CN115201245A - Rock core analysis method and device based on nuclear magnetic resonance simulation, equipment and medium - Google Patents
Rock core analysis method and device based on nuclear magnetic resonance simulation, equipment and medium Download PDFInfo
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Abstract
The embodiment of the application provides a rock core analysis method, a rock core analysis device, rock core analysis equipment and a rock core analysis medium based on nuclear magnetic resonance simulation, and belongs to the technical field of artificial intelligence. The method comprises the following steps: scanning a target sample to obtain a structural data volume; carrying out image processing on the structural data body to obtain a pore network model; performing simulation calculation according to the pore network model to obtain a target magnetization intensity attenuation curve; performing decomposition processing and inversion processing on the target magnetization attenuation curve to obtain core nuclear magnetic resonance T 2 Spectrum and nuclear magnetic resonance T of rock core 2 Performing morphological fitting treatment on the spectrum to obtain the saturation of the bound water of the rock core; screening out at least two simulation parameters from the rock core irreducible water saturation and the target magnetization intensity attenuation curve according to a preset wetting characteristic; 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; and analyzing data according to the at least two analog output data to obtain an analysis result. The method and the device can improve the accuracy of core analysis.
Description
Technical Field
The application relates to the technical field of artificial intelligence, in particular to a rock core analysis method and device based on nuclear magnetic resonance simulation, equipment and a medium.
Background
The nuclear magnetic resonance logging is a new logging technology for open hole well, is a logging method capable of directly measuring the free fluid seepage volume characteristic of any lithology Chu Jiceng, and has obvious superiority.
In the related technology, the nuclear magnetic resonance response of the core is simulated by adopting a random walking method in the physical simulation of the core of the digital core, but the influence of different wettabilities on the core is not considered, so that the accuracy of core analysis is low.
Disclosure of Invention
The embodiment of the application mainly aims to provide a rock core analysis method and device based on nuclear magnetic resonance simulation, electronic equipment and a medium, and aims to improve the accuracy of rock core analysis.
In order to achieve the above object, a first aspect of an embodiment of the present application provides a core analysis method based on nuclear magnetic resonance simulation, where the method includes:
scanning a target sample to obtain a structural data volume; the target sample is gravel with the brightness value meeting a preset brightness range;
carrying out image processing on the structural data body to obtain a pore network model;
performing simulation calculation according to the pore network model to obtain a target magnetization intensity attenuation curve;
performing inversion processing on the target magnetization intensity attenuation curve to obtain core nuclear magnetic resonance T 2 Spectrum and nuclear magnetic resonance T of said core 2 Performing morphological fitting treatment on the spectrum to obtain the saturation of the bound water of the rock core;
screening out target parameters from the rock core irreducible water saturation and the target magnetization intensity attenuation curve according to preset wetting characteristics; wherein the target parameters include: at least two simulation parameters;
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;
and carrying out data analysis according to at least two pieces of the analog output data to obtain an analysis result.
In some embodiments, before the scanning the target sample to obtain the structural data volume, the method further comprises:
screening the target sample from the original sample, which specifically comprises the following steps:
carrying out X-ray scanning on the original sample to obtain an original image;
acquiring a brightness value of the original image;
and acquiring the original sample with the brightness value meeting a preset brightness range to obtain the target sample.
In some embodiments, the scanning the target sample to obtain the structural data volume includes:
and scanning a preset area of the target sample according to a preset resolution to obtain the structural data volume.
In some embodiments, the image processing on the structural data volume to obtain a pore network model includes:
carrying out smooth noise reduction processing on the structural data body to obtain a preliminary data body;
performing threshold segmentation processing on the preliminary data volume to obtain a segmented data volume;
and carrying out reconstruction processing according to the segmentation data volume to obtain the pore network model.
In some embodiments, the performing a simulation calculation according to the pore network model to obtain a target magnetization decay curve includes:
performing simulation calculation on the pore network model according to a preset random walk algorithm to obtain an initial magnetization intensity attenuation curve;
and carrying out average calculation on the initial magnetization intensity attenuation curve to obtain the target magnetization intensity attenuation curve.
In some embodiments, the at least two simulation parameters include: water-wet simulation parameters, oil-wet simulation parameters and mixed-wetting simulation parameters; the at least two analog output data comprise: water-wet nuclear magnetic resonance echo information, oil-wet nuclear magnetic resonance echo information, and mixed-wetting nuclear magnetic resonance echo information; 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, including:
performing nuclear magnetic resonance simulation on the target sample according to the water-humidity simulation parameter to obtain the water-humidity nuclear magnetic resonance echo information;
performing nuclear magnetic resonance simulation on the target sample according to the oil-moisture simulation parameter to obtain oil-moisture nuclear magnetic resonance echo information;
and performing nuclear magnetic resonance simulation on the target sample according to the mixed wetting simulation parameters to obtain mixed wetting nuclear magnetic resonance echo information.
In some embodiments, the performing data analysis according to at least two of the analog output data to obtain the analysis result includes:
performing feature extraction on the water-wet nuclear magnetic resonance echo information to obtain water-wet echo features;
performing feature extraction on the oil-wet nuclear magnetic resonance echo information to obtain oil-wet echo features;
performing feature extraction on the mixed wetting resonance echo information to obtain mixed wetting echo features;
and performing characteristic analysis according to the water-wet echo characteristic, the oil-wet echo characteristic and the mixed wetting echo characteristic to obtain the analysis result.
