CN111239821B

CN111239821B - Carbonate reservoir pore structure prediction method, device, equipment and storage medium

Info

Publication number: CN111239821B
Application number: CN202010104055.2A
Authority: CN
Inventors: 赵建国; 潘建国; 李闯; 孙朗秋; 刘欣泽; 欧阳芳; 李智; 肖增佳
Original assignee: Northwest Branch Of Petrochina Co Ltd Exploration And Development Research Institute; China University of Petroleum Beijing
Current assignee: Northwest Branch Of Petrochina Co Ltd Exploration And Development Research Institute; China University of Petroleum Beijing
Priority date: 2020-02-20
Filing date: 2020-02-20
Publication date: 2021-02-12
Anticipated expiration: 2040-02-20
Also published as: CN111239821A

Abstract

The embodiment of the specification provides a carbonate reservoir pore structure prediction method, a carbonate reservoir pore structure prediction device, carbonate reservoir pore structure prediction equipment and a storage medium, wherein the method comprises the following steps: acquiring a longitudinal wave velocity data volume and a density data volume based on seismic prestack inversion; determining a first relation model of porosity, longitudinal wave velocity and density according to logging data, determining a second relation model of porosity, longitudinal wave velocity and density according to digital core data, and forming a third relation model according to the two models; acquiring a first pore structure factor based on logging data, acquiring a second pore structure factor based on digital core data, and forming a third pore structure factor according to the two pore structure factors; inputting the longitudinal wave velocity data volume and the density data volume into a third relation model to obtain a porosity data volume; and intersecting the longitudinal wave velocity data body with the porosity data body to obtain a pore structure data body. The embodiment of the specification can improve the accuracy of carbonate reservoir pore structure prediction.

Description

Carbonate reservoir pore structure prediction method, device, equipment and storage medium

Technical Field

The specification relates to the technical field of oil and gas exploration and development, in particular to a carbonate reservoir pore structure prediction method, device, equipment and storage medium.

Background

At present, reservoir characteristic parameter prediction in the prior art is commonly used in an attribute intersection mode adopted in production. Particularly for clastic rock reservoirs, the method of attribute intersection can relatively accurately obtain the porosity and the saturation of the reservoir, so that structural information of the reservoir is obtained from the porosity and the saturation.

However, in the carbonate reservoir, the pore structure is extremely complex due to the fact that the subsequent diagenetic transformation of the carbonate reservoir can cause the secondary pores to be very developed. The common pore structure types in the carbonate rock include a die casting hole, an intra-granular soluble hole, an inter-granular pore, a crack and the like, and the complex and variable pore structures can obviously influence the elastic property of the carbonate rock, so that the common attribute intersection mode cannot obtain accurate parameters of porosity and saturation and cannot obtain parameters of the pore structure. Therefore, how to improve the accuracy of predicting the pore structure of the carbonate reservoir becomes a technical problem to be solved urgently at present.

Disclosure of Invention

An object of the embodiments of the present specification is to provide a method, an apparatus, a device, and a storage medium for predicting a pore structure of a carbonate reservoir, so as to improve accuracy of predicting the pore structure of the carbonate reservoir.

In order to achieve the above object, in one aspect, the present specification provides a method for predicting a pore structure of a carbonate reservoir, including:

acquiring a longitudinal wave velocity data volume and a density data volume of a target block based on seismic prestack inversion;

determining a first relation model of porosity, longitudinal wave velocity and density according to the logging data of the target block, and determining a second relation model of porosity, longitudinal wave velocity and density according to the digital core data of the target block;

taking a weighted sum of the first relational model and the second relational model as a third relational model;

obtaining a first pore structure factor of the target block based on the logging data, and obtaining a second pore structure factor of the target block based on the digital core data;

(iii) taking a weighted sum of the first pore structure factor and the second pore structure factor as a third pore structure factor;

inputting the longitudinal wave velocity data volume and the density data volume into the third relation model to obtain a porosity data volume of the target block;

and intersecting the longitudinal wave velocity data volume with the porosity data volume to obtain a pore structure data volume of the target block.

In another aspect, an embodiment of the present specification further provides a device for predicting a pore structure of a carbonate reservoir, including:

the first data volume acquisition module is used for acquiring a longitudinal wave velocity data volume and a density data volume of a target block based on seismic prestack inversion;

the first model acquisition module is used for determining a first relation model of porosity, longitudinal wave velocity and density according to the logging data of the target block and determining a second relation model of porosity, longitudinal wave velocity and density according to the digital core data of the target block;

a second model obtaining module, configured to use a weighted sum of the first relationship model and the second relationship model as a third relationship model;

a first factor obtaining module, configured to obtain a first pore structure factor of the target block based on the logging data, and obtain a second pore structure factor of the target block based on the digital core data;

a second factor acquisition module for taking a weighted sum of the first pore structure factor and the second pore structure factor as a third pore structure factor;

a second data volume obtaining module, configured to input the longitudinal wave velocity data volume and the density data volume into the third relation model, so as to obtain a porosity data volume of the target block;

and the pore structure acquisition module is used for intersecting the longitudinal wave velocity data volume and the porosity data volume to acquire a pore structure data volume of the target block.

