Method and apparatus for predicting rock pore characteristics data

Abstract

The embodiment of the present application provides a method and device for predicting rock pore feature data, the method comprising: obtaining two-dimensional image information of a plurality of rock samples at respective corresponding preset temperatures, wherein the plurality of rock samples correspond to N sample groups, and the rock samples in the same sample group correspond to the same preset temperature; determining pore feature data corresponding to each sample group according to the two-dimensional image information corresponding to each rock sample; determining a target corresponding relationship between each preset temperature and each pore feature data according to the preset temperature corresponding to each sample group and the pore feature data corresponding to each sample group; and determining target pore feature data of a rock to be predicted at a target temperature according to the target corresponding relationship. The method for analyzing pore features according to rock sample data at a preset temperature can simulate the analysis of pore features of a rock at a preset temperature in an actual environment, and can ensure the prediction accuracy of the pore feature data.

Description

Technical Field

[0001] This application relates to technologies in the field of oil and gas exploration, and in particular to a method and apparatus for predicting rock porosity characteristics. Background Technology

[0002] Rocks contain numerous unevenly distributed pores, and accurately obtaining characteristic parameters of rock pore structure (such as pore size and throat distribution) is crucial for oil and gas development. Due to the influence of temperature, the pore structure of rocks changes significantly at different temperatures.

[0003] Therefore, temperature is an essential factor to consider in the study of rock pore structure. Currently, existing technologies typically involve heating the rock to a preset temperature and then cooling it. After cooling, the pore structure of the rock is analyzed using methods such as mercury intrusion porosimetry, CO2 or liquid nitrogen adsorption experiments, nuclear magnetic resonance (NMR) technology, and CT scanning.

[0004] However, existing methods for analyzing the pore structure of cooled rocks are not applicable to deep-to-ultra-deep rocks that have been subjected to long-term temperature effects, and cannot accurately describe the pore structure of deep-to-ultra-deep rocks under high-temperature conditions. Summary of the Invention

[0005] This application provides a method and apparatus for predicting rock pore characteristic data to solve the problem of low accuracy in describing the pore structure of deep to ultra-deep rocks under high temperature.

[0006] In a first aspect, embodiments of this application provide a method for predicting rock porosity characteristic data, including:

[0007] Two-dimensional image information of multiple rock samples at their respective preset temperatures is obtained, wherein the multiple rock samples correspond to N sample groups, and the rock samples in the same sample group correspond to the same preset temperature;

[0008] Based on the two-dimensional image information corresponding to each of the at least one rock sample, determine the pore feature data corresponding to each of the sample groups;

[0009] Based on the preset temperature and pore characteristic data corresponding to each sample group, determine the target correspondence between each preset temperature and each pore characteristic data;

[0010] Based on the target correspondence, the target pore characteristic data of the rock to be predicted at the target temperature are determined.

[0011] In one possible design, determining the target porosity characteristic data of the rock to be predicted at the target temperature based on the target correspondence includes:

[0012] If it is determined that a first preset temperature exists in the target correspondence that is the same as the target temperature, then the pore feature data corresponding to the first preset temperature is determined as the target pore feature data; or,

[0013] If it is determined that there is no first preset temperature in the target correspondence that is the same as the target temperature, then a second preset temperature and a third preset temperature corresponding to the target temperature are determined, wherein the second preset temperature is greater than the target temperature and the third preset temperature is less than the target temperature;

[0014] The target pore feature data is determined based on the pore feature data corresponding to the second preset temperature and the pore feature data corresponding to the third preset temperature.

[0015] In one possible design, determining the target pore characteristic data based on the pore characteristic data corresponding to the second preset temperature and the pore characteristic data corresponding to the third preset temperature includes:

[0016] Based on the pore feature data corresponding to the second preset temperature and the pore feature data corresponding to the third preset temperature, the evolution variable corresponding to the unit step length is determined, wherein the evolution variable is used to indicate the amount of change in pore feature data within the unit step length;

[0017] Determine the difference between the target temperature and the third preset temperature;

[0018] The target pore feature data are determined based on the difference and the evolution variable corresponding to the unit step size.

[0019] In one possible design, determining the pore feature data corresponding to each of the at least one rock sample group based on the corresponding two-dimensional image information includes:

[0020] Three-dimensional modeling is performed based on the two-dimensional image information corresponding to each rock sample to obtain the pore network model corresponding to each rock sample.

[0021] Based on the pore network model corresponding to each of the rock samples, the pore characteristic data corresponding to each sample group are determined.

[0022] In one possible design, determining the pore characteristic data corresponding to each sample group based on the pore network model corresponding to each of the rock samples includes:

[0023] Based on the pore network model corresponding to each of the rock samples, the pore feature data corresponding to each of the rock samples are determined.

[0024] For each of the sample groups, the average value of the pore characteristic data corresponding to each rock sample in the sample group is determined as the pore characteristic data corresponding to the sample group.

[0025] In one possible design, the pore characteristic data includes at least one of the following: pore size, channel size, and pore distribution.

[0026] The average value of the pore characteristic data corresponding to each rock sample in the sample group is determined as the pore characteristic data corresponding to the sample group, including:

[0027] The average pore size of each rock sample in the sample group is determined as the pore size of the sample group; and,

[0028] The average value of the gill circumference dimensions of each rock sample in the sample group is determined as the gill circumference dimension of the sample group; and,

[0029] The average value of the pore distribution corresponding to each rock sample in the sample group is determined as the pore distribution corresponding to the sample group.

[0030] In one possible design, each of the rock samples is collected from a target rock at the same depth, wherein the target rock is the rock with the least internal lithological differences among a plurality of candidate rocks;

[0031] The depth of the rock to be predicted is the same as the sampling depth of the target rock.

[0032] Secondly, embodiments of this application provide an apparatus for predicting rock porosity characteristic data, comprising:

[0033] The acquisition module is used to acquire two-dimensional image information of multiple rock samples at their respective preset temperatures, wherein the multiple rock samples correspond to N sample groups, and the rock samples in the same sample group correspond to the same preset temperature.

