A saturation model construction method, system, device, medium and program

Abstract

The application relates to a saturation model construction method, system, device, storage medium and computer program, and the method comprises the following steps: obtaining a piston core, reservoir data and logging data of a target reservoir, calculating saturation model interpretation parameters corresponding to each pore type according to the piston core; calculating pore structure parameters corresponding to the pore type according to the logging data; calculating the multiple core resistivity relationship of multiple pores according to the reservoir data, and constructing the quantitative relationship between the multiple core resistivity of multiple pores and the saturation model interpretation parameters and the pore structure parameters; and generating the saturation model of the target reservoir according to the quantitative relationship. The application improves the accuracy of the saturation model construction.

Description

Technical Field

[0001] This application relates to the field of complex reservoir saturation model construction technology, and in particular to a saturation model construction method, system, device, medium and program. Background Technology

[0002] Oil and gas saturation is a key parameter for quantitative evaluation of reservoir saturation through well logging. Due to the significant differences in electrical properties between oil and gas and formation water, saturation evaluation based on electrical properties has always been the primary method for quantitative well logging saturation assessment. Existing methods for determining saturation in reservoir evaluation mainly include direct measurement using closed core sampling, laboratory core analysis, and geophysical logging methods. Comparatively, direct measurement of water saturation from closed core sampling provides first-hand field data, but it is costly and difficult to implement widely. Laboratory core analysis is a relatively reliable method for obtaining water saturation, but the discrete nature of core analysis data leads to significant discontinuities in quantitative characterization; therefore, it is usually only used as a verification basis for theoretical analysis. Currently, the most widely used method for evaluating reservoir saturation is water saturation evaluation based on well logging data, primarily using electrical logging data, nuclear magnetic resonance (NMR) data, and sonic logging data. Constructing a saturation model based on laboratory core electrical property experiments and theoretical analysis, and then combining well logging curves and multiple influencing factors to evaluate reservoir water saturation, is currently an important means of reservoir water saturation evaluation.

[0003] Based on the development history of oil and gas exploration and combined with the characteristics of different types of reservoirs, oil and gas reservoirs are divided into two categories: (1) simple homogeneous reservoirs, mainly including medium- and high-porosity pure sandstone reservoirs and matrix-developed carbonate reservoirs; (2) complex heterogeneous reservoirs, mainly including argillaceous sandstone reservoirs and complex pore structure reservoirs (including low-porosity and low-permeability sandstone reservoirs, tight sandstone reservoirs and carbonate reservoirs with multiple pore media). For conventional reservoirs such as pure sandstone and carbonate rocks with matrix pore development, Archie's first and second formulas are commonly used to evaluate their water saturation. In argillaceous sandstone reservoirs and complex pore structure reservoirs, the classic Archie shows a "non-Archie" phenomenon. For argillaceous sandstone reservoirs, compared with conventional reservoirs, the curve is curved due to the complex conductivity of argillaceous material and the additional conductivity of argillaceous material. In complex reservoirs (low-porosity and low-permeability sandstone reservoirs, tight sandstone reservoirs, and carbonate reservoirs with well-developed multi-porous media), low-porosity and low-permeability sandstone reservoirs and tight sandstone reservoirs are characterized by low porosity and low permeability, well-developed matrix and dissolution pores, complex pore structures, and strong heterogeneity, all of which contribute to the non-Archie phenomenon. The complex pore structure is the main reason for this phenomenon. The pore structure of rocks mainly includes the geometry, size, distribution, and interconnection of pores and throats.

[0004] In conventional reservoirs, Archie's formula can be used to obtain saturation. However, non-Archie phenomena in argillaceous sandstone and complex reservoirs cause changes in saturation models based on Archie's formula. Saturation models based on Archie's formula are no longer applicable to conventional reservoirs. For argillaceous sandstone models, due to the additional conductivity of argillaceous material, conductivity models considering argillaceous content and distribution are needed. For reservoirs with complex pore structures, strong heterogeneity leads to changes in rock electrical parameters, thus rendering fixed-parameter Archie saturation models unusable. This has led to the development of modified Archie saturation models with variable parameters. While this method has some effectiveness, its regional limitations are a typical drawback. The emergence of "non-Archie" phenomena in complex pore structure reservoirs due to pore structure necessitates the development of saturation models based on pore structure. Given the multiple reservoir spaces in tight sandstone and carbonate rocks, including matrix porosity, dissolution pores, and fractures, research on saturation models for complex reservoirs based on the conductivity of multiple porous media is essential. How to accurately construct saturation models for complex reservoirs is a pressing problem to be solved. Summary of the Invention

[0005] This application provides a method, system, device, medium, and program for constructing a saturation model to solve the problem of poor accuracy in constructing saturation models in complex reservoirs.

[0006] Firstly, this application provides a method for constructing a saturation model, including:

[0007] Obtain the plunger core, reservoir data and logging data of the target reservoir, and calculate the saturation model interpretation parameters corresponding to each pore type based on the plunger core;

[0008] Calculate the pore structure parameters corresponding to the pore type based on the logging materials;

[0009] The resistivity relationship of multiple cores with multiple pores is calculated based on the reservoir data, and a quantitative relationship is constructed between the resistivity of multiple cores with multiple pores and the interpretation parameters of the saturation model and the pore structure parameters based on the resistivity relationship of multiple cores with multiple pores.

[0010] A saturation model of the target reservoir is generated based on the quantitative relationship.

[0011] In some embodiments, the step of calculating the saturation model interpretation parameters corresponding to each pore type based on the plunger core includes:

[0012] Core simulation was performed on the plunger core to obtain simulation parameters corresponding to the pore type;

[0013] The plunger core was excited by radio frequency pulses to obtain the echo signal of the plunger core;

[0014] The echo signal is inverted to obtain the transverse relaxation time spectrum corresponding to the plunger core.

[0015] Calculate the pore throat radius and core surface relaxation rate corresponding to the pore type based on the transverse relaxation time spectrum;

[0016] By combining the simulation parameters, the pore throat radius, and the core surface relaxation rate, the saturation model interpretation parameters corresponding to the pore type are obtained.

[0017] In some embodiments, calculating the pore structure parameters corresponding to the pore type based on the logging material includes:

[0018] The logging data is divided into conventional logging data, electrical imaging logging data, and nuclear magnetic resonance logging data.

[0019] Calculate the first pore structure parameters of the pores based on the conventional well logging data;

[0020] The electrical imaging logging data is image segmented to obtain a pore logging image, and the second pore structure parameters are calculated based on the pore logging image.

[0021] The optimal spatial configuration relationship characterizing the pore space is determined based on the nuclear magnetic resonance logging data, and the third pore structure parameters are calculated based on the optimal spatial configuration relationship.

[0022] The pore structure parameters are obtained by combining the first pore structure parameters, the second pore structure parameters, and the third pore structure parameters.

[0023] In some embodiments, calculating the second pore structure parameters based on the pore logging image includes:

[0024] Edge detection is performed on the porosity logging images to obtain the range of fracture curves and the range of dissolution cavities;

[0025] Calculate the crack structure parameters based on the crack curve range;

[0026] The structural parameters of the crack are calculated using the following formula:

[0027] W = CAR m b R xo (1-b)

[0028]

[0029] Where W represents the crack width, C represents the preset first instrument structure parameter, A represents the abnormal current area, and R... m R represents the resistivity of the mud.xo The value represents the resistivity of the intrusion band, b represents the preset second instrument structure parameter, and V. e Z0 represents the plate potential value, and I represents the basic half-width. a I represents the electrode current. b The electrode current represents the undisturbed formation or framework, z represents the displacement perpendicular to the fracture trajectory within the fracture curve range, and φ represents the current of the electrode in the undisturbed formation or framework. f W represents the porosity of the crack. i L represents the width of the i-th crack. i τ represents the length of the i-th fracture within a preset statistical window length L, D represents the well diameter in the logging data, and τ wfp Indicates crack tortuosity, qng represents crack dip angle, Rad wfp Indicates the crack radius;

[0030] Calculate the structural parameters of the dissolution cavities based on the range of the dissolution cavities;

[0031] By combining the crack structure parameters and the dissolution cavity structure parameters, the second pore structure parameters are obtained.

[0032] In some embodiments, calculating the multiple core resistivity relationship of multiple pores based on the reservoir data includes:

[0033] Identify the single pore type in the reservoir data;

[0034] Construct a three-dimensional core model corresponding to the single pore type, and calculate the core resistivity corresponding to the single pore type and multiple pores based on the three-dimensional core model;

[0035] The core resistivity relationship between the single core pore type and the multiple pore types is calculated based on the core resistivity.

[0036] In some embodiments, generating the saturation model of the target reservoir based on the quantitative relationship includes:

[0037] The saturation model is constructed using the following formula:

[0038]

[0039] Where Sw represents the saturation model, R w R represents the resistivity of formation water. t φ represents the original formation resistance. m_exp Represents the porosity index, n_exp represents the preset saturation index, Rad wvm φ represents the radius of the pore throat in the dissolution pore throat model. v φ b τ represents the porosity of the matrix porosity and the solution porosity, respectively.wvp τ represents the tortuosity of the pore body in the dissolution cavity throat model. wvt Rad represents the tortuosity of the throat passage in the dissolution cavity throat model. wvp Rad represents the pore volume radius of the dissolution cavity. wvt Rad represents the throat radius of the dissolution cavity. wbm Rad represents the radius of the pore throat in the matrix pore throat model. wbp Rad represents the pore volume radius of the matrix pores. wbt τ represents the throat radius of the matrix pores. wbp τ represents the tortuosity of the pore body in the matrix pore throat model. wbt This indicates the tortuosity of the larynx in the matrix pore pore laryngeal model.

[0040] Secondly, this application provides a saturation model construction apparatus, comprising:

[0041] The saturation model interpretation parameter calculation module is used to acquire the target reservoir's plunger core, reservoir data, and well logging data, and to calculate the saturation model interpretation parameters corresponding to each pore type based on the plunger core.

[0042] A pore structure parameter calculation module is used to calculate the pore structure parameters corresponding to the pore type based on the logging material.

[0043] The quantitative relationship construction module is used to calculate the multiple core resistivity relationship of multiple pores based on the reservoir data, and to construct the quantitative relationship between the multiple core resistivity of multiple pores and the interpretation parameters of the saturation model and the pore structure parameters based on the multiple core resistivity relationship.

[0044] The saturation model generation module is used to generate a saturation model of the target reservoir based on the quantitative relationship.

