Core analysis method and device, equipment and medium based on nuclear magnetic resonance simulation

Abstract

An embodiment of the present application provides a core analysis method, device, equipment and medium based on nuclear magnetic resonance simulation, belonging to the field of artificial intelligence technology. The method includes: scanning a target sample to obtain a structural data volume; performing image processing on the structural data volume to obtain a pore network model; performing simulation calculations according to the pore network model to obtain a target magnetization intensity decay curve; performing decomposition processing and inversion processing on the target magnetization intensity decay curve to obtain a core nuclear magnetic resonance T<subgt;2< / subgt; spectrum, and performing morphological method fitting processing on the core nuclear magnetic resonance T<subgt;2< / subgt> spectrum to obtain the core irreducible water saturation; screening at least two simulation parameters from the core irreducible water saturation and the target magnetization intensity decay curve according to preset wetting characteristics; performing nuclear magnetic resonance simulation on the target sample according to the at least two simulation parameters to obtain at least two simulation output data; performing data analysis according to the at least two simulation output data to obtain an analysis result. The application of the present invention can improve the accuracy of core analysis.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular, to a core analysis method, device, equipment, and medium based on nuclear magnetic resonance simulation. Background Technique

[0002] Nuclear magnetic resonance logging is a new logging technology for open-hole wells. It is a logging method that can directly measure the seepage volume characteristics of free fluids in any lithologic reservoir, with obvious advantages.

[0003] In related technologies, the physical simulation of digital cores uses the random walk method to simulate the nuclear magnetic resonance response of cores, but the influence of different wettabilities on cores is not considered, so the accuracy of core analysis is relatively low. Summary of the Invention

[0004] The main purpose of the embodiments of this application is to propose a core analysis method, device, electronic equipment, and medium based on nuclear magnetic resonance simulation, aiming to improve the accuracy of core analysis.

[0005] To achieve the above object, a first aspect of the embodiments of this application proposes a core analysis method based on nuclear magnetic resonance simulation, and the method includes:

[0006] Scanning a target sample to obtain a structural data volume; wherein, the target sample is gravel with a brightness value satisfying a preset brightness range;

[0007] Performing image processing on the structural data volume to obtain a pore network model;

[0008] Performing simulation calculations according to the pore network model to obtain a target magnetization intensity decay curve;

[0009] Performing inversion processing on the target magnetization intensity decay curve to obtain a core nuclear magnetic resonance T2 spectrum, and performing morphological method fitting processing on the core nuclear magnetic resonance T2 spectrum to obtain a core irreducible water saturation;

[0010] Selecting target parameters from the core irreducible water saturation and the target magnetization intensity decay curve according to a preset wetting characteristic; wherein, the target parameters include: at least two simulation parameters;

[0011] Performing nuclear magnetic resonance simulation on the target sample according to at least two of the simulation parameters to obtain at least two simulation output data;

[0012] Performing data analysis according to at least two of the simulation output data to obtain an analysis result.

[0013] In some embodiments, before scanning the target sample to obtain a structural data volume, the method further includes:

[0014] Screen out the target sample from the original sample, specifically including:

[0015] Perform X-ray scanning on the original sample to obtain an original image;

[0016] Obtain the brightness value of the original image;

[0017] Obtain the original sample whose brightness value satisfies a preset brightness range to obtain the target sample.

[0018] In some embodiments, scanning the target sample to obtain a structural data volume includes:

[0019] Scan a preset area of the target sample according to a preset resolution to obtain the structural data volume.

[0020] In some embodiments, performing image processing on the structural data volume to obtain a pore network model includes:

[0021] Perform smoothing and noise reduction processing on the structural data volume to obtain a preliminary data volume;

[0022] Perform threshold segmentation processing on the preliminary data volume to obtain a segmented data volume;

[0023] Perform reconstruction processing according to the segmented data volume to obtain the pore network model.

[0024] In some embodiments, performing simulation calculations according to the pore network model to obtain a target magnetization intensity decay curve includes:

[0025] Perform simulation calculations on the pore network model according to a preset random walk algorithm to obtain an initial magnetization intensity decay curve;

[0026] Perform average calculation on the initial magnetization intensity decay curve to obtain the target magnetization intensity decay curve.

[0027] In some embodiments, the at least two simulation parameters include: water-wet simulation parameters, oil-wet simulation parameters, and mixed-wet simulation parameters; the at least two simulation output data include: water-wet nuclear magnetic resonance echo information, oil-wet nuclear magnetic resonance echo information, and mixed-wet nuclear magnetic resonance echo information; performing nuclear magnetic resonance simulation on the target sample according to at least two of the simulation parameters to obtain at least two simulation output data includes:

[0028] Perform nuclear magnetic resonance simulation on the target sample according to the water-wet simulation parameters to obtain the water-wet nuclear magnetic resonance echo information;

[0029] Performing nuclear magnetic resonance simulation on the target sample according to the oil-wet simulation parameters to obtain the oil-wet nuclear magnetic resonance echo information;

[0030] A nuclear magnetic resonance simulation is performed on the target sample according to the mixed wetting simulation parameters to obtain the mixed wetting nuclear magnetic resonance echo information.

[0031] In some embodiments, performing data analysis based on at least two of the simulation output data to obtain the analysis result includes:

[0032] performing feature extraction on the water-wet nuclear magnetic resonance echo information to obtain water-wet echo features;

[0033] performing feature extraction on the oil-wet nuclear magnetic resonance echo information to obtain oil-wet echo features;

[0034] performing feature extraction on the mixed wetting resonance echo information to obtain mixed wetting echo features;

[0035] Feature analysis is performed based on the water-wetting echo feature, the oil-wetting echo feature, and the mixed-wetting echo feature to obtain the analysis result.