[0034] The first determining module is used to determine the pore feature data corresponding to each of the at least one rock sample based on the two-dimensional image information corresponding to each of the sample groups.

[0035] The second determining module is used to determine the target correspondence between each preset temperature and each pore feature data based on the preset temperature and pore feature data corresponding to each sample group.

[0036] The prediction module is used to determine the target pore characteristic data of the rock to be predicted at the target temperature based on the target correspondence.

[0037] In one possible design, the prediction module is specifically used to determine the target porosity characteristic data of the rock to be predicted at the target temperature based on the target correspondence.

[0038] If it is determined that a first preset temperature exists in the target correspondence that is the same as the target temperature, then the pore feature data corresponding to the first preset temperature is determined as the target pore feature data; or,

[0039] If it is determined that there is no first preset temperature in the target correspondence that is the same as the target temperature, then a second preset temperature and a third preset temperature corresponding to the target temperature are determined, wherein the second preset temperature is greater than the target temperature and the third preset temperature is less than the target temperature;

[0040] The target pore feature data is determined based on the pore feature data corresponding to the second preset temperature and the pore feature data corresponding to the third preset temperature.

[0041] In one possible design, the prediction module is specifically used to determine the target pore feature data based on the pore feature data corresponding to the second preset temperature and the pore feature data corresponding to the third preset temperature.

[0042] Based on the pore feature data corresponding to the second preset temperature and the pore feature data corresponding to the third preset temperature, the evolution variable corresponding to the unit step length is determined, wherein the evolution variable is used to indicate the amount of change in pore feature data within the unit step length;

[0043] Determine the difference between the target temperature and the third preset temperature;

[0044] The target pore feature data are determined based on the difference and the evolution variable corresponding to the unit step size.

[0045] In one possible design, the first determining module is specifically used to determine the pore feature data corresponding to each of the at least one rock sample group based on the corresponding two-dimensional image information of each sample.

[0046] Three-dimensional modeling is performed based on the two-dimensional image information corresponding to each rock sample to obtain the pore network model corresponding to each rock sample.

[0047] Based on the pore network model corresponding to each of the rock samples, the pore characteristic data corresponding to each sample group are determined.

[0048] In one possible design, the first determining module is specifically used to determine the pore feature data corresponding to each sample group based on the pore network model corresponding to each of the rock samples:

[0049] Based on the pore network model corresponding to each of the rock samples, the pore feature data corresponding to each of the rock samples are determined.

[0050] For each of the sample groups, the average value of the pore characteristic data corresponding to each rock sample in the sample group is determined as the pore characteristic data corresponding to the sample group.

[0051] In one possible design, the pore characteristic data includes at least one of the following: pore size, channel size, and pore distribution.

[0052] The average value of the pore characteristic data corresponding to each rock sample in the sample group is determined as the pore characteristic data corresponding to the sample group. The first determining module is specifically used for:

[0053] The average pore size of each rock sample in the sample group is determined as the pore size of the sample group; and,

[0054] The average value of the gill circumference dimensions of each rock sample in the sample group is determined as the gill circumference dimension of the sample group; and,

[0055] The average value of the pore distribution corresponding to each rock sample in the sample group is determined as the pore distribution corresponding to the sample group.

[0056] In one possible design, each of the rock samples is collected from a target rock at the same depth, wherein the target rock is the rock with the least internal lithological differences among a plurality of candidate rocks;

[0057] The depth of the rock to be predicted is the same as the sampling depth of the target rock.

[0058] Thirdly, embodiments of this application provide an apparatus for predicting rock porosity characteristic data, comprising:

[0059] Memory, used to store programs;

[0060] A processor for executing the program stored in the memory, wherein, when the program is executed, the processor is configured to perform the method described in the first aspect above and any of the various possible designs of the first aspect.

[0061] Fourthly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a computer, cause the computer to perform the methods described in the first aspect above and any of the various possible designs of the first aspect.

[0062] Fifthly, embodiments of this application provide a computer program product comprising: a computer program stored in a readable storage medium, wherein at least one processor of an electronic device can read the computer program from the readable storage medium, and the at least one processor executes the computer program to cause the electronic device to perform the method described in the first aspect above and any of the various possible designs of the first aspect.

[0063] This application provides a method and apparatus for predicting rock porosity feature data. The method includes: acquiring two-dimensional image information of multiple rock samples at their respective preset temperatures, wherein the multiple rock samples correspond to N sample groups, and rock samples in the same sample group correspond to the same preset temperature. Based on the two-dimensional image information of at least one rock sample, determine the porosity feature data corresponding to each sample group. Based on the preset temperature and porosity feature data corresponding to each sample group, determine a target correspondence between each preset temperature and each porosity feature data. Based on the target correspondence, determine the target porosity feature data of the rock to be predicted at the target temperature. Acquiring two-dimensional image information of multiple rock samples at their respective preset temperatures can simulate the two-dimensional image information of rocks at actual temperatures, ensuring the authenticity of the acquired rock data and further improving the prediction accuracy of rock porosity feature data. Attached Figure Description

[0064] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0065] Figure 1 The flowchart of the method for predicting rock porosity characteristic data provided in the embodiments of this application Figure 1 ;

[0066] Figure 2 A schematic diagram of a unit for a method of predicting rock porosity characteristic data provided in an embodiment of this application;

[0067] Figure 3 The flowchart of the method for predicting rock porosity characteristic data provided in the embodiments of this application Figure 2 ;

[0068] Figure 4 A schematic diagram illustrating the target correspondence provided in the embodiments of this application;

[0069] Figure 5A schematic diagram of the device for predicting rock porosity characteristic data provided in an embodiment of this application;

[0070] Figure 6 A schematic diagram of the hardware structure of the device for predicting rock porosity characteristic data provided in the embodiments of this application. Detailed Implementation

[0071] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0072] To facilitate understanding of the technical solution of this application, the relevant concepts involved in this application will be introduced first:

[0073] Currently, with breakthroughs in oil and gas exploration and development theories, technologies, and equipment, deep and ultra-deep onshore reservoirs have become an important area of ​​exploration and development in my country. Accurately obtaining characteristic parameters of rock pore structure (such as pore size and channel distribution) plays a crucial role in drilling engineering, oil and gas development plans, and the formulation of technical measures. Oil and gas reservoirs are generally buried deep underground, and their temperatures are generally higher than room temperature.