In order to achieve the above object, a second aspect of an embodiment of the present application provides a core analysis apparatus based on nuclear magnetic resonance simulation, including:
the scanning module is used for scanning the target sample to obtain a structural data volume; the target sample is gravel with the brightness value meeting a preset brightness range;
the image processing module is used for carrying out image processing on the structural data body to obtain a pore network model;
the calculation module is used for carrying out simulation calculation according to the pore network model to obtain a target magnetization intensity attenuation curve;
an inversion processing module for performing decomposition processing and inversion processing on the target magnetization attenuation curve to obtain core nuclear magnetic resonance T 2 Spectrum and nuclear magnetic resonance T of said core 2 Performing morphological fitting treatment on the spectrum to obtain the saturation of the bound water of the rock core;
the screening module is used for screening out target parameters from the rock core bound water saturation and the target magnetization intensity attenuation curve according to preset wetting characteristics; wherein the target parameters include: at least two simulation parameters;
the simulation module is used for carrying out nuclear magnetic resonance simulation on the target sample according to at least two simulation parameters to obtain at least two simulation output data;
and the analysis module is used for carrying out data analysis according to the at least two analog output data to obtain an analysis result.
In order 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 implementing connection communication between the processor and the memory, wherein the program, when executed by the processor, implements the method of the first aspect.
To achieve the above object, a fourth aspect of the embodiments of the present application proposes a storage medium, which is a computer-readable storage medium for computer-readable storage, and stores one or more programs, which are executable by one or more processors to implement the method of the first aspect.
According to the core analysis method and device based on nuclear magnetic resonance simulation, the electronic equipment and the medium, the analysis result is obtained by considering the influence on the saturation of the bound water of the core under different wetting characteristics, so that the digital core model for well logging correction can be accurately constructed according to the analysis result, and the guidance demonstration effect is played for the evaluation and the evaluation of the oil field reservoir stratum.
Drawings
Fig. 1 is a flow chart of a core analysis method based on nuclear magnetic resonance simulation provided in an embodiment of the present application;
FIG. 2 is a flow chart of a method for core analysis based on nuclear magnetic resonance simulation provided in another embodiment of the present application;
fig. 3 is a flowchart of step S101 in fig. 1;
fig. 4 is a flowchart of step S102 in fig. 1;
FIG. 5 is a flowchart of step S104 in FIG. 1;
FIG. 6 is a graph showing T in a core analysis method based on NMR simulation according to another embodiment of the present disclosure 2 A schematic of the spectrum;
FIG. 7 is a flowchart of step S106 in FIG. 1;
fig. 8 is a flowchart of step S107 in fig. 1;
fig. 9 is a schematic structural diagram of a core analysis apparatus based on nuclear magnetic resonance simulation according to an embodiment of the present application;
fig. 10 is a schematic hardware structure diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
It should be noted that although functional blocks are partitioned in a schematic diagram of an apparatus and a logical order is shown in a flowchart, in some cases, the steps shown or described may be performed in a different order than the partitioning of blocks in the apparatus or the order in the flowchart. The terms first, second and the like in the description and in the claims, and the drawings described above, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only and is not intended to be limiting of the application.
First, several terms referred to in the present application are resolved:
artificial Intelligence (AI): is a new technical science for researching and developing theories, methods, technologies and application systems for simulating, extending and expanding human intelligence; artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produces a new intelligent machine that can react in a manner similar to human intelligence, and research in this field includes robotics, language recognition, image recognition, natural language processing, and expert systems, among others. The artificial intelligence can simulate the information process of human consciousness and thinking. Artificial intelligence is also a theory, method, technique and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use the knowledge to obtain the best results.
Digital core: the digital core is an effective method for core analysis which is popular in recent years, is widely applied in the field of core analysis of sandstone, carbonate rock, shale and the like, and has great success. The basic principle is to complete digital core reconstruction by a certain algorithm based on a two-dimensional scanning electron microscope image or a three-dimensional CT scanning image and by using a computer image processing technology. Digital core modeling methods can be divided into two broad categories: physical experimental methods and numerical reconstruction methods. The physical experiment method is to take or scan a core sample by using an experimental instrument (such as a high-power optical microscope or an X-ray CT scanner) to obtain a large number of two-dimensional pictures of the core, and then superpose and reconstruct the two-dimensional pictures into a three-dimensional digital core through a modeling program or software, and mainly comprises a sequential slice imaging method, a laser scanning confocal microscope method and an X-ray CT scanning method. The numerical reconstruction method is a method for reconstructing a three-dimensional digital core by a random simulation method or a sedimentary rock process simulation method by using information contained in a two-dimensional picture on the basis of a small number of two-dimensional slice images.
Pore structure: the pore structure refers to the type, size, distribution and mutual communication relationship of pores and throats in the rock. The pore system of rock is composed of two parts, pore space and throat. The pores are the enlarged part of the system, and the fine part connecting the pores is called a throat.
Threshold segmentation: the threshold segmentation method is an image segmentation technology based on regions, and the principle is to divide image pixels into a plurality of classes. The image thresholding segmentation is the most common traditional image segmentation method, and becomes the most basic and widely applied segmentation technology in image segmentation due to simple implementation, small calculation amount and stable performance. It is particularly suitable for images where the object and background occupy different gray scale ranges. It not only can compress a great amount of data, but also greatly simplifies the analysis and processing steps, and thus is a necessary image preprocessing process before image analysis, feature extraction and pattern recognition in many cases. The purpose of image thresholding is to divide the set of pixels by gray level, each resulting subset forming a region corresponding to the real scene, each region having uniform properties within it, while adjacent regions do not have such uniform properties. Such a division can be achieved by choosing one or more threshold values from the grey scale.
