown. The method may include:

[0122] Step 1: Test the electromagnetic characteristics of the reservoir medium.

[0123] 1. Test of dielectric constant and loss factor:

[0124] Using a vector network analyzer combined with the relative dielectric constant resonator method, measure the dielectric constant and loss factor of different reservoir media (such as rock samples, coal samples, quartz sand, oil samples) at different frequencies.

[0125] 2. Conductivity test:

[0126] Using an insulation resistance tester, measure the conductivity of different reservoir media at different temperatures according to the conductivity standard GB / T1410 - 2006.

[0127] 3. Thermal property parameter test:

[0128] Using a thermal conductivity meter, measure the thermal diffusivity and specific heat capacity of different reservoir media at different temperatures, and determine parameters such as thermal conductivity and effective volume heat capacity.

[0129] Step 2: Radio frequency heating and temperature rise experiment.

[0130] 1. Experimental system setup:

[0131] Using the downhole radio frequency heating experimental system, set fixed frequency and power conditions to conduct heating experiments on different reservoir media.

[0132] 2. Temperature rise data acquisition:

[0133] Record the maximum temperature and temperature rise rate of different media within the same time to obtain radio frequency heating physical simulation experimental data.

[0134] Step 3: Electromagnetic wave propagation and loss calculation.

[0135] Solve the propagation and loss of electromagnetic waves in reservoir media through Maxwell's equations, input reasonable radio frequency heating power, radio frequency heating frequency, and radio frequency heating time, and calculate the electromagnetic heat source using Poynting's law.

[0136] Step 4: Establishment and solution of the electro-thermal coupling mathematical model.

[0137] 1. Establishment of the heat transfer equation:

[0138] Based on the energy transfer during electromagnetic heating, establish the heat transfer equation for the reservoir temperature distribution;

[0139] 2. Coupling of the electromagnetic field and the temperature field:

[0140] Substitute the electromagnetic heat source in Step 3 into the heat transfer equation of the reservoir to obtain the variation law of the reservoir temperature with time and space (model prediction result);

[0141] Verify the accuracy of the model by comparing the experimental data (Step 2) with the model prediction result;

[0142] Optimize the model parameters to improve the prediction accuracy and complete the coupling of the electromagnetic field and the temperature field.

[0143] Step Five: Construct a chart.

[0144] 1. Influence of physical property parameters on the heating effect:

[0145] Analyze multiple temperature coefficients in the temperature expression corresponding to the heat transfer equation, and judge the relationship between the heating rate and physical property parameters such as input power, effective volume heat capacity, conductivity, and relative permittivity.

[0146] 2. Establish a chart:

[0147] Based on the analysis results, establish a chart showing the influence of key parameters (such as relative permittivity and conductivity) on the temperature distribution (such as heating rate) to predict the heating effect of different reservoir media.

[0148] Step Six: Predict the heating effect based on the electro-thermal coupling mathematical model or the chart.

[0149] 1. Based on the model:

[0150] According to the electromagnetic property parameters (dielectric constant, conductivity, and magnetic permeability, etc.) and thermophysical property parameters (thermal conductivity, specific heat capacity, etc.) of the target reservoir medium, input them into the model to obtain the heating rate and the maximum temperature of the target reservoir medium under radio frequency heating conditions.

[0151] 2. Based on the chart:

[0152] According to the relative permittivity and conductivity, determine the heating rate in the chart, so as to determine the heating rate and the maximum temperature of the target reservoir medium under radio frequency heating conditions.

[0153] 3. Process optimization:

[0154] According to the prediction results, optimize the radio frequency heating process parameters (such as power, frequency, time) to improve the heating efficiency and energy utilization rate.

[0155] The radio frequency heating effect prediction method provided by the embodiments of the present application combines experimental data and theoretical analysis to explore the influence law of reservoir electromagnetic properties on the temperature distribution, providing a theoretical basis for predicting the energy absorption capacity and heating effect of the reservoir; through the analysis of the influence of the electromagnetic properties (relative permittivity and conductivity) of different reservoir media on the heating law during radio frequency heating, the asymptotic solution of the reservoir temperature can be used to predict the radio frequency heating effect of different reservoir media. Substituting the electromagnetic properties of different types of media into the chart can predict in advance the energy absorption capacity and heating effect of different reservoir media under radio frequency heating conditions.

