Reservoir permeability processing method, device, equipment and readable storage medium
By employing multi-angle pore structure characterization and permeability equation coefficient determination methods in reservoirs, the problem of low permeability prediction accuracy in reservoirs with complex pore structures has been solved, achieving higher accuracy permeability prediction.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA UNIV OF PETROLEUM (BEIJING)
- Filing Date
- 2023-01-09
- Publication Date
- 2026-05-08
AI Technical Summary
Existing technologies have low accuracy in predicting permeability in reservoirs with complex pore structures such as tight sandstone, and the parameters to be determined are difficult to determine accurately.
By obtaining experimental porosity, permeability, bound water saturation, and nuclear magnetic resonance T2 spectra of rock samples, and using the first and second pore structure characterization parameters, the pore structure is described from macroscopic and microscopic perspectives, the coefficients of the permeability equation are determined, and the target permeability equation is established.
It improves the accuracy of permeability prediction in reservoirs with complex pore structures, ensures the accuracy of permeability equation coefficients, and avoids parameter uncertainties caused by planning and solving methods.
Smart Images
Figure CN116046632B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of reservoir evaluation technology, and in particular to a reservoir permeability processing method, apparatus, equipment, and readable storage medium. Background Technology
[0002] In oil and gas exploration, permeability is an important indicator reflecting the fluid flow capacity within the pores of rocks. The permeability of rocks at different depths in a reservoir can represent the reservoir permeability at different depths, and thus reflect the oil and gas production in the reservoir at different depths. The accuracy of reservoir permeability prediction is crucial for reserve estimation, reservoir characterization, and quantitative description of remaining oil, and is of great significance to rock physics and reservoir evaluation research.
[0003] Currently, the commonly used methods for calculating reservoir permeability based on nuclear magnetic resonance logging technology are mostly derived from single pore structure characterization methods. However, this method has low permeability prediction accuracy in reservoirs with complex pore structures such as tight sandstone. Summary of the Invention
[0004] This application provides a reservoir permeability processing method, apparatus, device, and readable storage medium to solve the problem of low permeability prediction accuracy in reservoirs with complex pore structures.
[0005] In a first aspect, this application provides a method for treating reservoir permeability, comprising:
[0006] The experimental porosity, experimental permeability, experimental bound water saturation, and nuclear magnetic resonance T2 spectra of N rock samples from the sample reservoir are obtained; where N is an integer greater than or equal to 2, and the T2 spectra are obtained by performing nuclear magnetic resonance experiments on the rock samples in saturated water.
[0007] Based on the experimental porosity and experimental permeability of each rock sample, the first pore structure characterization parameter corresponding to each rock sample is obtained; the first pore structure characterization parameter is used to describe the pore structure of the rock sample from the perspective of the porosity of the rock sample.
[0008] The effective permeability of each rock sample was obtained based on the experimental porosity, experimental bound water saturation, and nuclear magnetic resonance T2 spectrum.
[0009] Based on the experimental permeability and effective seepage porosity of each rock sample, a second pore structure characterization parameter is obtained for each rock sample; the second pore structure characterization parameter is used to describe the pore structure of the rock sample from the perspective of the effective seepage porosity.
[0010] Based on the first pore structure characterization parameter and the second pore structure characterization parameter corresponding to each rock sample, the coefficients of the permeability equation are obtained; the permeability equation is used to characterize the mapping relationship between porosity, effective seepage porosity and permeability.
[0011] Substituting the coefficients of the obtained permeability equation into the permeability equation yields the target permeability equation; the target permeability equation is used to predict the permeability of the target reservoir at at least one depth.
[0012] Optionally, obtaining the first pore structure characterization parameters corresponding to each of the rock samples based on the experimental porosity and experimental permeability includes:
[0013] Based on the experimental porosity and experimental permeability of each rock sample, and the first pore structure characterization formula, the first pore structure characterization parameters corresponding to each rock sample are obtained.
[0014] The first pore structure characterization formula is:
[0015]
[0016] Where f is the first pore structure characterization parameter of the rock sample, K is the experimental permeability of the rock sample, φ is the experimental porosity of the rock sample, and K max φ is the preset maximum permeability of the rock sample. max This is the preset maximum porosity of the rock sample.
[0017] Optionally, obtaining the effective permeability porosity of each rock sample based on its experimental porosity, experimental bound water saturation, and nuclear magnetic resonance T2 spectrum includes:
[0018] Based on the nuclear magnetic resonance T2 spectra of each rock sample and the porosity calculation formula for large-diameter pores, the porosity of large-diameter pores in each rock sample is obtained.
[0019] The effective seepage porosity of each rock sample is obtained based on the experimental porosity, experimental bound water saturation, porosity of large-diameter pores, and the effective seepage porosity calculation formula.
[0020] The porosity calculation formula for the large-diameter pores is as follows:
[0021]
[0022] Where, φ b S represents the porosity of the large-diameter pores in the rock sample. T2(t) represents the porosity at time t in the T2 spectrum, T2b represents the target node time of the T2 spectrum, and T2max represents the maximum T2 time of the T2 spectrum; the porosity at times in the T2 spectrum that are greater than the target node time is the porosity of large-diameter pores.
[0023] The formula for calculating the effective seepage porosity is as follows:
[0024]
[0025] Where, φ m S represents the effective permeability porosity of the rock sample. wi The experimental bound water saturation of the rock sample is given.
[0026] Optionally, obtaining the second pore structure characterization parameters corresponding to each rock sample based on the experimental permeability and effective seepage porosity of each rock sample includes:
[0027] Based on the experimental permeability and effective seepage porosity of each rock sample, and the second pore structure characterization formula, the second pore structure characterization parameter Fa corresponding to each rock sample is obtained; wherein, the second pore structure characterization formula is: Fa=[Log(K)-Log(φ)] m )] / 2.
[0028] Optionally, obtaining the coefficients of the permeability equation based on the first pore structure characterization parameters and the second pore structure characterization parameters corresponding to each of the rock samples includes:
[0029] Based on the first pore structure characterization parameters and the second pore structure characterization parameters corresponding to each rock sample, the coefficients of the permeability equation are obtained using the characterization parameter equation.
[0030] The equation for the characterization parameter is:
[0031] f = k1 * Fa + k2
[0032] Where k1 and k2 are both coefficients of the permeability equation;
[0033] The permeability equation is:
[0034]
[0035] Where Ka is the predicted penetration rate, and φ a Porosity, φ m For effective seepage porosity.
