Method and device for predicting permeability of reservoir

Abstract

The present invention provides a method and device for predicting the permeability of a reservoir formation. The method for predicting the permeability of a reservoir formation includes: obtaining the porosity and the maximum pore radius of a current core; determining a corresponding permeability model according to the maximum pore radius; and inputting the porosity and the maximum pore radius into the corresponding permeability model to obtain the permeability of the reservoir formation. The present invention can improve the prediction efficiency and accuracy of the permeability of a reservoir formation, and reduce the development time and development cost of reservoir resources.

Description

Technical Field

[0001] The present invention relates to the technical field of reservoir exploration and development, and particularly, to a method and device for predicting the permeability of a reservoir in an oil reservoir. Background Art

[0002] With the increasing demand for energy, oil and gas development has entered the stage of unconventional oil and gas. Unconventional oil and gas consists of seven resources: tight oil (shale oil), tight sandstone gas (tight carbonate gas), coalbed methane, oil sand oil, shale gas, oil shale, and natural gas hydrate. Due to the formation and development of sedimentary basins in China having gone through two periods, namely the Paleozoic marine facies and the Mesozoic-Cenozoic continental facies, the geological conditions for the formation of unconventional oil and gas are very favorable, and the resources of unconventional oil and gas are abundant.

[0003] The reservoir is the soul of unconventional oil and gas research. The key to commercial development of various oil reservoirs lies in how to select high-quality reservoirs. As an effective means, reservoir classification and evaluation has been widely used in reservoir selection and production capacity prediction. This method is based on the geological characteristics of the study area. First, geological parameters that can represent the method block are selected, and then the reservoir is classified through a certain method to divide into poor, medium, and good grades. Especially for continuous classification and evaluation of reservoirs, it has guiding significance for subsequent exploration and development of oil reservoirs.

[0004] In recent years, scholars have conducted a large number of studies on reservoir classification of carbonate rocks, tight, shale, low-permeability and other reservoirs. Comparing these research results, it is found that there are significant differences in the evaluation criteria for the classification of the same type of reservoir, which are only applicable to specific blocks, and most studies do not compare the results before and after reservoir classification. Even if some studies use single-well production capacity, etc. to test whether the reservoir classification is correct, there are few studies on verifying the accuracy and feasibility of the reservoir classification standard based on the method of predicting permeability by multiple regression.

[0005] Pittman (1992), Kolodzie (1980), Aguilera (2002), etc. believe that the pore throat size at a certain mercury saturation can reflect the reservoir quality and fluid conduction ability. Winland determined the pore throat radius at a mercury saturation of 35% as the characteristic radius for dominant flow based on capillary pressure tests, named as R 35 , analyzed the capillary pressure data of mercury injection experiments on 312 hydrophilic core samples, and believed that the mercury saturation corresponding to the effective pore system that dominates rock seepage is 35%. An empirical relationship can be established between the sub-porosity, gas permeability, and pore throat radius using the mercury injection capillary pressure test data. However, the physical meaning of the above empirical formula is not clear and it is only applicable to carbonate rocks, and is not applicable to other sandstone, shale and other reservoir oil reservoirs, having limitations. Summary of the Invention

[0006] The main purpose of the embodiments of the present invention is to provide a method and device for predicting the permeability of a reservoir, so as to improve the prediction efficiency and accuracy of the permeability of the reservoir, and reduce the development time and cost of reservoir resources.

[0007] To achieve the above object, an embodiment of the present invention provides a method for predicting the permeability of a reservoir, including:

[0008] Obtaining the porosity and the maximum pore radius of the current core;

[0009] Determining the corresponding permeability model according to the maximum pore radius;

[0010] Inputting the porosity and the maximum pore radius into the corresponding permeability model to obtain the permeability of the reservoir.

[0011] In one embodiment, determining the corresponding permeability model according to the maximum pore radius includes:

[0012] Determining the core type of the current core according to the maximum pore radius;

[0013] Determining the permeability model corresponding to the core type.

[0014] In one embodiment, it further includes:

[0015] Obtaining the historical permeability, historical porosity and historical maximum pore radius of the historical core;

[0016] Classifying the historical cores according to the historical maximum pore radius;

[0017] Determining the permeability models of various types of cores according to the historical permeability, historical porosity and historical maximum pore radius of the classified historical cores.