In another aspect, the embodiments of the present specification further provide a computer device, including a memory, a processor, and a computer program stored on the memory, where the computer program, when executed by the processor, performs the following steps:

In another aspect, the embodiments of the present specification further provide a computer storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the following steps:

According to the technical scheme provided by the embodiment of the specification, in the embodiment of the specification, a relation model of the porosity of the target block, the longitudinal wave velocity and the density is obtained by combining the logging data of the target block and the digital core data; combining the logging data and the digital core data of the target block to obtain a pore structure factor of the target block; on the basis, a longitudinal wave velocity data volume and a density data volume of seismic prestack inversion of the target block are used as input and input into a relational model of the representation porosity of the target block and the longitudinal wave velocity and density, so that a more accurate porosity data volume of the target block can be obtained; and the longitudinal wave velocity data body of the seismic prestack inversion of the target block is intersected with the porosity data body of the target block, so that a more accurate pore structure data body of the target block can be obtained. And more reliable basic data are provided for reserve prediction, early-stage exploration and development, late-stage production monitoring and the like of the carbonate reservoir.

Drawings

In order to more clearly illustrate the embodiments of the present specification or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present specification, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort. In the drawings:

FIG. 1 is a flow chart of a carbonate reservoir pore structure prediction method in some embodiments of the present description;

FIG. 2 is a schematic cross-sectional view of a CT scan of a carbonate rock sample according to an embodiment of the present disclosure;

FIG. 3 is a schematic diagram illustrating the relationship between the longitudinal and transverse wave velocities of a carbonate rock sample according to the variation of the confining pressure in one embodiment of the present disclosure;

FIG. 4 is a graph illustrating the elastic modulus of a carbonate rock sample as a function of confining pressure in one embodiment of the present disclosure;

FIG. 5 shows the gray level redistribution result of the linear stretching method in one embodiment of the present disclosure;

FIG. 6a is a schematic diagram of a sample of an original CT scan (gray rectangle is the sampling portion) in one embodiment of the present disclosure;

FIG. 6b is a schematic view of a slice of the CT scan image taken from the original CT scan image shown in FIG. 6 a;

FIG. 6c is a schematic diagram illustrating the result of contrast enhancement processing performed on the slice of the CT scan image shown in FIG. 6 b;

FIG. 7a is a diagram illustrating the result of contrast enhancement processing performed on a CT scan image according to an embodiment of the present disclosure;

FIG. 7b is a schematic diagram illustrating the result of the anisotropic filtering process performed on the CT scan image shown in FIG. 7 a;

FIG. 8a is a schematic diagram illustrating the result of the anisotropic filtering process performed on the CT scan image according to an embodiment of the present disclosure;

FIG. 8b is a schematic diagram illustrating the result of edge enhancement processing performed on the CT scan image shown in FIG. 8 a;

FIG. 9a is a schematic diagram of a pre-processed CT scan image according to an embodiment of the present disclosure;

FIG. 9b is a schematic diagram illustrating the result of the image binarization process performed on the CT scan image shown in FIG. 9 a;

FIG. 10a is a schematic representation of a digital core of a carbonate rock sample number 3-1 in one embodiment of the present disclosure;

FIG. 10b is a schematic diagram of the pore structure of the digital core shown in FIG. 10 a;

FIG. 11 is a schematic diagram of gridding of a digital core in one embodiment of the present disclosure;

FIG. 12 is a graphical representation of the elastic properties versus porosity of a carbonate rock sample under classification as obtained in one embodiment of the present description;

13 a-13 c are schematic diagrams of log data obtained in one embodiment of the present disclosure;

FIG. 14 is a schematic diagram of a neural network in one embodiment of the present disclosure;

FIG. 15 is a schematic diagram illustrating the verification error between the porosity predicted by the neural network-trained model and the true porosity in one embodiment of the present disclosure;

FIG. 16 is a schematic cross-sectional view of a compressional velocity attribute data volume obtained based on seismic prestack AVO inversion in an embodiment of the present description;

FIG. 17 is a schematic cross-sectional view of a density attribute data volume obtained based on seismic pre-stack AVO inversion in one embodiment of the present description;

FIG. 18 is a cross-sectional view of a porosity data volume of a target block obtained in one embodiment of the present disclosure;

FIG. 19 is a schematic diagram illustrating an intersection of a porosity data volume and a compressional velocity data volume of a target block according to an embodiment of the present disclosure;

FIG. 20 is a cross-sectional view of a pore structure data volume of a target block obtained in an embodiment of the present disclosure;

FIG. 21 is a block diagram of a carbonate reservoir pore structure prediction device in some embodiments of the present description;

FIG. 22 is a block diagram of a computer device in some embodiments of the present description.