[0036] Optionally, the method further includes:
[0037] Obtain nuclear magnetic resonance logging data of the target reservoir;
[0038] The nuclear magnetic resonance logging data of the target reservoir are processed to obtain the T2 spectrum and bound water saturation of the target reservoir at multiple depths;
[0039] Based on the T2 spectrum of the target reservoir at multiple depths, the porosity of the target reservoir at multiple depths is obtained;
[0040] Based on the T2 spectrum of the target reservoir at multiple depths and the bound water saturation, the effective permeability porosity of the target reservoir at multiple depths is obtained.
[0041] Based on the effective permeability of the target reservoir at multiple depths, and the porosity, the predicted permeability of the target reservoir at multiple depths is obtained using the target permeability equation.
[0042] Optionally, the method further includes:
[0043] Output the permeability of the target reservoir at multiple depths.
[0044] Secondly, this application provides a reservoir permeability treatment apparatus, comprising:
[0045] The first acquisition module is used to acquire the experimental porosity, experimental permeability, experimental bound water saturation, and nuclear magnetic resonance T2 spectrum of N rock samples from the sample reservoir; where N is an integer greater than or equal to 2, and the T2 spectrum is obtained by performing nuclear magnetic resonance experiments on the rock samples in saturated water.
[0046] The second acquisition module is used to acquire the first pore structure characterization parameters corresponding to each rock sample based on the experimental porosity and experimental permeability of each rock sample; the first pore structure characterization parameters are used to describe the pore structure of the rock sample from the perspective of the porosity of the rock sample.
[0047] The third acquisition module is used to acquire the effective seepage porosity of each rock sample based on the experimental porosity, experimental bound water saturation, and nuclear magnetic resonance T2 spectrum of each rock sample.
[0048] The fourth acquisition module is used to acquire the second pore structure characterization parameters corresponding to each rock sample based on the experimental permeability and effective seepage porosity of each rock sample; the second pore structure characterization parameters are used to describe the pore structure of the rock sample from the perspective of the effective seepage porosity of the rock sample.
[0049] The fifth acquisition module is used to acquire the coefficients of the permeability equation based on the first pore structure characterization parameters and the second pore structure characterization parameters corresponding to each rock sample; the permeability equation is used to characterize the mapping relationship between porosity, effective seepage porosity and permeability.
[0050] The processing module is used to substitute the coefficients of the obtained permeability equation into the permeability equation to obtain the target permeability equation; the target permeability equation is used to predict the permeability of the target reservoir at at least one depth.
[0051] Thirdly, this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;
[0052] The memory stores computer-executed instructions;
[0053] The processor executes computer execution instructions stored in the memory to implement the reservoir permeability processing method as described in any one of the first aspects.
[0054] Fourthly, this application provides a computer-readable storage medium, comprising: computer-executable instructions stored in the computer-readable storage medium, wherein the computer-executable instructions, when executed by a processor, are used to implement the reservoir permeability processing method as described in any one of the first aspects.
[0055] Fifthly, this application provides a computer program product, including a computer program that, when executed by the processor, implements the reservoir permeability processing method as described in any of the first aspects.
[0056] In a sixth aspect, this application provides a chip on which a computer program is stored, and when the computer program is executed by the chip, it implements the reservoir permeability processing method as described in any of the first aspects.
[0057] The reservoir permeability processing method, apparatus, equipment, and readable storage medium provided in this application obtain experimental porosity, experimental permeability, experimental bound water saturation, and nuclear magnetic resonance T2 spectra of sample reservoir rock samples. This yields a first pore structure characterization parameter representing the pore structure of the rock sample from the perspective of porosity, and a second pore structure characterization parameter representing the pore structure of the rock sample from the perspective of effective permeable porosity, making the pore structure characterization of the rock sample more accurate. Furthermore, based on the mapping relationship between the first and second pore structure characterization parameters, the coefficients of the permeability equation are determined, resulting in a more precise target permeability equation representing the pore structure. The target permeability equation obtained using the method provided in this application has higher accuracy when used to predict the reservoir permeability of a target reservoir, improving the prediction accuracy of permeability in reservoirs with complex pore structures. Moreover, the introduced undetermined parameters, i.e., the coefficients of the permeability equation, can be accurately determined. Attached Figure Description
[0058] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.
[0059] Figure 1 A schematic flowchart illustrating a method for processing reservoir permeability provided in an embodiment of this application;
[0060] Figure 2 A schematic flowchart illustrating another method for processing reservoir permeability provided in an embodiment of this application;
[0061] Figure 3 A schematic diagram illustrating the mapping relationship between a first pore structure characterization parameter and a second structure characterization parameter, provided for embodiments of this application;
[0062] Figure 4 A schematic diagram showing the mapping relationship between the calculated permeability of a rock sample predicted by the reservoir permeability processing method provided in this application and the experimental permeability of the rock sample obtained experimentally, respectively, provided for an embodiment of this application;
[0063] Figure 5 A schematic flowchart illustrating another method for processing reservoir permeability provided in an embodiment of this application;
[0064] Figure 6 A schematic diagram showing a comparison between the permeability of a target reservoir predicted by the reservoir permeability processing method provided in this application and the Coates model in the prior art, and the experimental permeability of rock samples obtained from experiments, provided as an embodiment of this application.
[0065] Figure 7 A schematic diagram of a reservoir permeability treatment device provided in an embodiment of this application;
[0066] Figure 8 This is a schematic diagram of the structure of an electronic device 800 provided in an embodiment of this application.
[0067] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation
[0068] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.
[0069] First, the terms used in this application will be explained:
[0070] Rock sample: refers to a sample of rock, or simply a rock sample.
[0071] Porosity refers to the storage space formed by various pores, voids, fissures, and diagenetic fractures in a rock, which can store fluids. It is commonly expressed as porosity.
[0072] Porosity: refers to the ratio of the sum of the volumes of all pore spaces in a rock sample to the total volume of the rock sample, also known as total porosity.
[0073] Effective seepage porosity: refers to the ratio of the volume of pores participating in seepage (effective seepage porosity volume) in a rock sample to the total volume of the rock sample.
[0074] Seepage: refers to the flow of fluid in the porous medium of rock under dynamic action.
[0075] Permeability refers to the ability of a rock to be permeated by a fluid, and is usually expressed as permeability.
[0076] Permeability: refers to the ability of fluids to flow through the pores of a rock. The higher the permeability, the greater the ability of fluids to flow through the rock.