[0018] In one embodiment, it further includes:

[0019] Performing a mercury injection test on the current core to obtain a capillary pressure curve;

[0020] Determining the maximum pore radius according to the capillary pressure curve.

[0021] An embodiment of the present invention also provides an apparatus for predicting the permeability of a reservoir, including:

[0022] A pore acquisition module, configured to obtain the porosity and the maximum pore radius of the current core;

[0023] A permeability model determination module, configured to determine the corresponding permeability model according to the maximum pore radius;

[0024] The current core permeability module is used to input the porosity and the maximum pore radius into the corresponding permeability model to obtain the reservoir permeability of the oil reservoir.

[0025] In one embodiment, the permeability model determination module includes:

[0026] The core type unit is used to determine the core type of the current core according to the maximum pore radius;

[0027] The permeability model determination unit is used to determine the permeability model corresponding to the core type.

[0028] In one embodiment, it further includes:

[0029] The historical data acquisition module is used to acquire the historical permeability, historical porosity and historical maximum pore radius of the historical core;

[0030] The classification module is used to classify the historical cores according to the historical maximum pore radius;

[0031] The core permeability determination module is used to determine the permeability models of various types of cores according to the historical permeability, historical porosity and historical maximum pore radius of the classified historical cores.

[0032] In one embodiment, it further includes:

[0033] The capillary pressure curve module is used to perform mercury injection tests on the current core to obtain the capillary pressure curve;

[0034] The maximum pore radius module is used to determine the maximum pore radius according to the capillary pressure curve.

[0035] An embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored on the memory and running on the processor. When the processor executes the computer program, the steps of the oil reservoir permeability prediction method are implemented.

[0036] An embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the oil reservoir permeability prediction method are implemented.

[0037] An embodiment of the present invention also provides a computer program product, including a computer program / instructions. When the computer program / instructions are executed by a processor, the steps of the oil reservoir permeability prediction method are implemented.

[0038] The method and device for predicting the permeability of a reservoir formation according to an embodiment of the present invention first determine the corresponding permeability model based on the maximum pore radius, and then input the porosity and the maximum pore radius into the corresponding permeability model to improve the prediction efficiency and accuracy of the permeability of the reservoir formation, and reduce the development time and development cost of reservoir resources. Description of the Drawings

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0040] Figure 1 It is a flowchart of the method for predicting the permeability of a reservoir formation according to an embodiment of the present invention;

[0041] Figure 2 It is a schematic diagram of a core with capillary bundles;

[0042] Figure 3 It is a schematic diagram of a capillary bundle model based on the fractal theory;

[0043] Figure 4 It is a flowchart of determining the permeability models of various types of cores according to an embodiment of the present invention;

[0044] Figure 5 It is a schematic diagram of the capillary pressure curve of a type of core according to an embodiment of the present invention;

[0045] Figure 6 It is a schematic diagram of the capillary pressure curve of a second type of core according to an embodiment of the present invention;

[0046] Figure 7 It is a schematic diagram of the capillary pressure curve of a third type of core according to an embodiment of the present invention;

[0047] Figure 8 It is a comparison diagram of the preset permeability and the historical permeability of a type of core;

[0048] Figure 9 It is a comparison diagram of the preset permeability and the historical permeability of a second type of core;

[0049] Figure 10 It is a comparison diagram of the preset permeability and the historical permeability of a third type of core;

[0050] Figure 11 It is a structural block diagram of the device for predicting the permeability of a reservoir formation according to an embodiment of the present invention;

[0051] Figure 12 It is a structural block diagram of a computer device according to an embodiment of the present invention. Detailed implementation manners

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0053] Those skilled in the art know that the implementation manners of the present invention can be realized as a system, a device, an equipment, a method or a computer program product. Therefore, the present disclosure can be specifically realized in the following forms, namely: completely hardware, completely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0054] In the technical solutions of the present invention, the acquisition, storage, use, processing, etc. of data all comply with the relevant provisions of national laws and regulations.

[0055] The terms related to the present invention are explained as follows:

[0056] Capillary force: The additional surface effect of the liquid level rising or falling in a capillary tube is called capillary pressure or capillary force.

[0057] Fractal dimension: The pore distribution has statistical self-similarity, and the fractal dimension is introduced to describe the distribution characteristics of pores. It is an effective supplement to parameters such as the porosity of rocks and can better characterize the fractal characteristics of the pore structure of rocks. As the porosity increases, the fractal dimension of the pore structure also increases. Moreover, under the same porosity, the fractal dimensions of the pore structures are not the same. The more complex the pore structure, the larger its fractal dimension.