[0074] Deep to ultra-deep rocks exist within a specific geothermal field environment. Temperature effects cause microscopic changes in these rocks, influencing the macroscopic geometric characteristics of their pore structure. Therefore, temperature is an essential factor to consider in rock pore structure research. Currently, existing techniques typically involve heating the rock to a preset temperature and then cooling it to room temperature. The pore structure is then analyzed at room temperature using methods such as mercury intrusion porosimetry, CO2 or liquid nitrogen adsorption experiments, nuclear magnetic resonance (NMR), and CT scanning to obtain the results of the rock pore structure study.

[0075] However, existing methods for analyzing the pore structure of cooled rocks are not applicable to deep-to-ultra-deep rocks that have been subjected to long-term temperature effects, and cannot accurately describe the pore structure of deep-to-ultra-deep rocks under high-temperature conditions.

[0076] Based on the aforementioned problems, this application proposes the following technical concept: To address the inability of existing technologies to accurately describe the pore structure of rocks at deep-to-ultra-deep high temperatures, this application conducts pore structure analysis on multiple rocks at different preset temperatures to obtain pore structure feature data corresponding to the rocks at different preset temperatures. Based on the pore structure feature data corresponding to the rocks at different preset temperatures, a correspondence between the preset temperature and the rock pore structure feature data is established. Based on the correspondence between the preset temperature and the rock pore structure feature data, and the rock to be predicted at the target temperature, the pore structure feature data corresponding to the rock to be predicted is obtained. On the one hand, the rock pore structure feature data obtained by conducting rock structure analysis on rocks at preset temperatures is relatively close to the actual pore structure data of the rocks, and has the advantage of high accuracy in describing rock pore structure. On the other hand, based on the correspondence between the preset temperature and the rock pore structure feature data, the pore structure features of the rock to be predicted at the target temperature can be predicted, which provides a simple and reliable method for obtaining rock pore structure feature analysis.

[0077] Based on the technical concept described above, the method for predicting rock porosity characteristic data provided in this application will be described in detail below with reference to a specific embodiment. It is worth noting that the execution subject in each embodiment of this application is a device with control functions, such as a processor or microprocessor. This embodiment does not limit the specific implementation of this execution subject, as long as it can perform control processing. Figure 1 and Figure 2 To introduce, Figure 1 The flowchart of the method for predicting rock porosity characteristic data provided in the embodiments of this application Figure 1 , Figure 2 This is a schematic diagram of a unit for a method of predicting rock pore characteristic data provided in an embodiment of this application.

[0078] like Figure 1 As shown, the method includes:

[0079] S101. Acquire two-dimensional image information of multiple rock samples at their respective preset temperatures, wherein the multiple rock samples correspond to N sample groups, and the rock samples in the same sample group correspond to the same preset temperature.

[0080] In this embodiment, the two-dimensional image information is used to indicate multiple two-dimensional images of the rock obtained after multiple scans using a three-dimensional X-ray microscope. Specifically, in computed tomography (CT), X-rays are used to scan a rotating object in multiple directions, and the detector receives X-ray information of different intensities through this layer. The information is then processed by a computer to achieve image reconstruction. The resulting two-dimensional images use different colors to clearly show the internal structure and defects of the object being examined.

[0081] Applying CT technology to the field of oil and gas exploration, X-ray CT technology can quickly and accurately analyze the particle morphology and structural size of the tested rock, including the physical properties of special rock strata such as igneous rocks and coal seams. It can also accurately calculate the morphology and size of pores and channels in the reservoir, revealing the morphology and size of channels, and finally obtaining multiple two-dimensional images that record the rock's characteristic information.

[0082] In one possible implementation, two-dimensional image information of multiple rock samples at their respective preset temperatures is acquired. The multiple rock samples correspond to N sample groups, and rock samples within the same sample group correspond to the same preset temperature.

[0083] Next, we will further illustrate the situation of rock samples through a specific example.

[0084] like Figure 2 As shown, multiple rock samples are collected by the sample acquisition unit 201, all from the same collection depth. The sample grouping unit 202 groups the rock samples into multiple sample groups. Next, the sample heating unit 203 heats each sample group to its corresponding preset temperature, which is different for each sample group. For example, after grouping, six sample groups (N=6) are obtained, each with a different preset temperature, such as 40℃, 60℃, 80℃, 100℃, 120℃, and 140℃. Additionally, the sample acquisition unit 201 sends the collection depth of the rock samples to the data processing unit 205.

[0085] It should be noted that during the heating of each rock sample to its corresponding preset temperature, the heating is carried out at a preset rate, for example, 0.5℃ / min. Once each group of rock samples reaches its corresponding preset temperature, this preset temperature is maintained for a preset time, for example, 1 hour.

[0086] All rock samples in each sample group at a preset temperature were scanned multiple times using a three-dimensional X-ray microscope, with the distance between any two adjacent scan positions being the same. After the X-ray scanning operation was completed, two-dimensional image information of multiple rock samples at their respective preset temperatures was obtained.

[0087] In this embodiment, the specific settings of the preset temperature and preset duration and the specific methods of acquiring two-dimensional image information are merely exemplarily introduced. It is not intended to limit the specific settings of the preset temperature and preset duration and the specific methods of acquiring two-dimensional image information. The specific settings of the preset temperature and preset duration and the specific methods of acquiring two-dimensional image information can be selected according to actual needs.

[0088] S102. Based on the two-dimensional image information corresponding to at least one rock sample, determine the pore feature data corresponding to each sample group.

[0089] In one possible implementation, for each rock sample, after performing three-dimensional reconstruction based on the two-dimensional image information corresponding to each rock sample, a pore network model is established for each rock sample to obtain the pore feature data corresponding to each rock sample.