[0077] A reservoir is a rock formation with interconnected pores that allows oil and gas to be stored and seeped into it. The storage capacity of a reservoir is determined by its rock physical properties, typically including porosity and permeability. Porosity determines the reservoir's storage capacity, while permeability determines the flow capacity of the reservoir. Generally speaking, the greater the porosity and the higher the permeability, the better the reservoir.
[0078] Reservoir permeability: refers to the permeability of rock at a certain depth in a reservoir.
[0079] Bound water refers to water that is partially stored in very small pores in the oil layer or attached to the surface of sandstone particles, and does not flow even under great pressure.
[0080] Bound water saturation: refers to the ratio of the volume of bound water to the total pore volume.
[0081] Nuclear magnetic resonance logging is a logging method that measures the free propagation of hydrogen nuclei in the formation within the Earth's magnetic field. Its results mainly describe the rock physical properties and pore fluid properties of the reservoir.
[0082] As can be seen from the above explanation, determining reservoir permeability is of great significance for rock physics and reservoir evaluation research. The following is a brief description of existing methods for determining reservoir permeability, and the problems associated with each method.
[0083] Currently, the determination of reservoir permeability often employs calculation methods based on nuclear magnetic resonance logging technology, mainly including the following approaches:
[0084] Existing technology 1: Reservoir permeability is calculated using SDR and Coates permeability models based on nuclear magnetic resonance logging. The SDR and Coates permeability models proposed by this type of method are based on theoretical formulas such as those of Kozeny-Carmen and Timur, characterizing the pore structure from a single perspective. For example, the SDR model characterizes the pore structure from the geometric mean of the T2 distribution, and then calculates the permeability. Therefore, this type of method performs well in conventional reservoirs with medium to high porosity and permeability, but its accuracy is low in reservoirs with complex pore structures such as tight sandstone with low porosity and low permeability. The aforementioned conventional reservoirs with medium to high porosity and permeability refer to conventional reservoirs with medium to high porosity and permeability; similarly, reservoirs with low porosity and low permeability refer to reservoirs with low porosity and low permeability.
[0085] The reservoirs with the aforementioned complex pore structure can be, for example, clastic sandstone, shale, or carbonate rock reservoirs containing dense sandstone. Reservoirs with complex pore structures generally have the characteristics of low porosity and low permeability.
[0086] Existing technology 2: A method for calculating permeability based on the three pore components of nuclear magnetic resonance T2 spectra. This type of method divides the nuclear magnetic resonance T2 spectrum into three pore components, determines the contribution rate of different pore components to permeability, and then applies it to nuclear magnetic resonance logging data. However, the undetermined parameters of this method are approximate values obtained through a programming solution, making it difficult to accurately determine the parameters in practical applications.
[0087] In view of this, this application provides a reservoir permeability processing method. By employing two different pore structure characterization methods—rock sample porosity and effective permeable porosity—the pore structure characterization becomes more accurate. Furthermore, the permeability equation is obtained through the relationship between these two pore structure characterization methods, which more accurately represents the relationship between permeability and pore structure. This results in more accurate permeability predictions using this permeability equation, improving the prediction accuracy of reservoir permeability in complex pore structures and solving the problem of low prediction accuracy of reservoir permeability in complex pore structures in the prior art. Simultaneously, the undetermined parameters in this permeability equation can be accurately determined through the mapping relationship between the two pore structure characterization methods, avoiding the aforementioned problem of needing to plan and solve parameters. This simultaneously improves the prediction accuracy and adaptability of reservoir permeability.
[0088] The subject of this application may be an electronic device such as a computer with processing capabilities, or a chip or chip module with processing capabilities.
[0089] The following describes the technical solution of this application and how it solves the above-mentioned technical problems, using a computer as the executing entity as an example, in conjunction with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.
[0090] Figure 1 This is a schematic flowchart illustrating a method for processing reservoir permeability, provided in an embodiment of this application.
[0091] like Figure 1 As shown, the method includes:
[0092] S101. Obtain the experimental porosity, experimental permeability, experimental bound water saturation, and nuclear magnetic resonance T2 spectrum of N rock samples from the sample reservoir.
[0093] N is an integer greater than or equal to 2, and the T2 spectrum is obtained from the nuclear magnetic resonance experiment of the above rock sample in saturated water. T2 is the transverse relaxation time.
[0094] The experimental porosity, permeability, and bound water saturation values mentioned above can be obtained by conducting experiments on the porosity, permeability, and bound water saturation values of the N rock samples, or by processing the above nuclear magnetic resonance T2 spectra.
[0095] The experimental porosity, permeability, bound water saturation, and nuclear magnetic resonance T2 spectrum data mentioned above can be obtained through communication between the host computer of the experimental instrument and the computer performing this step, or through an external device that stores the above data, such as a USB flash drive.
[0096] It should be understood that the data acquisition methods used in the above experiments can refer to existing technologies.
[0097] S102. Based on the experimental porosity and permeability of each rock sample, obtain the first pore structure characterization parameters corresponding to each rock sample.
[0098] As mentioned above, the pore structure of a rock sample is related not only to its porosity but also to its permeability. Therefore, the porosity and permeability of a rock sample can be used to characterize its pore structure.
[0099] The aforementioned first pore structure characterization parameter is used to describe the pore structure of the rock sample from the perspective of its porosity. It can be obtained by solving the equation representing the mapping relationship between the porosity and permeability of the rock sample.
[0100] For example, by using the experimental porosity and permeability of the rock samples mentioned above, as well as the preset maximum porosity and maximum permeability of the rock samples, an equation containing the first pore structure characterization parameters is established, and the first pore structure characterization formula is further obtained, thereby obtaining the first pore structure characterization parameters.
[0101] S103. Based on the experimental porosity, experimental bound water saturation, and nuclear magnetic resonance T2 spectrum of each rock sample, obtain the effective permeable porosity of each rock sample.
[0102] As mentioned earlier, not all pores in a rock sample possess seepage capacity; for example, pores may contain bound water. Therefore, the pores that affect the permeability of a rock sample are actually effective seepage pores with seepage capacity.
[0103] The aforementioned effective seepage porosity refers to the porosity of effective seepage pores with seepage capacity, which is obtained by correcting the porosity of the rock sample.
[0104] For example, in rock samples, large-diameter pores and small-diameter pores are often connected in parallel, but the seepage capacity is often determined by the small-diameter pores. Therefore, the experimental porosity of the rock sample can be corrected from the perspective of the porosity of the large-diameter pores, combined with the experimental bound water saturation, to obtain the effective seepage porosity.