[0036] To achieve the above objectives, a second aspect of an embodiment of the present application provides a core analysis device based on nuclear magnetic resonance simulation, the device comprising:

[0037] A scanning module is used to scan a target sample to obtain a structural data volume; wherein the target sample is gravel whose brightness value meets a preset brightness range;

[0038] An image processing module, configured to perform image processing on the structural data volume to obtain a pore network model;

[0039] A calculation module, configured to perform simulation calculations based on the pore network model to obtain a target magnetization intensity attenuation curve;

[0040] an inversion processing module, configured to decompose and invert the target magnetization intensity attenuation curve to obtain a core nuclear magnetic resonance T2 spectrum, and perform morphological fitting processing on the core nuclear magnetic resonance T2 spectrum to obtain the core irreducible water saturation;

[0041] A screening module is used to screen target parameters from the core irreducible water saturation and the target magnetization intensity attenuation curve according to preset wettability characteristics; wherein the target parameters include: at least two simulation parameters;

[0042] a simulation module, configured to perform a nuclear magnetic resonance simulation on the target sample according to at least two simulation parameters to obtain at least two simulation output data;

[0043] An analysis module for performing data analysis based on at least two of the simulation output data to obtain an analysis result.

[0044] To achieve the above object, a third aspect of the embodiments of the present application provides an electronic device, which includes a memory, a processor, a program stored on the memory and executable on the processor, and a data bus for realizing the connection and communication between the processor and the memory. When the program is executed by the processor, the method described in the first aspect above is realized.

[0045] To achieve the above object, a fourth aspect of the embodiments of the present application provides a storage medium, which is a computer-readable storage medium for computer-readable storage. The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to realize the method described in the first aspect above.

[0046] The core analysis method and device, electronic device and medium based on nuclear magnetic resonance simulation proposed in the present application can accurately construct a digital core model for well logging correction according to the analysis result by considering the influence of different wetting characteristics on the irreducible water saturation of the core, which plays a guiding and exemplary role in oilfield reservoir evaluation and assessment. Description of the Drawings

[0047] Figure 1 is a flowchart of the core analysis method based on nuclear magnetic resonance simulation provided by the embodiments of the present application;

[0048] Figure 2 is a flowchart of the core analysis method based on nuclear magnetic resonance simulation provided by another embodiment of the present application;

[0049] Figure 3 is Figure 1 a flowchart of step S101 in

[0050] Figure 4 is Figure 1 a flowchart of step S102 in

[0051] Figure 5 is Figure 1 a flowchart of step S104 in

[0052] Figure 6 is a schematic diagram of the T2 spectrum in the core analysis method based on nuclear magnetic resonance simulation provided by another embodiment of the present application;

[0053] Figure 7 is Figure 1 a flowchart of step S106 in

[0054] Figure 8 isFigure 1 The flowchart of step S107 in

[0055] Figure 9 is a schematic structural diagram of a core analysis device based on nuclear magnetic resonance simulation provided by an embodiment of the present application;

[0056] Figure 10 is a schematic hardware structure diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0057] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0058] It should be noted that although functional module division is performed in the device schematic diagram and the logical sequence is shown in the flowchart, in some cases, the steps shown or described may be executed in a different module division in the device or a different sequence in the flowchart. Terms such as "first" and "second" in the description, claims and the above-mentioned drawings are used to distinguish similar objects and do not necessarily need to describe a specific order or sequence.

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application.

[0060] First, several nouns involved in the present application are analyzed:

[0061] Artificial intelligence (AI): It is a new technical science that studies, develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence; artificial intelligence is a branch of computer science. Artificial intelligence attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a way similar to human intelligence. The research in this field includes robots, speech recognition, image recognition, natural language processing and expert systems, etc. Artificial intelligence can simulate the information process of human consciousness and thinking. Artificial intelligence also uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results of theories, methods, technologies and application systems.

[0062] Digital Core: Digital core is an effective method for core analysis that has emerged in recent years and is widely used in core analysis fields such as sandstone, carbonate rock, and shale, achieving great success. Its basic principle is based on two-dimensional scanning electron microscope images or three-dimensional CT scan images, using computer image processing technology to complete the reconstruction of digital cores through certain algorithms. Digital core modeling methods can be divided into two categories: physical experiment methods and numerical reconstruction methods. Physical experiment methods refer to using experimental instruments (such as high-power optical microscopes or X-ray CT scanners, etc.) to photograph or scan core samples to obtain a large number of two-dimensional core pictures, and then using modeling programs or software to stack and reconstruct the two-dimensional pictures into three-dimensional digital cores, mainly including serial section imaging method, laser scanning confocal microscopy method, and X-ray CT scanning method. Numerical reconstruction method is a method to reconstruct three-dimensional digital cores based on a small number of two-dimensional thin slice images, using the information contained in the two-dimensional pictures and through random simulation method or process simulation method of sedimentary rock.

[0063] Pore Structure: Pore structure refers to the types, sizes, distributions, and their interconnected relationships of pores and throats within a rock. The pore system of a rock consists of two parts: pores and throats. Pores are the enlarged parts in the system, and the narrow parts connecting the pores are called throats.

[0064] Threshold Segmentation: Threshold segmentation method is a region-based image segmentation technique. The principle is to divide the image pixel points into several categories. Image thresholding segmentation is a traditional and most commonly used image segmentation method. Because of its simple implementation, small computational amount, and relatively stable performance, it has become the most basic and widely used segmentation technique in image segmentation. It is especially suitable for images where the target and the background occupy different gray-level ranges. It can not only greatly compress the data volume but also greatly simplify the analysis and processing steps. Therefore, in many cases, it is a necessary image preprocessing process before image analysis, feature extraction, and pattern recognition. The purpose of image thresholding is to divide the pixel set according to the gray level, and each subset obtained forms a region corresponding to the real scene. Each region has consistent attributes internally, while adjacent regions do not have such consistent attributes. Such a division can be achieved by selecting one or more thresholds starting from the gray level.

[0065] Magnetization Intensity: There are two origins of the magnetic dipole moment generated by the magnetization of a substance. One is that the electrons inside the atom, due to the action of an external magnetic field, the magnetic moment generated by their orbital motion will perform Larmor precession, thus generating an additional magnetic moment, which accumulates and condenses. The other is that after applying an external static magnetic field, the spins of the particles in the substance are "magnetized" and tend to align along the magnetic field direction. These magnetic dipoles formed by spins can be regarded as small magnets and can be represented by vectors, which is the classical description for spin-related magnetic analysis.