[0090] The pore characteristic data includes at least one of the following: pore size, channel size, and pore distribution.

[0091] In this embodiment, the specific implementation method of determining pore feature data based on two-dimensional image information of rock samples is merely exemplarily introduced, and is not intended to limit the specific implementation method of determining pore feature data based on two-dimensional image information of rock samples. The specific implementation method of determining pore feature data based on two-dimensional image information of rock samples can be selected according to actual needs.

[0092] Based on the pore characteristic data of each of the multiple rock samples, the pore characteristic data of each sample group were determined.

[0093] like Figure 2 The data acquisition unit 204 acquires two-dimensional image information corresponding to each sample group heated to each preset temperature. The data processing unit 205 processes the acquired two-dimensional image information to obtain the pore feature data corresponding to each sample group.

[0094] S103. Based on the preset temperature and pore characteristic data of each sample group, determine the target correspondence between each preset temperature and each pore characteristic data.

[0095] In this embodiment, for example, pore feature data includes pore size, channel size, and pore distribution.

[0096] In one possible implementation, a target correspondence between each preset temperature and each pore characteristic data is determined based on the preset temperature corresponding to each sample group and the pore characteristic data corresponding to each sample group. Specifically, the target correspondence includes the pore size, channel size, and pore distribution of the rock group corresponding to each preset temperature.

[0097] like Figure 2 As shown, based on the preset temperature and pore characteristic data of each sample group, the data processing unit 205 determines the target correspondence between each preset temperature and each pore characteristic data.

[0098] S104. Based on the target correspondence, determine the target pore characteristic data of the rock to be predicted at the target temperature.

[0099] It should be noted that the rock samples to be predicted and the rock samples corresponding to the target were collected at the same depth.

[0100] In one possible implementation, based on the target temperature of the rock to be predicted and the target correspondence, it is determined whether a preset temperature exists in the target correspondence that matches the target temperature. If it does, the pore feature data corresponding to the preset temperature that matches the target temperature is determined as the target pore feature data of the rock to be predicted at the target temperature. If it does not exist, the evolution of the pore feature data per unit temperature is determined based on the target correspondence. The target pore feature data of the rock to be predicted at the target temperature is then determined based on the evolution of the pore feature data per unit temperature.

[0101] like Figure 2 As shown, based on the target correspondence, the data processing unit 205 is used to determine the target pore characteristic data of the rock to be predicted at the target temperature.

[0102] In this embodiment, the implementation method of determining the target pore characteristic data of the rock to be predicted at the target temperature is merely an exemplary introduction, and is not intended to limit the implementation method of determining the target pore characteristic data of the rock to be predicted at the target temperature. The implementation method of determining the target pore characteristic data of the rock to be predicted at the target temperature can be selected according to actual needs, as long as it is determined according to the target correspondence.

[0103] The method for predicting rock pore feature data provided in this application includes: acquiring two-dimensional image information of multiple rock samples at their respective preset temperatures, wherein the multiple rock samples correspond to N sample groups, and rock samples in the same sample group correspond to the same preset temperature. Based on the two-dimensional image information corresponding to at least one rock sample, determining the pore feature data corresponding to each sample group. Based on the preset temperature and pore feature data corresponding to each sample group, determining the target correspondence between each preset temperature and each pore feature data. Based on the target correspondence, determining the target pore feature data of the rock to be predicted at the target temperature. Acquiring two-dimensional image information of multiple rock samples at their respective preset temperatures can simulate the two-dimensional image information of rocks at actual temperatures, ensuring the authenticity of the acquired rock data and further improving the prediction accuracy of rock pore feature data.

[0104] Based on the above embodiments, the method for predicting rock pore characteristic data provided in this application will be further described below with reference to a specific embodiment. Figures 3 to 4 To introduce, Figure 3 The flowchart of the method for predicting rock porosity characteristic data provided in the embodiments of this application Figure 2 , Figure 4 This is a schematic diagram illustrating the target correspondence provided in the embodiments of this application.

[0105] like Figure 3 As shown, the method includes:

[0106] S301. Acquire two-dimensional image information of multiple rock samples at their respective preset temperatures, wherein the multiple rock samples correspond to N sample groups, and the rock samples in the same sample group correspond to the same preset temperature.

[0107] The implementation of steps S301 and S101 is similar, and will not be described in detail here.

[0108] In one possible implementation, the rock sample may be, for example, a plunger core. The plunger core has a diameter greater than 20 mm and less than 30 mm, and a height greater than 60 mm and less than 100 mm, or a diameter of 25 mm and a height of 80 mm.

[0109] In another possible implementation, the diameter of the plunger core is 25 mm and the height of the plunger core is 80 mm.

[0110] Additionally, it should be emphasized that multiple rock samples were collected from the same depth of the target rock. The target rock was the one with the least internal lithological variation among the candidate rocks.

[0111] Next, we will introduce the specific implementation process of identifying the target rock from multiple candidate rocks.

[0112] First, the lithology of multiple candidate rocks is obtained. The lithological information of the rocks includes, but is not limited to, mineral composition, mineral grain size, pore size, and pore distribution.

[0113] Secondly, based on the lithology of multiple candidate rocks, the rock with the smallest lithological difference compared to the other rocks is selected as the target rock. The number of target rocks can be one or more.

[0114] In this embodiment, the selection of rock samples is merely an exemplary description and is not intended as a selection process. The selection of rock samples can be made according to actual needs.

[0115] S302. Perform three-dimensional modeling based on the two-dimensional image information corresponding to each rock sample to obtain the pore network model corresponding to each rock sample.

[0116] In this embodiment, a three-dimensional model is created for each rock sample based on its corresponding two-dimensional image information. After the three-dimensional modeling process is completed, a pore network model corresponding to each rock sample is obtained.

[0117] S303. Based on the pore network model corresponding to each rock sample, determine the pore characteristic data corresponding to each rock sample.