[0105] By combining experimental bound water saturation and nuclear magnetic resonance T2 spectrum, the influence of pores containing bound water and other non-permeable pores is eliminated, and the experimental porosity of rock samples is corrected to effective permeable porosity, which can more accurately reflect the influence of pore structure on permeability.
[0106] S104. Based on the experimental permeability and effective seepage porosity of each rock sample, obtain the corresponding second pore structure characterization parameters for each rock sample.
[0107] The aforementioned second pore structure characterization parameter is used to describe the pore structure of the rock sample from the perspective of its effective seepage porosity. It can be obtained by solving the equation representing the mapping relationship between the effective seepage porosity and the permeability of the rock sample.
[0108] For example, by using the experimental permeability and effective seepage porosity of the above-mentioned rock samples, an equation containing the above-mentioned second pore structure characterization parameters is established, and the second pore structure characterization formula is further obtained, thereby obtaining the above-mentioned second pore structure characterization parameters.
[0109] As mentioned above, the first pore structure characterization parameter describes the pore structure of the rock sample from the perspective of porosity, that is, from a macroscopic perspective. The second pore structure characterization parameter describes the pore structure of the rock sample from the perspective of effective seepage porosity, that is, from a microscopic perspective. Combining both macroscopic and microscopic perspectives to characterize the pore structure of the rock sample improves the accuracy of characterizing complex pore structures and avoids the problem of low accuracy when characterizing the pore structure of the rock sample from a single perspective.
[0110] S105. Obtain the coefficients of the permeability equation based on the first pore structure characterization parameters and the second pore structure characterization parameters corresponding to each rock sample.
[0111] As mentioned above, the permeability of rock samples is related to their pore structure. Therefore, the coefficients of the permeability equation can be obtained by using the first and second pore structure characterization parameters obtained from different perspectives to characterize the pore structure, so as to more accurately characterize the mapping relationship between the permeability and pore structure of the rock samples in the above-mentioned reservoir.
[0112] The coefficients of the permeability equation described above are used to characterize the mapping relationship between the first pore structure characterization parameter and the second pore structure characterization parameter. They can be obtained by solving the equation representing the mapping relationship between the first pore structure characterization parameter and the second pore structure characterization parameter.
[0113] For example, a characterization parameter equation containing the coefficients of the permeability equation is established using the first pore structure characterization parameter and the second pore structure characterization parameter. The coefficients of the permeability equation are then obtained using the N sets of first and second pore structure characterization parameters obtained above. For example, the coefficients of the permeability equation can be obtained using methods such as the least squares method.
[0114] Therefore, the coefficients of this permeability equation can not only characterize the mapping relationship between the first pore structure characterization parameter and the second pore structure characterization parameter, that is, characterize the relationship between the pore structure of the rock sample described from macroscopic and microscopic perspectives, but also be accurately determined.
[0115] S106. Substitute the coefficients of the obtained permeability equation into the permeability equation to obtain the target permeability equation.
[0116] By using the coefficients of the permeability equation, which can accurately characterize the mapping relationship between the permeability and pore structure of the rock sample in the above-mentioned reservoir, the obtained target permeability equation can more realistically and accurately reflect the mapping relationship between the permeability, porosity, and effective seepage porosity of the rock sample in the target reservoir.
[0117] It should be noted that the target reservoir may be the same reservoir as the sample reservoir, or the target reservoir may be a reservoir of the same type of rock in the same region as the sample reservoir, that is, the target reservoir and the sample reservoir are in the same region and the rocks have similar physical properties.
[0118] The permeability equation described above is used to characterize the mapping relationship between porosity, effective permeability, and permeability. It can be derived from the mapping relationship between porosity, effective permeability, and permeability.
[0119] For example, based on the mapping relationship between porosity, effective permeable porosity and permeability, a permeability equation is established. The coefficients of the permeability equation obtained above are substituted into this permeability equation to obtain the target permeability equation.
[0120] Therefore, the target permeability equation for the sample reservoir obtained through steps S101 to S106 characterizes the pore structure of the rock sample from two perspectives: porosity and effective permeability. This improves the prediction accuracy of the target permeability equation in reservoirs with complex pore structures. Simultaneously, the coefficients of the permeability equation can be accurately determined. This avoids both the low permeability prediction accuracy caused by characterizing the pore structure of the rock sample from a single perspective and the difficulty in accurately determining parameters due to using a programming solution method to obtain the values of the undetermined parameters.
[0121] A more accurate target permeability equation obtained from rock samples of the target reservoir can be used to predict the permeability of the target reservoir at at least one depth, improving the accuracy of the predicted reservoir permeability. It should be noted that the reservoir permeability of the target reservoirs described above can be obtained using the aforementioned target permeability equation.
[0122] The reservoir permeability processing method provided in this application obtains experimental porosity, experimental permeability, experimental bound water saturation, and nuclear magnetic resonance T2 spectra of sample reservoir rock samples. This yields a first pore structure characterization parameter representing the pore structure of the rock sample from the perspective of porosity, and a second pore structure characterization parameter representing the pore structure of the rock sample from the perspective of effective permeable porosity, making the pore structure characterization of the rock sample more accurate. Then, based on the mapping relationship between the first and second pore structure characterization parameters, the coefficients of the permeability equation are determined, resulting in a more accurate target permeability equation representing the pore structure. The target permeability equation obtained using the method provided in this application has higher accuracy when used to predict the reservoir permeability of a target reservoir, improving the prediction accuracy of permeability in reservoirs with complex pore structures. Furthermore, the introduced undetermined parameters, i.e., the coefficients of the permeability equation, can be accurately determined.
[0123] The following section uses tight sandstone, a reservoir with a complex pore structure, as an example to explain in detail how to obtain the coefficients of the target permeability equation and then determine the target permeability equation.
[0124] Figure 2 This is a schematic flowchart illustrating another method for processing reservoir permeability provided in an embodiment of this application. Figure 2 As shown, the method includes:
[0125] S201. Obtain experimental porosity, experimental permeability, experimental bound water saturation, and nuclear magnetic resonance T2 spectrum of N rock samples from the sample reservoir.
[0126] S202. Based on the experimental porosity and permeability of each rock sample, and the first pore structure characterization formula, obtain the first pore structure characterization parameters corresponding to each rock sample.
[0127] The formula for characterizing the first pore structure mentioned above is:
[0128]
[0129] Where f is the first pore structure characterization parameter of the rock sample, K is the experimental permeability of the rock sample, φ is the experimental porosity of the rock sample, and K max φ is the preset maximum permeability of the rock sample. max This is the preset maximum porosity of the rock sample.