[0045] Thirdly, this application provides a computer device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described above.

[0046] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0047] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the methods described above.

[0048] This application provides a saturation model construction method, system, equipment, medium, and program. It calculates the interpretation parameters of the saturation model corresponding to each pore type using plunger core samples, allowing for multi-faceted interpretation of the saturation model for each pore type. It calculates the pore structure parameters corresponding to each pore type based on well logging materials, ensuring that pore structure parameters are considered in the saturation model construction. Furthermore, it calculates the quantitative relationship between multiple core resistivity and the interpretation parameters of the saturation model, as well as the pore structure parameters, based on reservoir data. This allows for the construction of a saturation model using pore types and pore structure parameters, resulting in a more accurate saturation model and improving the accuracy of reservoir fluid identification and reservoir productivity evaluation. Attached Figure Description

[0049] The present application will be described in more detail below based on embodiments and with reference to the accompanying drawings:

[0050] Figure 1 A flowchart illustrating a saturation model construction method provided in this application embodiment;

[0051] Figure 2 A schematic diagram of a pore structure model of a X-ray tube provided in an embodiment of this application;

[0052] Figure 3 This application provides a schematic diagram of the structure of a spherical pore spectrum and a tubular throat spectrum corresponding to a matrix pore NMR T2 spectrum.

[0053] Figure 4 This application provides a schematic diagram of the structure of a spherical pore spectrum and a tubular throat spectrum corresponding to the NMR T2 spectrum of a dissolution cavity;

[0054] Figure 5 A schematic diagram of the structure of matrix pores and dissolution cavities provided in an embodiment of this application;

[0055] Figure 6 This is a schematic diagram of the structure of a dual-pore core model provided in an embodiment of this application;

[0056] Figure 7 A schematic diagram of finite element simulation results of resistivity and series resistivity provided in an embodiment of this application;

[0057] Figure 8 This is a schematic diagram of the structure of a pharyngeal cavity model provided in an embodiment of this application;

[0058] Figure 9 A schematic diagram of a core saturation index provided in an embodiment of this application;

[0059] Figure 10 A schematic diagram of the water saturation of different research sections provided for embodiments of this application;

[0060] Figure 11 A schematic diagram of the functional modules of a saturation model construction device provided in an embodiment of this application;

[0061] Figure 12 This is a schematic diagram of the structure of an electronic device that provides a saturation model construction method according to an embodiment of this application.

[0062] In the accompanying drawings, the same parts are referred to by the same reference numerals, and the drawings are not drawn to scale. Detailed Implementation

[0063] To enable those skilled in the art to better understand the technical solutions of this application, and to fully understand and implement the process of how this application uses technical means to solve technical problems and achieve corresponding technical effects, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, not all of them. The embodiments of this application and the various features within them can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of this application.

[0064] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0065] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0066] This application provides a method for constructing a saturation model. The execution entity of this saturation model construction method includes, but is not limited to, at least one of the following: a server, a terminal, or other electronic devices that can be configured to execute the system provided in this application. In other words, the saturation model construction method can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0067] Example 1

[0068] Figure 1 This is a flowchart illustrating a saturation model construction method provided in an embodiment of this application, as shown below. Figure 1 As shown, the saturation model construction method includes:

[0069] S1. Obtain the plunger core, reservoir data, and well logging data of the target reservoir, and calculate the saturation model interpretation parameters corresponding to each pore type based on the plunger core.

[0070] In one embodiment, a plunger core refers to a columnar sample in a rock, particularly in reservoir rocks such as sandstone, used to study pore structure and fluid flow characteristics. These plunger samples are imaged using different scanning methods to characterize pore structures at different scales, including matrix pores, dissolution pores, intergranular pores, intragranular pores, mold pores, fractures, etc.

[0071] Specifically, the calculation of saturation model interpretation parameters corresponding to each pore type based on the plunger core includes:

[0072] Core simulation was performed on the plunger core to obtain simulation parameters corresponding to the pore type;

[0073] The plunger core was excited by radio frequency pulses to obtain the echo signal of the plunger core;

[0074] The echo signal is inverted to obtain the transverse relaxation time spectrum corresponding to the plunger core.

[0075] Calculate the pore throat radius and core surface relaxation rate corresponding to the pore type based on the transverse relaxation time spectrum;

[0076] By combining the simulation parameters, the pore throat radius, and the core surface relaxation rate, the saturation model interpretation parameters corresponding to the pore type are obtained.

[0077] In one embodiment, core simulation is performed using methods such as brine saturation, centrifugation, weighing, four-stage method, brine salinity, formation water analysis data, and gas expansion method. Specifically, brine saturation is used, centrifugation is used to obtain cores with different water saturation levels, weighing is used to obtain water saturation Sw, four-stage method is used to measure core resistivity Rt, brine resistivity Rw is obtained based on brine salinity, saturation parameters are obtained by fitting the Rt-Sw curve in a double logarithmic coordinate system to the rock electrical experiment, and core porosity is obtained by the gas expansion method.

[0078] Specifically, the step of performing core simulation on the plunger core to obtain simulation parameters corresponding to the pore type includes:

[0079] Construct a three-dimensional digital core corresponding to the plunger core;

[0080] Core simulation parameters were constructed based on the described plunger core.

[0081] The plunger core is simulated based on the three-dimensional digital core and the core simulation parameters to obtain the simulation parameters corresponding to the pore type.

[0082] In one embodiment, the three-dimensional data is a three-dimensional structural model obtained by three-dimensional scanning of a plunger core. The core model parameters are determined by using plunger cores with different preset pore types, thereby applying the conditions of the core model to the three-dimensional data core to simulate the plunger core and obtain the corresponding simulation parameters.

[0083] Furthermore, core simulations can be performed using corresponding software based on the different simulation parameters required. For example, finite element software can simulate core resistivity Rt, while Petrel software can simulate porosity, permeability, water saturation, etc. Formation water resistivity can be obtained from formation water analysis data, and mud resistivity can be obtained based on field records.

[0084] In one embodiment, the radio frequency pulse is a Carr-Purcell-Meiboom-Gill (CPMG) sequence, a nuclear magnetic resonance pulse sequence used to measure the transverse relaxation time (T2) of the sample.

[0085] In one embodiment, the step of exciting the plunger core with a radio frequency pulse to obtain the echo signal of the plunger core includes:

[0086] Obtain preset radio frequency pulse parameters, and write excitation code based on the radio frequency pulse parameters;

[0087] Based on the excitation code, a preset nuclear magnetic resonance device is used to excite a pulse sequence to the plunger core;

[0088] The pulse sequence is sampled for echo to obtain the echo signal of the plunger core.

[0089] In one embodiment, the radio frequency pulse parameters include attributes such as the shape, phase, amplitude, and duration of the radio frequency pulse on the plunger core, as well as parameters such as echo interval, number of data acquisitions, number of sampling points, and sampling time. The excitation code written using the radio frequency pulse parameters can control the nuclear magnetic resonance device to apply a series of 180° pulses after the 90° radio frequency pulse, generating multiple echo signals.

[0090] Specifically, the excitation pulse sequence can be a 90° radio frequency pulse followed by a 180° flip pulse applied after time TE / 2, and then a sampling command is initiated at the appropriate time to sample the signal. An echo peak appears at time TE, followed by a 180° pulse to collect the echo. Multiple 180° pulses are applied sequentially in the ±Y direction, and the corresponding echoes are sampled sequentially within the corresponding time windows. This process is repeated every TR (Repetition Time) for NS cycles (NS being the number of superpositions, i.e., the number of times a single sequence is executed), resulting in multiple echo signals from the plunger core.

[0091] In one embodiment, the transverse relaxation time spectrum can be measured by using the echo signal from the plunger core while reducing the influence of magnetic field inhomogeneity on the measurement results, thereby improving the measurement accuracy of the transverse relaxation time spectrum.

[0092] In one embodiment, inversion is the recovery or reconstruction of the original signal or parameters from the observed echo signal. By inverting the echo signal, the transverse relaxation time (T2) distribution information of the plunger core can be extracted to obtain the transverse relaxation time spectrum.

[0093] In one embodiment, the step of inverting the echo signal to obtain the transverse relaxation time spectrum corresponding to the plunger core includes:

[0094] Construct a system of linear equations for the echo signal;

[0095] Singular value decomposition was performed on the linear equation system to obtain the transverse relaxation time spectrum corresponding to the plunger core.

[0096] In one embodiment, the functional expression of the echo curve can be obtained from the echo signal, an appropriate decay time for each relaxation component is set, the coefficient matrix is ​​calculated, and a system of linear equations is constructed using the coefficient matrix.

[0097] Furthermore, the function expression corresponding to the echo signal can be fitted using preset inversion software to obtain the T2 (lateral relaxation time) spectrum, which is the lateral relaxation time spectrum corresponding to the plunger core.

[0098] In one embodiment, the distribution information of the transverse relaxation time (T2) of the pore type corresponding to the plunger core can be extracted by the transverse relaxation time spectrum, which provides a basis for subsequent calculation of the pore throat radius and the relaxation rate of the core surface.

[0099] In one embodiment, the pore throat radius refers to the narrow channel in a porous medium, i.e., the pore throat. When the pore throat is described using a cylinder, the corresponding cylinder radius is used. The size of the pore throat radius has a significant impact on reservoir properties: the larger the pore throat radius, the better the reservoir properties, the smaller the seepage resistance, and the greater the development potential; conversely, the worse the reservoir properties, the smaller the development potential, and the greater the development difficulty. Core surface relaxation refers to the influence of the rock particle surface on the relaxation process. In nuclear magnetic resonance (NMR) technology, core surface relaxation is a key parameter used to calculate pore size distribution.

[0100] In one embodiment, calculating the pore throat radius and core surface relaxation rate corresponding to the pore type based on the transverse relaxation time spectrum includes:

[0101] The transverse relaxation time spectrum is split into a spherical pore time spectrum and a tubular throat time spectrum.

[0102] Calculate the optimal configuration relationship between the pore time spectrum of the sphere and the time spectrum of the tubular throat;

[0103] Calculate the arithmetic mean and geometric mean of the sphere pore time spectrum and the tubular throat time spectrum based on the optimal configuration relationship;

[0104] The radius and tortuosity of the spherical pores and tubular throats are calculated based on the arithmetic mean and the geometric mean.

[0105] Calculate the pore throat radius corresponding to the pore type based on the radius and the tortuosity;

[0106] Calculate the harmonic mean and specific surface area of ​​the transverse relaxation time spectrum;

[0107] The core surface relaxation rate corresponding to the pore type is calculated based on the harmonic average value and the specific surface area.