Magnetization intensity: the magnetic dipole moment produced by magnetization of a substance has two origins. One is formed by accumulating and condensing extra magnetic moments generated by electrons in atoms and magnetic moments generated by the movement of the rail regions of the electrons under the action of an external magnetic field. Alternatively, upon application of a static magnetic field, the spins of the particles within the material become "magnetized" and tend to align with the direction of the magnetic field. These spin-formed magnetic dipoles can be viewed as a small magnet, which can be represented as a vector, as a classical description of spin-dependent magnetic analysis.
Relaxation: relaxation refers to a process of gradually returning from a certain state to an equilibrium state in a certain gradual physical process. In high-energy physics, under the action of an external radio frequency pulse RF (B1), after nuclear magnetic resonance occurs and reaches a stable high-energy state, the whole process is called a relaxation process, namely a process of physical state recovery from the moment when the external radio frequency disappears to the moment before the magnetic resonance occurs.
Relaxation time: an indicator for measuring relaxation. (1) The time interval between the disruption of the original balance and the establishment of the new balance. (2) Refers to the time required for the system to change from the original equilibrium position to 1/e (e is the base of the natural logarithm) of its equilibrium value.
Random walk (random walk): random walk is also called random walk, and the like, and means that a future development step and direction cannot be predicted based on past performance. The core concept means that the conservation quantities of any irregular walker correspond to a diffusion transport law respectively, and the law is close to Brownian motion and is an ideal mathematical state of the Brownian motion.
Singular value decomposition: singular Value Decomposition (SVD) has wide application in dimension reduction, data compression, recommendation systems and the like, any matrix can be subjected to singular value decomposition, an SVD algorithm is derived in a progressive manner by orthogonal transformation without changing included angles among base vectors, row dimension reduction and column dimension reduction are understood by covariance meaning, and finally the data compression principle of SVD is introduced.
Irreducible water saturation: from the oil and gas migration perspective, when oil and gas are transported from an oil-producing formation to a sandstone reservoir, due to the wettability difference of oil, water and gas on rocks and the action of capillary force, the transported oil and gas cannot completely displace water in rock pores, and a certain amount of water remains in the rock pores. Most of the water is distributed and remained in corners and fine pores where the rock particles contact or adsorbed on the surfaces of the rock skeleton particles. This portion of water is almost immobile due to the particular distribution and presence, and is therefore referred to as immobile water. Also, since the presence and distribution of this water is significantly affected by the nature of the solids, it is also referred to as irreducible water or residual water, and the corresponding saturation is referred to as irreducible water saturation.
T 2 Spectrum (T) 2 spectrum)：T 2 The spectrum is a time constant describing the process of recovery of the transverse component of the nuclear magnetization and is therefore called the transverse relaxation time. The transverse relaxation process is caused by the exchange of energy within the nuclear spin system and is therefore also referred to as spin-spin relaxationThe time of relaxation.
Nuclear magnetic resonance echo signal: spin echo signals obtained by nuclear magnetic resonance logging can provide important information such as pore fluid content, fluid properties, fluid distribution and the like, so that parameters such as formation porosity, permeability, fluid saturation, fluid type and the like can be kept and determined, and the method becomes a novel geophysical logging method with wide application.
The influence of different wettabilities on nuclear magnetic resonance simulation is not considered during core analysis, so that the core is not accurately analyzed directly according to single water humidity.
Based on this, the embodiment of the application provides a core analysis method, a core analysis device, equipment and a medium based on nuclear magnetic resonance simulation, aiming at improving the accuracy of core analysis and enabling the construction of a digital core model to be more accurate.
The method, the apparatus, the device and the medium for core analysis based on nuclear magnetic resonance simulation provided in the embodiments of the present application are specifically described in the following embodiments, and first, the method for core analysis based on nuclear magnetic resonance simulation in the embodiments of the present application is described.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
The artificial intelligence base technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.
The embodiment of the application provides a rock core analysis method based on nuclear magnetic resonance simulation, and relates to the technical field of artificial intelligence. The rock core analysis method based on nuclear magnetic resonance simulation provided by the embodiment of the application can be applied to a terminal, a server side and software running in the terminal or the server side. In some embodiments, the terminal may be a smartphone, tablet, laptop, desktop computer, or the like; the server side can be configured into an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, and cloud servers for providing basic cloud computing services such as cloud service, a cloud database, cloud computing, cloud functions, cloud storage, network service, cloud communication, middleware service, domain name service, security service, CDN (content delivery network) and big data and artificial intelligence platforms; the software may be an application or the like that implements a core analysis method based on nuclear magnetic resonance simulation, but is not limited to the above form.
The application is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
Fig. 1 is an alternative flowchart of a core analysis method based on nuclear magnetic resonance simulation provided in an embodiment of the present application, and the method in fig. 1 may include, but is not limited to, steps S101 to S107.
Step S101, scanning a target sample to obtain a structural data volume; the target sample is gravel with the brightness value meeting a preset brightness range;
step S102, carrying out image processing on the structure data volume to obtain a pore network model;
step S103, carrying out simulation calculation according to the pore network model to obtain a target magnetization intensity attenuation curve;
step S104, performing decomposition processing and inversion processing on the target magnetization intensity attenuation curve to obtain a rock core nuclear magnetic resonance T2 spectrum, and performing morphological fitting processing on the rock core nuclear magnetic resonance T2 spectrum to obtain rock core irreducible water saturation;
s105, screening out target parameters from the rock core bound water saturation and the target magnetization intensity attenuation curve according to preset wetting characteristics; wherein, the target parameters include: at least two simulation parameters;
step S106, 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;
and S107, performing data analysis according to the at least two analog output data to obtain an analysis result.