[0130] The aforementioned preset maximum permeability of the rock sample can be either the theoretical maximum permeability of that type of rock sample or the maximum experimental permeability obtained after numerous experiments. The preset maximum porosity of the rock sample is similar and will not be elaborated further here. For example, the theoretical maximum permeability of clastic rocks is typically taken as 8271.1 md, and the theoretical maximum porosity of clastic rocks is typically taken as 0.475 v / v. It should be noted that dense sandstone is a type of clastic rock.
[0131] S203. Based on the nuclear magnetic resonance T2 spectrum of each rock sample and the porosity calculation formula for large-diameter pores, obtain the porosity of large-diameter pores in each rock sample.
[0132] The porosity calculation formula for the above-mentioned large-diameter pores is as follows:
[0133]
[0134] Where, φ b S represents the porosity of the large-diameter pores in the above rock sample. T2(t) represents the porosity at time t in the T2 spectrum, T2b is the target node time of the T2 spectrum, and T2max is the maximum T2 time of the T2 spectrum; the porosity corresponding to times in the T2 spectrum greater than the above target node time is the porosity of large-diameter pores. For example, T2b is taken as 100ms in dense sandstone and 200ms in medium-high porosity and permeability sandstone.
[0135] It should be noted that the T2 spectrum can be correlated with the pore size. The T2 time is used as the node time to divide large-pore and small-pore pores. T2 time greater than T2b corresponds to large-pore pores, and T2 time less than T2b corresponds to small-pore pores. The amplitude of the T2 spectrum is the porosity of the pore.
[0136] S204. Based on the experimental porosity, experimental bound water saturation, porosity of large-diameter pores, and the effective seepage porosity calculation formula for each rock sample, obtain the effective seepage porosity of each rock sample.
[0137] The formula for calculating the effective seepage porosity is as follows:
[0138]
[0139] Where, φ m S represents the effective seepage porosity of the aforementioned rock sample. wi The experimental bound water saturation of the above rock samples is given.
[0140] S205. Based on the experimental permeability and effective seepage porosity of each rock sample, and the second pore structure characterization formula, obtain the second pore structure characterization parameter Fa corresponding to each rock sample.
[0141] The formula for characterizing the second pore structure is: Fa=[Log(K)-Log(φ)] m )] / 2.
[0142] S206. Based on the first pore structure characterization parameters and the second pore structure characterization parameters corresponding to each rock sample, obtain the coefficients of the permeability equation using the characterization parameter equation.
[0143] The above characterization parameter equations are:
[0144] f = k1 * Fa + k2
[0145] Where k1 and k2 are both coefficients of the above permeability equation.
[0146] By substituting the data of the N sets of first pore structure characterization parameters and second pore structure characterization parameters obtained above into the characterization parameter equation, the coefficients k1 and k2 of the permeability equation can be accurately determined.
[0147] Figure 3This is a schematic diagram illustrating the mapping relationship between a first pore structure characterization parameter and a second structure characterization parameter, provided for an embodiment of this application.
[0148] For example, 94 reservoir rock samples were selected, and the experimental permeability, experimental porosity, experimental bound water saturation, and nuclear magnetic resonance T2 spectra of each sample were obtained. The mapping relationship between the first pore structure characterization parameters and the second structure characterization parameters was determined, and the coefficients k1 and k2 of the aforementioned permeability equation were as follows: Figure 3 As shown. Where k1 = -4.47831, k2 = 12.27509. Figure 3 R represents the correlation coefficient; the closer R is to 1, the better the fit. Specifically, R = 0.88065 indicates that the values of coefficients k1 and k2 are relatively accurate, meaning their values can be accurately determined.
[0149] S207. Substitute the coefficients of the permeability equation obtained above into the permeability equation to obtain the target permeability equation.
[0150] The target permeability equation is shown below for example:
[0151]
[0152] Where Ka is the predicted penetration rate, and φ a Porosity, φ m For effective seepage porosity.
[0153] It should be understood that the formulas or equations provided in the embodiments of this application are merely illustrative examples of formulas or equations. In specific implementations, the formulas or equations may be appropriately modified according to actual calculation needs or the characteristics of the reservoir to which the rock sample belongs, and this is not limited.
[0154] Figure 4 This diagram illustrates a comparison of the mapping relationship between the calculated permeability of a rock sample predicted by the reservoir permeability processing method provided in this application and the experimental permeability of the rock sample obtained experimentally, respectively.
[0155] For example, the mapping relationship between the experimental permeability of the 94 reservoir rock samples obtained above, the calculated permeability predicted by the reservoir permeability processing method provided in this application, and the calculated permeability predicted by the Coates model in the prior art is as follows: Figure 4 As shown.
[0156] from Figure 4It can be seen that the calculated permeability of the rock samples predicted by the reservoir permeability processing method provided in this application is closer to the experimental permeability of the rock samples, and the correlation coefficient R is closer to 1. Furthermore, comparing the calculated permeability of the rock samples predicted by the reservoir permeability processing method provided in this application with the experimental permeability, the average relative error is 0.42%, and the average absolute error is 3.07 md. In contrast, the results predicted by the Coates model have an average relative error of 2.45% and an average absolute error of 13.8 md.
[0157] That is, the reservoir permeability processing method provided in this application has higher accuracy in predicting rock sample permeability.
[0158] The reservoir permeability processing method provided in this application corrects the porosity of rock samples from the perspective of large-diameter pores to effective permeable porosity. Then, using experimental permeability, experimental porosity, experimental bound water saturation, and nuclear magnetic resonance T2 spectrum, it obtains a first pore structure characterization parameter and a second structure characterization parameter representing the pore structure of the rock sample from two perspectives, determining the coefficients k1 and k2 of the permeability equation, and thus obtaining the target permeability equation. The target permeability equation determined by the reservoir permeability processing method provided in this application more accurately characterizes the pore structure of rock samples from both the perspectives of porosity and effective permeable porosity, improving the prediction accuracy of permeability in reservoirs with complex pore structures. Furthermore, it introduces only two undetermined parameters, and these two parameters can be accurately determined.
[0159] The following example, taking a sample reservoir as the aforementioned tight sandstone reservoir, and the target reservoir and sample reservoir belonging to the same reservoir, illustrates how to use the target permeability equation to obtain the permeability of the target reservoir at multiple depths.