[0108] In one embodiment, the structural model corresponding to the pore type includes two parts: spherical pores and tubular throats. Based on the correspondence between the radius of the tubular throat and the radius of the spherical pore in the spherical tube pore structure model, the configuration relationship between the spherical pores and tubular throats in all groups of spherical tube pore structure models is enumerated. Based on the configuration relationship, the difference between the echo signal and the original signal corresponding to the NMR T2 spectrum split into the spherical pore time spectrum (T2s spectrum) representing the spherical pore and the tubular throat time spectrum (T2c spectrum) representing the tubular throat is enumerated to obtain the optimal configuration relationship. The arithmetic mean and geometric mean of the spherical pore time spectrum and the tubular throat time spectrum under the optimal configuration relationship are calculated respectively. Then, the radius and tortuosity of the spherical pore and the tubular throat are calculated respectively based on the arithmetic mean and the geometric mean.

[0109] The radius and tortuosity of the spherical pores and tubular throats are calculated using the formulas shown below:

[0110]

[0111] Among them, Rad wp The radius of the pores in the sphere is represented by ρ, the relaxation rate of the core surface is represented by T. 2SAM τ represents the arithmetic mean of the pore size of a sphere. wp T represents the tortuosity of the pores in a sphere. 2SGM Rad represents the geometric mean of the porosity of a sphere. wt The radius of the tubular throat is represented by ρ, the relaxation rate of the core surface is represented by T. 2CAM τ represents the arithmetic mean of the tubular larynx. wt T represents the tortuosity of the tubular larynx. 2CGM This represents the geometric mean of the tubular throat.

[0112] Furthermore, due to the formulas for porosity and pore structure parameters in the pore throat model:

[0113]

[0114] Where φ represents porosity, A and L represent the cross-sectional area and length of the rock physical volume model containing the pore throat cavity, respectively, and τ wp The tortuosity of the porous body, i.e., the pore length L wp The ratio of τ to the length L of the laryngeal cavity model wt ψ represents the tortuosity of the larynx. wp The cross-sectional area A of the porous body wp The ratio of ψ to the cross-sectional area A of the rock physical volume model wt The cross-sectional area A of the larynx wp The ratio of the cross-sectional area A of the rock physical volume model to the given cross-sectional area, Ks represents the preset shape factor, Rad wp Rad represents the pore volume radius.wt Rad represents the throat radius. wm This indicates the radius of the throat cavity.

[0115] In detail, the pore throat radius Rad can be calculated using the least squares method based on the porosity obtained from core experiments. wm .

[0116] Furthermore, for signals excited by radio frequency pulses in nuclear magnetic resonance (NMR) technology, molecules will shift due to diffusion during the time (Δ) between two gradient pulses. By measuring these shifts, the self-diffusion coefficient D of the molecules can be calculated. When exciting a plunger core with radio frequency pulses, a series of echo signals can be obtained by changing the magnetic field gradient, gradient pulse width, or time (Δ) between gradient pulses.

[0117] Based on the above diffusion coefficient and the fitting of the radius with the tortuosity, the specific surface area is obtained as shown in the following formula:

[0118]

[0119] Where D(t) represents the apparent diffusion coefficient, and D0 represents the free diffusion coefficient. The diffusion time is a higher-order quantity, where ρ represents the relaxation rate of the core surface, ρ represents the tortuosity, and t represents the diffusion time. Indicates the surface area.

[0120] The self-diffusion coefficient of the echo signal can be calculated using the PGSE (Pulse Gradient Spin Echo) technique. Specific surface area, also known as specific surface area, refers to the specific surface area of ​​a solid material. The harmonic mean of the transverse relaxation time spectrum represents the reciprocal of the transverse relaxation time spectrum.

[0121] Specifically, the formula for the relaxation rate of the core surface is expressed as:

[0122]

[0123] in, The value represents the harmonic mean of the transverse relaxation time spectrum, and ρ represents the relaxation rate of the core surface. T1 represents the specific surface area, and T2 represents the transverse relaxation time spectrum.

[0124] In one embodiment, a formula combining the relaxation rate of the core surface and the radius of the pore throat can be used to calculate the pore throat radius and the relaxation rate of the core surface corresponding to different pore types. This allows for the characterization of the pore throat and pore size distribution of different pore types, thereby improving the accuracy of the calculation of the interpretation parameters of the saturation model.

[0125] In one embodiment, the saturation model is one of the core parameters in the quantitative evaluation of oil and gas reservoirs. It is used to describe the relationship between the saturation and resistivity of fluids in the reservoir, thereby realizing the conversion of well logging resistivity information into reservoir saturation parameters. By interpreting parameters through the saturation model, the non-Achia characteristics of reservoirs with complex pore structures can be accurately described, which can adapt to different oil and gas exploration and development needs.

[0126] In one embodiment, simulation parameters, pore throat radius, and core surface relaxation rate are used as interpretation parameters for each pore type in the saturation model. This allows for the determination of saturation model interpretation parameters based on reservoir logging data and tubing models, enabling multi-faceted interpretation of the saturation model corresponding to pore types and effectively improving the accuracy of saturation model interpretation parameter calculation.

[0127] S2. Calculate the pore structure parameters corresponding to the pore type based on the logging material.

[0128] In one embodiment, well logging data refers to data and information about the characteristics of underground reservoirs obtained through well logging techniques during oil and gas exploration and development. This data is crucial for understanding and assessing the reservoir's rock physical properties, fluid properties, and connectivity, and is essential for the reservoir under study.

[0129] In detail, well logging data can include conventional well logging data, electrical imaging well logging data, and nuclear magnetic resonance (NMR) well logging data. Conventional well logging data mainly refers to the logging methods used in oil and gas exploration and development, including logging in exploration wells, appraisal wells, and development wells. Electrical imaging well logging data uses sensor array scanning or rotational scanning downhole to collect a large amount of formation information along the longitudinal, circumferential, or radial direction of the wellbore. After being transmitted to the surface, image processing technology is used to obtain two-dimensional images of the wellbore or three-dimensional images within a certain detection depth around the wellbore. Through electrical imaging images and formation dip angles, various formation interfaces, fractures, and structural morphologies can be intuitively and qualitatively identified. NMR well logging data can be acquired using pre-polarized methods and spin echo methods. NMR logging instruments can provide three types of information that conventional logging instruments cannot provide: fluid content, fluid characteristics, pore size, and porosity, thus providing richer formation geological data.

[0130] In one embodiment, calculating the pore structure parameters corresponding to the pore type based on the logging material includes:

[0131] The logging data is divided into conventional logging data, electrical imaging logging data, and nuclear magnetic resonance logging data.

[0132] Calculate the first pore structure parameters of the pores based on the conventional well logging data;

[0133] The electrical imaging logging data is image segmented to obtain a pore logging image, and the second pore structure parameters are calculated based on the pore logging image.

[0134] The optimal spatial configuration relationship characterizing the pore space is determined based on the nuclear magnetic resonance logging data, and the third pore structure parameters are calculated based on the optimal spatial configuration relationship.

[0135] The pore structure parameters are obtained by combining the first pore structure parameters, the second pore structure parameters, and the third pore structure parameters.

[0136] In one embodiment, conventional logging data includes curve measurement methods such as natural gamma, spontaneous potential, and borehole diameter three-lithology curves, shallow, medium, and deep three-resistivity curves, and sonic, neutron, and density three-porosity curves. Electrical imaging logging data is acquired through electrical imaging technology, and nuclear magnetic resonance logging data is acquired through nuclear magnetic resonance technology. Therefore, conventional logging data, electrical imaging logging data, and nuclear magnetic resonance logging data can be classified according to their data sources. Corresponding pore structure parameters can be calculated based on different logging data to improve the accuracy of pore structure parameter calculation.

[0137] In one embodiment, three-porosity logging data from conventional well logging can provide formation porosity information. The total porosity, matrix porosity, and fracture porosity can be calculated from conventional well logging data as the first pore structure parameter.

[0138] In one embodiment, calculating the first pore structure parameter of the pores based on the conventional well logging data includes:

[0139] Calculate the formation density logging porosity, neutron logging porosity, and primary intergranular porosity based on the conventional logging data.

[0140] The total porosity is calculated based on the neutron logging porosity and the density logging porosity.

[0141] The porosity of the matrix and the fracture porosity are calculated based on the total porosity and the primary intergranular porosity.

[0142] The first pore structure parameter is obtained by combining the total porosity, the matrix porosity, and the crack porosity.

[0143] In one embodiment, the three-porosity logging data in conventional logging data can provide formation porosity information, wherein neutron logging porosity and density logging porosity reflect the total formation porosity, while sonic velocity logging mainly reflects the primary intergranular porosity.

[0144] In detail, the formation density logging porosity, neutron logging porosity, and primary intergranular porosity are calculated using the following formulas:

[0145]

[0146] Where, φ D ρ represents density logging porosity. ma ρ represents the density value of the rock skeleton in conventional well logging data. b ρ represents the density value measured by density logging in conventional well logging data. f φ represents the density value of pore fluid in conventional well logging data. N Φ represents the porosity in neutron logging. ma Φ f Φ N φ represents the hydrogen content index measured in conventional well logging data, specifically the rock skeleton, pore fluid, and neutron logging data. S Indicates the primary intergranular porosity, Δt ma Δt f Δt and Δt represent the time differences of rock skeleton, pore fluid, and logging time difference measurements in conventional logging data, respectively.

[0147] In one embodiment, the total porosity is calculated using the following formula:

[0148]

[0149] Where, φ t φ represents total porosity. D φ represents density logging porosity. N This represents the porosity from neutron logging. Further, the porosity of the matrix porosity and fracture porosity is calculated using the following formula:

[0150] φ f =φ t -φ s ,φ b =φ s

[0151] Where, φ f φ represents the porosity of the crack pores. t φ represents total porosity. S Indicates the primary intergranular porosity, φ b This indicates the porosity of the matrix pores.

[0152] In one embodiment, by aggregating the total porosity, the matrix porosity, and the fracture porosity, the porosity of different pore types can be calculated, thereby improving the accuracy and comprehensiveness of pore structure parameter calculation.