In steps S101 to S107 illustrated in the embodiment of the present application, a structural data volume is obtained by scanning a target sample, an image of the structural data volume is processed to obtain a pore network model, the pore network model is then processed to obtain a target magnetization intensity attenuation curve, and the target magnetization intensity attenuation curve is inverted to obtain a core nmr T 2 Spectrum and nuclear magnetic resonance T of rock core 2 Performing morphological fitting processing on the spectrum to obtain the rock core bound water saturation, screening out at least two simulation parameters from the rock core bound water saturation and a target magnetization intensity attenuation curve according to preset wetting characteristics, performing nuclear magnetic resonance simulation according to the at least two simulation parameters to obtain two simulation output data, and finally performing data analysis on the at least two simulation output data to analyze the rule in the at least two simulation output data to obtain an analysis result. Therefore, by considering the influence on the saturation of the irreducible water of the rock core under different wetting characteristics to obtain an analysis result, a digital rock core model for well logging correction can be accurately constructed according to the analysis result, and the method plays a role in evaluating and evaluating the oil field reservoirTo the guidance and demonstration functions.
In step S101 of some embodiments, the target sample is a core plug sample, and the structural data volume is obtained by scanning the core plug sample. The method comprises the steps of scanning a core plunger sample, selecting a preset area, scanning the preset area with a preset resolution to obtain a structural data body, wherein the structural data body is a three-dimensional pore structure data body.
In step S102 of some embodiments, the structural data volume is subjected to image processing to reconstruct a three-dimensional pore structure to obtain a pore network model, so that the pore network model is easy to construct.
In step S103 of some embodiments, a target magnetization decay curve is obtained by performing a simulation calculation according to the pore network model, wherein the target magnetization decay curve is a magnetization decay curve in the matrix, and the target magnetization decay curve is a decay curve of magnetization with time, specifically, the magnetization is the magnetization of the micropore area of the core.
In step S104 of some embodiments, the target magnetization decay curve is inverted to obtain the core nmr T 2 Spectrum and nuclear magnetic resonance T of rock core 2 And performing morphological fitting treatment on the spectrum to obtain the saturation of the irreducible water in the rock core. Therefore, the T of the pore structure of the digital core reflecting the core plunger sample is obtained by carrying out inversion processing on the target magnetization attenuation curve 2 Spectrum due to T 2 The spectrum includes immobile bound water as well as mobile water of macroporous structure, thus according to T 2 T of morphological characteristics of spectrum 2 And the cutoff value automatic confirmation method can obtain the irreducible water saturation in the rock core so as to obtain the irreducible water saturation of the rock core.
In step S105 of some embodiments, the T of the core is modeled to simulate different wetting characteristics of the core 2 Selecting at least two simulation parameters from the rock core irreducible water saturation and target magnetization intensity attenuation curves according to the preset wetting characteristics under the condition of map change, wherein each simulation parameter corresponds to the preset wetting characteristic, so that the rock core irreducible water saturation and target magnetization intensity attenuation curves are selected according to the wetting characteristicsAnd (4) at least two simulation parameters are obtained, and the influence of each wetting characteristic on the nuclear magnetic resonance of the rock core can be determined according to the simulation parameters.
In step S106 of some embodiments, a 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, where each simulation parameter corresponds to one simulation output data. And the simulation output data corresponds to nuclear magnetic resonance echo signals influenced by the wetting characteristics, so that the nuclear magnetic resonance influence of different wetting characteristics on the rock is judged through the nuclear magnetic resonance echo signals.
In step S107 of some embodiments, analog output data is obtained, and at least two analog output data are subjected to data analysis one by one to analyze different wetting characteristic pairs T 2 And (3) analyzing the rule of the graph to obtain an analysis result under the influence of the graph, and more accurately establishing a digital core model for well logging correction according to the analysis result.
Referring to fig. 2, in some embodiments, the method for core analysis based on nmr simulation further comprises:
and screening out a target sample from the original sample.
Note that, to verify T under different wetting characteristics 2 The change of the map is more accurate, and a target sample meeting the requirement needs to be screened from an original sample, namely, gravel with the brightness value meeting the preset brightness range is obtained. The required target sample needs to remove holes/cracks in the fracture-cavity rock and large gravels in the gravel rock so as to obtain the target sample with higher matrix components, so that the nuclear magnetic resonance simulation technology analysis of the rock core is more accurate.
The step of screening the target sample from the original sample may include, but is not limited to, the steps S201 to S203:
step S201, carrying out X-ray scanning on an original sample to obtain an original image;
step S202, acquiring the brightness value of an original image;
step S203, obtaining an original sample with a brightness value meeting a preset brightness range to obtain a target sample.
In step S201 of some embodiments, an original image is obtained by performing an X-ray scanning on an original sample, and according to the compton effect, components with different densities in the original sample have different X-ray absorption coefficients, and the difference in the X-ray absorption coefficients of the original sample represents a brightness difference on an imaged image, so that a target sample with a high matrix component is screened out by the brightness difference on the original image.
In step S202 of some embodiments, by acquiring brightness values of the original image, and brightness values of original samples of different density components are different, the density component of the original sample can be determined by the brightness values.
In step S203 of some embodiments, the original sample with the brightness value satisfying the preset brightness range is obtained to obtain the target sample, and therefore, the target sample with higher matrix composition in the original sample is selected according to the brightness difference to eliminate large rocks such as holes, cracks and conglomerates inside the fracture-cavity rock, so as to improve the T of the different wetting characteristics to T 2 And (4) analysis accuracy of the map.
Referring to fig. 3, in some embodiments, step S101 may include, but is not limited to, step S301 to step S303:
step S301, scanning a preset area of the target sample according to a preset resolution ratio to obtain a structural data volume.