[0160] Figure 5 This is a schematic flowchart illustrating another method for processing reservoir permeability provided in an embodiment of this application. Figure 5 As shown, the method includes:
[0161] S501. Obtain nuclear magnetic resonance logging data of the target reservoir.
[0162] The aforementioned nuclear magnetic resonance logging data refers to the data obtained when using nuclear magnetic resonance logging on the target reservoir.
[0163] The aforementioned nuclear magnetic resonance logging data can be acquired through communication between the host computer of the experimental instrument and the computer performing this step, or through an external device storing the aforementioned logging data, such as a USB flash drive.
[0164] It should be understood that the above-mentioned nuclear magnetic resonance logging can be performed with reference to existing technologies.
[0165] S502. Process the nuclear magnetic resonance logging data of the above-mentioned target reservoir to obtain the T2 spectrum and bound water saturation of the above-mentioned target reservoir at multiple depths.
[0166] It should be understood that the processing of nuclear magnetic resonance logging data for the aforementioned target reservoirs can be carried out with reference to existing technologies.
[0167] It should be noted that the nuclear magnetic resonance (NMR) logging data of the aforementioned target reservoir yielded T2 spectrum and bound water saturation curves. These T2 spectrum and bound water saturation curves reflect the mapping relationship between the T2 spectrum and bound water saturation at different depths of the target reservoir. Therefore, the NMR logging data of the aforementioned target reservoir can be used to obtain the T2 spectrum and bound water saturation of the target reservoir at multiple depths.
[0168] S503. Based on the T2 spectra of the target reservoir at multiple depths, obtain the porosity of the target reservoir at multiple depths.
[0169] It should be noted that the porosity of the target reservoir at multiple depths can be obtained with reference to existing technologies.
[0170] S504. Based on the T2 spectrum of the target reservoir at multiple depths and the bound water saturation, obtain the effective permeability porosity of the target reservoir at multiple depths.
[0171] The effective permeability porosity of the target reservoir at a certain depth is obtained using the formulas given in steps S203 and S204 of the aforementioned embodiment. Specifically, the porosity of large-diameter pores at that depth is first obtained using the T2 spectrum, and then the effective permeability porosity at that depth is obtained by combining this with the bound water saturation. Furthermore, the effective permeability porosity of the target reservoir at multiple depths is obtained through the above method.
[0172] S505. Based on the effective permeability of the target reservoir at multiple depths and the porosity, the predicted permeability of the target reservoir at multiple depths is obtained using the aforementioned target permeability equation.
[0173] It should be noted that before obtaining the predicted permeability of the target reservoir at multiple depths using the aforementioned target permeability equation, the target permeability equation of the target reservoir needs to be determined according to the aforementioned steps S201-S107. That is, the coefficients k1 and k2 of the aforementioned target permeability equation are first determined using the experimental permeability, experimental porosity, experimental bound water saturation and nuclear magnetic resonance T2 spectrum of N groups of rock samples from the aforementioned sample reservoir.
[0174] By using the target permeability equation, which can accurately reflect the mapping relationship between the permeability, porosity, and effective permeability of the sample reservoir rock sample, the permeability of the target reservoir rock sample located in the same reservoir as the sample reservoir can be accurately predicted, and the permeability of the target reservoir at multiple depths can be predicted.
[0175] Therefore, by substituting the effective permeability and porosity of the target reservoir at multiple depths obtained above into the target permeability equation of the target reservoir after determination, the permeability of the target reservoir at multiple depths can be obtained.
[0176] S506. Output the permeability of the target reservoir at multiple depths.
[0177] It should be noted that after obtaining the permeability of the target reservoir at multiple depths, the reservoir permeability of the target reservoir can be obtained. For example, the maximum value among the permeability at multiple depths can be taken as the reservoir permeability of the target reservoir.
[0178] The reservoir permeability processing method provided in this application first determines two undetermined parameters k1 and k2 of the target permeability equation using experimental permeability, experimental porosity, experimental bound water saturation, and nuclear magnetic resonance T2 spectra of N groups of rock samples from the sample reservoir. Then, the porosity and effective permeable porosity of the target reservoir at multiple depths are obtained by processing the nuclear magnetic resonance logging data of the target reservoir and substituted into the target permeability equation. This allows for the prediction of the permeability of the target reservoir at multiple depths using the target permeability equation, thereby obtaining the reservoir permeability of the target reservoir. This method not only improves the prediction accuracy of reservoir permeability in reservoirs with complex pore structures, but also ensures that all introduced undetermined parameters can be accurately determined, thus improving the accuracy and adaptability of reservoir permeability prediction.
[0179] Figure 6 This diagram illustrates a comparison between the permeability of a target reservoir predicted using the reservoir permeability processing method provided in this application and the Coates model in the prior art, and the experimental permeability of rock samples obtained from experiments.
[0180] like Figure 6 As shown, column ① represents the depth of the target reservoir; column ② represents a comparison between the permeability of the target reservoir predicted using the Coates model in the prior art and the experimental permeability of rock samples obtained experimentally; and column ③ represents a comparison between the permeability of the target reservoir predicted using the reservoir permeability processing method provided in this application and the experimental permeability of rock samples obtained experimentally. In columns ② and ③, the curves represent the predicted permeability of the target reservoir at different depths, and the black dots represent the experimental permeability of rock samples at that depth.
[0181] from Figure 6As can be seen, the reservoir permeability processing method provided in this application predicts permeability curves at different depths of the target reservoir with more points of overlap with experimental permeability. In other words, the permeability predicted by the reservoir permeability processing method provided in this application is closer to the experimental permeability of rock samples at the corresponding depths. Therefore, the reservoir permeability predicted by the reservoir permeability processing method provided in this application has high accuracy.
[0182] Figure 7 This is a schematic diagram of a reservoir permeability treatment device provided in an embodiment of this application. Figure 7 As shown, the device includes: a first acquisition module 11, a second acquisition module 12, a third acquisition module 13, a fourth acquisition module 14, a fifth acquisition module 15, and a processing module 16. Optionally, the device may also include a prediction module 17 and / or an output module 18.
[0183] The first acquisition module 11 is used to acquire the experimental porosity, experimental permeability, experimental bound water saturation, and nuclear magnetic resonance T2 spectrum of N rock samples from the sample reservoir; where N is an integer greater than or equal to 2, and the T2 spectrum is obtained by performing nuclear magnetic resonance experiments on the rock samples with saturated water.
[0184] The second acquisition module 12 is used to acquire the first pore structure characterization parameters corresponding to each rock sample based on the experimental porosity and experimental permeability of each rock sample; the first pore structure characterization parameters are used to describe the pore structure of the rock sample from the perspective of the porosity of the rock sample.