Detailed Description

In order to make those skilled in the art better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only a part of the embodiments of the present specification, and not all of the embodiments. All other embodiments obtained by a person skilled in the art based on the embodiments in the present specification without any inventive step should fall within the scope of protection of the present specification.

Referring to fig. 1, a carbonate reservoir pore structure prediction method of some embodiments of the present description may include the steps of:

s101, acquiring a longitudinal wave velocity data volume and a density data volume of a target block based on seismic prestack inversion.

S102, determining a first relation model of porosity, longitudinal wave velocity and density according to the logging data of the target block, and determining a second relation model of porosity, longitudinal wave velocity and density according to the digital core data of the target block.

S103, taking the weighted sum of the first relation model and the second relation model as a third relation model.

S104, acquiring a first pore structure factor of the target block based on the logging data, and acquiring a second pore structure factor of the target block based on the digital core data.

S105, taking the weighted sum of the first pore structure factor and the second pore structure factor as a third pore structure factor.

And S106, inputting the longitudinal wave velocity data volume and the density data volume into the third relation model to obtain a porosity data volume of the target block.

And S107, intersecting the longitudinal wave velocity data volume with the porosity data volume to obtain a pore structure data volume of the target block.

Therefore, in the embodiment of the specification, a relation model of the porosity of the target block, the velocity and the density of the longitudinal wave is obtained by combining the logging data of the target block and the digital core data; combining the logging data and the digital core data of the target block to obtain a pore structure factor of the target block; on the basis, a longitudinal wave velocity data volume and a density data volume of seismic prestack inversion of the target block are used as input and input into a relational model of the representation porosity of the target block and the longitudinal wave velocity and density, so that a more accurate porosity data volume of the target block can be obtained; and the longitudinal wave velocity data body of the seismic prestack inversion of the target block is intersected with the porosity data body of the target block, so that a more accurate pore structure data body of the target block can be obtained. And more reliable basic data are provided for reserve prediction, early-stage exploration and development, late-stage production monitoring and the like of the carbonate reservoir.

In an embodiment of the present specification, the longitudinal wave velocity data volume and the density data volume based on seismic prestack inversion may be, for example, longitudinal wave velocity data volume and density data volume obtained based on seismic AVO (Amplitude variation with offset) inversion. The seismic AVO inversion can be carried out on the target block in advance, so that the inversion result including the longitudinal wave velocity data volume, the density data volume and the like of the target block is obtained. Thus, the longitudinal wave velocity data volume and the density data volume of the target block can be obtained by reading the inversion result. Of course, this description is not so limited, and in other embodiments, other seismic prestack inversion techniques may be employed as desired.

In an embodiment of the present disclosure, the target block may be logged in advance, so as to obtain corresponding logging data. The well log data may generally include density, compressional velocity, shear velocity, and porosity data. And the porosity, the longitudinal wave velocity and the density of the target block based on the logging data can be obtained by reading the logging data. On the basis, the longitudinal wave velocity and the density in the logging data can be used as input, the porosity in the logging data can be used as target output, and a preset machine learning model (such as a neural network) is trained, so that a relation curve of the porosity and the longitudinal wave velocity and the density can be obtained, namely, a first relation model of the porosity and the longitudinal wave velocity and the density is determined. When the relation is fitted, the neural network is easier to add in a data set compared with a common nonlinear fitting algorithm, and the relation capable of being represented is more complex, so theoretically, as long as an approximate relation exists between the porosity and the velocity and the density of the longitudinal wave, no matter how complex the relation is, a relation curve representing the porosity, the velocity and the density of the longitudinal wave can be found through training of the neural network.

For example, in an exemplary embodiment of the present disclosure, log data (compressional velocity, density, and porosity) for 3 wells (i.e., well 1, well 2, and well 3) in a work zone may be as shown in fig. 13 a-13 c. On the basis of the logging data, a neural network is used for training, as shown in fig. 14, so that a curve representing the relationship between the porosity and the velocity and the density of the longitudinal wave can be obtained. In an exemplary embodiment, the data set corresponding to the logging data may be divided into a training set and a validation set, and the validation set accounts for 20% of the total data. After the porosity obtained by using the trained relationship curve is used, the porosity can be compared with the real porosity, and the prediction accuracy of the relationship curve can be verified through the comparison, for example, as shown in fig. 15.