In step S301 of some embodiments, the target sample is scanned, and the target sample is scanned to determine a preset region, and then the preset region is scanned with a preset resolution to obtain the structural data volume. Specifically, scanning a preset area of the target sample species with matrix components meeting preset requirements, wherein the size of the preset area is not more than 4mm multiplied by 4mm, so as to perform high-resolution scanning on the preset area with preset resolution, and the preset resolution is between 1 and 2 micrometers. Therefore, the structural data body meeting the requirement is obtained by scanning a preset area of the target sample at a preset resolution.
Referring to fig. 4, in some embodiments, step S102 includes, but is not limited to, steps S401 to S403:
step S401, carrying out smooth noise reduction processing on the structural data volume to obtain a preliminary data volume;
step S402, carrying out threshold segmentation processing on the preliminary data volume to obtain a segmented data volume;
and S403, carrying out reconstruction processing according to the segmented data volume to obtain a pore network model.
In step S401 of some embodiments, since the structural data volume obtained by direct scanning has a noise signal, the structural data volume is subjected to a smoothing noise reduction process to obtain a preliminary data volume, so as to remove the noise signal of the structural data to obtain the preliminary data volume.
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 according to the segmented data volumes to obtain a pore network model, so as to reconstruct the segmented data volume after image processing to obtain a pore network model, where the pore network model corresponds to a three-dimensional pore structure of the core.
Referring to fig. 5, in some embodiments, step S104 includes, but is not limited to, step S501 to step S502:
s501, performing simulation calculation on the aperture network model according to a preset random walk algorithm to obtain an initial magnetization intensity attenuation curve;
step S502, average calculation is carried out on the initial magnetization intensity attenuation curve to obtain a target magnetization intensity attenuation curve.
In step S501 of some embodiments, a simulation calculation is performed on the pore network model based on a random walk algorithm to calculate a magnetization decay curve of the matrix of the target sample, and the initial magnetization decay curve is a time-normalized magnetization decay of the pore fluid.
It should be noted that the decay of the normalized magnetization M (t) of the pore fluid with time can be expressed as formula (1):
in the formula, T 2S Is the transverse surface relaxation time of the fluid, M 2S (t) the surface relaxation magnetization of the fluid at time t, M 2D (t) is the diffusion relaxation magnetization of the fluid at time t.
Wherein, in the saturated fluid medium, the attenuation of the magnetization vector follows the Bloch-Torrey equation, wherein the Bloch-Torrey equation is as the formula (2):
the boundary conditions of the Bloch-Torrey equation are set as formula (3):
wherein, the magnetization vector at the initial moment is formula (4):
where ρ is the surface relaxation rate, m (r, t) represents the magnetization vector, γ is the gyromagnetic ratio of proton, and B z Is a static magnetic field, T 2 For transverse relaxation time, M (0) is the total magnetization at the initial moment, V P Is the total pore volume.
Therefore, as shown in the formula (1), the relaxation magnetization only corresponds to the relaxation time T 2B In relation to, and the type of fluid determines the relaxation time T 2B So that the relaxation time T is determined by the fluid type 2B So as to obtain the curve of relaxation magnetization intensity and time. Therefore, in the random walk algorithm, a pore network model is used for simulation calculation, a walk mode is selected according to the relation between the distance d from the particle to the nearest particle mark surface obtained by calculation and the diffusion radius, and when d (less than 3 epsilon, epsilon is the diffusion radius of the traditional random walk method) is smaller, the traditional random walk algorithm is adopted, namely the diffusion radius r = epsilon; when d is more than or equal to 3 epsilon,the first travel-time approach is used, i.e. diffusion radius d = r. The time step Δ t can be calculated as formula (5) from the following formula:
where Δ t is a time step, and within the time step Δ t, the particle diffuses from the initial time position [ x (t), y (t), z (t) ] to the position of the next time, and [ x (t + Δ t), y (t + Δ t), z (t + Δ t) ] can be expressed as formula (6):
in the formula, the selection range of theta is more than or equal to 0 and less than or equal to pi; the selection range of phi is more than or equal to 0 and less than or equal to 2 pi.
When the traditional random walk algorithm is used, the particles may collide with the surface of the rock particles in the diffusion process, and the probability that the particles are absorbed after collision is assumed to be delta 2 The surface relaxation magnetization of the particles is 1-delta 2 Attenuation, delta 2 The calculation formula of (2) is formula (7):
if the particles are not absorbed after the collision, the particles will elastically collide at the interface and maintain a continuous change in phase and amplitude. Therefore, the surface relaxation magnetization M at time t is obtained in the whole numerical simulation process 2S (t) can be expressed as formula (8):
in the formula, N 2 (t) is the total number of particles not absorbed at time t; n is a radical of 2 (0) Is the total number of particles at the initial moment.
Therefore, after normalization, the diffusion relaxation magnetization of each particle can be calculated from the cosine of its phase. The phase shift φ of the particle due to spin for a time step Δ t can be calculated from equation (9):
in the formula, normal () is a gaussian random function. In order to meet the requirement of data acquisition of the CPMG pulse sequence, when t = (n + 1/2) TE, the phase is reversed, namely phi (t) = -phi (t); the echo is acquired when t = nTE, when the diffusion relaxation magnetization is the cosine sum of all the particle spin dephasing, i.e. equation (10):
in summary, the normalized echo data amplitude M (nTE) simulated by the random walk algorithm can be expressed as formula (11):
in step S502 of some embodiments, after the initial magnetization attenuation curve is obtained, average calculation is performed on the initial magnetization attenuation curves of a plurality of target samples to obtain a target magnetization attenuation curve of a micropore area of the core, so that analysis of the core according to the target magnetization attenuation curve is more accurate.