[0185] The third acquisition module 13 is used to acquire the effective seepage porosity of each rock sample based on the experimental porosity, experimental bound water saturation, and nuclear magnetic resonance T2 spectrum of each rock sample.
[0186] The fourth acquisition module 14 is used to acquire the second pore structure characterization parameters corresponding to each rock sample based on the experimental permeability and effective seepage porosity of each rock sample; the second pore structure characterization parameters are used to describe the pore structure of the rock sample from the perspective of the effective seepage porosity of the rock sample.
[0187] The fifth acquisition module 15 is used to acquire the coefficients of the permeability equation based on the first pore structure characterization parameters and the second pore structure characterization parameters corresponding to each rock sample; the permeability equation is used to characterize the mapping relationship between porosity, effective seepage porosity and permeability.
[0188] The processing module 16 is used to substitute the coefficients of the obtained permeability equation into the permeability equation to obtain the target permeability equation; the target permeability equation is used to predict the permeability of the target reservoir at at least one depth.
[0189] In one possible implementation, the second acquisition module 12 is specifically used to acquire the first pore structure characterization parameters corresponding to each rock sample based on the experimental porosity and experimental permeability of each rock sample, as well as the first pore structure characterization formula.
[0190] The first pore structure characterization formula is:
[0191]
[0192] Where f is the first pore structure characterization parameter of the rock sample, K is the experimental permeability of the rock sample, φ is the experimental porosity of the rock sample, and K max φ is the preset maximum permeability of the rock sample. max This is the preset maximum porosity of the rock sample.
[0193] One possible implementation is that the third acquisition module 13 is specifically used to acquire the porosity of the large-diameter pores in each rock sample based on the nuclear magnetic resonance T2 spectrum of each rock sample and the porosity calculation formula of the large-diameter pores; and to acquire the effective seepage porosity of each rock sample based on the experimental porosity, experimental bound water saturation, porosity of the large-diameter pores, and the effective seepage porosity calculation formula of each rock sample.
[0194] The porosity calculation formula for the large-diameter pores is as follows:
[0195]
[0196] Where, φ b S represents the porosity of the large-diameter pores in the rock sample. T2 (t) represents the porosity at time t in the T2 spectrum, T2b represents the target node time of the T2 spectrum, and T2max represents the maximum T2 time of the T2 spectrum; the porosity at times in the T2 spectrum that are greater than the target node time is the porosity of large-diameter pores.
[0197] The formula for calculating the effective seepage porosity is as follows:
[0198]
[0199] Where, φ m S represents the effective permeability porosity of the rock sample. wi The experimental bound water saturation of the rock sample is given.
[0200] One possible implementation is that the fourth acquisition module 14 is specifically used to acquire the second pore structure characterization parameter Fa corresponding to each rock sample based on the experimental permeability and effective seepage porosity of each rock sample, and the second pore structure characterization formula; wherein, the second pore structure characterization formula is: Fa=[Log(K)-Log(φ)] m )] / 2.
[0201] One possible implementation is that the fifth acquisition module 15 is specifically used to obtain the coefficients of the permeability equation based on the first pore structure characterization parameters and the second pore structure characterization parameters corresponding to each rock sample, using the characterization parameter equation.
[0202] The equation for the characterization parameter is:
[0203] f = k1 * Fa + k2
[0204] Where k1 and k2 are both coefficients of the permeability equation;
[0205] The permeability equation is:
[0206]
[0207] Where Ka is the predicted penetration rate, and φ a Porosity, φ m For effective permeability porosity.
[0208] One possible implementation involves a prediction module 17, which acquires nuclear magnetic resonance (NMR) logging data of the target reservoir; processes the NMR logging data of the target reservoir to acquire the T2 spectrum and bound water saturation of the target reservoir at multiple depths; acquires the porosity of the target reservoir at multiple depths based on the T2 spectrum of the target reservoir at multiple depths; acquires the effective permeability porosity of the target reservoir at multiple depths based on the T2 spectrum of the target reservoir at multiple depths and the bound water saturation; and obtains the predicted permeability of the target reservoir at multiple depths using the target permeability equation based on the effective permeability porosity and the porosity of the target reservoir at multiple depths.
[0209] One possible implementation is an output module 18, which outputs the permeability of the target reservoir at multiple depths.
[0210] The reservoir permeability treatment device provided in this application can perform the permeability treatment method in the above method embodiments. Its implementation principle and technical effect are similar, and will not be described again here.
[0211] Figure 8 This is a schematic diagram of the structure of an electronic device 800 provided in an embodiment of this application. Figure 8As shown, the electronic device 800 may include at least one processor 801 and a memory 802, such as a computer, tablet computer or other electronic device with processing capabilities.
[0212] Memory 802 is used to store programs. Specifically, the program may include program code, which includes computer operation instructions. Memory 802 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.
[0213] The processor 801 is used to execute computer execution instructions stored in the memory 802 to implement the reservoir permeability processing method described in the foregoing method embodiments. The processor 801 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.
[0214] The electronic device 800 may also include a communication interface 803, through which it can communicate and interact with external devices. Such external devices may be, for example, the host computer of the aforementioned experimental instrument, a USB flash drive, etc.
[0215] In practical implementation, if the communication interface 803, memory 802, and processor 801 are implemented independently, they can be interconnected via a bus to complete communication. 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., but this does not imply that there is only one bus or one type of bus.
[0216] Optionally, in a specific implementation, if the communication interface 803, memory 802, and processor 801 are integrated on a single chip, then the communication interface 803, memory 802, and processor 801 can communicate through an internal interface.
[0217] This application also provides a computer-readable storage medium, which may include various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a disk, or an optical disk. Specifically, the computer-readable storage medium stores program instructions, which are used for the reservoir permeability processing method in the above embodiments.
[0218] This application also provides a program product including executable instructions stored in a readable storage medium. At least one processor of an electronic device 800 can read the executable instructions from the readable storage medium, and the at least one processor executes the executable instructions to cause the electronic device 800 to implement the reservoir permeability processing methods provided in the various embodiments described above.
[0219] This application also provides a chip on which a computer program is stored. When the computer program is executed by the chip, it implements the reservoir permeability processing method provided in various embodiments.
[0220] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.
[0221] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.