[0066] Relaxation: Relaxation refers to the process of gradually returning to the equilibrium state in a gradual physical process. In high-energy physics, under the action of an externally applied radio-frequency pulse RF(B1), after the atomic nucleus undergoes nuclear magnetic resonance and reaches a stable high-energy state, from the moment the externally applied radio frequency disappears until it returns to the magnetic moment state before nuclear magnetic resonance occurs, this entire process is called the relaxation process, that is, the process of physical state recovery.

[0067] Relaxation time: An index for measuring the relaxation effect. (1) The time interval between the disruption of the original equilibrium of the system and the establishment of a new equilibrium. (2) It refers to the time required for the system to change from its original equilibrium position to 1 / e (e is the base of the natural logarithm) of its equilibrium value.

[0068] Random walk: A random walk, also known as a random stroll or random walk, means that based on past performance, the future development steps and directions cannot be predicted. The core concept is that any conserved quantity carried by an irregular walker corresponds to a diffusion transport law, which is close to Brownian motion and is the ideal mathematical state of Brownian motion.

[0069] Singular value decomposition: Singular value decomposition (SVD) has wide applications in dimensionality reduction, data compression, recommendation systems, etc. Any matrix can be decomposed by singular value decomposition. In this paper, the SVD algorithm is gradually derived through orthogonal transformation without changing the angle between the basis vectors, and the row and column dimensionality reduction are understood in terms of the meaning of covariance. Finally, the data compression principle of SVD is introduced.

[0070] Bound water saturation: From the perspective of oil and gas migration, when oil and gas migrate from the source rock to the sandstone reservoir, due to the difference in wettability of oil, water, and gas to the rock and the action of capillary force, the migrating oil and gas cannot completely displace the water in the rock pores, and a certain amount of water will remain in the rock pores. Most of this water is distributed and remains in the corners and micro-pores at the contact of rock particles or adsorbed on the surface of the rock skeleton particles. Due to its special distribution and existence state, this part of the water is almost immobile, so it is called immobile water. Also, because the existence and distribution of this part of the water are significantly affected by the solid properties, it is also called bound water or residual water, and the corresponding saturation is called bound water saturation.

[0071] T₂ spectrum (T₂ spectrum): The T₂ spectrum is the time constant that describes the recovery process of the transverse component of nuclear magnetization intensity, so it is called the transverse relaxation time. The transverse relaxation process is caused by the internal energy exchange of the nuclear spin system, so it is also called the spin-spin relaxation time.

[0072] Nuclear magnetic resonance echo signal: The spin echo signal obtained from nuclear magnetic resonance logging can provide important information such as pore fluid content, fluid properties, and fluid distribution, and thus help determine parameters such as formation porosity, permeability, fluid saturation, and fluid type. It has become a widely used new method for geophysical logging.

[0073] When analyzing cores, the influence of different wettabilities on nuclear magnetic resonance simulation is not considered, so it is inaccurate to directly analyze cores based on a single water-wet condition.

[0074] Based on this, the embodiments of the present application provide a core analysis method, device, equipment, and medium based on nuclear magnetic resonance simulation, aiming to improve the accuracy of core analysis and make the construction of digital core models more accurate.

[0075] The core analysis method, device, equipment, and medium based on nuclear magnetic resonance simulation provided by the embodiments of the present application are specifically described through the following embodiments. First, the core analysis method based on nuclear magnetic resonance simulation in the embodiments of the present application is described.

[0076] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results.

[0077] Artificial intelligence basic technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, and mechatronics. Artificial intelligence software technologies mainly include several major directions such as computer vision technology, robotics, biometric technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0078] The core analysis method based on nuclear magnetic resonance simulation provided by the embodiments of the present application relates to the field of artificial intelligence technology. The core analysis method based on nuclear magnetic resonance simulation provided by the embodiments of the present application can be applied to a terminal, or to a server side, or can also be software running on a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or can be configured as a server cluster or a distributed system composed of multiple physical servers, or can also be configured as a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the core analysis method based on nuclear magnetic resonance simulation, etc., but is not limited to the above forms.

[0079] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and so on. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in a distributed computing environment where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0080] Figure 1 is an optional flowchart of the core analysis method based on nuclear magnetic resonance simulation provided by the embodiments of the present application, Figure 1 The method in may include but is not limited to steps S101 to S107.

[0081] Step S101, scanning a target sample to obtain a structural data volume; wherein, the target sample is a gravel whose brightness value satisfies a preset brightness range;

[0082] Step S102, performing image processing on the structural data volume to obtain a pore network model;

[0083] Step S103, performing simulation calculations according to the pore network model to obtain a target magnetization intensity decay curve;

[0084] Step S104: Decompose and invert the target magnetization decay curve to obtain the core nuclear magnetic resonance T2 spectrum, and perform morphological fitting on the core nuclear magnetic resonance T2 spectrum to obtain the core irreducible water saturation;

[0085] Step S105: Select target parameters from the core irreducible water saturation and the target magnetization decay curve according to the preset wetting characteristics; among them, the target parameters include at least two simulation parameters;

[0086] Step S106: Perform nuclear magnetic resonance simulation on the target sample according to at least two simulation parameters to obtain at least two simulation output data;

[0087] Step S107: Perform data analysis on at least two simulation output data to obtain an analysis result.

[0088] Steps S101 to S107 shown in the embodiments of the present application: Obtain a structural data volume by scanning the target sample, perform image processing on the structural data volume to obtain a pore network model, then perform image processing on the pore network model to obtain a target magnetization decay curve, perform inversion processing on the target magnetization decay curve to obtain the core nuclear magnetic resonance T2 spectrum, perform morphological fitting on the core nuclear magnetic resonance T2 spectrum to obtain the core irreducible water saturation, select at least two simulation parameters from the core irreducible water saturation and the target magnetization decay curve according to the preset wetting characteristics, perform nuclear magnetic resonance simulation according to at least two simulation parameters to obtain two simulation output data, and finally perform data analysis on at least two simulation output data to analyze the laws in at least two simulation output data to obtain an analysis result. Therefore, by considering the influence of different wetting characteristics on the core irreducible water saturation to obtain an analysis result, a digital core model for well logging correction can be accurately constructed according to the analysis result, which plays a guiding and exemplary role in oilfield reservoir evaluation and assessment.