[0058] Displacement pressure: It refers to the capillary pressure of the largest connected pores in the pore system. The value obtained by making a tangent to the flat part of the capillary force curve and intersecting the vertical axis is the displacement pressure. The displacement pressure is one of the main parameters for dividing the performance of rock reservoirs, which reflects both the concentration degree of the pore throats of rocks and the size of the concentrated pore throats.

[0059] Maximum pore radius: Corresponding to the displacement pressure is the maximum pore radius.

[0060] Darcy's law: The law that describes the linear relationship between the seepage velocity of water in saturated soil and the hydraulic gradient, also known as the linear seepage law.

[0061] In view of the fact that the prior art cannot accurately predict the permeability of reservoir formations, the embodiments of the present invention provide a method for predicting the permeability of reservoir formations. Based on the derived permeability expression of the maximum pore radius, on the basis of the physical properties parameters of the mercury injection data of the core, determining the pore type to which the core belongs according to the maximum pore radius can realize the classification of reservoir formations and the prediction of permeability, reducing the development time and development cost of reservoir resources. The present invention will be described in detail below with reference to the accompanying drawings.

[0062] Figure 1 is the flow chart of the method for predicting the permeability of reservoir formations in the embodiments of the present invention. As Figure 1 shown, the method for predicting the permeability of reservoir formations includes:

[0063] S101: Obtain the porosity and the maximum pore radius of the current core.

[0064] Before executing S101, it further includes:

[0065] Perform a mercury injection test on the current core to obtain a capillary pressure curve, and determine the maximum pore radius according to the capillary pressure curve.

[0066] In specific implementation, the porosity of the current core can be measured by the saturated water weighing method; apply an external pressure to inject the non-wetting phase mercury into the rock pores. As the external pressure increases, the mercury saturation of the core increases, and finally the capillary force curve of the current core is obtained. The maximum pore radius of the current core can be calculated by using the capillary force curve in combination with the interpolation method.

[0067] S102: Determine the corresponding permeability model according to the maximum pore radius.

[0068] In one embodiment, S102 includes:

[0069] Determine the core type of the current core according to the maximum pore radius; determine the permeability model corresponding to the core type.

[0070] In specific implementation, when the maximum pore radius is greater than or equal to 1.476 μm, the core is a type I core; when the maximum pore radius is greater than 0.2453 μm and less than 1.476 μm, the core is a type II core; when the maximum pore radius is less than or equal to 0.2453 μm, the core is a type III core.

[0071] S103: Input the porosity and the maximum pore radius into the corresponding permeability model to obtain the permeability of the reservoir formation.

[0072] In specific implementation, the permeability of the reservoir formation can be obtained through the following formula:

[0073]

[0074] Among them, k is the permeability of the reservoir rock formation, with the unit of md; a j is the first constant coefficient of the permeability model corresponding to the j-th type of core, and b j is the second constant coefficient of the permeability model corresponding to the j-th type of core, and c j is the third constant coefficient of the permeability model corresponding to the j-th type of core, φ is the porosity, with the unit of %; r max is the maximum pore radius, with the unit of μm.

[0075] Figure 1 The execution subject of the reservoir rock formation permeability prediction method shown is a computer. From Figure 1 the process shown, it can be seen that the reservoir rock formation permeability prediction method and device according to the embodiments of the present invention first determine the corresponding permeability model according to the maximum pore radius, and then input the porosity and the maximum pore radius into the corresponding permeability model to improve the prediction efficiency and accuracy of the reservoir rock formation permeability, and reduce the development time and development cost of reservoir resources.

[0076] Figure 2 is a schematic diagram of a core with capillary bundles. Figure 3 is a schematic diagram of a capillary bundle model based on fractal theory. As Figure 2 and Figure 3 shown, it is assumed that the pore space of the reservoir consists of a bundle of tortuous capillaries with different sizes, and the pore size distribution thereof can be characterized by fractal theory:

[0077]

[0078] Among them, N(ξ≥r) is the number of pores with a pore radius greater than or equal to r, r max is the maximum pore radius, with the unit of μm; r is the pore radius, with the unit of μm; D f is the fractal dimension, and in two-dimensional space, 0 < D f < 2.