In an embodiment of the present specification, the determining the second relationship model of porosity and velocity and density of longitudinal waves according to the digital core data of the target block may include the following steps:

and (I) acquiring the rock physical parameters of the specified rock sample of the target block.

In the embodiments of the present specification, the petrophysical parameters may include, for example, porosity, diagenetic mineral composition, pore type, density, and longitudinal and transverse wave velocities varying with the confining pressure in a specific frequency band. In one embodiment, the obtaining may receive, for example, an externally input petrophysical parameter of the specified rock sample. In another embodiment, the obtaining may be, for example, reading the petrophysical parameters recorded with the specified rock sample from the specified storage path.

In an embodiment of the present specification, the petrophysical parameters of the specified rock sample can be obtained in advance by:

(1) the method can select a plurality of representative carbonate rock samples of the reservoir, generally select at least 5 rock samples, roughly cover crack types, hole types and hole-crack types by the pore types of the rock samples, and can carry out pretreatment such as salt washing, oil washing, drying and the like on the rock samples.

(2) The selected representative carbonate rock sample can be subjected to helium measurement of porosity, or the porosity can be measured through nuclear magnetic resonance, so that the more accurate porosity of the representative rock sample can be obtained for subsequent calibration.

(3) The selected representative carbonate rock sample can be subjected to X-Ray Diffraction (XRD) mineral analysis to obtain diagenetic minerals which are used as mineral skeleton modulus parameter input for subsequent elastic property simulation.

(4) The selected representative carbonate rock sample can be subjected to sub-mirror slice analysis to obtain qualitative semi-quantitative pore types for subsequent calibration.

(5) The density measurement and the longitudinal and transverse wave velocity measurement which changes with the ambient pressure under the high-frequency ultrasonic frequency band can be carried out on the selected representative carbonate rock sample for subsequent calibration.

Wherein, the steps (2) to (5) can be finely adjusted in the laboratory measurement according to actual conditions according to requirements.

For example, in an exemplary embodiment of the specification, 7 dolomites were selected as representative carbonate reservoir rocks, respectively designated Dolo-1, Dolo-2, Dolo-3-1, Dolo-3-2, Dolo-4-1, Dolo-4-2, Dolo-5, with a rock sample diameter of 38mm (or 25mm in diameter), and a CT scan imaging resolution of 20.7678 μm per voxel. The 7 horizontal slices of carbonate rock sample digital core data are shown in fig. 2. By gas porosity measurements on these 7 rock samples, the measured porosity values shown in table 1 below were obtained, as can be seen in table 1: these rock samples have very narrow porosity distributions (0.6% to 3.71%). In addition, XRD mineral analysis of the 7 rock samples showed that the rock samples contained an average of about 7.4% quartz and 92.6% dolomite. In terms of mineral components, which is a relatively special place of a carbonate rock sample, the carbonate rock can be regarded as a monomineral rock, and when elastic property simulation is subsequently performed by using finite elements, the elastic property of the framework phase of the digital core binarization image can be simply set to be the modulus of the monomineral rock.

TABLE 1 physical Properties of measured porosity and calculated porosity of rock sample

Rock sample numbering	Measured porosity (%)	Calculated porosity (%)
			Dolo-1	3.14	1.26
Dolo-2	2.89	1.92
			Dolo-3-1	3.71	2.61
Dolo-3-2	2.67	1.09
			Dolo-4-1	0.97	0.81
Dolo-4-2	2.25	2.14
			Dolo-5	0.60	0.46

Taking a Dolo-3-1 rock sample as an example, a schematic diagram of the longitudinal and transverse wave speeds of the Dolo-3-1 rock sample as a function of confining pressure is shown in FIG. 3; a schematic representation of the modulus of elasticity of a Dolo-3-1 rock sample as a function of confining pressure is shown in FIG. 4.

And (II) constructing a digital core based on the petrophysical parameters and the CT scanning image of the specified rock sample.

In an embodiment of the present specification, the constructing a digital core based on the petrophysical parameter and the CT scan image of the specified rock sample may include the following steps:

(1) and acquiring a CT scanning image of the rock sample. The CT scanning image can be an original image section obtained by carrying out CT scanning imaging on a selected representative carbonate rock sample in advance.

(2) And carrying out image binarization processing on the CT scanning image, and constructing a digital core according to the obtained binarized image.