[0118] In one possible implementation, pore feature data for each rock sample is obtained based on the pore network model corresponding to each rock sample.

[0119] The pore characteristic data includes, but is not limited to, pore size, channel size, and pore distribution.

[0120] S304. For each sample group, the average value of the pore characteristic data corresponding to each rock sample in the sample group is determined as the pore characteristic data corresponding to the sample group.

[0121] Based on the above steps, the pore characteristic data corresponding to each rock sample were obtained. Next, the pore characteristic data corresponding to the sample group were determined.

[0122] In one possible implementation, the average value of the pore characteristic data corresponding to each rock sample in the sample group is determined as the pore characteristic data corresponding to the sample group.

[0123] The following example will be used to explain how to determine the pore characteristic data corresponding to the sample group.

[0124] For example, a sample set includes three rock samples: rock sample 1, rock sample 2, and rock sample 3. The pore characteristic data for these three rock samples includes pore size, channel size, and pore distribution. Specifically, the pore characteristic data for rock sample 1 are as follows:

[0125] Pore ​​size = a1, throat size = b1, and pore distribution = c1; Pore characteristic data of rock sample No. 2 are: pore size = a2, throat size = b2, and pore distribution = c2; Pore characteristic data of rock sample No. 3 are: pore size = a3, throat size = b3, and pore distribution = c3. Then the pore characteristic data corresponding to this sample group are: pore size A1 = (a1 + a2 + a3) / 3, throat size B1 = (b1 + b2 + b3) / 3, and pore distribution C1 = (c1 + c2 + c3) / 3.

[0126] In this embodiment, the implementation method of determining the pore characteristic data corresponding to the sample group is merely exemplified, and is not intended to limit the implementation method of determining the pore characteristic data corresponding to the sample group. The implementation method of determining the pore characteristic data corresponding to the sample group can be selected according to the requirements, as long as it is determined based on the pore characteristic data corresponding to each rock sample in the sample group.

[0127] S305. Based on the preset temperature and pore characteristic data of each sample group, determine the target correspondence between each preset temperature and each pore characteristic data.

[0128] In this embodiment, the target correspondence is used to indicate the one-to-one correspondence between each preset temperature and each pore characteristic data.

[0129] In one possible implementation, the target correspondence between each preset temperature and each pore characteristic data is determined based on the preset temperature and pore characteristic data corresponding to each sample group. The target correspondence can be in the form of a table, for example.

[0130] Next, we will introduce the implementation method of determining the correspondence between targets using a specific example. For example, there are six sample groups, and the temperatures corresponding to the six sample groups are: T1, T2, T3, T4, T5, T. The pore characteristic data of each group include: pore size, channel size, and pore size. The pore characteristic data for the first sample group are: pore size = A1, throat size = B1, and pore distribution = C1; the pore characteristic data for the second sample group are: pore size = A2, throat size = B2, and pore distribution = C2; the pore characteristic data for the third sample group are: pore size = A3, throat size = B3, and pore distribution = C3; the pore characteristic data for the fourth sample group are: pore size = A4, throat size = B4, and pore distribution = C4; the pore characteristic data for the fifth sample group are: pore size = A5, throat size = B5, and pore distribution = C5; the pore characteristic data for the sixth sample group are: pore size = A6, throat size = B6, and pore distribution = C6. Based on the pore characteristic data of these six sample groups, the target correspondence between each preset temperature and each pore characteristic data is determined, as follows: Figure 4 As shown.

[0131] In another possible implementation, the target correspondence between each preset temperature and each pore characteristic data is determined based on the preset temperature and pore characteristic data corresponding to each sample group. The form of the target correspondence can be a function expression, as shown in Formula 1.

[0132] GTi(A i B i C i )=f(T i Formula 1, i∈N

[0133] Among them, GTi() is used to indicate the function related to the pore structure of the rock, f() is used to indicate the function related to temperature, and A, B, and C are used to indicate the pore size, throat size, and pore distribution in the characteristic parameters of rock pores, respectively.

[0134] It is important to emphasize that, based on the target correspondence, the characteristic parameters of rock pores corresponding to each preset temperature can be determined. When the rock temperature is T... i At that time, the corresponding pore characteristic data are: A = A i B = B i C = C i .

[0135] In this embodiment, the implementation method of the target correspondence is only described as an example, and is not intended to limit the implementation method of the target correspondence. The implementation method of the target correspondence can be selected according to actual needs.

[0136] S306. Determine whether there is a first preset temperature in the target correspondence relationship that is the same as the target temperature. If it exists, execute S307; otherwise, execute S308.

[0137] Based on step S305 above, after determining the target correspondence between each preset temperature and each pore characteristic data, the pore characteristic data of the rock to be predicted are determined according to the target correspondence and the target temperature of the rock to be predicted.

[0138] It is important to emphasize that there is a one-to-one correspondence between each preset temperature and each pore characteristic data point in the target correspondence. Based on each preset temperature, the corresponding pore characteristic data point can be determined.

[0139] Therefore, before determining the porosity characteristic data of the rock to be predicted, it is necessary to determine whether there is a first preset temperature in the target correspondence that is the same as the target temperature. If it exists, proceed to step S307; otherwise, proceed to step S308.

[0140] S307. Determine the pore feature data corresponding to the first preset temperature as the target pore feature data.

[0141] If there is a first preset temperature in the target correspondence that is the same as the target temperature of the rock to be predicted, then the pore feature data corresponding to the first preset temperature in the target correspondence is determined as the target pore feature data of the rock to be predicted at the target temperature.

[0142] S308. Determine the second preset temperature and the third preset temperature corresponding to the target temperature, wherein the second preset temperature is greater than the target temperature and the third preset temperature is less than the target temperature.

[0143] If there is no first preset temperature in the target correspondence that matches the target temperature of the rock to be predicted, then a second preset temperature and a third preset temperature corresponding to the target temperature are determined from among the preset temperatures in the target correspondence. The second preset temperature is greater than the target temperature, and the third preset temperature is less than the target temperature.