[0079] Assuming that the pore size distribution is continuous, differentiating the above formula can deduce the number of pores with a pore radius between r and r + dr:

[0080]

[0081] On the cross-section of the core cylinder, the number of pores with a pore radius between r and r + dr can be expressed as:

[0082]

[0083] Among them, φ is the porosity, with the unit of %; d is the diameter of the core cylinder, with the unit of cm.

[0084] The flow rate q in a single capillary can be expressed as:

[0085]

[0086] Among them, q is the flow rate with pore radius r, in cm 3 / s; Δp is the pressure difference across the capillary ends, in atm; μ is the fluid viscosity, in mPa·s; τ is the pore tortuosity, and L is the core length, in cm.

[0087] The total flow rate of the fractal porous medium can be calculated by the following formula:

[0088]

[0089] Among them, Q is the total flow rate, in cm 3 / s; q(r) is the flow rate with pore radius r.

[0090] According to Darcy's law, the permeability can be expressed as:

[0091]

[0092] Among them, k is the (reservoir) permeability, in mD; A is the cross-sectional area of the core cylinder, in cm 2 .

[0093] In the actual reservoir pore structure, r max >> r min , so the above formula can be rewritten as:

[0094]

[0095] Taking the logarithm of both sides of the equation, we can get:

[0096]

[0097] Since the change range of the fractal dimension is extremely small, only the influence of and on the permeability needs to be considered, so the above formula can be rewritten as:

[0098]

[0099] Among them, a is the first constant coefficient, b is the second constant coefficient, and c is the third constant coefficient.

[0100] Figure 4 is the flowchart for determining the permeability model of various cores in the embodiments of the present invention. As Figure 4 shown, the reservoir permeability prediction method further includes:

[0101] S201: Obtain the historical permeability, historical porosity, and historical maximum pore radius of the historical core.

[0102] In specific implementation, 42 groups of cores can be obtained, the historical permeability (gas-measured permeability) of the historical cores can be measured, and the historical porosity of the cores can be measured by the saturated water weighing method.

[0103] An external pressure is applied to inject non-wetting mercury into the rock pores. As the external pressure increases, the mercury saturation of the core increases, and finally the capillary pressure curve of the core is obtained. The historical maximum pore radius of the core is calculated by using the capillary pressure curve in combination with the interpolation method.

[0104] S202: Classify the historical cores according to the historical maximum pore radius.

[0105] Figure 5 It is a schematic diagram of the capillary pressure curve of a type of core in the embodiment of the present invention. Figure 6 It is a schematic diagram of the capillary pressure curve of a type II core in the embodiment of the present invention. Figure 7 It is a schematic diagram of the capillary pressure curve of a type III core in the embodiment of the present invention. As Figures 5 - 7 shown, the core classification can be determined based on the maximum pore radius. Based on the statistical morphological features of the three types of capillary pressure curves, with the capillary pressure of 0.5 MPa and 3 MPa as the boundaries, combined with the maximum radius corresponding to the displacement pressure, the cores are divided into: type I cores (r max ≥1.476 μm), type II cores (0.2453 μm < r max < 1.476 μm) and type III cores (r max ≤0.2453 μm). Taking the capillary pressure curves of some groups of cores as an example, when the core r max ≥1.476 μm, that is, when the capillary pressure P C ≤0.5 MPa, such cores belong to type I cores; when 0.2453 μm < r max < 1.476 μm, that is, 0.5 MPa < P C < 3 MPa, such cores belong to type II cores; when r max ≤0.2453 μm, that is, P C ≤3 MPa, such cores belong to type III cores. Thus, the core classification based on the maximum pore radius can be realized.

[0106] S203: Determine the permeability models of various types of cores according to the historical permeability, historical porosity, and historical maximum pore radius of the classified historical cores.

[0107] For example, there are 9 groups of type I cores, 22 groups of type II cores, and 11 groups of type III cores. According to the derivation formula fit the data of the three types of cores, and at the same time compare and verify the permeability obtained from the fitting formula of the three types of cores with the experimental permeability, and give the evaluation error formula of the fitting accuracy:

[0108]

[0109] Among them, R 2 is the coefficient of determination, and N is the number of permeabilities; y i is the predicted value of the i-th permeability; is the actual value of the i-th permeability (historical permeability); is the average value of the actual values of the permeabilities, RMSE is the root mean square error, and MAPE is the mean absolute percentage error.