In an embodiment of the present specification, the performing an image binarization process on the CT scan image may include the following steps:

(21) contrast enhancement

The original image of the CT scan may be first cropped, typically by selecting a cube with a side length of 1000 voxels. The gray scale of the cut CT image is mainly distributed in a very low-value area, and image information is displayed in a dark mode from the visual observation of the image, so that the narrow-distribution gray scale histogram distribution can cause difficulty in subsequent image segmentation, and the contrast of the image can be enhanced. The contrast enhancement operation transforms the originally narrow distribution gray level histogram into a wide distribution histogram by a gray scale transformation. The gray level transformation formula (1) is selected to realize the functions:

wherein f (x, y) represents the gray value of a pixel point at the coordinate (x, y) on the original two-dimensional image, g (x, y) represents the value of the same point after gray conversion, and a, b, c and d are constants.

(22) Filtering and de-noising

In some embodiments of the present disclosure, a global filtering algorithm such as median filtering, mean filtering, or gaussian filtering may be used to perform denoising processing on the image. The global filtering algorithm treats all pixel points on the image uniformly, and information such as crack textures and the like is easy to smooth. The carbonate rock has more abundant pore structure types compared with other types of rock types, particularly complex structures such as cracks, textures and holes are distributed in the rock types, and the complex pore structure influences the elastic property of the carbonate rock and is one of important research contents in geophysical exploration of the carbonate rock. Therefore, the filtering and denoising algorithm which can reasonably remove noise in the image and effectively retain pore structure information such as cracks, textures, holes and the like is obtained, and is an important task in the carbonate rock digital core image processing. In an embodiment of the present specification, the desired function may be implemented by using an anisotropic filtering algorithm, which may specifically be as shown in formulas (2) to (4):

in the above formula (2), I_t(s) a gray function representing the original image, I_t+1(s) represents the gray function of the filtered image, s is the spatial position of the pixel point on the imageSetting a coordinate, wherein lambda is a constant for controlling diffusion intensity, the larger the lambda value is, the smoother the filtered image is, and g is_K(x) Is a diffusion function, wherein x is an image gradient value, K is an image gradient threshold parameter for judging the noise and microstructure signals, a gradient below K is judged as a noise signal, a gradient above K is judged as a useful microstructure signal,

the difference value between the pixel point and the adjacent pixels in all directions is expressed in formula (4), eta_sThe symbol in four directions of north, south, east, west, etc. around the pixel S is represented by { N, S, E, W }. K is set equal to the gradient corresponding to the 90% value of the gradient histogram integral (cumulative sum) in each iteration of iterative equation (2).

(23) Edge enhancement

Image edge enhancement on the digital core can make the image edge clearer. The main difficulty with edge enhancement is that the sharpening filter easily amplifies all the noise in the image. In an embodiment of the present specification, edge detection may be used for edge enhancement. Since the edges between the solid particles and the pores generally correspond to regions of high gradient, the gradient can be used to extract the edge signal. By means of the cumulative sum function of the image gradients, a gradient point can be determined, above which the gradient accumulation amounts to 90% of the total value, and a high gradient is considered as indicating a phase boundary. Therefore, the edge region between the solid particles and the pores can be completely identified, and image segmentation is carried out on the basis, so that segmentation uncertainty is reduced, and a better image segmentation effect is obtained.

(24) Image segmentation

After the processing steps (21) to (23) are carried out, image binarization processing can be carried out on the carbonate rock digital core image, so that an initial digital core can be established, and elastic property simulation can be carried out subsequently.

In the embodiments of the present specification, the pore structure is extracted from the CT image, and the pore pixels in each image are identified, which is called image segmentation in image processing. Image segmentation techniques are generally classified into geometric segmentation methods and cluster segmentation methods. The former is a method of analyzing the gray histogram geometry to determine the segmentation threshold, and the latter is a method of determining the optimal threshold by the statistical property difference between two sets of data in the image. The clustering segmentation method mainly comprises the following three steps:

(241) and because each pixel point in the image corresponds to a gray value, the gray value of all the pixel points in the image is regarded as a data set by the clustering algorithm.

(242) Because the CT image is a gray image, the gray distribution generally comprises two types of 8bit (0-255) or 16bit (0-65535), taking 8bit as an example, the threshold value is taken from 0 to 255 for 256 values, and each threshold value divides the data set into two types.