[0144] In the following introduction, all will be in the form of Figure 4 Taking the target correspondence in the model and the target temperature of the rock to be predicted as 130℃ as an example, the specific implementation method of this embodiment will be described in detail.

[0145] First, the second and third preset temperatures corresponding to the target temperature are explained in detail. Based on the preset temperatures in the target correspondence and the target temperature of the rock to be predicted, the minimum value among the preset temperatures greater than the target temperature is determined as the second preset temperature; and the maximum value among the preset temperatures less than the target temperature is determined as the third preset temperature.

[0146] For example, the preset temperatures corresponding to the six sample groups in the target correspondence relationship are as follows from smallest to largest: T1 = 40℃, T2 = 60℃, T3 = 80℃, T4 = 100℃, T5 = 120℃, and T6 = 140℃. If the target temperature of the rock to be predicted is 130℃, then the second preset temperature is 140℃ and the third preset temperature is 120℃.

[0147] It is important to emphasize that the smaller the difference between the second and third preset temperatures and the target temperature, the higher the accuracy of determining the target porosity characteristics of the rock to be predicted.

[0148] In this embodiment, the selection of the second preset temperature and the third preset temperature is described by way of example, and is not intended to limit the selection of the second preset temperature and the third preset temperature. The selection of the second preset temperature and the third preset temperature can be made according to actual needs.

[0149] S309. Based on the pore characteristic data corresponding to the second preset temperature and the pore characteristic data corresponding to the third preset temperature, determine the evolution variable corresponding to the unit step length, wherein the evolution variable is used to indicate the amount of change in the pore characteristic data within the unit step length.

[0150] In this embodiment, the unit step size is used to indicate the unit change in temperature, λ. For example, the unit change λ = 2°C.

[0151] After determining the unit step size, based on the pore characteristic data corresponding to the second preset temperature and the pore characteristic data corresponding to the third preset temperature, the evolution variable corresponding to the unit step size is determined, wherein the evolution variable is used to indicate the amount of change in pore characteristic data within the unit step size.

[0152] For example, Figure 4 In the target correspondence, the second preset temperature corresponds to a temperature of 140℃, and the pore size, throat size, and pore distribution in the pore feature data are A6, B6, and C6, respectively; the third preset temperature corresponds to a temperature of 120℃, and the pore size, throat size, and pore distribution in the pore feature data are A5, B5, and C5, respectively.

[0153] Therefore, the evolution variables of pore size, throat size, and pore distribution per unit step length in the pore feature data are respectively: V A =(A6-A5) / λ,V B =(B6-B5) / λ,V C = (C6-C5) / λ.

[0154] In this embodiment, the implementation method of determining the evolution amount of pore feature data corresponding to a unit step size is merely exemplified and is not intended to limit the implementation method of determining the evolution amount of pore feature data corresponding to a unit step size. The implementation method of determining the evolution amount of pore feature data corresponding to a unit step size can be selected according to the implementation requirements.

[0155] S310. Determine the difference between the target temperature and the third preset temperature.

[0156] Based on the target temperature of the rock to be predicted and the third preset temperature, determine the difference D between the target temperature and the third preset temperature. value .

[0157] As shown in step S308, the target temperature is 130℃, and the third preset temperature is 120℃. Therefore, the difference D between the target temperature and the third preset temperature is... value =130℃-120℃=10℃.

[0158] S311. Determine the target pore characteristic data based on the difference and the evolution variable corresponding to the unit step size.

[0159] After determining the difference between the target temperature and the third preset temperature, as well as the evolution variables of the pore feature data corresponding to the unit step size, based on the above steps, the next step is to determine the target pore feature data corresponding to the rock to be predicted.

[0160] Based on the difference D between the target temperature and the third preset temperature value The unit step size λ and the evolution variable V of the pore characteristic data corresponding to the unit step size. A V B V C Determine the pore size A in the pore feature data of the target rock to be predicted. 目标 1. Throat size B 目标 and pore distribution C 目标 The implementation methods are as follows: A 目标 =A5+D value / λ×V A B 目标 =B5+D value / λ×V B C 目标 =C5+D value / λ×V C .

[0161] In this embodiment, the implementation method of determining target pore feature data is merely exemplified and is not intended to limit the implementation method of determining target pore feature data. The implementation method of determining target pore feature data can be selected according to actual needs.

[0162] The method for predicting rock pore feature data provided in this application includes: acquiring two-dimensional image information of multiple rock samples at their respective preset temperatures, wherein the multiple rock samples correspond to N sample groups, and rock samples in the same sample group correspond to the same preset temperature. Performing three-dimensional modeling processing based on the two-dimensional image information of each rock sample to obtain a pore network model corresponding to each rock sample. Determining the pore feature data corresponding to each rock sample based on the pore network model corresponding to each rock sample. For each sample group, determining the average value of the pore feature data corresponding to each rock sample in the sample group as the pore feature data corresponding to the sample group. Determining a target correspondence between each preset temperature and each pore feature data based on the preset temperature and the pore feature data corresponding to each sample group. If a first preset temperature with the same value as the target temperature is found in the target correspondence, the pore feature data corresponding to the first preset temperature is determined as the target pore feature data. Alternatively, if a first preset temperature with the same value as the target temperature is not found in the target correspondence, a second preset temperature and a third preset temperature corresponding to the target temperature are determined, wherein the second preset temperature is greater than the target temperature, and the third preset temperature is less than the target temperature. Based on the pore characteristic data corresponding to the second and third preset temperatures, the evolutionary variable corresponding to each unit step size is determined. This evolutionary variable indicates the change in pore characteristic data within a unit step size. The difference between the target temperature and the third preset temperature is determined. Based on this difference and the evolutionary variable corresponding to each unit step size, the target pore characteristic data is determined. This method of analyzing the pore characteristics of rock samples at preset temperatures can approximately simulate the pore characteristic analysis of rocks at preset temperatures in actual environments. This plays a crucial role in improving the prediction accuracy of methods for predicting pore characteristic data. Furthermore, based on the target correspondence, the evolutionary variable corresponding to the pore characteristic data at each temperature unit step size is determined. Based on the target correspondence and the evolutionary variable corresponding to the pore characteristic data at each unit step size, the target pore characteristic data of the rock to be predicted at the target temperature is determined. This not only improves prediction accuracy but also simplifies obtaining the target pore characteristic data of the rock to be predicted and expands the specific methods for determining the pore characteristic data of rocks.