[0156] Furthermore, by accurately measuring the electromagnetic properties of reservoir media, such as dielectric constant, conductivity, and permeability, and their dynamic variation laws with frequency and temperature, the absorption and energy conversion capabilities of different media for radio frequency electromagnetic waves can be deeply analyzed. In addition, through radio frequency heating experiments, the transmission and loss mechanisms of electromagnetic waves in the reservoir are studied, and the temperature field distribution and its evolution law caused by radio frequency heating are analyzed, especially the non-uniform heating effect under multi-media conditions. Through mutual verification of the two parts of the experiments, this technology has important application value in reservoir thermal field control and energy consumption optimization.

[0157] Furthermore, based on the thermoelectric coupling mathematical heat transfer model of electromagnetic heating, substituting the variation laws of the measured thermoelectric property parameters of different reservoir media with frequency and temperature into the heat transfer equation, the changes of the downhole reservoir with the increase of heating time are simulated. Combining with the asymptotic solution of the reservoir temperature distribution, the influence law of the electromagnetic properties of different media on the temperature distribution is explored, and a chart is established using the asymptotic solution of the reservoir temperature. This chart can predict in advance the energy absorption capacity and heating effect of reservoir media under radio frequency heating conditions, providing a theoretical basis for optimizing the radio frequency heating process.

[0158] Figure 5 The structural schematic diagram of the radio frequency heating effect prediction device provided by this application is as Figure 5 shown. The radio frequency heating effect prediction device 50 provided in this embodiment includes:

[0159] An acquisition module 501, configured to acquire the physical property parameters of the target reservoir, where the physical property parameters include electromagnetic property parameters and thermophysical property parameters, and among them, the electromagnetic property parameters at least include conductivity and relative dielectric constant;

[0160] A prediction module 502, configured to obtain the heating effect of the target reservoir under radio frequency heating conditions based on the prediction model and the physical property parameters, and the prediction model is obtained by optimizing a preset heat transfer equation based on the electromagnetic properties and thermophysical properties of the reservoir.

[0161] In a possible implementation manner, the prediction module 502 can also be used to:

[0162] Acquire reservoir data corresponding to multiple historical moments within a historical time period, where the reservoir data corresponding to each historical moment includes: the type of reservoir medium corresponding to a reservoir, physical property parameters, and the electromagnetic source information of the reservoir under radio frequency heating conditions;

[0163] For each reservoir data with the same type of reservoir medium, obtain the actual heating effect of the reservoir;

[0164] Determine the electromagnetic heat source according to the electromagnetic property parameters and electromagnetic source information of the reservoir;

[0165] Input the electromagnetic heat source and the thermal physical property parameters among the physical property parameters into a preset heat transfer equation to obtain a predicted heating effect;

[0166] Optimize the preset heat transfer equation according to the actual heating effect and the predicted heating effect to obtain an electrothermal coupling model;

[0167] Construct a prediction chart based on the electrothermal coupling model, and the prediction chart is used to indicate the heating rate under different physical property parameters;

[0168] Among them, the prediction model includes an electrothermal coupling model and a prediction chart.

[0169] In a possible implementation manner, the prediction module 502 can also be used for:

[0170] Analyze the propagation characteristics of electromagnetic waves in the reservoir based on Maxwell's equations, the electromagnetic property parameters of the reservoir, and the electromagnetic source information to obtain the spatial distributions of the electric field and the magnetic field;

[0171] Determine the electromagnetic heat source based on Poynting's law, the electromagnetic property parameters of the reservoir, and the spatial distributions of the electric field and the magnetic field.

[0172] In a possible implementation manner, the prediction module 502 can also be used for:

[0173] Solve the electrothermal coupling model to obtain the temperature expression of the reservoir under radio frequency heating conditions and multiple temperature coefficients in the temperature expression;

[0174] Based on the temperature expression, conduct a sensitivity analysis on the temperature of the reservoir to determine the change trend of each temperature coefficient over time;

[0175] Update the temperature expression according to the change trend of each temperature coefficient over time to obtain a target temperature expression and target temperature coefficients in the target temperature expression;

[0176] Determine the key parameters affecting the temperature distribution in the reservoir according to the correlation relationship between the target temperature coefficients and the physical property parameters. The key parameters at least include conductivity and relative permittivity;

[0177] Construct a prediction chart according to the key parameters and the target temperature expression.