After the target magnetization attenuation curve is obtained, the target magnetization attenuation curve is subjected to spectrum decomposition by adopting an SVD singular value decomposition method to obtain T reflecting the pore structure of the digital core of the plunger sample 2 Spectrum, T 2 The spectrum includes immobile bound water as well as mobile water of macroporous structure. Thus, based on T 2 T of morphological characteristics of spectrum 2 And obtaining the saturation of the rock core bound water by using an automatic cutoff value confirmation method.
Specifically, according to the pair T 2 The morphological characteristics of the spectra are analyzed and,the centrifugal spectrum can be extracted and constructed from the saturation spectrum by means of data fitting, and then the T is determined according to the constructed centrifugal spectrum and the actually measured saturation spectrum 2 Cutoff values and calculating therefrom the corresponding T 2 The spectrum represents the irreducible water saturation of the core sample or the actual reservoir.
For various different types of saturation T 2 And (4) spectrum fitting by adopting different centrifugal spectrum fitting processing methods. FIG. 6 shows a unimodal saturated T 2 A schematic diagram of the normal distribution form of the spectrum, wherein the fitting function is formula (12):
in the formula: 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. Thus, according to T by equation (12) 2 Spectral construction T 2 And cutting off the value to determine the core irreducible water saturation, so that the core irreducible water saturation is easy to calculate.
Referring to fig. 7, in some embodiments, the at least two simulation parameters include: water-wet simulation parameters, oil-wet simulation parameters and mixed-wetting simulation parameters; the at least two analog output data comprise: water-wet nuclear magnetic resonance echo information, oil-wet nuclear magnetic resonance echo information, and hybrid wetting nuclear magnetic resonance echo information. Step S106 includes, but is not limited to, steps S701 to S703:
step S701, performing nuclear magnetic resonance simulation on a target sample according to the water-humidity simulation parameters to obtain water-humidity nuclear magnetic resonance echo information;
step S702, performing nuclear magnetic resonance simulation on a target sample according to the oil-wet simulation parameters to obtain oil-wet nuclear magnetic resonance echo information;
and step S703, performing nuclear magnetic resonance simulation on the target sample according to the mixed wetting simulation parameters to obtain mixed wetting nuclear magnetic resonance echo information.
In step S701 of some embodiments, nuclear magnetic resonance simulation of oil-water one-way flow needs to be performed on the target sample to respectively construct different relaxation models according to different wetting characteristicsTo analyze T under different wetting characteristics 2 The change of the map can be determined according to T 2 And determining the oil-water distribution condition in the rock core by the map so as to construct a more accurate digital rock core model. Therefore, the relaxation model is constructed according to the water-humidity simulation parameters by obtaining the water-humidity simulation parameters, so that the nuclear magnetic resonance echo signals are the water-humidity nuclear magnetic resonance echo information.
Specifically, the water-humidity simulation parameters include: volume relaxation magnetization of water, surface relaxation magnetization of water, diffusion relaxation magnetization of water, volume relaxation magnetization of oil, surface relaxation magnetization of oil, diffusion relaxation magnetization of oil, water saturation, oil saturation, hydrogen index of water, hydrogen index of oil. Because the oil component exists in the center of macropores, the water component is in contact with the surface of rock particles and is characterized by oil-in-water. Therefore, under the water-wet condition, the rock pores contain oil-water two phases, and the oil is not influenced by surface relaxation, so that the water-wet nuclear magnetic resonance echo information is obtained as formula (13):
in the formula, M wB (t) is the volume relaxation magnetization of water; m wS (t) is the surface relaxation magnetization of water; m wD (t) is the diffusion relaxation magnetization of water; m oB (t) is the volume relaxation magnetization of the oil; m oS (t) is the surface relaxation magnetization of the oil; m oD (t) is the diffusion relaxation magnetization of the oil; s w Is the water saturation; s o Is the oil saturation; HI (high-intensity) w Is the hydrogen index of water; HI (high-intensity) o Is the hydrogen index of the oil. Therefore, the nuclear magnetic resonance condition of the core under the water-wet condition can be analyzed by performing nuclear magnetic resonance simulation according to the water-wet simulation parameters to obtain the water-wet nuclear magnetic resonance echo information.
In step S702 of some embodiments, in the small pores, the small pores keep the water-wet property unchanged, and only bound water exists, the water component exists in the middle of the pores in the large pores, and the oil component is in contact with the surface of the rock particles, which is a "water-in-oil" feature. Therefore, under the oil-wet condition, when oil-water two phases in rock pores face each other, water moisture is kept in small pores, bound water is influenced by volume relaxation, surface relaxation and diffusion relaxation, oil moisture is in large pores, water is not influenced by surface relaxation, and oil is influenced by volume relaxation and diffusion relaxation. Therefore, the oil-wet simulation parameters include water-wet simulation parameters and irreducible water saturation, so that the nuclear magnetic resonance simulation is performed on the target sample according to the oil-wet simulation parameters, and the obtained oil-wet nuclear magnetic resonance echo information is formula (14):
in the formula, S wi To irreducible water saturation.
In step S703 of some embodiments, the core surface has both oleophilic portions and hydrophilic portions, i.e., is determined to be mixed wet. In the mixed wetting condition, bound water in small pores is still only influenced by three relaxations of water, and oil and water in large pores are both influenced by volume relaxation and surface relaxation, namely diffusion relaxation. Thus, the mixed wetting simulation parameters include: therefore, 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 as shown in formula (15):
therefore, as can be seen from the formula (15), the nmr simulation is performed according to the mixed wetting simulation parameters to obtain the mixed wetting nmr echo information, i.e., the three nmr echo information can be compared to obtain T under different wetting conditions 2 And (4) map change conditions.