[0163] Figure 5 A schematic diagram of the device for predicting rock porosity characteristic data provided in an embodiment of this application. Figure 5 As shown, the device 50 includes: an acquisition module 501, a first determination module 502, a second determination module 503, and a prediction module 504.

[0164] The acquisition module 501 is used to acquire two-dimensional image information of multiple rock samples at their respective preset temperatures, wherein the multiple rock samples correspond to N sample groups, and the rock samples in the same sample group correspond to the same preset temperature.

[0165] The first determining module 502 is used to determine the pore feature data corresponding to each of the sample groups based on the two-dimensional image information corresponding to each of the at least one rock sample.

[0166] The second determining module 503 is used to determine the target correspondence between each preset temperature and each pore feature data based on the preset temperature and pore feature data corresponding to each sample group.

[0167] The prediction module 504 is used to determine the target pore characteristic data of the rock to be predicted at the target temperature based on the target correspondence.

[0168] In one possible design, the prediction module 504 is specifically used to determine the target porosity characteristic data of the rock to be predicted at the target temperature based on the target correspondence.

[0169] If it is determined that a first preset temperature exists in the target correspondence that is the same as the target temperature, then the pore feature data corresponding to the first preset temperature is determined as the target pore feature data; or,

[0170] If it is determined that there is no first preset temperature in the target correspondence that is the same as the target temperature, then a second preset temperature and a third preset temperature corresponding to the target temperature are determined, wherein the second preset temperature is greater than the target temperature and the third preset temperature is less than the target temperature;

[0171] The target pore feature data is determined based on the pore feature data corresponding to the second preset temperature and the pore feature data corresponding to the third preset temperature.

[0172] In one possible design, the prediction module 504 is specifically used to determine the target pore feature data based on the pore feature data corresponding to the second preset temperature and the pore feature data corresponding to the third preset temperature.

[0173] Based on the pore feature data corresponding to the second preset temperature and the pore feature data corresponding to the third preset temperature, the evolution variable corresponding to the unit step length is determined, wherein the evolution variable is used to indicate the amount of change in pore feature data within the unit step length;

[0174] Determine the difference between the target temperature and the third preset temperature;

[0175] The target pore feature data are determined based on the difference and the evolution variable corresponding to the unit step size.

[0176] In one possible design, the first determining module 502 is specifically used to determine the pore feature data corresponding to each of the at least one rock sample group based on the two-dimensional image information corresponding to each sample group:

[0177] Three-dimensional modeling is performed based on the two-dimensional image information corresponding to each rock sample to obtain the pore network model corresponding to each rock sample.

[0178] Based on the pore network model corresponding to each of the rock samples, the pore characteristic data corresponding to each sample group are determined.

[0179] In one possible design, the first determining module 502 is specifically used to determine the pore feature data corresponding to each sample group based on the pore network model corresponding to each of the rock samples:

[0180] Based on the pore network model corresponding to each of the rock samples, the pore feature data corresponding to each of the rock samples are determined.

[0181] For each of the sample groups, the average value of the pore characteristic data corresponding to each rock sample in the sample group is determined as the pore characteristic data corresponding to the sample group.

[0182] In one possible design, the pore characteristic data includes at least one of the following: pore size, channel size, and pore distribution.

[0183] The average value of the pore characteristic data corresponding to each rock sample in the sample group is determined as the pore characteristic data corresponding to the sample group. The first determining module 502 is specifically used for:

[0184] The average pore size of each rock sample in the sample group is determined as the pore size of the sample group; and,

[0185] The average value of the gill circumference dimensions of each rock sample in the sample group is determined as the gill circumference dimension of the sample group; and,

[0186] The average value of the pore distribution corresponding to each rock sample in the sample group is determined as the pore distribution corresponding to the sample group.

[0187] In one possible design, each of the rock samples is collected from a target rock at the same depth, wherein the target rock is the rock with the least internal lithological differences among a plurality of candidate rocks;

[0188] The depth of the rock to be predicted is the same as the sampling depth of the target rock.

[0189] The apparatus provided in this embodiment can be used to execute the technical solutions of the above method embodiments. Its implementation principle and technical effects are similar, and will not be described again here.

[0190] Figure 6 A schematic diagram of the hardware structure of the device for predicting rock porosity characteristic data provided in the embodiments of this application is shown below. Figure 6 As shown, the predictive rock porosity feature data device 60 of this embodiment includes: a processor 601 and a memory 602; wherein

[0191] Memory 602 is used to store instructions executed by the computer;

[0192] The processor 601 is used to execute computer execution instructions stored in the memory to implement the various steps of the method for predicting rock porosity characteristic data in the above embodiments. For details, please refer to the relevant descriptions in the foregoing method embodiments.

[0193] Alternatively, the memory 602 can be either standalone or integrated with the processor 601.

[0194] When the memory 602 is set up independently, the predictive rock porosity feature data device also includes a bus 603 for connecting the memory 602 and the processor 601.

[0195] This application provides a computer-readable storage medium storing computer-executable instructions. When a processor executes the computer-executable instructions, it implements the method for predicting rock porosity feature data executed by the above-mentioned device for predicting rock porosity feature data.

[0196] This application also provides a computer program product, which includes a computer program stored in a readable storage medium. At least one processor of an electronic device can read the computer program from the readable storage medium, and the at least one processor executes the computer program to cause the electronic device to perform the solution provided in any of the above embodiments.

[0197] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.

[0198] The integrated modules implemented as software functional modules described above can be stored in a computer-readable storage medium. These software functional modules, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of this application.