[0153] In one embodiment, since electrical imaging logging data is obtained by using sensor array scanning or rotational scanning measurements downhole to collect a large amount of formation information along the longitudinal, circumferential, or radial direction of the wellbore, and then transmitting it to the surface, image processing technology is used to obtain a two-dimensional image of the wellbore or a three-dimensional image of the wellbore within a certain detection depth. Generally speaking, in electrical imaging logging data, the high-resistivity rock skeleton appears as a bright color, while formation water exists in dissolution pores and fracture pores, which have low resistivity and appear as a dark color in the electrical imaging data. Fractures are imaged as dark sinusoidal curves, and dissolution pores appear as irregular clumps, spots, or near-circular shapes in the electrical imaging logging data. Therefore, image segmentation can be performed on the electrical imaging logging data to identify pore logging images that effectively represent the pore, fracture, and cavity parts of the rock in the electrical imaging logging data.

[0154] In one embodiment, the step of performing graphic segmentation on the electrical imaging logging data to obtain a porosity logging image includes:

[0155] Calculate the grayscale histogram of each logging image in the electrical imaging logging data;

[0156] The threshold for maximizing the inter-class variance is calculated based on the gray-level histogram, and the well logging image is segmented using the maximum inter-class difference method based on the threshold to obtain a segmented image;

[0157] Median filtering is applied to the segmented image to obtain a porosity logging image.

[0158] In one embodiment, the inter-class variance of the foreground and background regions at each threshold can be calculated using a grayscale histogram. By iterating through all possible thresholds, the threshold that maximizes the inter-class variance is found. This threshold segments the image into foreground and background, and the foreground and background are used as the segmented image.

[0159] Furthermore, since the Otsu's inter-class difference segmentation algorithm is quite sensitive to noise and uneven illumination, it may lead to poor segmentation results. Therefore, median filtering can be used to filter and eliminate noise to obtain more accurate porosity logging images.

[0160] In one embodiment, the second pore structure parameters include the porosity of cracks and dissolution cavities, as well as the radius and tortuosity of cracks.

[0161] Specifically, the step of calculating the second pore structure parameters based on the pore logging image includes:

[0162] Edge detection is performed on the porosity logging images to obtain the range of fracture curves and the range of dissolution cavities;

[0163] Calculate the crack structure parameters based on the crack curve range;

[0164] Calculate the structural parameters of the dissolution cavities based on the range of the dissolution cavities;

[0165] By combining the crack structure parameters and the dissolution cavity structure parameters, the second pore structure parameters are obtained.

[0166] In one embodiment, the black sine curve in the electro-imaging data is regarded as an open fracture. The range of the black sine curve is delineated by edge detection, and the fracture width (aperture) is calculated. At the same time, the fracture in the porosity logging image can be identified by the electro-imaging logging software ciflog, and the dip angle and dip direction of the fracture can be directly obtained. Then, the fracture porosity, radius and tortuosity can be calculated to obtain the fracture structure parameters.

[0167] In one embodiment, the crack structure parameters are calculated using the following formula:

[0168] W = CAR m b R xo (1-b)

[0169]

[0170] Where W represents the crack width, C represents the preset first instrument structure parameter, A represents the abnormal current area, and R... m R represents the resistivity of the mud. xo The value represents the resistivity of the intrusion band, b represents the preset second instrument structure parameter, and V. e Z0 represents the plate potential value, and I represents the basic half-width. a I represents the electrode current. b The electrode current represents the undisturbed formation or framework, z represents the displacement perpendicular to the fracture trajectory within the fracture curve range, and φ represents the current of the electrode in the undisturbed formation or framework. f W represents the porosity of the crack. i L represents the width of the i-th crack. i τ represents the length of the i-th fracture within a preset statistical window length L, D represents the well diameter in the logging data, and τ wfp Indicates crack tortuosity, qng represents crack dip angle, Rad wfp Indicates the crack radius.

[0171] In detail, the first instrument structural parameter can be a parameter related to the instrument when acquiring electrical imaging logging data. For example, the first instrument structural parameter can be 0.004801, and the second instrument structural parameter can be 0.863, which is related to the instrument's own structure. Simultaneously, the instrument can measure parameters such as mud resistivity, invasion zone resistivity, and electrode current of the reservoir corresponding to the electrical imaging logging data. For example, the electrode current I... a It is a function of the vertical position z of the instrument when it crosses the crack.

[0172] In one embodiment, for irregular clumps, spots, or near-circular dissolution holes, edge detection delineates the distribution range of the dissolution holes, and point statistics are used to measure the segmented dissolution hole image portion. The number of segmented dissolution holes and the number of pixels in each dissolution hole are counted to obtain the porosity of the dissolution holes. In this application, the porosity of the dissolution holes is used as the structural parameter of the dissolution holes.

[0173] Specifically, the structural parameters of the dissolution pores are calculated using the following formula:

[0174]

[0175] Where, φ v VP represents the structural parameters of the dissolution pores. e denoted by , represents the number of pixels representing the pores in the e-th dissolution hole within the dissolution hole range, N represents the total number of dissolution holes within the dissolution hole range, and TP represents the total number of points within the dissolution hole range.

[0176] In one embodiment, the second pore structure parameter can be used to calculate the pore structure parameters when the logging data is electrical imaging logging data, thereby improving the universality and accuracy of the pore structure parameter calculation.

[0177] In one embodiment, the maximum sphere method is used to extract a pore network model representing the pore space. The basic unit of the pore network model is a sphere-tube pore structure model. The length of the sphere-tube pore structure model is twice the equivalent pore radius Rade. The sphere-tube pore structure model includes two parts: spherical pores and tubular throats. The radius of the spherical pores is Rads, and the radius of the tubular throat is Radc (e.g., Radc). Figure 2 As shown in the figure, Cd represents the ratio of the tubular throat radius to the pore radius of the sphere in the sphere pore structure model, reflecting the spatial configuration relationship of the sphere pore structure model; the pore surface area of ​​the sphere is SFs, the surface area of ​​the tubular throat is SFc, and the total pore space surface area of ​​the sphere pore structure model is SFt.

[0178] Furthermore, the pore radius Rad of the sphere in the sphere-tube pore structure model can be given. s With equivalent pore radius Rad e The relationship is as follows:

[0179]

[0180] Wherein, Cs represents the relationship between the average radius of the grouped pores and the radius of the spherical pores, and is generally taken as 3;

[0181] The relationship Cd between the radius of the tubular throat and the radius of the sphere pores in the given sphere tube pore structure model is as follows:

[0182]

[0183] Among them, Rad c Rad represents the radius of the tubular throat. s Indicates the radius of the pores in the sphere;

[0184] Furthermore, the surface areas of the tubular throat and the pores of the sphere in the given sphere tube pore structure model are shown below:

[0185] SF c =π×Rad c 2 ×(Rad e -Rad c ),(3)

[0186] SF s =4π×Rad s 2, (4)

[0187] Among them, SF c Rad represents the surface area of ​​the tubular larynx. c Rad represents the radius of the tubular throat. e SF represents the equivalent pore radius. s Rad represents the pore surface area of ​​a sphere. s Indicates the radius of the pores in the sphere;

[0188] The total surface area of ​​the pore space in the X-ray tube pore structure model is shown below:

[0189] SF t =SF s +SF c (5)

[0190] Among them, SF t SF represents the total surface area of ​​the pore space. c SF represents the surface area of ​​the tubular larynx. s This represents the surface area of ​​the pores in a sphere.

[0191] Furthermore, the pore space of rocks can be characterized by multiple sets of sphere-tube pore structure models with different equivalent pore radii.

[0192] In detail, determining the optimal spatial configuration relationship characterizing the pore space based on the nuclear magnetic resonance logging data includes:

[0193] Based on the transverse relaxation time spectrum in the nuclear magnetic resonance logging data, the number of model groups for the pore structure model of the PV tube representing the pore space is determined.

[0194] Calculate the configuration relationship between the spherical pores and the tubular throat in the spherical tube pore structure model based on the number of model groups.

[0195] Calculate the echo train representing the pore space under each configuration relationship, and determine the optimal configuration relationship based on the echo train.

[0196] In one embodiment, the transverse relaxation time spectrum (NMR T2 spectrum) consists of multiple relaxation times and their amplitudes, i.e.

[0197] T2=(T 2y Am y ),(y=1,2,…,…),(6)

[0198] In the formula, T 2y Am represents the y-th relaxation time or the y-th placement point value in the T2 spectrum. y Let y be the spectral amplitude corresponding to the y-th relaxation time.

[0199] Furthermore, based on the NMR T2 spectrum, the pore space can be characterized as n sets of X-ray tube pore structure models with different equivalent pore radii. The quantitative relationship between the equivalent pore radius and relaxation time of the X-ray tube pore structure model is as follows:

[0200] Rad ei =3ρT 2y (7)

[0201] Among them, Rad ei T represents the equivalent pore radius. 2y Let y represent the y-th relaxation time in the T2 spectrum, and ρ represent the surface relaxation rate.

[0202] Furthermore, the configuration relationships between the spherical pores and tubular throats in all groups of PV tube pore structure models are enumerated. For example, the configuration relationship for the i-th group of PV tube pore structure models is as follows:

[0203]

[0204] In the formula, Rad ci Rad represents the radius of the tubular throat in the i-th group of PV tube pore structure models. si Cd represents the radius of the spherical pores in the i-th group of spherical tube pore structure models. i It represents the ratio of the tubular throat radius to the pore radius of the sphere in the i-th group of sphere-tube pore structure models.

[0205] Will Cd i Discretize into 6 values.

[0206]

[0207] Therefore, for n sets of X-ray tube pore structure models, there are 6 possible configuration relationships for each set of X-ray tube pore structure models. Thus, there are a total of 6 possible configuration relationships for X-ray tube pore structure models across all sets. n indivual.

[0208] Based on the complete configuration relationships between spherical pores and tubular throats in all groups of spherical tube pore structure models, the NMR T2 spectrum is enumerated and decomposed into T2 spectra T representing the spherical pores. 2s and the T2 NMR spectrum characterizing the tubular larynx 2c .