(243) And calculating the ratio of the inter-class variance to the intra-class variance of the two types of data after each threshold classification, wherein the threshold corresponding to the point with the maximum ratio is the threshold found by the clustering segmentation method. Similar to the common threshold segmentation idea, the gray values of the two types of separated data are reassigned, generally, the maximum difference values are respectively assigned, and the process is called image binarization processing. In the clustering segmentation algorithm, the Otsu algorithm is based on the characteristic of a single threshold on a gray level histogram, so that the method is particularly suitable for the single mineral rock sample. And the mineral composition of the carbonate rock is single, and the Otsu algorithm is used for segmenting the CT image, so that a good effect is obtained.

(3) Optimizing the digital core to have a porosity consistent with the porosity in the petrophysical parameter.

Based on the binary digital core data body obtained in the above steps, the ratio of the porosity to the total volume, that is, the porosity determined based on the digital core, can be obtained, and for all selected carbonate rock samples, the porosity obtained based on the digital core can be compared with the porosity measured by helium and NMR, so that the porosity calibration work can be realized. If the porosity estimated based on the digital core and the actually measured porosity exceed the tolerance of the error, the process flow of the binary image can be returned again, and parameter optimization and fine adjustment are carried out on all sub-steps in the process flow, namely the steps (21) to (24), until the porosity estimated based on the digital core is consistent with the actually measured porosity, so that the optimized digital core can be obtained. In addition, the optimized digital core can be compared with the pore type obtained based on the sheet analysis in the step, so that the pore structure type of the optimized digital core can be judged for subsequent analysis.

For example, in an exemplary embodiment of the specification, when a contrast enhancement process is performed on a CT scan image of a Dolo-3-1 rock sample using a gray scale transformation formula shown in formula 1, the gray scale redistribution result of the image may be as shown in fig. 5, the original very narrow gray scale distribution (dark black data points) is broadened to a full gray scale distribution (light black data points), and the peak distribution characteristics of the void phase and the solid phase are significantly enhanced. The pore space and the solid transition zone are well widened in gray scale distribution, so that reasonable image contrast enhancement is completed.

Fig. 6a shows a Dolo-3-1 rock sample original scanning image, and the original image of CT scanning is clipped, that is, a cube with a side length of 1000 voxels is selected, so that the original image after being diced as shown in fig. 6b can be obtained. Such a narrowly distributed gray-scale histogram distribution causes difficulty in subsequent image segmentation, and thus contrast enhancement of an image can be performed. The contrast enhancement can be realized by the formula (2), that is, the original narrow distribution gray scale histogram is converted into the wide distribution histogram by gray scale conversion, the gray scale distribution of the transition band between the pore phase and the solid phase is widened, and the result after the contrast enhancement is shown in fig. 6c, so that the selection of the subsequent image segmentation threshold value can be better facilitated.

The results shown in fig. 7a can be obtained using the anisotropic filtering methods shown in equations (2) to (4). With reference to fig. 7a and 7b, after anisotropic filtering, the noise signals inside the solid particles and inside the pores are successfully smoothed, and the edge signals between the solid particles and the pores are well preserved.

Edge enhancement is performed using an edge detection processing flow. Since the edges between the solid particles and the pores generally correspond to regions of high gradient, the gradient can be used to extract the edge signal. The gradient point above which the gradient accumulation is 90% of the total value can be determined by the cumulative sum function of the image gradients, and a high gradient can be considered as a high gradient indicating phase boundary. With reference to fig. 8a and 8b, it can be seen that the method works well, and the edge regions between the solid particles and the pores are completely identified, and image segmentation based on the identified edge regions is beneficial to reducing segmentation uncertainty, so as to obtain a better image segmentation effect.

By subjecting one edge-enhanced image slice of the Dolo3-1 rock sample (as shown in fig. 9 a) to binarization image processing, a binarized image as shown in fig. 9b can be obtained. In fig. 9b the black color indicates pores and the off-white color indicates solid particles. In this way, by performing the same binarization image processing on each slice of the Dolo3-1 rock sample, the result after binarization image processing, i.e. the 3D rock skeleton diagram shown in fig. 10a, can be finally obtained, where fig. 10a shows that both the rock solid phase and the pore phase exist. At the moment, the measured porosity in a laboratory and the estimated porosity of the digital core can be used for mutual verification in the step, the rationality of the image processing flow is calibrated, if the error is exceeded, the image processing flow is returned again for parameter adjustment optimization and other processing, and the image processing step is implemented again. A schematic of the pore structure of the digital core shown in figure 10b can thus be obtained.