[0089] In step S101 of some embodiments, the target sample is a core plug sample, and a structural data volume is obtained by scanning the core plug sample. Among them, after scanning the core plug sample, a preset area is selected, and the preset area is scanned with a preset resolution to obtain a structural data volume, and the structural data volume is a three-dimensional pore structure data volume.

[0090] In step S102 of some embodiments, the structural data volume is subjected to image processing to reconstruct the three-dimensional pore structure to obtain a pore network model, making the construction of the pore network model simple.

[0091] In step S103 of some embodiments, by performing simulation calculations according to the pore network model, a target magnetization intensity decay curve is obtained. Among them, the target magnetization intensity decay curve is the magnetization intensity decay curve in the matrix, and the target magnetization intensity decay curve is a curve of the decay of magnetization intensity over time. Specifically, the magnetization intensity is the magnetization intensity in the micropore region of the core.

[0092] In step S104 of some embodiments, the target magnetization intensity decay curve is inversely processed to obtain the core nuclear magnetic resonance T2 spectrum, and the core nuclear magnetic resonance T2 spectrum is morphologically fitted to obtain the irreducible water saturation in the core. Therefore, by inversely processing the target magnetization intensity decay curve to obtain the T2 spectrum reflecting the pore structure of the digital core of the core plug sample, since the T2 spectrum includes immobile irreducible water and mobile water in the macropore structure, the irreducible water saturation in the core can be obtained by the automatic confirmation method of the T2 cut-off value based on the morphological characteristics of the T2 spectrum to obtain the irreducible water saturation of the core.

[0093] In step S105 of some embodiments, in order to simulate the change of the T2 map of the core under different wetting characteristics of the core, at least two simulation parameters are selected from the irreducible water saturation of the core and the target magnetization intensity decay curve through the preset wetting characteristics, and each simulation parameter corresponds to the preset wetting characteristics. Therefore, by selecting at least two simulation parameters from the irreducible water saturation of the core and the target magnetization intensity decay curve according to the wetting characteristics, the influence of each wetting characteristic on the nuclear magnetic resonance of the core can be determined according to the simulation parameters.

[0094] In step S106 of some embodiments, nuclear magnetic resonance simulation is performed on the target sample through the simulation parameters to construct a relaxation model according to the simulation parameters, and the simulation output data is determined through the relaxation model, and each simulation parameter corresponds to a simulation output data. Among them, the simulation output data corresponds to the nuclear magnetic resonance echo signal affected by the wetting characteristics, so as to judge the influence of different wetting characteristics on the nuclear magnetic resonance of the rock through the nuclear magnetic resonance echo signal.

[0095] In step S107 of some embodiments, the simulation output data is obtained, and at least two simulation output data are analyzed one by one to analyze the influence of different wetting characteristics on the T2 map, so as to analyze the law therein to obtain the analysis result, and the digital core model for logging correction can be more accurately established according to the analysis result.

[0096] Please refer to Figure 2 , in some embodiments, the core analysis method based on nuclear magnetic resonance simulation further includes:

[0097] Selecting a target sample from the original sample.

[0098] It should be noted that in order to more accurately verify the changes in the T2 spectrum under different wetting characteristics, it is necessary to screen out target samples that meet the requirements from the original samples, that is, to obtain gravel with brightness values within a preset brightness range. Among them, the required target samples need to exclude the holes / fractures inside the fracture-vuggy rocks and the large gravel in the sandy conglomerate to obtain target samples with a higher matrix composition, so as to make the nuclear magnetic resonance simulation technology analysis of the core more accurate.

[0099] Among them, screening out target samples from the original samples may include, but is not limited to, steps S201 to S203:

[0100] Step S201, perform X-ray scanning on the original sample to obtain an original image;

[0101] Step S202, obtain the brightness value of the original image;

[0102] Step S203, obtain the original sample with a brightness value within a preset brightness range to obtain a target sample.

[0103] In step S201 of some embodiments, by performing X-ray scanning on the original sample to obtain an original image, according to the Compton effect, components with different densities in the original sample have different X-ray absorption coefficients, and the different X-ray absorption coefficients of the original sample are manifested as brightness differences in the imaging image. Therefore, target samples with a higher matrix composition are screened out through the brightness differences in the original image.

[0104] In step S202 of some embodiments, by obtaining the brightness value of the original image, and the brightness values of the original samples with different density components are different, the density components of the original samples can be judged through the brightness values.

[0105] In step S203 of some embodiments, obtain the original sample with a brightness value within a preset brightness range to obtain a target sample. Therefore, by selecting the target sample with a higher matrix composition in the original sample according to the brightness difference, the holes, fractures inside the fracture-vuggy rocks and the large rocks in the sandy conglomerate are excluded to improve the analysis accuracy of different wetting characteristics on the T2 spectrum.

[0106] Please refer to Figure 3 , in some embodiments, step S101 may include, but is not limited to, steps S301 to S303:

[0107] Step S301, scan a preset area of the target sample according to a preset resolution to obtain a structural data volume.

[0108] In some embodiments, step S301 involves scanning the target sample. The target sample is first scanned to determine a predetermined region, and then the predetermined region is scanned at a predetermined resolution to obtain a structural data volume. Specifically, a predetermined region of the target sample whose matrix components meet predetermined requirements is scanned, and the size of the predetermined region does not exceed 4 mm × 4 mm × 4 mm. The predetermined region is scanned at a predetermined resolution, and the predetermined resolution is between 1 and 2 μm. Thus, a structural data volume that meets the requirements is obtained by scanning the predetermined region of the target sample at the predetermined resolution.

[0109] See also Figure 4 In some embodiments, step S102 includes but is not limited to steps S401 to S403:

[0110] Step S401, performing smoothing and noise reduction processing on the structure data volume to obtain a preliminary data volume;

[0111] Step S402, performing threshold segmentation processing on the preliminary data volume to obtain a segmented data volume;

[0112] Step S403: Reconstruct the segmented data volume to obtain a pore network model.