[0209] For all groups of sphere-tube pore structure models, the configuration of sphere pores and tubular throat structures is 6 n The k-th configuration relation (Cd) in the relation 1k Cd 2k ,…,Cd ik ,…,Cd nk )(i=1,2,…,n;k=1,2,…,6 n For example, it can be represented as:

[0210] 1. Calculate the size of the spherical pores and the size of the tubular throat in each group of spherical tube pore structure models under the k-th configuration relationship, and back-calculate the relaxation time T of the spherical pores. 2sik and the relaxation time T of the tubular larynx 2cik Based on T2 relaxation time T 2y The equivalent pore radius Rad for each group of PV tube pore structure models is calculated using formula (7). ei Based on the configuration relationship Cd ik Formulas (1) and (2) are used to calculate the sphere pore radius Rad of the i-th group of sphere tube pore structure models. sik and the radius of the tubular larynx Rad cik ;

[0211] Based on the pore radius of the sphere Rad sik and the radius of the tubular larynx Rad cik Based on formula (7), the nuclear magnetic relaxation time T corresponding to the spherical pores and tubular throats is calculated. 2sik and T 2cik As shown below:

[0212]

[0213] Among them, T 2sik T 2cik Rad represents the T2 relaxation time corresponding to the sphere pores and tubular throats in the i-th group of the sphere-tube pore structure model under the current k-th configuration relationship. sik Rad cikρ represents the radius of the sphere pores and tubular throat in the i-th group of sphere-tube pore structure models under the current configuration relationship, and ρ represents the surface relaxation rate.

[0214] 2. Calculate the surface area of ​​the sphere pores and the surface area of ​​the tubular throat in each group of sphere-tube pore structure models under the current k-th configuration relationship. Calculate the surface area SF of the tubular throat in the i-th group of sphere-tube pore structures according to formulas (3), (4) and (5). sik Surface area of ​​sphere pores SF sik And the total surface area of ​​the pore space in the X-ray tube pore structure SF tik ;

[0215] 3. Calculate the volume of the spherical pores and the volume of the tubular throat in each group of spherical tube pore structure models under the current k-th configuration relationship, based on the relaxation time T in the NMR T2 spectrum. 2y amplitude Am y The porosity Am of the sphere under the current k-th configuration relationship is calculated based on the surface area ratio method. sik With the volume Am of the tubular larynx cik :

[0216]

[0217] In the formula, Am i SF represents the spectral amplitude corresponding to the relaxation time in the T2 NMR spectrum. cik SF sik SF tik These represent the surface areas of the tubular throat, the pores of the sphere, and the total surface area of ​​the pore space in the i-th group of the sphere-tube pore structure model under the current k-th configuration relationship;

[0218] 4. Obtain the NMR T2 spectrum representing the porosity of the sphere under the current k-th configuration relationship. 2sk and characterization of the tubular laryngeal tract T2 NMR spectrum 2ck .

[0219] Rad of the sphere pores in all groups of tube models calculated in steps 1 and 3 under the current k-th configuration relationship. sik With amplitude Am sik and the radius of the tubular larynx Rad cik With amplitude Am cik The spherical pore spectrum and the tubular throat spectrum were constructed:

[0220] T 2sk =(T 2sik Am sik ), (i=1,2,…,n; k=1,2,…,6 n ),(14),

[0221] T 2ck =(T2cik Am cik ), (i=1,2,…,n; k=1,2,…,6 n ),(15),

[0222] In one embodiment, the NMR T2 spectrum characterizing the spherical pores is obtained based on the configuration relationship between the spherical pores and the tubular throat in all X-ray tube pore structure models. 2sk and characterization of the tubular laryngeal tract T2 NMR spectrum 2ck And inversely calculate the echo train that characterizes the pore space.

[0223] For all groups of ball tube pore structure models, the ball rod structure configuration is 6 n The k-th configuration relation (Cd) in the relation 1k Cd 2k ,…,Cd ik ,…,Cd nk ) (i=1,2,…,n; k=1,2,…,6 n For example:

[0224] 1. Based on the current k-th configuration relationship, the NMR T2 spectrum characterizing the porosity of the sphere. 2sk and characterization of the tubular laryngeal tract T2 NMR spectrum 2ck Calculate the echo train M characterizing the pore space. sck :

[0225]

[0226] Where t represents the echo train time, M sck This is the echo train calculated from the spherical pore spectrum and tubular throat spectrum obtained based on the X-ray tube model under the current k-th configuration relationship.

[0227] 2. Based on the NMR T2 spectrum (Formula 6), calculate the original echo train M. ori :

[0228]

[0229] Where t is the echo train time; M ori The echo train is calculated from the inverse of the T2 NMR spectrum.

[0230] 3. Calculate the calculated echo train Msck and the original echo train M in the sphere pore structure model and the tubular throat spectrum under the current k-th configuration relationship. ori The difference M errork :

[0231] M errork =|M sck -M ori |,(k=1,2,…,6 n),(18)

[0232] In the formula, k represents the k-th configuration relationship, and M sck For the current k-th configuration relationship, the echo train is obtained by inverse calculation of the spherical pore spectrum and the tubular throat spectrum based on the X-ray tube model; M ori The echo train is calculated from the inverse of the T2 NMR spectrum.

[0233] Furthermore, the minimum echo train error and the corresponding configuration relationship between the spherical pores and tubular throats in each group of X-ray tube pore structure models are selected as the optimal configuration relationship between the spherical pores and tubular throats in the X-ray tube pore structure model. The optimal configuration relationship is expressed as:

[0234] (Cd1 * Cd2 * ,…,Cd i * ,…,Cd n * ),(19)

[0235] Among them, Cd1 * This represents the ratio of the radius of the tubular throat to the radius of the spherical pore in the i-th group of X-ray tube models, reflecting the configuration relationship of the pore structure models in this group of X-ray tubes. Based on the optimal configuration relationship between the spherical pores and the tubular throat in all X-ray tube pore structure models, the above steps are used to decompose the NMR T2 spectrum into the optimal spherical pore spectrum T. 2s * And the optimal tubular laryngeal spectrum T 2c * As shown in the following formula:

[0236] T 2s * =(T 2sy * Am sy * ),(y=1,2,…,…)

[0237] T 2c * =(T 2cy * Am cy * ),(y=1,2,…,…)

[0238] Among them, T 2sy * Indicates the spherical pore size T 2s Am is the y-th relaxation time or the y-th placement point value in the spectrum. sy * Indicates the spherical pore size T 2s The spectral amplitude T corresponding to the y-th relaxation time or the y-th placement point value in the spectrum. 2cy* T represents the tubular larynx 2c Am is the y-th relaxation time or the y-th placement point value in the spectrum. sy * T represents the tubular larynx 2c The spectral amplitude corresponding to the y-th relaxation time or the y-th placement point value in the spectrum.

[0239] In one embodiment, based on the optimal configuration relationship between spherical pores and tubular throats in all X-ray tube pore structure models, the matrix pore NMR T2 spectra, and the corresponding spherical pore spectrum T2bs* and tubular throat spectrum T2bc* are obtained respectively, as follows: Figure 3 As shown; and the T2 NMR spectrum of the dissolution pores, the corresponding T2vs* spectrum of the spherical pores and the T2vc* spectrum of the tubular throats, as shown. Figure 4 As shown.

[0240] In one embodiment, the third pore structure parameters include the pore radius and tortuosity of a sphere, and the pore radius and tortuosity of a tubular throat.

[0241] Specifically, the calculation of the third pore structure parameters based on the optimal spatial configuration relationship includes:

[0242] Calculate the arithmetic mean and geometric mean of the sphere pore time spectrum and the tubular throat time spectrum based on the optimal configuration relationship;

[0243] The radius and tortuosity of the spherical pores and tubular throats are calculated based on the arithmetic mean and the geometric mean.

[0244] The radius and tortuosity of the spherical pores and the radius and tortuosity of the tubular throat are combined to obtain the third pore structure parameters.

[0245] The radius and tortuosity of the pores in a sphere and the throat in a tubular structure can be calculated using the following formula:

[0246]

[0247] Among them, Rad wp The radius of the pores in the sphere is represented by ρ, and the surface relaxation rate is represented by T. 2SAM τ represents the arithmetic mean of the pore size of a sphere. wp T represents the tortuosity of the pores in a sphere. 2SGM Rad represents the geometric mean of the porosity of a sphere. wt The radius of the tubular throat is represented by ρ, the relaxation rate of the core surface is represented by T. 2CAM τ represents the arithmetic mean of the tubular larynx. wt T represents the tortuosity of the tubular larynx. 2CGM This represents the geometric mean of the tubular throat.

[0248] In one embodiment, by calculating the third pore structure parameter, the structural parameters of the pore structure can be calculated when the logging data is nuclear magnetic resonance logging data, thereby improving the universality and accuracy of the pore structure parameter calculation.

[0249] In one embodiment, the pore structure parameters corresponding to the logging data are obtained through the first pore structure parameter, the second pore structure parameter, and the third pore structure parameter. The pore structure parameters can be calculated separately when the logging data is conventional logging data, electrical imaging logging data, and nuclear magnetic resonance logging data. The pore structure parameters can be considered in the saturation model constructed from various logging data, thereby improving the accuracy of the calculation of pore structure parameters in the reservoir saturation model corresponding to the logging data.

[0250] S3. Calculate the multiple core resistivity relationship of multiple pores based on the reservoir data, and construct a quantitative relationship between the multiple core resistivity of multiple pores and the interpretation parameters of the saturation model and the pore structure parameters based on the multiple core resistivity relationship.

[0251] In one embodiment, reservoir data includes core images, geological data, and well logging data from rock samples obtained during geological exploration and oil and gas development. Core images refer to images of physical cross-sections of underground rocks obtained through drilling, which can intuitively display the rock's lithology, composition, structure, porosity, permeability, and other parameters. Geological data refers to original geological data, results geological data, and physical geological data such as rock cores and various specimens in the form of text, charts, and audio-visual materials generated during geological work. Well logging data is obtained by using various logging instruments manufactured using physical principles such as electricity, magnetism, sound, heat, and nuclear physics to measure the physical properties of underground rock formations and information on oil and gas-bearing and water-bearing layers within the well.

[0252] In one embodiment, calculating the multi-core resistivity relationship of multiple pores based on the reservoir data includes:

[0253] Identify the single pore type in the reservoir data;

[0254] Construct a three-dimensional core model corresponding to the single pore type, and calculate the core resistivity corresponding to the single pore type and multiple pores based on the three-dimensional core model;

[0255] The core resistivity relationship between the single core pore type and the multiple pore types is calculated based on the core resistivity.

[0256] Furthermore, a single pore type refers to a pore structure in a rock that has only one specific size or shape. This can be a single pore developed from matrix pores and dissolution pores, as well as a single pore from fractures, such as intergranular dissolution pores, intercrystalline dissolution pores, caverns, dissolution pores, dissolution fissures, etc.