[0110] Table 1

[0111]

[0112]

[0113] Table 1 is the permeability model table of three kinds of cores. Figure 8 is a comparison chart of the preset permeability and the historical permeability of a kind of core. Figure 9 is a comparison chart of the preset permeability and the historical permeability of the second kind of core. Figure 10 is a comparison chart of the preset permeability and the historical permeability of the third kind of core. Figures 8 - 10 The abscissa in Figures 8 - 10 is the predicted permeability, and the ordinate is the gas logging permeability (historical permeability, actual permeability). As shown in Table 1, 2 Taking R 2 and MAPE as evaluation indexes, the permeability prediction effects of the first type of reservoir and the third type of reservoir are ideal. The comparison between the actual value and the predicted value is close to the 45° diagonal line, and their R 2 are 0.9839 and 0.8544 respectively. In terms of a single RMSE evaluation, the RMSE of the second type of reservoir is 0.0169, which is smaller than the RMSE value of the first type of reservoir, indicating that the fitting effect of the second type of reservoir is better than that of the first type. Therefore, in terms of the overall error evaluation, the classification effect of the present invention has reached the standard of reservoir classification in the oil reservoir, and the verification meets the requirements.

[0114] The specific process of the embodiment of the present invention is as follows:

[0115] 1. Obtain the historical permeability, historical porosity, and historical maximum pore radius of the historical core.

[0116] 2. Classify the historical core according to the historical maximum pore radius.

[0117] 3. Determine the permeability model of each type of core according to the historical permeability, historical porosity, and historical maximum pore radius of the classified historical core.

[0118] 4. Perform a mercury injection test on the current core to obtain a capillary pressure curve, and determine the maximum pore radius according to the capillary pressure curve.

[0119] 5. Determine the core type of the current core based on the maximum pore radius to determine the corresponding permeability model.

[0120] 6. Obtain the porosity of the current core, and input the porosity and the maximum pore radius into the corresponding permeability model to obtain the reservoir permeability.

[0121] In summary, the present invention only needs to obtain the maximum pore radius of the core to classify the reservoir types. According to the corresponding core porosity and the maximum pore radius, the permeability can be further predicted. The invention involves fewer relevant parameters, the reservoir classification process is simple, and the permeability prediction accuracy is high.

[0122] Based on the same inventive concept, an embodiment of the present invention further provides a device for predicting reservoir permeability. Since the principle of the device for solving problems is similar to that of the method for predicting reservoir permeability, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0123] Figure 11 It is a structural block diagram of the device for predicting reservoir permeability in an embodiment of the present invention. As Figure 11 shown, the device for predicting reservoir permeability includes:

[0124] A porosity acquisition module for acquiring the porosity and the maximum pore radius of the current core;

[0125] A permeability model determination module for determining the corresponding permeability model according to the maximum pore radius;

[0126] A current core permeability module for inputting the porosity and the maximum pore radius into the corresponding permeability model to obtain the reservoir permeability.

[0127] In one embodiment, the permeability model determination module includes:

[0128] A core type unit for determining the core type of the current core according to the maximum pore radius;

[0129] A permeability model determination unit for determining the permeability model corresponding to the core type.

[0130] In one embodiment, it further includes:

[0131] A historical data acquisition module for acquiring the historical permeability, historical porosity, and historical maximum pore radius of the historical core;

[0132] A classification module for classifying the historical cores according to the historical maximum pore radius;

[0133] A core permeability determination module, which is used to determine the permeability model of each type of core according to the historical permeability, historical porosity, and historical maximum pore radius of the classified historical cores.

[0134] In one embodiment, it further includes:

[0135] A capillary pressure curve module, which is used to perform mercury injection tests on the current core to obtain a capillary pressure curve;

[0136] A maximum pore radius module, which is used to determine the maximum pore radius according to the capillary pressure curve.

[0137] In summary, the reservoir permeability prediction device according to the embodiment of the present invention first determines the corresponding permeability model according to the maximum pore radius, and then inputs the porosity and the maximum pore radius into the corresponding permeability model to improve the prediction efficiency and accuracy of the reservoir permeability, and reduce the development time and development cost of reservoir resources.