Referring to fig. 8, in some embodiments, step S107 may include, but is not limited to, steps S801 to S804:
step S801, extracting characteristics of the water-wet nuclear magnetic resonance echo information to obtain water-wet echo characteristics;
step S802, extracting the characteristics of the oil-wet nuclear magnetic resonance echo information to obtain oil-wet echo characteristics;
step S803, performing feature extraction on the mixed wetting resonance echo information to obtain mixed wetting echo features;
and step S804, performing characteristic analysis according to the water-wet echo characteristic, the oil-wet echo characteristic and the mixed wetting echo characteristic to obtain an analysis result.
In the above steps S801 to S804, the water-wet echo feature is obtained by performing feature extraction on the water-wet nuclear magnetic resonance echo information, the oil-wet echo feature is obtained by performing feature extraction on the oil-wet nuclear magnetic resonance echo information, and the mixed wetting echo feature is obtained by performing feature extraction on the mixed wetting resonance echo information, so that the influence of different wetting features on the nuclear magnetic resonance simulation can be more clearly analyzed according to the law in the water-wet echo feature, the oil-wet echo feature, and the mixed wetting echo feature, that is, a digital core model for well logging correction can be accurately established according to the analysis result, and a guidance and demonstration effect is provided for oil field reservoir evaluation and evaluation.
Referring to fig. 9, an embodiment of the present application further provides a core analysis apparatus based on nuclear magnetic resonance simulation, which may implement the core analysis based on nuclear magnetic resonance simulation, and the apparatus includes:
a scanning module 901, configured to scan a target sample to obtain a structural data volume; the target sample is gravel with the brightness value meeting a preset brightness range;
an image processing module 902, configured to perform image processing on the structural data volume to obtain a pore network model;
a calculating module 903, configured to perform simulation calculation according to the pore network model to obtain a target magnetization attenuation curve;
an inversion processing module 904 for performing decomposition processing and inversion processing on the target magnetization attenuation curve to obtain a core nuclear magnetic resonance T 2 Spectrum and nuclear magnetic resonance T of rock core 2 Performing morphological fitting treatment on the spectrum to obtain the saturation of the bound water of the rock core;
the screening module 905 is used for screening out target parameters from the rock core bound water saturation and the target magnetization intensity attenuation curve according to preset wetting characteristics; wherein, the target parameters include: at least two simulation parameters;
a simulation module 906, configured to perform nuclear magnetic resonance simulation on the target sample according to the at least two simulation parameters to obtain at least two simulation output data;
and the analysis module 907 is configured to perform data analysis according to the at least two pieces of analog output data to obtain an analysis result.
The specific implementation of the core analysis apparatus based on nuclear magnetic resonance simulation is substantially the same as the specific implementation of the core analysis method based on nuclear magnetic resonance simulation, and is not described herein again.
An embodiment of the present application further provides an electronic device, where the electronic device includes: the core analysis method based on the nuclear magnetic resonance simulation comprises a memory, a processor, a program which is stored on the memory and can run on the processor, and a data bus for realizing connection communication between the processor and the memory, wherein when the program is executed by the processor, the core analysis method based on the nuclear magnetic resonance simulation is realized. The electronic equipment can be any intelligent terminal including a tablet computer, a vehicle-mounted computer and the like.
Referring to fig. 10, fig. 10 illustrates a hardware structure of an electronic device according to another embodiment, where the electronic device includes:
the processor 101 may be implemented by a general-purpose CPU (central processing unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits, and is configured to execute a relevant program to implement the technical solution provided in the embodiment of the present application;
the memory 102 may be implemented in a form of a Read Only Memory (ROM), a static storage device, a dynamic storage device, or a Random Access Memory (RAM). The memory 102 may store an operating system and other application programs, and when the technical solution provided in the embodiments of the present specification is implemented by software or firmware, related program codes are stored in the memory 102, and the processor 101 is used to call and execute the core analysis method based on nuclear magnetic resonance simulation according to the embodiments of the present application;
an input/output interface 103 for implementing information input and output;
the communication interface 104 is configured to implement communication interaction between the device and other devices, and may implement communication in a wired manner (e.g., USB, network cable, etc.) or in a wireless manner (e.g., mobile network, WIFI, bluetooth, etc.);
a bus 105 that transfers information between various components of the device (e.g., the processor 101, the memory 102, the input/output interface 103, and the communication interface 104);
wherein the processor 101, the memory 102, the input/output interface 103 and the communication interface 104 are communicatively connected to each other within the device via a bus 105.
The embodiment of the application also provides a storage medium, which is a computer-readable storage medium used for computer-readable storage, and 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 core analysis method based on nuclear magnetic resonance simulation.
The memory, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory located remotely from the processor, and these remote memories may be connected to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
According to the recommendation method, the recommendation device, the electronic equipment and the storage medium, the analysis result is obtained by considering the influence on the irreducible water saturation of the rock core under different wetting characteristics, so that the digital rock core model for well logging correction can be accurately constructed according to the analysis result, and the guidance and demonstration effects are played for evaluation and evaluation of the oil field reservoir stratum.
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 to the technical solutions provided in the embodiments of the present application, and it is obvious to those skilled in the art that the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems with the evolution of technology and the emergence of new application scenarios.
It will be appreciated by those skilled in the art that the solutions shown in fig. 1-8 are not intended to limit the embodiments of the present application and may include more or fewer steps than those shown, or some of the steps may be combined, or different steps may be included.
The above-described embodiments of the apparatus are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may also be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
One of ordinary skill in the art will appreciate that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof.