[0257] In one embodiment, identifying a single pore type in the reservoir data includes:

[0258] The reservoir data is classified to obtain different categories of category data;

[0259] Extract the data features of each data item in the aforementioned categories;

[0260] Identify the single pore type in the reservoir data based on the data characteristics.

[0261] In one embodiment, reservoir data is categorized into core images, geological data, and well logging data based on their type. Then, the unique pore type within the reservoir data is identified using the characteristics of these different data types. For example, core images are subjected to feature extraction to identify dissolution pores. A pre-constructed complex porous media model is used to analyze the well logging response characteristics in the well logging data, thereby identifying the unique pore type within the well logging data. The pore type is defined as matrix pores or dissolution pores (see reference [link]). Figure 5 As shown.

[0262] In detail, by identifying the single pore type in the reservoir data, the pore type of the core can be determined, and then a three-dimensional digital core model of the single pore type in the reservoir data can be simulated.

[0263] In one embodiment, a three-dimensional core model is a technique that depicts the microstructure of a rock core in the form of images or data. It enables detailed characterization of the rock's microstructure at the pore scale, thereby achieving microscale characterization of the core and quantitatively studying the influence of various microscopic factors on the physical properties of the rock.

[0264] In one embodiment, constructing the three-dimensional core model corresponding to the single pore type includes:

[0265] Obtain the core CT scan image corresponding to the reservoir data, and perform image filtering on the core CT scan image to obtain the filtered scan image;

[0266] The filtered scan image is segmented according to the single pore type to obtain pore scan images corresponding to different pore types;

[0267] The pore scanning image is reconstructed in three dimensions to obtain a three-dimensional core model corresponding to the single pore type.

[0268] In one embodiment, an X-ray CT scanner is used to scan a core sample to obtain X-ray CT scan images, which can acquire three-dimensional grayscale images of the core. Since the density differences of different components lead to different X-ray absorption coefficients, the core's framework and pore space can be distinguished. Therefore, an appropriate threshold can be used to segment the filtered scan images to differentiate the pore scan images corresponding to different pore types.

[0269] Furthermore, the core CT scan images can be filtered to eliminate noise, for example, by median filtering. And computer software (such as Avizo software) can be used to accurately reconstruct the pore scan images in three dimensions to build a three-dimensional digital core model.

[0270] In one embodiment, multiple porosity refers to the presence of multiple pores of different sizes and types within a reservoir (such as rock, soil, etc.). These pores intertwine to form the complex pore structure within the reservoir.

[0271] In one embodiment, calculating the core resistivity corresponding to the single pore type and multiple pore types based on the three-dimensional core model includes:

[0272] By nesting the three-dimensional core models corresponding to the single pore type, a multi-pore three-dimensional core model is obtained.

[0273] The three-dimensional core model and the multi-pore three-dimensional core model are discretized to obtain discretized units.

[0274] Finite element analysis is performed on the discretized unit to obtain the core resistivity corresponding to the single pore type and the multiple pores.

[0275] In one embodiment, discretizing the three-dimensional core model to obtain discretized elements is the basis for finite element analysis. Transforming the continuous three-dimensional core model into discrete elements can convert the complex three-dimensional structure into a finite number of small elements, simplifying the solution of core resistivity.

[0276] In one embodiment, nesting three-dimensional core models involves combining nested models into a complete three-dimensional digital core. This can be achieved by assigning different values ​​to models of different components and then combining them through addition or multiplication operations. For example, in the matrix pore model, the values ​​for pores and skeleton are set to 0 and 1, respectively, while in the dissolution cavity pore model, the values ​​for pores and skeleton are set to 2 and 1, respectively.

[0277] For example, see Figure 6 As shown, based on the core nesting technique, matrix pores are nested into the dissolution cavity core to obtain a dual-pore core model with matrix voids and dissolution pores.

[0278] Among them, the three-dimensional core model can be analyzed by using preset finite element software to obtain the core resistivity corresponding to single pore type and multiple pore types.

[0279] In one embodiment, core resistivity is closely related to factors such as porosity, water saturation, and mineral composition of the rocks in the reservoir. It can be used to estimate reservoir characteristics and fluid properties, and to describe the strength of the rocks' electrical conductivity. Core resistivity measurement is of great significance in fields such as petroleum and geological exploration.

[0280] In one embodiment, the multiple core resistivity relationship describes the conductive connection between individual pores that make up the multiple pores. For example, if the multiple pores have cracks and dissolution cavities, then the core resistivity relationship between cracks and dissolution cavities is analyzed.

[0281] In one embodiment, calculating the multiple core resistivity relationship between the single core pore type and the multiple pore types based on the core resistivity includes:

[0282] A series-parallel conductivity model was used to analyze the resistivity of the core corresponding to the single pore type to obtain the series-parallel conductivity form of the multiple pores.

[0283] The theoretical core resistivity of the multi-pore structure is derived based on the series-parallel conduction configuration.

[0284] The relationship between the multiple core resistivity and the single core pore type is determined based on the theoretical core resistivity and the core resistivity corresponding to the multiple pores.

[0285] In one embodiment, the series-parallel conduction form describes the conduction form between different phases (such as solid skeleton, pore fluid, etc.) in the reservoir medium, including the conduction form between different gap types, such as series, parallel, series-parallel, etc.

[0286] Furthermore, the series-parallel conductivity model theory is a theory used to describe the interaction between the conductivity of different phases (such as solid skeletons, pore fluids, etc.) in porous media, and to describe the results of different pore types under different conductivity forms.

[0287] Furthermore, the theoretical resistivity of a three-dimensional core model with multiple pores under different series and parallel conduction modes is derived using the series-parallel conduction model theory. For example, the resistivity of different pores in series and in parallel is calculated. The theoretical resistivity is compared with the resistivity obtained from finite element analysis to determine the core resistivity relationship between different pore types in a multi-pore structure, i.e., the series-parallel conduction mode. For instance, the resistivity of a core with dual pore development (matrix pores and dissolution cavities) obtained from finite element simulation is similar to that obtained from series resistivity simulation (e.g.,...). Figure 7As shown in the figure, the subsequent theoretical derivation uses the interconnected conductivity of matrix pores and dissolution pores.

[0288] In one embodiment, the series and parallel conduction patterns between different pore types can be determined by the core resistivity relationship, which in turn allows for the analysis of the relationship between the resistivity of multi-pore cores and the resistivity of a single pore type, providing a basis for subsequent quantitative calculations of multiple parameters.

[0289] In one embodiment, the quantitative relationship of multiple parameters is the quantitative relationship between the resistivity of multi-pore core and the pore type and pore structure parameters, which can be derived from the quantitative relationship between the resistivity of a single pore type and the pore structure parameters.

[0290] In one embodiment, constructing a quantitative relationship between the multiple core resistivity of the multiple pores and the interpretation parameters of the saturation model and the pore structure parameters based on the multiple core resistivity relationship includes:

[0291] Construct a pore throat model for the single pore type;

[0292] Quantitative relationships of pore structure parameters for the single pore type were calculated based on the pore throat model and the core resistivity.

[0293] Based on the multiple core resistivity relationship and the quantitative relationship of the pore structure parameters, the quantitative relationship between the multiple core resistivity of the multiple pores and the interpretation parameters of the saturation model and the pore structure parameters is calculated.

[0294] In one embodiment, the pore-throat model is a model used to describe the pore structure of porous media, simplifying the pore structure in rocks as a combination of pores and pore throats. In this model, enlarged spaces are defined as pores, while relatively narrow channels are defined as pore throats. This model typically uses spheres to represent pores and cylinders to represent pore throats. The pore-throat model simplifies the pore structure, decomposing it into a large number of regular pores and pore throats, thus forming a network of pores and pore throats.

[0295] In one embodiment, constructing the pore throat model of the single pore type includes:

[0296] The single pore type is divided into pore types to obtain the pore type classification;

[0297] Calculate the pore throat parameters corresponding to the pore type classification;

[0298] Based on the pore throat parameters, construct the pore throat model corresponding to the pore type classification.

[0299] In one embodiment, the rock physical volume model for the pore-throat cavity includes two parts: the throat and the pore body. For matrix pores and dissolution cavities, the radii of the throat and the pore body are different, while the pores of the fracture have similar radii to the throat. It is also assumed that the cross-sectional areas of the pore body, the throat, and the pore-throat cavity have the same shape. Therefore, the cross-sectional areas of the pore body, the throat, and the pore-throat cavity are proportional to their radii.

[0300] In one embodiment, the pore throat parameters can be calculated using the maximum sphere method. For example, for any point in the three-dimensional digital model corresponding to the pore type, find the maximum inscribed sphere with that point as the center. After removing redundant spheres, obtain the set of all non-redundant inscribed spheres describing the pore space. Define the radius of the inscribed sphere with the largest local radius as the pore radius and the radius of the inscribed sphere with the smallest local radius as the pore throat radius.

[0301] like Figure 8 As shown, Figure 8 In the figure, (a) represents the pore throat model of matrix pores and dissolution pores, and (b) represents the pore throat model corresponding to the crack. Where Awt is the cross-sectional area of ​​the throat, Awp is the cross-sectional area of ​​the pore body, Lwt is the throat length, Lwp is the pore body length, A is the cross-sectional area of ​​the pore throat model, and L is the pore throat length.

[0302] In one embodiment, by constructing a pore-throat model, the relationship between the core resistivity and pore structure parameters of a single pore type can be derived based on the assumption that the pores and throats conduct electricity in series.

[0303] In one embodiment, using a pore-throat model of a single pore type, and based on the assumption that the pores and throats are connected in series for conduction, a quantitative relationship between the resistivity of a single pore type and the pore structure parameters can be derived.

[0304] For example, in a pore-throat model completely saturated with formation water and its equivalent conductivity model, the total resistance of one throat is r. wt :

[0305]

[0306] Where, r wt For the laryngeal cavity model, R is the laryngeal resistance. w For the resistivity of formation water, A wt L is the cross-sectional area of ​​the throat. wt This refers to the length of the larynx.

[0307] The resistance r0 of the entire laryngeal cavity is equal to the resistance r of the laryngeal passage. wt With pore volume resistance r wp sum:

[0308]

[0309] Where r0 represents the pore-laryngeal resistance of the pore-laryngeal model, r wt The laryngeal resistance, r, represents the laryngeal cavity resistance in the foramen laryngeal model. wp R represents the volume resistance of the pores. w L represents the resistivity of formation water. wp A represents the length of the pore volume. wp A represents the cross-sectional area of ​​the porous volume. wt L is the cross-sectional area of ​​the throat. wt Ks represents the length of the larynx, and Rad represents the preset shape factor. wp Rad represents the pore volume radius. wt Indicates the radius of the throat.