[0178] In a possible implementation manner, the prediction module 502 can also be used for:

[0179] Based on the electrothermal coupling model and the physical property parameters in the prediction model, obtain the heating effect of the target reservoir under radio frequency heating conditions;

[0180] Or,

[0181] Based on the prediction chart in the prediction model, the conductivity and relative permittivity in the physical property parameters, the heating effect of the target reservoir under radio frequency heating conditions is obtained.

[0182] In a possible implementation, the prediction module 502 can also be used to:

[0183] Associate the reservoir medium type of the reservoir with the corresponding electrothermal coupling model and prediction chart to obtain a prediction sub-model;

[0184] Based on the prediction sub-models corresponding to all reservoir medium types, obtain a prediction model;

[0185] Correspondingly,

[0186] Based on the prediction model and physical property parameters, the heating effect of the target reservoir under radio frequency heating conditions is obtained, including:

[0187] Obtain the reservoir medium type of the target reservoir;

[0188] Determine the prediction sub-model corresponding to the reservoir medium type of the target reservoir in the prediction model;

[0189] Based on the prediction sub-model and physical property parameters, obtain the heating effect of the target reservoir under radio frequency heating conditions.

[0190] The radio frequency heating effect prediction device provided in this embodiment can execute the method provided in the above method embodiment, and its implementation principle and technical effect are similar, which will not be elaborated here in this embodiment.

[0191] Figure 6 It is a schematic structural diagram of the electronic device provided in this application. As Figure 6 shown, the electronic device 60 provided in this embodiment includes: at least one processor 601 and a memory 602. Optionally, the device 60 further includes a communication component 603. Among them, the processor 601, the memory 602, and the communication component 603 are connected through a bus 604.

[0192] In a specific implementation process, at least one processor 601 executes the computer execution instructions stored in the memory 602, so that at least one processor 601 executes the above method.

[0193] The specific implementation process of the processor 601 can refer to the above method embodiment, and its implementation principle and technical effect are similar, which will not be elaborated here in this embodiment.

[0194] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU for short), or other general-purpose processors, digital signal processors (DSP for short), application specific integrated circuits (ASIC for short), etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the method disclosed in combination with the invention can be directly implemented by the execution of the hardware processor, or implemented by the combination of the hardware and software modules in the processor.

[0195] The memory may include a random access memory (RAM), and may also include a non-volatile memory (NVM), such as at least one disk memory.

[0196] The bus may be an industry standard architecture (ISA) bus, a peripheral component interconnect (PCI) bus, an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, the bus in the drawings of this application is not limited to only one bus or one type of bus.

[0197] This application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0198] This application also provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the processor executes the computer-executable instructions, the above method is implemented.

[0199] The above-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a magnetic disk or an optical disk. The readable storage medium can be any available medium accessible by a general-purpose or special-purpose computer.

[0200] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be part of the processor. The processor and the readable storage medium can be located in an Application Specific Integrated Circuits (ASIC). Of course, the processor and the readable storage medium can also exist as discrete components in a device.

[0201] The division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Additionally, the couplings or direct couplings or communication connections shown or discussed among each other can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0202] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0203] In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0204] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in each embodiment of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks or optical discs that can store program codes.

[0205] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps including those of the above method embodiments; and the foregoing storage medium includes various media such as ROM, RAM, magnetic disk, or optical disc that can store program codes.

[0206] Finally, it should be noted that: After considering the specification and practicing the invention disclosed herein, those skilled in the art will readily conceive of other embodiments of the present invention. The present invention is intended to cover any variations, uses, or adaptations of the present invention, which follow the general principles of the present invention and include known common knowledge or conventional technical means in the technical field not disclosed by the present invention. It is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is only limited by the appended claims.

Claims

1. A method for predicting the radio frequency heating effect, characterized in that, Including: Obtain physical property parameters of a target reservoir, where the physical property parameters include electromagnetic property parameters and thermophysical property parameters, and among them, the electromagnetic property parameters at least include conductivity and relative permittivity; Based on a prediction model and the physical property parameters, obtain the heating effect of the target reservoir under radio frequency heating conditions, where the prediction model is obtained by optimizing a preset heat transfer equation based on the electromagnetic properties and thermophysical properties of the reservoir.