[0113] In step S401 of some embodiments, since the structure data volume obtained by direct scanning has noise signals, the structure data volume is smoothed and denoised to obtain a preliminary data volume, so as to remove the noise signals of the structure data.

[0114] In step S403 of some embodiments, the preliminary data volume is segmented into corresponding segmented data volumes by performing threshold segmentation processing on the preliminary data volume, and then reconstruction processing is performed based on the segmented data volumes to obtain a pore network model, so as to reconstruct the segmented data volumes after image processing to obtain a pore network model, and the pore network model corresponds to the three-dimensional pore structure of the core.

[0115] See also Figure 5 In some embodiments, step S104 includes but is not limited to steps S501 to S502:

[0116] Step S501, simulating and calculating the pore network model according to a preset random walk algorithm to obtain an initial magnetization intensity attenuation curve;

[0117] Step S502 : averaging the initial magnetization intensity decay curve to obtain a target magnetization intensity decay curve.

[0118] In step S501 of some embodiments, the pore network model is simulated and calculated based on the random walk algorithm to obtain the magnetization intensity decay curve of the matrix of the target sample, and the initial magnetization intensity decay curve is the decay of the normalized magnetization intensity of the pore fluid over time.

[0119] It should be noted that the decay of the normalized magnetization intensity M(t) of the pore fluid over time can be expressed by formula (1):

[0120]

[0121] In the formula, T 2S is the transverse surface relaxation time of the fluid, M 2S (t) is the surface relaxation magnetization intensity of the fluid at time t, M 2D (t) is the diffusion relaxation magnetization intensity of the fluid at time t.

[0122] Among them, in a saturated fluid medium, the decay of the magnetization vector follows the Bloch-Torrey equation, where the Bloch-Torrey equation is as shown in formula (2):

[0123]

[0124] Set the boundary conditions of the Bloch-Torrey equation as formula (3):

[0125]

[0126] Among them, the magnetization vector at the initial moment is formula (4):

[0127]

[0128] In the formula, ρ is the surface relaxation rate, m(r, t) represents the magnetization vector, γ is the gyromagnetic ratio of the proton, B z is the static magnetic field, T2 is the transverse relaxation time, M(0) is the total magnetization intensity at the initial moment, V P is the total pore volume.

[0129] Therefore, it can be seen from formula (1) that the relaxation magnetization intensity is only related to the relaxation time T 2B , and the type of fluid determines the relaxation time T 2B , so the relaxation time T is determined by the fluid type 2B, the curve of relaxation magnetization intensity versus time can be obtained. Therefore, in the random walk algorithm, simulation calculations are performed using the pore network model. According to the relationship between the distance d from the particle to the nearest particle surface and the diffusion radius calculated, the walking mode is selected. When d (<3ε, where ε is the diffusion radius of the traditional random walk method) is small, the traditional random walk algorithm is used, i.e., the diffusion radius r = ε; when d ≥ 3ε, the first travel time method is used, i.e., the diffusion radius d = r. The time step Δt can be calculated by the following formula as shown in formula (5):

[0130]

[0131] In the formula, Δt is the time step, and within the time step Δt, the particle diffuses from the initial position [x(t), y(t), z(t)] to the next position, and [x(t + Δt), y(t + Δt), z(t + Δt)] can be expressed as formula (6):

[0132]

[0133] In the formula, the selection range of θ is 0 ≤ θ ≤ π; the selection range of φ is 0 ≤ φ ≤ 2π.

[0134] When using the traditional random walk algorithm, the particle may collide with the rock particle surface during the diffusion process. Assuming the probability that the particle is absorbed after collision is δ2, then the surface relaxation magnetization intensity of the particle decays by 1 - δ2, and the calculation formula of δ2 is formula (7):

[0135]

[0136] If the particle is not absorbed after collision, the particle will undergo an elastic collision on the interface and maintain continuous changes in phase and amplitude. Therefore, during the entire numerical simulation process, the surface relaxation magnetization intensity M 2S (t) can be expressed as formula (8):

[0137]

[0138] In the formula, N2(t) is the total number of particles not absorbed at time t; N2(0) is the total number of particles at the initial time.

[0139] Therefore, after normalization, the diffusion relaxation magnetization intensity of each particle can be calculated from the cosine of its phase. Within a time step Δt, the phase shift φ generated by the particle due to spin can be calculated by formula (9):

[0140] ]>

[0141] Wherein, Normal() is a Gaussian random function. To meet the requirements of data acquisition by the CPMG pulse sequence, when t = (n + 1 / 2)TE, the phase is reversed, i.e., φ(t) = -φ(t); when t = nTE, the echo is collected. At this time, the diffusion relaxation magnetization intensity is the cosine sum of the spin dephasing of all particles, that is, formula (10):

[0142]

[0143] In summary, it can be known that the normalized echo data amplitude M(nTE) simulated by the random walk algorithm can be expressed as formula (11):

[0144]

[0145] In step S502 of some embodiments, after obtaining the initial magnetization intensity decay curve, the initial magnetization intensity decay curves of multiple target samples are averaged to obtain the target magnetization intensity decay curve of the core micropore region, so as to analyze the core more accurately according to the target magnetization intensity decay curve.

[0146] It should be noted that after obtaining the target magnetization intensity decay curve, the SVD singular value decomposition method is used to deconvolute the target magnetization intensity decay curve to obtain the T2 spectrum reflecting the pore structure of the digital core of the plug sample. The T2 spectrum includes immobile bound water and mobile water in the macropore structure. Therefore, the T2 cut-off value automatic confirmation method based on the morphological characteristics of the T2 spectrum obtains the core bound water saturation.

[0147] Specifically, according to the analysis of the morphological characteristics of the T2 spectrum, by means of data fitting, the "centrifugal spectrum" can be extracted and constructed from the saturated spectrum, and then the T2 cut-off value is determined according to the constructed centrifugal spectrum and the actually measured saturated spectrum, and the bound water saturation of the core sample or the actual reservoir represented by the corresponding T2 spectrum is calculated therefrom.