[0310] Based on the definition of pores, the porosity of each pore can be calculated using the following formula:

[0311]

[0312] Where φ represents porosity, A and L represent the cross-sectional area and length of the rock physical volume model containing the pore throat cavity, respectively, and τ wp The tortuosity of the porous body, i.e., the pore length L wp The ratio of τ to the length L of the laryngeal cavity model wt ψ represents the tortuosity of the larynx. wp The cross-sectional area A of the porous body wp The ratio of ψ to the cross-sectional area A of the rock physical volume model wt The cross-sectional area A of the larynx wp The ratio of the cross-sectional area A of the rock physical volume model to the given cross-sectional area, Ks represents the preset shape factor, Rad wp Rad represents the pore volume radius. wt Rad represents the throat radius. wm This indicates the radius of the larynx.

[0313] In detail, the quantitative relationship between different individual pore types and pore structure parameters can be calculated, as shown below:

[0314] Resistivity R0 of fully water-bearing rocks and resistivity R of formation water w The quantitative relationships between the pore structure parameters are expressed as follows:

[0315]

[0316] Where R0 represents the resistivity of a completely water-bearing rock, τ wp The tortuosity of the porous body, i.e., the pore length L wp The ratio of τ to the length L of the laryngeal cavity model wt ψ represents the tortuosity of the larynx. wpThe cross-sectional area A of the porous body wp The ratio of ψ to the cross-sectional area A of the rock physical volume model wt The cross-sectional area A of the larynx wp The ratio of the cross-sectional area A of the rock physical volume model to the given cross-sectional area, Ks represents the preset shape factor, Rad wp Rad represents the pore volume radius. wt Rad represents the throat radius. wm This indicates the radius of the larynx.

[0317] The quantitative relationships between resistivity R1, porosity φ, and pore structure parameters in fully hydrous matrix pore-developed rocks are expressed as follows:

[0318]

[0319] Among them, R w Rad represents the resistivity of formation water. wbm τ represents the radius of the pore throat in the matrix pore throat model. wbp τ represents the tortuosity of the pore body in the matrix pore throat model. wbt Rad represents the tortuosity of the laryngeal passage in the matrix pore pore laryngeal cavity model. wbp Rad represents the pore volume radius of the matrix pores. wbt This indicates the throat radius of the matrix pores.

[0320] The porosity φ of a completely hydrated matrix is ​​expressed as:

[0321]

[0322] Among them, Rad wbm τ represents the radius of the pore throat in the matrix pore throat model. wbp τ represents the tortuosity of the pore body in the matrix pore throat model. wbt Rad represents the tortuosity of the laryngeal passage in the matrix pore pore laryngeal cavity model. wbp Rad represents the pore volume radius of the matrix pores. wbt This indicates the throat radius of the matrix pores.

[0323] Furthermore, the quantitative relationships between resistivity R2, porosity φ, and pore structure parameters of rocks with fully developed water-bearing dissolution cavities are expressed as follows:

[0324]

[0325] Among them, R w Rad represents the resistivity of formation water. wvm τ represents the radius of the pore throat in the dissolution pore throat model. wvp τ represents the tortuosity of the pore body in the dissolution cavity throat model.wvt Rad represents the tortuosity of the throat passage in the dissolution cavity throat model. wvp Rad represents the pore volume radius of the dissolution cavity. wvt This indicates the throat radius of the dissolution cavity.

[0326]

[0327] Among them, Rad wvm τ represents the radius of the pore throat in the dissolution pore throat model. wvp τ represents the tortuosity of the pore body in the dissolution cavity throat model. wvt Rad represents the tortuosity of the throat passage in the dissolution cavity throat model. wvp Rad represents the pore volume radius of the dissolution cavity. wvt This indicates the throat radius of the dissolution cavity.

[0328] Furthermore, the quantitative relationships between resistivity R3, porosity φ, and pore structure parameters of fully water-bearing fractured rocks are expressed as follows:

[0329]

[0330] Among them, R w Rad represents the resistivity of formation water. wfm τ represents the radius of the pore-throat cavity in the pore-throat cavity model of crack development. wfp Rad represents the tortuosity of the pore body in the fracture development pore throat model. wfp The radius of the pore body indicating the development of cracks.

[0331]

[0332] Among them, Rad wfm τ represents the radius of the pore-throat cavity in the pore-throat cavity model of crack development. wfp Rad represents the tortuosity of the pore body in the fracture development pore throat model. wfp The radius of the pore body indicating the development of cracks.

[0333] In one embodiment, the quantitative relationship of multiple parameters is the quantitative relationship between the resistivity of multi-pore core and the pore type and pore structure parameters, which can be derived from the quantitative relationship between the resistivity of a single pore type and the pore structure parameters.

[0334] In one embodiment, the step of calculating the quantitative relationship of multiple parameters of the core resistivity of the multiple pores based on the multiple core resistivity relationship includes:

[0335] The pore correlation and series-parallel relationship of the multiple pores are determined based on the multiple core resistivity relationship;

[0336] The resistivity of the fully saturated formation water in the core with multiple pores is calculated based on the pore correlation, the series-parallel relationship, and the parameter quantitative relationship.

[0337] The quantitative relationship of multiple parameters of the core resistivity of the multi-pore core was constructed based on the resistivity of the fully saturated formation water in the core.

[0338] In one embodiment, the pore correlation relationship is that there are several pore types in the multi-pore structure, for example, two or three types, etc., and the multiple pores are connected in series, parallel, series-to-parallel, or parallel-to-series conductive forms, resulting in a series-parallel relationship. Furthermore, the resistivity of the fully saturated formation water in the multi-pore core can be expressed as the resistivity of the fully saturated formation water in the multi-pore core.

[0339] The resistivity of multiple cores with multiple pore types can be expressed as:

[0340] 1. When two pore types are connected in series for conductivity, the resistivity R of multiple cores is... fnco This can be expressed as the cascade result of the resistivity of pore type I and pore type II:

[0341]

[0342] Among them, R fnco The resistivity of multiple rock cores is represented by φ. II φ I R represents the porosity of pore type II and pore type I, respectively. II0 R I0 These represent the core resistivity for pore type II and pore type I, respectively.

[0343] 2. When two pore types are conducting electricity in parallel, the resistivity R of multiple cores... fnco This can be expressed as the parallel result of the resistivity of pore type I and pore type II:

[0344]

[0345] Among them, R fnco Represents the resistivity of multiple cores, φ II φ I R represents the porosity of pore type II and pore type I, respectively. II0 R I0 These represent the core resistivity for pore type II and pore type I, respectively.

[0346] 3. When three pore types are connected in series for conductivity, the resistivity R of the multiple core is... fnco This can be represented as the series result of the resistivity of pore type I, pore type II, and pore type III:

[0347]

[0348] Among them, R fnco Represents the resistivity of multiple cores, φ I φ II φ III R represents the porosity of the three pore types respectively. I0 R II0 R III0 These represent the core resistivity for pore types I, II, and III, respectively.

[0349] 4. When three pore types are connected in parallel for conductivity, the resistivity R of the multiple core is... fnco This can be expressed as the parallel result of the resistivity of pore type I, pore type II, and pore type III:

[0350]

[0351] Among them, R fnco Represents the resistivity of multiple cores, φ I φ II φ III R represents the porosity of the three pore types respectively. I0 R II0 R III0 These represent the core resistivity for pore types I, II, and III, respectively.

[0352] 5. When two of the three pore types are initially connected in parallel and then connected in series with the third type to conduct electricity, the resistivity R of the multiple core is... fnco This can be represented as the result of pore type I and pore type II first connected in parallel and then connected in series with pore type III:

[0353]

[0354] Among them, R fnco Represents the resistivity of multiple cores, φ I φ II φ III R represents the porosity of the three pore types respectively. I0 R II0 R III0 These represent the core resistivity for pore types I, II, and III, respectively.

[0355] 6. When two of the three pore types are connected in series and then connected in parallel with the third type to conduct electricity, the resistivity R of the multiple core is... fnco This can be represented as the result of pore type I and pore type II first connected in series and then connected in parallel with pore type III:

[0356]

[0357] Among them, R fnco Represents the resistivity of multiple cores, φ I φ II φ III R represents the porosity of the three pore types respectively. I0 R II0 R III0 These represent the core resistivity for pore types I, II, and III, respectively.

[0358] Furthermore, based on Archie's first formula, a quantitative relationship can be established between the resistivity of cores with multiple pore types and the pore type alone, yielding a quantitative relationship between the resistivity of cores with multiple pore types and the pore type and pore structure parameters, as shown in the following formula:

[0359]

[0360] Where R represents the core resistivity of a single pore type, R w R represents the resistivity of formation water. fnco R and φ represent multiple pore types. m_exp This indicates the preset porosity index.

[0361] in, It can represent the formation factors of a reservoir and is used to describe the scaling factor between resistivity and pore water resistivity.

[0362] In one embodiment, multiple core resistivity can characterize the quantitative relationship between the core resistivity of multiple pores and pore type and pore structure parameters, effectively improving the accuracy of reservoir conductivity mechanism research.

[0363] Furthermore, the quantitative relationship between the resistivity of the multi-pore core and the interpretation parameters of the saturation model, and the pore structure parameters, includes:

[0364] Calculate the quantitative relationship using the following formula:

[0365]

[0366] Where R represents the core resistivity of a single pore type, R w φ represents the resistivity of formation water. m_exp R represents the porosity index. fnco R represents the resistivity of cores with multiple pore types. adwvm Rad represents the radius of the pore throat in the dissolution pore throat model. wvp φ represents the radius of the pore volume of the dissolution cavity. v φ bτ represents the porosity of the matrix porosity and the solution porosity, respectively. wvp τ represents the tortuosity of the pore body in the dissolution cavity throat model. wvt Rad represents the tortuosity of the throat passage in the dissolution cavity throat model. wvt Rad represents the throat radius of the dissolution cavity. wbm Rad represents the radius of the pore throat in the matrix pore throat model. wbp Rad represents the pore volume radius of the matrix pores. wbt τ represents the throat radius of the matrix pores. wbp τ represents the tortuosity of the pore body in the matrix pore throat model. wbt This indicates the tortuosity of the larynx in the matrix pore pore laryngeal model.

[0367] S4. Generate the saturation model of the target reservoir based on the quantitative relationship.