[0199] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0200] The memory may include high-speed RAM, and may also include non-volatile storage (NVM), such as at least one disk storage device, and may also be a USB flash drive, external hard drive, read-only memory, disk or optical disc, etc.

[0201] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0202] The aforementioned storage medium can be implemented from any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium can be any available medium accessible to general-purpose or special-purpose computers.

[0203] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0204] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application 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 or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for predicting rock porosity characteristics, characterized in that, include: Multiple rock samples were collected using a sample collection unit. Each rock sample was collected from the target rock at the same depth. The target rock was the rock with the smallest internal lithological differences among multiple candidate rocks. Multiple rock samples were grouped to obtain multiple sample groups, each corresponding to a different preset temperature. Each sample group is heated to its corresponding preset temperature at a preset rate by the sample heating unit, and the preset temperature is maintained for a preset time after reaching the preset temperature. During the holding period at the preset temperature, two-dimensional image information of multiple rock samples at their respective preset temperatures is acquired. The multiple rock samples correspond to N sample groups, and the rock samples in the same sample group correspond to the same preset temperature. The two-dimensional image information is a three-dimensional information data volume of the internal space of the sample obtained by three-dimensional X-ray microscopy. Three-dimensional modeling is performed based on the two-dimensional image information of each rock sample to obtain the pore network model of each rock sample. Based on the pore network model corresponding to each rock sample, the pore characteristic data corresponding to each sample group are determined. Based on the preset temperature and pore characteristic data of each sample group, the target correspondence between each preset temperature and each pore characteristic data is determined. If it is determined that there is no first preset temperature in the target correspondence that is the same as the target temperature of the rock to be predicted, then the second preset temperature and the third preset temperature corresponding to the target temperature are determined, the second preset temperature is greater than the target temperature, and the third preset temperature is less than the target temperature; Based on the pore characteristic data corresponding to the second preset temperature and the pore characteristic data corresponding to the third preset temperature, determine the evolution variable corresponding to the unit step size; Determine the difference between the target temperature and the third preset temperature; Based on the difference and the evolution variables corresponding to the unit step size, the target porosity characteristic data of the rock to be predicted are determined; The evolution variables for pore size, throat size, and pore distribution per unit step length in the pore feature data are as follows: ； The unit step size indicates the unit change in temperature. The pore size, throat size, and pore distribution in the pore feature data corresponding to the second preset temperature are as follows: The pore size, throat size, and pore distribution in the pore feature data corresponding to the third preset temperature are as follows: .

2. The method according to claim 1, characterized in that, If it is determined that there exists a first preset temperature in the target correspondence that is the same as the target temperature, then the pore feature data corresponding to the first preset temperature is determined as the target pore feature data.

3. The method according to claim 1, characterized in that, The step of determining the pore feature data corresponding to each sample group based on the pore network model corresponding to each of the rock samples includes: Based on the pore network model corresponding to each of the rock samples, the pore feature data corresponding to each of the rock samples are determined. For each of the sample groups, the average value of the pore characteristic data corresponding to each rock sample in the sample group is determined as the pore characteristic data corresponding to the sample group.

4. The method according to claim 3, characterized in that, The pore characteristic data includes at least one of the following: pore size, channel size, and pore distribution; The average value of the pore characteristic data corresponding to each rock sample in the sample group is determined as the pore characteristic data corresponding to the sample group, including: The average pore size of each rock sample in the sample group is determined as the pore size of the sample group; and, The average value of the gill circumference dimensions of each rock sample in the sample group is determined as the gill circumference dimension of the sample group; and, The average value of the pore distribution corresponding to each rock sample in the sample group is determined as the pore distribution corresponding to the sample group.

5. An apparatus for predicting rock porosity characteristics, characterized in that, include: The acquisition module is used to acquire multiple rock samples through a sample acquisition unit. Each rock sample is acquired from a target rock at the same depth, which is the rock with the smallest internal lithological differences among multiple candidate rocks. The depth of the rock to be predicted is the same as the acquisition depth of the target rock. The multiple rock samples are grouped to obtain multiple sample groups, each corresponding to a different preset temperature. Each sample group is heated to its corresponding preset temperature at a preset rate through a sample heating unit, and held at the preset temperature for a preset duration. During the preset temperature holding period, two-dimensional image information of multiple rock samples at their respective preset temperatures is acquired. The multiple rock samples correspond to N sample groups, and rock samples in the same sample group correspond to the same preset temperature. The two-dimensional image information is a three-dimensional information data volume of the sample's internal space acquired by a three-dimensional X-ray microscope. The first determining module is used to perform three-dimensional modeling processing based on the two-dimensional image information corresponding to each rock sample to obtain the pore network model corresponding to each rock sample; and to determine the pore feature data corresponding to each sample group based on the pore network model corresponding to each rock sample. The second determining module is used to determine the target correspondence between each preset temperature and each pore characteristic data based on the preset temperature corresponding to each sample group and the pore characteristic data corresponding to each sample group. The prediction module is used to determine a second preset temperature and a third preset temperature corresponding to the target temperature if there is no first preset temperature in the target correspondence that is the same as the target temperature of the rock to be predicted. The second preset temperature is greater than the target temperature and the third preset temperature is less than the target temperature. Based on the pore characteristic data corresponding to the second preset temperature and the pore characteristic data corresponding to the third preset temperature, determine the evolution variable corresponding to the unit step size; Determine the difference between the target temperature and the third preset temperature; Based on the difference and the evolution variables corresponding to the unit step size, the target porosity characteristic data of the rock to be predicted are determined; The evolution variables for pore size, throat size, and pore distribution per unit step length in the pore feature data are as follows: ； The unit step size indicates the unit change in temperature. The pore size, throat size, and pore distribution in the pore feature data corresponding to the second preset temperature are as follows: The pore size, throat size, and pore distribution in the pore feature data corresponding to the third preset temperature are as follows: .

6. An apparatus for predicting rock porosity characteristics, characterized in that, include: Memory, used to store programs; A processor for executing the program stored in the memory, wherein when the program is executed, the processor is configured to perform the method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, Includes instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1 to 4.

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