The terms "first," "second," "third," "fourth," and the like in the description of the application and the above-described figures, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Moreover, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in the present application, "at least one" means one or more, "a plurality" means two or more. "and/or" for describing an association relationship of associated objects, indicating that there may be three relationships, e.g., "a and/or B" may indicate: only A, only B and both A and B are present, wherein A and B may be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of single item(s) or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the above-described division of units is only one type of division of logical functions, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes multiple instructions for causing a computer device (which may be a personal computer, a server, or a network device) to perform all or part of the steps of the method of the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing programs, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The preferred embodiments of the present application have been described above with reference to the accompanying drawings, and the scope of the claims of the embodiments of the present application is not limited thereto. Any modifications, equivalents and improvements that may occur to those skilled in the art without departing from the scope and spirit of the embodiments of the present application are intended to be within the scope of the claims of the embodiments of the present application.
Claims (10)
1. A core analysis method based on nuclear magnetic resonance simulation is characterized by comprising the following steps:
scanning a target sample to obtain a structural data volume; the target sample is gravel with the brightness value meeting a preset brightness range;
performing image processing on the structural data body to obtain a pore network model;
performing simulation calculation according to the pore network model to obtain a target magnetization intensity attenuation curve;
inverting the target magnetization decay curvePerforming to obtain nuclear magnetic resonance T of rock core 2 Spectrum and nuclear magnetic resonance T of said core 2 Performing morphological fitting treatment on the spectrum to obtain the saturation of the bound water of the rock core;
screening out target parameters from the rock core irreducible water saturation and the target magnetization intensity attenuation curve according to preset wetting characteristics; wherein the target parameters include: at least two simulation parameters;
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;
and carrying out data analysis according to at least two pieces of the analog output data to obtain an analysis result.
2. The method of claim 1, wherein prior to scanning the target sample to obtain the structural data volume, the method further comprises:
screening the target sample from the original sample, which specifically comprises the following steps:
carrying out X-ray scanning on the original sample to obtain an original image;
acquiring a brightness value of the original image;
and acquiring the original sample with the brightness value meeting a preset brightness range to obtain the target sample.
3. The method of claim 2, wherein scanning the target sample to obtain the structural data volume comprises:
and 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, wherein said image processing of said structural data volume to obtain a pore network model comprises:
carrying out smooth noise reduction processing on the structural data body to obtain a preliminary data body;
performing threshold segmentation processing on the preliminary data volume to obtain a segmented data volume;
and carrying out reconstruction processing according to the segmentation data volume to obtain the pore network model.
5. The method according to any one of claims 1 to 3, wherein the performing a simulation calculation according to the pore network model to obtain a target magnetization decay curve comprises:
performing simulation calculation on the pore network model according to a preset random walk algorithm to obtain an initial magnetization intensity attenuation curve;
and carrying out average calculation on the initial magnetization intensity attenuation curve to obtain the target magnetization intensity attenuation curve.
6. The method of any of claims 1 to 3, wherein the at least two simulation parameters comprise: water-wet simulation parameters, oil-wet simulation parameters and mixed-wetting simulation parameters; the at least two analog output data comprise: water-wet nuclear magnetic resonance echo information, oil-wet nuclear magnetic resonance echo information, and mixed-wetting nuclear magnetic resonance echo information; 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, including:
performing nuclear magnetic resonance simulation on the target sample according to the water-humidity simulation parameter to obtain the water-humidity nuclear magnetic resonance echo information;
performing nuclear magnetic resonance simulation on the target sample according to the oil-moisture simulation parameter to obtain oil-moisture nuclear magnetic resonance echo information;
and performing nuclear magnetic resonance simulation on the target sample according to the mixed wetting simulation parameters to obtain mixed wetting nuclear magnetic resonance echo information.
7. The method of claim 6, wherein said analyzing data from at least two of said analog output data to obtain said analysis result comprises:
performing feature extraction on the water-wet nuclear magnetic resonance echo information to obtain water-wet echo features;
performing characteristic extraction on the oil-wet nuclear magnetic resonance echo information to obtain oil-wet echo characteristics;
performing feature extraction on the mixed wetting resonance echo information to obtain mixed wetting echo features;
and performing characteristic analysis according to the water-wet echo characteristic, the oil-wet echo characteristic and the mixed wetting echo characteristic to obtain the analysis result.
8. A core analysis device based on nuclear magnetic resonance simulation, the device comprising:
the scanning module is used for scanning the target sample to obtain a structural data volume; the target sample is gravel with the brightness value meeting a preset brightness range;
the image processing module is used for carrying out image processing on the structural data body to obtain a pore network model;
the calculation module is used for carrying out simulation calculation according to the pore network model to obtain a target magnetization intensity attenuation curve;
an inversion processing module for performing inversion processing on the target magnetization attenuation curve to obtain core nuclear magnetic resonance T 2 Spectrum and nuclear magnetic resonance T of said core 2 Performing morphological fitting treatment on the spectrum to obtain the saturation of the bound water of the rock core;
the screening module is used for screening out target parameters from the rock core bound water saturation and the target magnetization intensity attenuation curve according to preset wetting characteristics; wherein the target parameters include: at least two simulation parameters;
the simulation module is used for carrying out nuclear magnetic resonance simulation on the target sample according to at least two simulation parameters to obtain at least two simulation output data;
and the analysis module is used for carrying out data analysis according to the at least two analog output data to obtain an analysis result.
9. An electronic device, characterized in that the electronic device comprises a memory, a processor, a program stored on the memory and executable on the processor, and a data bus for enabling a connection communication between the processor and the memory, which program, when executed by the processor, realizes the steps of the method according to any one of claims 1 to 7.
10. A storage medium, being a computer readable storage medium, for computer readable storage, characterized in that the storage medium stores one or more programs executable by one or more processors to implement the steps of the method of any one of claims 1 to 7.
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