In this exemplary embodiment, the laboratory helium measured a porosity of 3.711%, and the segmentation resulted in an imaged porosity slightly less than the helium porosity, which was 2.297%. This is because carbonate rocks are very heterogeneous and can have pore sizes as small as hundreds of nanometers and as large as a few centimeters. Further, the image resolution and the imaging view are incompatible, and the imaging view is reduced as the resolution is higher. So once the field of view is fixed, its imaging resolution is also uniquely invariant. One reasonable explanation for the phenomenon that the imaged porosity is slightly less than the experimental porosity is: there are always some porosity in the carbonate rock, which is even smaller than the CT resolution and cannot be imaged, so the porosity estimated by CT imaging will be less than that measured by laboratory helium. Since the pore structure cannot be fully resolved, the contact area of the particles and the pores on the CT image may appear pasty. So that the segmentation process generates unavoidable errors. While a relatively reliable segmentation process typically results in a low porosity after segmentation.

And processing the CT data of the remaining six dolomites by using the same processing method, and obtaining a corresponding binarized three-dimensional digital core model. The porosity of the dolomite rock sample is tested by the helium porosity and permeability measuring instrument under the laboratory condition, the accuracy of the established three-dimensional digital core model is verified, and the measured data are shown in the table 1. Overall, it can be seen that the calculated porosity of the digital core of the seven dolomites is slightly lower than the laboratory helium porosity measurement. Due to the strong heterogeneity of carbonate rock, its pore size distribution is very wide (hundreds of nanometers to centimeters). Typically, a portion of the pore size is smaller than the imaging resolution and is clearly displayed on the CT image, thus resulting in a digital core imaging porosity that is slightly less than the laboratory helium porosity. However, the actually measured porosity well proves the rationality of the image processing flow in the process of establishing the digital core image, and in addition, even if slight errors exist, the research idea and the flow proposed by the invention are enough for further regular research.

In addition, in the present exemplary embodiment, the type of pore structure obtained by the slice analysis can be compared with the pore network model shown in fig. 10b, so as to give the rough type of pore structure of the rock sample.

(III) reading the specified data of the digital core; the specified data includes porosity, longitudinal wave velocity, and density.

And (IV) training the specified data based on a preset machine learning model to obtain a second relation model of porosity, longitudinal wave velocity and density.

For example, in an embodiment of the present specification, the longitudinal wave velocity and density of the digital core may be used as input, the porosity of the digital core may be used as target output, and a preset neural network model is trained, so that a relationship curve between the porosity and the longitudinal wave velocity and density may be obtained, that is, a second relationship model between the porosity and the longitudinal wave velocity and density may be determined.

Due to the complexity of the pore structure of carbonate rock, particularly the influence of secondary porosity, the relationship between porosity and velocity and density is also complicated. The porosity predicted based on seismic prestack inversion alone can become very unreliable. The porosity information in the log data or digital core data is known and also relatively accurate. In some embodiments of the present disclosure, in order to make the predicted porosity accurate, the relationship between the porosity and the velocity and density of longitudinal waves can be found from the two data. Therefore, the weighted sum of the first relation model and the second relation model is used as the third relation model, so that the accuracy of the carbonate reservoir pore structure prediction can be further improved. When the first relational model and the second relational model are subjected to weighted summation, the weight coefficients of the first relational model and the second relational model can be set as required. For example, in an exemplary embodiment of the present specification, the weight coefficients of the first relational model and the second relational model may each be 0.5.

In an embodiment of the present disclosure, the obtaining of the first pore structure factor of the target block based on the well log data may be according to a formula

And (4) obtaining.

Wherein rho is the density of the target block based on the logging data, rho_mDensity, ρ, of solid phase mineral framework in pore phase based on well log data for a target block_fDensity of fluid or gas filled in pore phase of target block based on logging data, phi is porosity of target block based on logging data, K is bulk modulus of target block based on logging data, V_PFor the longitudinal wave velocity V of the target block based on the logging data and changing with the confining pressure under the specified frequency band_SThe target block is based on the transverse wave speed of logging data which changes with the confining pressure under the specified frequency band, mu is based on the loggingShear modulus of well data, F_kIs an intermediate variable, K_mBulk modulus, K, of solid phase mineral framework in pore phase based on well log data for target block_fAnd f is an intermediate variable, and gamma is a pore structure factor of the target block based on the logging data.

In another embodiment of the present disclosure, the obtaining of the second pore structure factor of the target block based on the digital core data may also be according to the above calculation formula

And (4) obtaining. Wherein rho is the density of the target block based on the digital core, rho_mDensity, rho, of solid phase mineral framework in pore phase based on digital core for target block_fDensity of fluid or gas filled in the digital core-based pore phase for the target block, [ phi ] is porosity of the target block based on the digital core, [ K ] is bulk modulus of the target block based on the digital core, V_PFor a target block based on the longitudinal wave velocity V of the digital core changing with the confining pressure under the specified frequency band_SThe transverse wave speed of the target block based on the digital core and changing with the confining pressure under the designated frequency band, mu is the shear modulus of the target block based on the digital core, F_kIs an intermediate variable, K_mBulk modulus, K, of solid phase mineral framework in pore phase based on digital core for target block_fAnd f is the bulk modulus of the fluid or gas filled in the pore phase of the digital core based on the target block, f is an intermediate variable, and gamma is the pore structure factor of the digital core based on the target block.