[0138] The embodiment of the present invention also provides a specific implementation manner of a computer device that can implement all steps in the reservoir permeability prediction method in the above embodiment. Figure 12 It is a structural block diagram of the computer device in the embodiment of the present invention. Refer to Figure 12 , and the computer device specifically includes the following:

[0139] A processor 1201 and a memory 1202.

[0140] The processor 1201 is used to call the computer program in the memory 1202. When the processor executes the computer program, it implements all steps in the reservoir permeability prediction method in the above embodiment. For example, when the processor executes the computer program, it implements the following steps:

[0141] Obtain the porosity and the maximum pore radius of the current core;

[0142] Determine the corresponding permeability model according to the maximum pore radius;

[0143] Input the porosity and the maximum pore radius into the corresponding permeability model to obtain the reservoir permeability.

[0144] In summary, the computer device according to the embodiment of the present invention first determines the corresponding permeability model according to the maximum pore radius, and then inputs the porosity and the maximum pore radius into the corresponding permeability model to improve the prediction efficiency and accuracy of the reservoir permeability, and reduce the development time and development cost of reservoir resources.

[0145] An embodiment of the present invention also provides a computer-readable storage medium capable of implementing all steps in the reservoir permeability prediction method in the above embodiment. A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, all steps in the reservoir permeability prediction method in the above embodiment are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0146] Obtain the porosity and the maximum pore radius of the current core;

[0147] Determine the corresponding permeability model according to the maximum pore radius;

[0148] Input the porosity and the maximum pore radius into the corresponding permeability model to obtain the reservoir permeability.

[0149] In summary, the computer-readable storage medium of the embodiment of the present invention first determines the corresponding permeability model according to the maximum pore radius, and then inputs the porosity and the maximum pore radius into the corresponding permeability model to improve the prediction efficiency and accuracy of the reservoir permeability, and reduce the development time and development cost of reservoir resources.

[0150] An embodiment of the present invention also provides a computer program product capable of implementing all steps in the reservoir permeability prediction method in the above embodiment. The computer program product includes computer programs / instructions. When the computer programs / instructions are executed by a processor, all steps in the reservoir permeability prediction method in the above embodiment are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0151] Obtain the porosity and the maximum pore radius of the current core;

[0152] Determine the corresponding permeability model according to the maximum pore radius;

[0153] Input the porosity and the maximum pore radius into the corresponding permeability model to obtain the reservoir permeability.

[0154] In summary, the computer program product of the embodiment of the present invention first determines the corresponding permeability model according to the maximum pore radius, and then inputs the porosity and the maximum pore radius into the corresponding permeability model to improve the prediction efficiency and accuracy of the reservoir permeability, and reduce the development time and development cost of reservoir resources.

[0155] In the above specific embodiments, the purpose, technical solutions, and beneficial effects of the present invention are further described in detail. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0156] Those skilled in the art can also understand that the various illustrative logical blocks, units, and steps listed in the embodiments of the present invention can be implemented by electronic hardware, computer software, or a combination of both. To clearly show the interchangeability of hardware and software, the above-mentioned various illustrative components, units, and steps have generally described their functions. Whether such functions are implemented by hardware or software depends on the specific application and the design requirements of the entire system. Those skilled in the art can use various methods to implement the described functions for each specific application, but such implementation should not be construed as exceeding the scope of protection of the embodiments of the present invention.

[0157] The various illustrative logical blocks, or units, or devices described in the embodiments of the present invention can all be implemented or operate the described functions through a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array, or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or any combination of the above designs. The general-purpose processor can be a microprocessor. Optionally, the general-purpose processor can also be any conventional processor, controller, microcontroller, or state machine. The processor can also be implemented by a combination of computing devices, such as a digital signal processor and a microprocessor, multiple microprocessors, one or more microprocessors combined with a digital signal processor core, or any other similar configuration.

[0158] The steps of the methods or algorithms described in the embodiments of the present invention can be directly embedded in hardware, software modules executed by a processor, or a combination of the two. The software modules can be stored in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium in the art. Exemplarily, the storage medium can be connected to the processor so that the processor can read information from the storage medium and write information to the storage medium. Optionally, the storage medium can also be integrated into the processor. The processor and the storage medium can be provided in an ASIC, and the ASIC can be provided in a user terminal. Optionally, the processor and the storage medium can also be provided in different components of the user terminal.