2. The method according to claim 1, wherein The method further includes: Obtain reservoir data corresponding to multiple historical moments within a historical time period, where the reservoir data corresponding to each historical moment includes: the reservoir medium type corresponding to a reservoir, physical property parameters, and electromagnetic source information of the reservoir under radio frequency heating conditions; For each reservoir data with the same reservoir medium type, obtain the actual heating effect of the reservoir; Determine an electromagnetic heat source according to the electromagnetic property parameters of the reservoir and the electromagnetic source information; Input the electromagnetic heat source and the thermophysical property parameters in the physical property parameters into the preset heat transfer equation to obtain a predicted heating effect; Optimize the preset heat transfer equation according to the actual heating effect and the predicted heating effect to obtain an electrothermal coupling model; Construct a prediction chart based on the electrothermal coupling model, where the prediction chart is used to indicate the heating rate under different physical property parameters; Among them, the prediction model includes the electrothermal coupling model and the prediction chart.

3. The method according to claim 2, wherein The determining an electromagnetic heat source according to the electromagnetic property parameters of the reservoir and the electromagnetic source information includes: Based on Maxwell's equations, the electromagnetic property parameters of the reservoir, and the electromagnetic source information, analyze the propagation characteristics of electromagnetic waves in the reservoir to obtain the spatial distribution of the electric field and the magnetic field; Determine the electromagnetic heat source based on Poynting's law, the electromagnetic property parameters of the reservoir, and the spatial distribution of the electric field and the magnetic field.

4. The method according to claim 2, wherein The constructing a prediction chart based on the electrothermal coupling model includes: Solve the electrothermal coupling model to obtain the temperature expression of the reservoir under radio frequency heating conditions and multiple temperature coefficients in the temperature expression; Based on the temperature expression, conduct a sensitivity analysis on the temperature of the reservoir to determine the change trend of each temperature coefficient over time; Update the temperature expression according to the change trend of each temperature coefficient over time to obtain a target temperature expression and target temperature coefficients in the target temperature expression; Determine key parameters affecting the temperature distribution in the reservoir according to the correlation relationship between the target temperature coefficients and the physical property parameters, where the key parameters at least include conductivity and relative permittivity; Construct a prediction chart according to the key parameters and the target temperature expression.

5. The method according to claim 2, wherein The obtaining the heating effect of the target reservoir under radio frequency heating conditions based on the prediction model and the physical property parameters includes: Based on the electrothermal coupling model in the prediction model and the physical property parameters, obtain the heating effect of the target reservoir under radio frequency heating conditions; Or, Based on the prediction chart in the prediction model, the conductivity and relative permittivity in the physical property parameters, the heating effect of the target reservoir under radio frequency heating conditions is obtained.

6. The method according to claim 2, wherein The method further includes: Associating the reservoir medium type of the reservoir with the corresponding electro-thermal coupling model and prediction chart to obtain a prediction sub-model; Based on the prediction sub-models corresponding to all reservoir medium types, a prediction model is obtained; Correspondingly, The obtaining of the heating effect of the target reservoir under radio frequency heating conditions based on the prediction model and the physical property parameters includes: Obtaining the reservoir medium type of the target reservoir; Determining the prediction sub-model corresponding to the reservoir medium type of the target reservoir in the prediction model; Based on the prediction sub-model and the physical property parameters, the heating effect of the target reservoir under radio frequency heating conditions is obtained.

7. A radio frequency heating effect prediction device, characterized in that It includes: An acquisition module for acquiring the physical property parameters of the target reservoir, where the physical property parameters include electromagnetic property parameters and thermophysical property parameters, and among them, the electromagnetic property parameters at least include conductivity and relative permittivity; A prediction module for obtaining the heating effect of the target reservoir under radio frequency heating conditions based on the prediction model and the physical property parameters, and the prediction model is obtained by optimizing a preset heat transfer equation based on the electromagnetic properties and thermophysical properties of the reservoir.

8. An electronic device, characterized in that, It includes: A memory, a processor; The memory stores computer execution instructions; The processor executes the computer execution instructions stored in the memory, so that the processor executes the method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, Computer execution instructions are stored in the computer-readable storage medium, and when the computer execution instructions are executed, they are used to implement the method according to any one of claims 1-6.

10. A computer program product, characterized in that, It includes a computer program, and when the computer program is executed, it implements the method according to any one of claims 1-6.

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