[0148] For various different types of saturated T2 spectra, different centrifugal spectrum fitting processing methods are adopted. Such as Figure 6 is a schematic diagram of the normal distribution morphology of a single-peak saturated T2 spectrum, and its fitting function is formula (12):

[0149]

[0150] Wherein: A is the amplitude of the function; u is the expected value of the function; q is the variance of the function; k is the number of normal distributions. Therefore, the T2 cut-off value is constructed according to the T2 spectrum by formula (12) to determine the core bound water saturation, making the calculation of the core bound water saturation simple.

[0151] Please refer to Figure 7, in some embodiments, at least two simulation parameters include: water-wet simulation parameters, oil-wet simulation parameters, and mixed-wet simulation parameters; at least two simulation output data include: water-wet nuclear magnetic resonance echo information, oil-wet nuclear magnetic resonance echo information, and mixed-wet nuclear magnetic resonance echo information. Step S106 includes but is not limited to steps S701 to S703:

[0152] Step S701, perform nuclear magnetic resonance simulation on the target sample according to the water-wet simulation parameters to obtain water-wet nuclear magnetic resonance echo information;

[0153] Step S702, perform nuclear magnetic resonance simulation on the target sample according to the oil-wet simulation parameters to obtain oil-wet nuclear magnetic resonance echo information;

[0154] Step S703, perform nuclear magnetic resonance simulation on the target sample according to the mixed-wet simulation parameters to obtain mixed-wet nuclear magnetic resonance echo information.

[0155] In step S701 of some embodiments, it is necessary to perform nuclear magnetic resonance simulation of one-way oil-water flow on the target sample to respectively construct different relaxation models according to different wetting characteristics, so as to analyze the change of the T2 spectrum under different wetting characteristics, that is, the oil-water distribution in the core can be determined according to the T2 spectrum, so as to construct a more accurate digital core model. Therefore, by obtaining the water-wet simulation parameters, a relaxation model is constructed according to the water-wet simulation parameters to obtain the nuclear magnetic resonance echo signal as the water-wet nuclear magnetic resonance echo information.

[0156] Specifically, the water-wet simulation parameters include: the volume relaxation magnetization intensity of water, the surface relaxation magnetization intensity of water, the diffusion relaxation magnetization intensity of water, the volume relaxation magnetization intensity of oil, the surface relaxation magnetization intensity of oil, the diffusion relaxation magnetization intensity of oil, the water saturation, the oil saturation, the hydrogen index of water, and the hydrogen index of oil. Since the oil component is in the center of the large pores and the water component is in contact with the rock particle surface, it is a "water-in-oil" characteristic. Therefore, in the water-wet case, there are two phases of oil and water in the rock pores, and the oil is not affected by surface relaxation. So the water-wet nuclear magnetic resonance echo information is formula (13):

[0157]

[0158] In the formula, M wB (t) is the volume relaxation magnetization intensity of water; M wS (t) is the surface relaxation magnetization intensity of water; M wD (t) is the diffusion relaxation magnetization intensity of water; M oB (t) is the volume relaxation magnetization intensity of oil; M oS (t) is the surface relaxation magnetization intensity of oil; M oD (t) is the diffusion relaxation magnetization intensity of oil; S wis the water saturation; S o is the oil saturation; HI w is the hydrogen index of water; HI o is the hydrogen index of oil. Therefore, by performing nuclear magnetic resonance simulation according to the water-wet simulation parameters to obtain the water-wet nuclear magnetic resonance echo information, the nuclear magnetic resonance situation of the core under water-wet conditions can be analyzed.

[0159] In step S702 of some embodiments, in small pores, the property of the small pores remaining water-wet remains unchanged, and only bound water exists. The water component in the large pores exists in the middle of the pores, while the oil component contacts on the surface of the rock particles, showing the "water-in-oil" characteristic. Therefore, in the oil-wet case, when there are oil and water phases in the rock pores, the small pores remain water-wet, and the bound water is affected by volume relaxation, surface relaxation, and diffusion relaxation. The large pores are oil-wet, the water is not affected by surface relaxation, and the oil is affected by both volume relaxation and volume relaxation and diffusion relaxation. Therefore, the oil-wet simulation parameters include the water-wet simulation parameters and the bound water saturation. So, performing nuclear magnetic resonance simulation on the target sample according to the oil-wet simulation parameters, the oil-wet nuclear magnetic resonance echo information is formula (14):

[0160]

[0161] In the formula, S wi is the bound water saturation.

[0162] In step S703 of some embodiments, there are both oil-wet and water-wet parts on the core surface, that is, it is determined as mixed wetting. In the case of mixed wetting, the bound water in the small pores is still only affected by the three relaxations of water, and both the oil and water in the large pores are affected by volume relaxation, surface relaxation, and diffusion relaxation. Therefore, the mixed-wetting simulation parameters include: water-wet simulation parameters and oil-wet simulation parameters. Therefore, performing nuclear magnetic resonance simulation on the target sample according to the mixed-wetting simulation parameters, the mixed-wetting nuclear magnetic resonance echo information is as shown in formula (15):

[0163]

[0164] Therefore, it can be seen from formula (15) that by performing nuclear magnetic resonance simulation according to the mixed-wetting simulation parameters to obtain the mixed-wetting nuclear magnetic resonance echo information, the change of the T2 spectrum under different wetting conditions can be obtained by comparing the three nuclear magnetic resonance echo information.

[0165] Please refer to Figure 8 , in some embodiments, step S107 may include but is not limited to steps S801 to S804:

[0166] Step S801, extracting the characteristics of the water-wet nuclear magnetic resonance echo information to obtain the water-wet echo characteristics;

[0167] Step S802: Extract features from the oil-wet nuclear magnetic resonance echo information to obtain oil-wet echo features;

[0168] Step S803: Extract features from the mixed-wet resonance echo information to obtain mixed-wet echo features;

[0169] Step S804: Conduct feature analysis based on the water-wet echo features, oil-wet echo features, and mixed-wet echo features to obtain an analysis result.