[0159] In one or more exemplary designs, the functions described in embodiments of the present invention may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored on a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium. A computer-readable medium includes both computer storage media and communication media that facilitate transfer of a computer program from one place to another. The storage media may be any available media that can be accessed by a general purpose or special purpose computer. By way of example, and not limitation, such computer-readable media can include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store program code in the form of instructions or data structures and that can be accessed by a general purpose or special purpose computer, or a general purpose or special purpose processor. In addition, any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave, it is included in the definition of computer-readable medium. Disk and disc include compact disk, laser disk, optical disk, DVD, floppy disk, and Blu-ray disk, where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

Claims

1. A method for predicting the permeability of an oil reservoir formation, characterized in that, Including: Obtain the porosity and the maximum pore radius of the current core; Determine the corresponding permeability model according to the maximum pore radius; Input the porosity and the maximum pore radius into the corresponding permeability model to obtain the reservoir permeability; Among them, determining the corresponding permeability model according to the maximum pore radius includes: Determine the core type of the current core according to the maximum pore radius. Among them, when the maximum pore radius is greater than or equal to 1.476 μm, the core is a type-I core; when the maximum pore radius is greater than 0.2453 μm and less than 1.476 μm, the core is a type-II core; when the maximum pore radius is less than or equal to 0.2453 μm, the core is a type-III core; Determine the permeability model corresponding to the core type; Among them, the formula for the reservoir permeability is: Where k is the permeability of the reservoir formation, with the unit of md; a j is the first constant coefficient of the permeability model corresponding to the j-th type of core, b j is the second constant coefficient of the permeability model corresponding to the j-th type of core, c j is the third constant coefficient of the permeability model corresponding to the j-th type of core, φ is the porosity, with the unit of %; r max is the maximum pore radius, with the unit of μm.

2. The method for predicting the permeability of an oil reservoir according to claim 1, characterized in that Also including: Obtain the historical permeability, historical porosity, and historical maximum pore radius of the historical core; Classify the historical core according to the historical maximum pore radius; Determine the permeability models of various types of cores according to the historical permeability, historical porosity, and historical maximum pore radius of the classified historical cores.

3. The method for predicting the permeability of an oil reservoir according to claim 1, wherein Also including: Perform mercury injection test on the current core to obtain the capillary pressure curve; Determine the maximum pore radius according to the capillary pressure curve.

4. An oil reservoir permeability prediction device, characterized in that, Including: Pore acquisition module, used to obtain the porosity and the maximum pore radius of the current core; Permeability model determination module, used to determine the corresponding permeability model according to the maximum pore radius; Current core permeability module, used to input the porosity and the maximum pore radius into the corresponding permeability model to obtain the reservoir permeability; Among them, the permeability model determination module includes: Core type unit, used to determine the core type of the current core according to the maximum pore radius. When the maximum pore radius is greater than or equal to 1.476 μm, the core is a type-I core; when the maximum pore radius is greater than 0.2453 μm and less than 1.476 μm, the core is a type-II core; when the maximum pore radius is less than or equal to 0.2453 μm, the core is a type-III core; Permeability model determination unit, used to determine the permeability model corresponding to the core type; Among them, the formula for the reservoir permeability is: where k is the permeability of the reservoir formation, with the unit of md; a j is the first constant coefficient of the permeability model corresponding to the j-th type of core, b j is the second constant coefficient of the permeability model corresponding to the j-th type of core, c j is the third constant coefficient of the permeability model corresponding to the j-th type of core, φ is the porosity, with the unit of %; r max is the maximum pore radius, with the unit of μm.

5. The reservoir permeability prediction device according to claim 4, characterized in that, Also including: Historical data acquisition module, used to obtain the historical permeability, historical porosity, and historical maximum pore radius of the historical core; Classification module, used to classify the historical core according to the historical maximum pore radius; Core permeability determination module, used to determine the permeability models of various types of cores according to the historical permeability, historical porosity, and historical maximum pore radius of the classified historical cores.

6. The reservoir permeability prediction device according to claim 4, characterized in that, Also including: Capillary pressure curve module, used to perform mercury injection test on the current core to obtain the capillary pressure curve; Maximum pore radius module, used to determine the maximum pore radius according to the capillary pressure curve.

7. A computer device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the reservoir permeability prediction method according to any one of claims 1 to 3.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the reservoir permeability prediction method according to any one of claims 1 to 3.

9. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by a processor, the steps of the reservoir permeability prediction method according to any one of claims 1 to 3 are implemented.

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