Claims
1. A method for treating reservoir permeability, characterized in that, The method includes: The experimental porosity, experimental permeability, experimental bound water saturation, and nuclear magnetic resonance T2 spectra of N rock samples from the sample reservoir are obtained; where N is an integer greater than or equal to 2, and the T2 spectra are obtained by performing nuclear magnetic resonance experiments on the rock samples in saturated water. Based on the experimental porosity and experimental permeability of each rock sample, the first pore structure characterization parameter corresponding to each rock sample is obtained; the first pore structure characterization parameter is used to describe the pore structure of the rock sample from the perspective of the porosity of the rock sample. The effective permeability of each rock sample was obtained based on the experimental porosity, experimental bound water saturation, and nuclear magnetic resonance T2 spectrum. Based on the experimental permeability and effective seepage porosity of each rock sample, a second pore structure characterization parameter is obtained for each rock sample; the second pore structure characterization parameter is used to describe the pore structure of the rock sample from the perspective of the effective seepage porosity. Based on the first pore structure characterization parameter and the second pore structure characterization parameter corresponding to each rock sample, the coefficients of the permeability equation are obtained; the permeability equation is used to characterize the mapping relationship between porosity, effective seepage porosity and permeability. Substituting the coefficients of the obtained permeability equation into the permeability equation yields the target permeability equation; the target permeability equation is used to predict the permeability of the target reservoir at at least one depth.
2. The method according to claim 1, characterized in that, The step of obtaining the first pore structure characterization parameters corresponding to each rock sample based on the experimental porosity and experimental permeability includes: Based on the experimental porosity and experimental permeability of each rock sample, and the first pore structure characterization formula, the first pore structure characterization parameters corresponding to each rock sample are obtained. The first pore structure characterization formula is: in, These are the first pore structure characterization parameters of the rock sample. The experimental permeability of the rock sample is given. The experimental porosity of the rock sample is given. The preset maximum permeability of the rock sample, This is the preset maximum porosity of the rock sample.
3. The method according to claim 2, characterized in that, The method of obtaining the effective permeability porosity of each rock sample based on its experimental porosity, experimental bound water saturation, and nuclear magnetic resonance T2 spectrum includes: Based on the nuclear magnetic resonance T2 spectra of each rock sample and the porosity calculation formula for large-diameter pores, the porosity of large-diameter pores in each rock sample is obtained. The effective seepage porosity of each rock sample is obtained based on the experimental porosity, experimental bound water saturation, porosity of large-diameter pores, and the effective seepage porosity calculation formula. The porosity calculation formula for the large-diameter pores is as follows: in, The porosity of the large-diameter pores in the rock sample is given. This represents the porosity at time t in the T2 spectrum. The target node time for the T2 spectrum. The maximum T2 time of the T2 spectrum; the porosity corresponding to the time in the T2 spectrum that is greater than the target node time is the porosity of the large-diameter pores; The formula for calculating the effective seepage porosity is as follows: in, The effective permeability porosity of the rock sample is... The experimental bound water saturation of the rock sample is given.
4. The method according to claim 3, characterized in that, The step of obtaining the second pore structure characterization parameters corresponding to each rock sample based on the experimental permeability and effective seepage porosity of each rock sample includes: Based on the experimental permeability and effective seepage porosity of each rock sample, and the second pore structure characterization formula, the corresponding second pore structure characterization parameters for each rock sample are obtained. The formula for characterizing the second pore structure is: .
5. The method according to claim 4, characterized in that, The step of obtaining the coefficients of the permeability equation based on the first pore structure characterization parameters and the second pore structure characterization parameters corresponding to each of the rock samples includes: Based on the first pore structure characterization parameters and the second pore structure characterization parameters corresponding to each rock sample, the coefficients of the permeability equation are obtained using the characterization parameter equation. The equation for the characterization parameter is: in, , All of these are coefficients of the permeability equation; The permeability equation is: in, For the predicted penetration rate, Porosity For effective permeability porosity.
6. The method according to any one of claims 1-5, characterized in that, The method further includes: Obtain nuclear magnetic resonance logging data of the target reservoir; The nuclear magnetic resonance logging data of the target reservoir are processed to obtain the T2 spectrum and bound water saturation of the target reservoir at multiple depths; Based on the T2 spectrum of the target reservoir at multiple depths, the porosity of the target reservoir at multiple depths is obtained; Based on the T2 spectrum of the target reservoir at multiple depths and the bound water saturation, the effective permeability porosity of the target reservoir at multiple depths is obtained. Based on the effective permeability of the target reservoir at multiple depths, and the porosity, the predicted permeability of the target reservoir at multiple depths is obtained using the target permeability equation.
7. The method according to claim 6, characterized in that, The method further includes: Output the permeability of the target reservoir at multiple depths.
8. A reservoir permeability treatment device, characterized in that, The device includes: The first acquisition module is used to acquire the experimental porosity, experimental permeability, experimental bound water saturation, and nuclear magnetic resonance T2 spectrum of N rock samples from the sample reservoir; where N is an integer greater than or equal to 2, and the T2 spectrum is obtained by performing nuclear magnetic resonance experiments on the rock samples in saturated water. The second acquisition module, connected to the first acquisition module, is used to acquire the first pore structure characterization parameters corresponding to each rock sample based on the experimental porosity and experimental permeability of each rock sample; the first pore structure characterization parameters are used to describe the pore structure of the rock sample from the perspective of the porosity of the rock sample. The third acquisition module, connected to the first acquisition module, is used to acquire the effective permeable porosity of each rock sample based on the experimental porosity, experimental bound water saturation, and nuclear magnetic resonance T2 spectrum of each rock sample. The fourth acquisition module, connected to the first acquisition module and the third acquisition module respectively, is used to acquire the second pore structure characterization parameters corresponding to each rock sample based on the experimental permeability and effective seepage porosity of each rock sample; the second pore structure characterization parameters are used to describe the pore structure of the rock sample from the perspective of the effective seepage porosity of the rock sample. The fifth acquisition module, connected to the second acquisition module and the fourth acquisition module respectively, is used to acquire the coefficients of the permeability equation based on the first pore structure characterization parameter and the second pore structure characterization parameter corresponding to each rock sample; the permeability equation is used to characterize the mapping relationship between porosity, effective seepage porosity and permeability; The processing module, connected to the fifth acquisition module, is used to substitute the coefficients of the acquired permeability equation into the permeability equation to obtain the target permeability equation; the target permeability equation is used to predict the permeability of the target reservoir at at least one depth.
9. An electronic device, characterized in that, The electronic device includes: a processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the reservoir permeability processing method as described in any one of claims 1-7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the reservoir permeability processing method as described in any one of claims 1 to 7.