[0170] In the above steps S801 to S804, by extracting features from the water-wet nuclear magnetic resonance echo information to obtain water-wet echo features, extracting features from the oil-wet nuclear magnetic resonance echo information to obtain oil-wet echo features, and extracting features from the mixed-wet resonance echo information to obtain mixed-wet echo features, the rules therein can be analyzed based on the water-wet echo features, oil-wet echo features, and mixed-wet echo features, so as to more clearly understand the influence of different wetting characteristics on nuclear magnetic resonance simulation. Then, a digital core model for well logging correction can be accurately established according to the analysis result, which plays a guiding and exemplary role in oilfield reservoir evaluation and assessment.

[0171] Please refer to Figure 9 , this embodiment of the present application also provides a core analysis device based on nuclear magnetic resonance simulation, which can implement the above-mentioned core analysis based on nuclear magnetic resonance simulation. The device includes:

[0172] A scanning module 901, configured to scan a target sample to obtain a structural data volume; wherein, the target sample is gravel with a brightness value satisfying a preset brightness range;

[0173] An image processing module 902, configured to perform image processing on the structural data volume to obtain a pore network model;

[0174] A calculation module 903, configured to perform simulation calculations based on the pore network model to obtain a target magnetization intensity decay curve;

[0175] An inversion processing module 904, configured to perform decomposition processing and inversion processing on the target magnetization intensity decay curve to obtain a core nuclear magnetic resonance T2 spectrum, and perform morphological method fitting processing on the core nuclear magnetic resonance T2 spectrum to obtain the core irreducible water saturation;

[0176] A screening module 905, configured to screen out target parameters from the core irreducible water saturation and the target magnetization intensity decay curve according to a preset wetting characteristic; wherein, the target parameters include: at least two simulation parameters;

[0177] A simulation module 906, configured to perform nuclear magnetic resonance simulation on the target sample according to at least two simulation parameters to obtain at least two simulation output data;

[0178] An analysis module 907 is configured to perform data analysis based on at least two simulation output data to obtain an analysis result.

[0179] The specific implementation manner of the core analysis device based on nuclear magnetic resonance simulation is substantially the same as the specific embodiments of the core analysis method based on nuclear magnetic resonance simulation described above, and will not be elaborated herein.

[0180] An embodiment of the present application further provides an electronic device, which includes: a memory, a processor, a program stored on the memory and executable on the processor, and a data bus for implementing connection communication between the processor and the memory. When the program is executed by the processor, the core analysis method based on nuclear magnetic resonance simulation described above is implemented. The electronic device may be any intelligent terminal including a tablet computer, a vehicle-mounted computer, etc.

[0181] Please refer to Figure 10 , Figure 10 which schematically shows the hardware structure of an electronic device according to another embodiment. The electronic device includes:

[0182] A processor 101, which may be implemented by using a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is configured to execute relevant programs to implement the technical solutions provided by the embodiments of the present application;

[0183] A memory 102, which may be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM), etc. The memory 102 may store an operating system and other application programs. When implementing the technical solutions provided by the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 102 and are called by the processor 101 to execute the core analysis method based on nuclear magnetic resonance simulation of the embodiments of the present application;

[0184] An input / output interface 103, which is configured to implement information input and output;

[0185] A communication interface 104, which is configured to implement communication interaction between this device and other devices, and may implement communication through a wired manner (such as USB, network cable, etc.) or through a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0186] A bus 105, which transmits information between various components of the device (such as the processor 101, the memory 102, the input / output interface 103, and the communication interface 104);

[0187] Among them, the processor 101, the memory 102, the input / output interface 103, and the communication interface 104 are communicatively connected to each other inside the device through the bus 105.

[0188] The embodiment of the present application also provides a storage medium, which is a computer-readable storage medium for computer-readable storage. The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the above-mentioned core analysis method based on nuclear magnetic resonance simulation.

[0189] As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory can include high-speed random access memory, and can also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory may optionally include a memory remotely located relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above networks include, but are not limited to, the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0190] The recommendation method, recommendation device, electronic device, and storage medium provided by the embodiments of the present application can accurately construct a digital core model for well logging correction according to the analysis result by considering the influence on the irreducible water saturation of the core under different wetting characteristics, and play a guiding and exemplary role in oilfield reservoir evaluation and assessment.

[0191] The embodiments described in the embodiments of the present application are for more clearly illustrating the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art can know that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.

[0192] Those skilled in the art can understand that Figure 1-8 the technical solutions shown do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than those shown, or combine certain steps, or different steps.

[0193] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0194] Those of ordinary skill in the art will understand that all or some of the steps in the methods disclosed above, and the functional modules / units in systems and devices, can be implemented as software, firmware, hardware, or a suitable combination thereof.

[0195] As used in the description of the present application and the above drawings, the terms "first", "second", "third", "fourth", etc. (if any) 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 may be interchanged under appropriate circumstances so that the embodiments of the present application described herein can be implemented in an order different from those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0196] It should be understood that in the present application, "at least one (item)" means one or more, and "a plurality" means two or more. "And / or" is used to describe the association relationship of associated objects and indicates that three relationships may exist. For example, "A and / or B" may mean: only A exists, only B exists, and both A and B exist simultaneously. Here, A and B may be singular or plural. The character " / " generally indicates that the associated objects before and after are in an "or" relationship. "At least one (one) of the following" or a similar expression means any combination of these items, including any combination of single items (ones) or plural items (ones). For example, at least one (one) of a, b, or c may mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c may be single or multiple.

[0197] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the above division of units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other may be through some interfaces, and the indirect coupling or communication connection of devices or units may be in an electrical, mechanical, or other form.

[0198] The units described above as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed over multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0199] In addition, in each embodiment of the present application, each functional unit can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0200] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes multiple instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in each embodiment of the present application. The aforementioned storage medium includes: various media that can store programs such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs.