[0368] In one embodiment, the saturation model is one of the key parameters in oil and gas reservoir evaluation, involving the quantitative analysis of fluid saturation in the reservoir. The saturation model can improve the accuracy of well logging water saturation evaluation, enhance reservoir fluid identification capabilities, and increase the accuracy of reservoir productivity evaluation.

[0369] In one embodiment, a saturation model is constructed using the following formula:

[0370]

[0371] Where Sw represents the saturation model, R w R represents the resistivity of formation water. t φ represents the original formation resistance. m_exp Represents the porosity index, n_exp represents the preset saturation index, Rad wvm φ represents the radius of the pore throat in the dissolution pore throat model. v φ b τ represents the porosity of the matrix porosity and the solution porosity, respectively. wvp τ represents the tortuosity of the pore body in the dissolution cavity throat model. wvt Rad represents the tortuosity of the throat passage in the dissolution cavity throat model. wvp Rad represents the pore volume radius of the dissolution cavity. wvt Rad represents the throat radius of the dissolution cavity. wbm Rad represents the radius of the pore throat in the matrix pore throat model. wbp Rad represents the pore volume radius of the matrix pores. wbt τ represents the throat radius of the matrix pores. wbp τ represents the tortuosity of the pore body in the matrix pore throat model.wbt This indicates the tortuosity of the larynx in the matrix pore pore laryngeal model.

[0372] Among them, the core saturation index n_exp is obtained based on core electrical experimental data, such as Figure 9 As shown.

[0373] In one embodiment, the water saturation of different study sections of the reservoir can be calculated using a saturation model; see details below. Figure 10 As shown, channel 12 is the well logging water saturation channel, and SW_mpore is the new water saturation value.

[0374] Furthermore, by identifying pore types, using logging evaluation methods based on pore structure parameters, studying conductivity mechanisms, and constructing saturation models based on pore types and pore structure parameters, the accuracy of logging water saturation evaluation can be improved, as well as the ability to identify reservoir fluids and the accuracy of reservoir productivity evaluation.

[0375] Example 2

[0376] like Figure 11 The diagram shown is a functional block diagram of a saturation model construction device 100 provided in this embodiment.

[0377] The saturation model construction device 100 of the present invention can be installed in an electronic device. Depending on the functions implemented, the saturation model construction device 100 may include a saturation model interpretation parameter calculation module 101, a pore structure parameter calculation module 102, a quantitative relationship construction module 103, and a saturation model generation module 104. The module described in this invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and are stored in the memory of the electronic device.

[0378] In this embodiment, the functions of each module / unit are as follows:

[0379] The saturation model interpretation parameter calculation module 101 is used to acquire the target reservoir's plunger core, reservoir data, and well logging data, and to calculate the saturation model interpretation parameters corresponding to each pore type based on the plunger core.

[0380] The pore structure parameter calculation module 102 is used to calculate the pore structure parameters corresponding to the pore type based on the logging material.

[0381] The quantitative relationship construction module 103 is used to calculate the multiple core resistivity relationship of multiple pores based on the reservoir data, and to construct a quantitative relationship between the multiple core resistivity of multiple pores and the interpretation parameters of the saturation model and the pore structure parameters based on the multiple core resistivity relationship.

[0382] The saturation model generation module 104 is used to generate a saturation model of the target reservoir based on the quantitative relationship.

[0383] Example 3

[0384] Figure 12 This is a schematic diagram of the structure of an electronic device that provides a saturation model construction method according to an embodiment of this application.

[0385] Based on the above embodiments, this embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the above embodiments.

[0386] In some embodiments of this example, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the steps of the method described in the above embodiments.

[0387] In some embodiments of this example, a computer program product is provided, including a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the method described in the above embodiments.

[0388] The processor may include, but is not limited to, one or more processors or microprocessors. Each processor may be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic component, for executing the methods in the above embodiments.

[0389] Computer-readable storage media can be implemented by any type of volatile or non-volatile storage device or a combination thereof, including but not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, and computer storage media (e.g., hard disks, floppy disks, solid-state drives, removable disks, CD-ROMs, DVD-ROMs, Blu-ray discs, etc.).

[0390] Computer-readable storage media may also store at least one computer-executable program, such as computer-readable instructions. Computer-readable storage media include, but are not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Computer-readable storage media may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the computer-readable storage medium, the various methods described above can be performed.

[0391] In addition, the computer device may include (but is not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (e.g., keyboard, mouse, speakers, etc.).

[0392] The processor can communicate with external devices via the communication interface of the I / O bus through wired or wireless networks.

[0393] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product / computer program product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.

[0394] In the embodiments provided in this application, it should be understood that the disclosed systems and methods can also be implemented in other ways. The system embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0395] It should be noted that, in this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element limited by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0396] Although the embodiments disclosed in this application are as described above, the above content is merely for the purpose of facilitating understanding of this application and is not intended to limit this application. Any person skilled in the art to which this application pertains may make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in this application; however, the scope of patent protection of this application shall still be determined by the scope defined in the appended claims.

Claims

1. A method for constructing a saturation model, characterized in that, The method includes: Obtain the plunger core, reservoir data and logging data of the target reservoir, and calculate the saturation model interpretation parameters corresponding to each pore type based on the plunger core; Calculate the pore structure parameters corresponding to the pore type based on the logging materials; The resistivity relationship of multiple cores with multiple pores is calculated based on the reservoir data, and a quantitative relationship is constructed between the resistivity of multiple cores with multiple pores and the interpretation parameters of the saturation model and the pore structure parameters based on the resistivity relationship of multiple cores with multiple pores. A saturation model of the target reservoir is generated based on the quantitative relationship.

2. The saturation model construction method according to claim 1, characterized in that, The calculation of saturation model interpretation parameters corresponding to each pore type based on the plunger core includes: Core simulation was performed on the plunger core to obtain simulation parameters corresponding to the pore type; The plunger core was excited by radio frequency pulses to obtain the echo signal of the plunger core; The echo signal is inverted to obtain the transverse relaxation time spectrum corresponding to the plunger core. Calculate the pore throat radius and core surface relaxation rate corresponding to the pore type based on the transverse relaxation time spectrum; By combining the simulation parameters, the pore throat radius, and the core surface relaxation rate, the saturation model interpretation parameters corresponding to the pore type are obtained.

3. The saturation model construction method according to claim 1, characterized in that, The step of calculating the pore structure parameters corresponding to the pore type based on the logging material includes: The logging data is divided into conventional logging data, electrical imaging logging data, and nuclear magnetic resonance logging data. Calculate the first pore structure parameters of the pores based on the conventional well logging data; The electrical imaging logging data is image segmented to obtain a pore logging image, and the second pore structure parameters are calculated based on the pore logging image. The optimal spatial configuration relationship characterizing the pore space is determined based on the nuclear magnetic resonance logging data, and the third pore structure parameters are calculated based on the optimal spatial configuration relationship. The pore structure parameters are obtained by combining the first pore structure parameters, the second pore structure parameters, and the third pore structure parameters.

4. The saturation model construction method according to claim 3, characterized in that, The calculation of the second pore structure parameters based on the pore logging image includes: Edge detection is performed on the porosity logging images to obtain the range of fracture curves and the range of dissolution cavities; Calculate the crack structure parameters based on the crack curve range; The structural parameters of the crack are calculated using the following formula: W=CAR m b R xo (1-b) Where W represents the crack width, C represents the preset first instrument structure parameter, A represents the abnormal current area, and R... m R represents the resistivity of the mud. xo The value represents the resistivity of the intrusion band, b represents the preset second instrument structure parameter, and V. e Z0 represents the plate potential value, and I represents the basic half-width. a I represents the electrode current. b The electrode current represents the undisturbed formation or framework, z represents the displacement perpendicular to the fracture trajectory within the fracture curve range, and φ represents the current of the electrode in the undisturbed formation or framework. f W represents the porosity of the crack. i L represents the width of the i-th crack. i τ represents the length of the i-th fracture within a preset statistical window length L, D represents the well diameter in the logging data, and τ wfp Indicates crack tortuosity, qng represents crack dip angle, Rad wfp Indicates the crack radius; Calculate the structural parameters of the dissolution cavities based on the range of the dissolution cavities; By combining the crack structure parameters and the dissolution cavity structure parameters, the second pore structure parameters are obtained.

5. The saturation model construction method according to claim 1, characterized in that, The calculation of the multi-core resistivity relationship of multiple pores based on the reservoir data includes: Identify the single pore type in the reservoir data; Construct a three-dimensional core model corresponding to the single pore type, and calculate the core resistivity corresponding to the single pore type and multiple pores based on the three-dimensional core model; The core resistivity relationship between the single core pore type and the multiple pore types is calculated based on the core resistivity.

6. The saturation model construction method according to claim 1, characterized in that, The step of generating the saturation model of the target reservoir based on the quantitative relationship includes: The saturation model is constructed using the following formula: Where Sw represents the saturation model, R w R represents the resistivity of formation water. t φ represents the original formation resistance. m_exp Represents the porosity index, n_exp represents the preset saturation index, Rad wvm φ represents the radius of the pore throat in the dissolution pore throat model. v φ b τ represents the porosity of the matrix porosity and the solution porosity, respectively. wvp τ represents the tortuosity of the pore body in the dissolution cavity throat model. wvt Rad represents the tortuosity of the throat passage in the dissolution cavity throat model. wvp Rad represents the pore volume radius of the dissolution cavity. wvt Rad represents the throat radius of the dissolution cavity. wbm Rad represents the radius of the pore throat in the matrix pore throat model. wbp Rad represents the pore volume radius of the matrix pores. wbt τ represents the throat radius of the matrix pores. wbp τ represents the tortuosity of the pore body in the matrix pore throat model. wbt This indicates the tortuosity of the larynx in the matrix pore pore laryngeal model.

7. A saturation model construction device, characterized in that, The device includes: The saturation model interpretation parameter calculation module is used to acquire the target reservoir's plunger core, reservoir data, and well logging data, and to calculate the saturation model interpretation parameters corresponding to each pore type based on the plunger core. A pore structure parameter calculation module is used to calculate the pore structure parameters corresponding to the pore type based on the logging material. The quantitative relationship construction module is used to calculate the multiple core resistivity relationship of multiple pores based on the reservoir data, and to construct the quantitative relationship between the multiple core resistivity of multiple pores and the interpretation parameters of the saturation model and the pore structure parameters based on the multiple core resistivity relationship. The saturation model generation module is used to generate a saturation model of the target reservoir based on the quantitative relationship.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 6.

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