[0201] The preferred embodiments of the embodiments of the present application have been described above with reference to the accompanying drawings, and thus do not limit the scope of the rights of the embodiments of the present application. Any modification, equivalent replacement, and improvement made by those skilled in the art without departing from the scope and essence of the embodiments of the present application shall be within the scope of the rights of the embodiments of the present application.

Claims

1. A core analysis method based on nuclear magnetic resonance simulation, characterized in that The method includes: Scanning a target sample to obtain a structural data volume; wherein, the target sample is gravel with a brightness value satisfying a preset brightness range; Performing image processing on the structural data volume to obtain a pore network model; Performing simulation calculations according to the pore network model to obtain a target magnetization intensity decay curve; Performing inversion processing on the target magnetization intensity decay curve to obtain a core nuclear magnetic resonance T2 spectrum, and performing morphological method fitting processing on the core nuclear magnetic resonance T2 spectrum to obtain the core irreducible water saturation; Selecting target parameters from the core irreducible water saturation and the target magnetization intensity decay curve according to a preset wetting characteristic; wherein, the target parameters include: at least two simulation parameters; the at least two simulation parameters include: a water-wet simulation parameter, an oil-wet simulation parameter, and a mixed wetting simulation parameter; Performing nuclear magnetic resonance simulation on the target sample according to the water-wet simulation parameter to obtain water-wet nuclear magnetic resonance echo information; Performing nuclear magnetic resonance simulation on the target sample according to the oil-wet simulation parameter to obtain oil-wet nuclear magnetic resonance echo information; wherein, the oil-wet simulation parameter includes the water-wet simulation parameter and the core irreducible water saturation; Performing nuclear magnetic resonance simulation on the target sample according to the mixed wetting simulation parameter to obtain mixed wetting nuclear magnetic resonance echo information; wherein, the mixed wetting simulation parameter includes the water-wet simulation parameter and the oil-wet simulation parameter; Performing data analysis according to at least two simulation output data to obtain an analysis result, so as to establish a digital core model for well logging correction according to the analysis result; wherein, at least two of the simulation output data include the water-wet nuclear magnetic resonance echo information, the oil-wet nuclear magnetic resonance echo information, and the mixed wetting nuclear magnetic resonance echo information.

2. The method according to claim 1, wherein Before scanning the target sample to obtain a structural data volume, the method further includes: Selecting the target sample from the original sample, specifically including: Performing X-ray scanning on the original sample to obtain an original image; Obtaining the brightness value of the original image; Obtaining the original sample with the brightness value satisfying the preset brightness range to obtain the target sample.

3. The method according to claim 2, characterized in that, Scanning the target sample to obtain a structural data volume includes: Scanning a preset area of the target sample according to a preset resolution to obtain the structural data volume.

4. The method according to any one of claims 1 to 3, characterized in that, Performing image processing on the structural data volume to obtain a pore network model includes: Performing smoothing and noise reduction processing on the structural data volume to obtain a preliminary data volume; Performing threshold segmentation processing on the preliminary data volume to obtain a segmented data volume; Performing reconstruction processing according to the segmented data volume to obtain the pore network model.

5. The method according to any one of claims 1 to 3, characterized in that, Performing simulation calculations according to the pore network model to obtain a target magnetization intensity decay curve includes: Performing simulation calculations on the pore network model according to a preset random walk algorithm to obtain an initial magnetization intensity decay curve; Performing average calculation on the initial magnetization intensity decay curve to obtain the target magnetization intensity decay curve.

6. The method according to claim 1, characterized in that, Performing data analysis according to at least two simulation output data to obtain the analysis result includes: Extract features from the water-wet nuclear magnetic resonance echo information to obtain water-wet echo features; Extract features from the oil-wet nuclear magnetic resonance echo information to obtain oil-wet echo features; Extract features from the mixed-wet nuclear magnetic resonance echo information to obtain mixed-wet echo features; Perform feature analysis based on the water-wet echo features, the oil-wet echo features, and the mixed-wet echo features to obtain the analysis result.

7. A core analysis device based on nuclear magnetic resonance simulation, characterized in that, The device includes: A scanning module for scanning a target sample to obtain a structural data volume; wherein, the target sample is gravel with a brightness value satisfying a preset brightness range; An image processing module for performing image processing on the structural data volume to obtain a pore network model; A calculation module for performing simulation calculations based on the pore network model to obtain a target magnetization decay curve; An inversion processing module for performing inversion processing on the target magnetization decay curve to obtain a core nuclear magnetic resonance T2 spectrum, and performing morphological fitting processing on the core nuclear magnetic resonance T2 spectrum to obtain a core irreducible water saturation; A screening module for screening target parameters from the core irreducible water saturation and the target magnetization decay curve according to preset wetting characteristics; wherein, the target parameters include: at least two simulation parameters; the at least two simulation parameters include: water-wet simulation parameters, oil-wet simulation parameters, and mixed-wet simulation parameters; A simulation module for performing nuclear magnetic resonance simulation on the target sample according to the water-wet simulation parameters to obtain water-wet nuclear magnetic resonance echo information; performing nuclear magnetic resonance simulation on the target sample according to the oil-wet simulation parameters to obtain oil-wet nuclear magnetic resonance echo information; wherein, the oil-wet simulation parameters include the water-wet simulation parameters and the core irreducible water saturation; performing nuclear magnetic resonance simulation on the target sample according to the mixed-wet simulation parameters to obtain mixed-wet nuclear magnetic resonance echo information; wherein, the mixed-wet simulation parameters include the water-wet simulation parameters and the oil-wet simulation parameters; An analysis module for performing data analysis based on at least two simulation output data to obtain an analysis result, so as to establish a digital core model for logging correction according to the analysis result; wherein, the at least two simulation output data include the water-wet nuclear magnetic resonance echo information, the oil-wet nuclear magnetic resonance echo information, and the mixed-wet nuclear magnetic resonance echo information.

8. An electronic device, characterized in that, The electronic device includes a memory, a processor, a program stored on the memory and executable on the processor, and a data bus for realizing connection communication between the processor and the memory. When the program is executed by the processor, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A storage medium, the storage medium being a computer-readable storage medium for computer-readable storage, characterized in that, The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the method according to any one of claims 1 to 6.

Images