In some embodiments of the present disclosure, in the second pore structure factor of the target block obtained based on the digital core data, the digital core may be a meshed digital core, and the elastic property corresponding to the meshed digital core may be obtained through simulation by a Finite Element Method (FEM) elastic property simulator, specifically as follows:

before gridding, the digital core as a 3D data model resembles the data of a cube of a magic cube, while gridding resembles the division of the cube into many small cubic grids that make up the cube, each of which is also a small 3D digital core data volume (also referred to as a sub-grid or digital core volume).

After gridding, the digital core body represented by each sub-grid can be obtained easily by the gridded digital core body. Because the data of the sub-grids in the gridded digital core is huge, a sub-grid set with a very wide porosity span can be obtained. In the case where coring is abnormally trapped, and often only a limited number of cores are available, the porosity profile is often very narrow if the porosity is predicted directly from the limited number of cores. In the embodiment of the present specification, since the digital core based on gridding can obtain a sub-grid set with a very wide porosity span, it is beneficial to obtain a more comprehensive porosity distribution.

In an embodiment of the present description, each sub-grid may be gridded with hexahedral cells according to the CT scan resolution, and each sub-grid (i.e., voxel) is equal to the CT scan resolution. In addition, the mineral skeleton modulus of each voxel can be assigned according to the XRD mineral analysis result obtained in the above steps.

For example, in an exemplary embodiment of the specification, fig. 11 shows a gridded 3D digital core of a certain rock sample, and by gridding, each rock sample can obtain 125 sub-grid data sets, so that for 7 rock samples in the exemplary embodiment, 125 × 7 — 875 sub-grid data sets can be obtained. The porosity (i.e., porosity estimated using a digital core) can be easily obtained for each sub-image in 875 sub-meshes. Since the data of the sub-grids is large, a sub-grid set with a very wide porosity span can be obtained. In addition, the mineral framework modulus assignment can be performed on the digital rock core body corresponding to each sub-grid according to the XRD mineral analysis result.

In an embodiment of the present specification, the performing a finite element elastic property simulation on the gridded digital core may include the following steps:

(1) and determining the parameters of the sub-grid framework. Since the mineral skeleton modulus for each voxel has been assigned in step S103. From which the skeleton parameters of each sub-mesh can be determined.

(2) And receiving input mineral framework mineral parameters.

(3) Receiving an input fluid modulus.

(4) The elastic property of each sub lattice can be simulated and obtained by using a Finite Element Method (FEM) elastic property simulator (namely Finite Element algorithm).

In one embodiment of the present disclosure, the FEM elastic property simulator may be, for example, a line elastic finite element method proposed by Garboczi (1995): each voxel in the three-dimensional digital core model is regarded as a line elastic finite element grid, so that a complicated grid division process (which is essentially equal to a standard hexahedral grid division solving area) is avoided. The method applies periodic boundary conditions to the model, so that the solving problem of elastic displacement distribution can be converted into a physical problem solved by the minimum potential energy principle, and the mathematical solution is a typical functional extreme value solving problem in a finite element method. For example, Arns (2002) mentions that the functional derivative equation can be iteratively converged by using a fast conjugate gradient method to approach the minimum energy of the system, and finally, a numerical solution is obtained, i.e., the stress σ in six directions_xx,σ_yy,σ_zz,σ_xz,σ_yz,σ_xyStrain epsilon with six directions_xx,ε_yy,ε_zz,ε_xz,ε_yz,ε_xyAnd then the equivalent elastic modulus is obtained.

For example, in an exemplary embodiment of the specification, for each digital core body represented by the obtained 875 sub-meshes, finite element numerical simulation of each data volume using a FEM elasticity simulator may be performed to obtain the elastic properties of the digital core body represented by the sub-mesh data volume. At the same time, the pore structure parameter γ of the digital rock core body represented by each sub-grid data body can also be calculated quantitatively. For a large number of sub-grid data volumes obtained by segmentation, the porosity phi, the elastic properties (such as longitudinal and transverse wave velocities and the like) and the pore structure factor gamma of each sub-grid can be calculated. For the present exemplary embodiment, the elastic properties versus porosity relationship under classification for carbonate rock samples as shown in fig. 12 may be obtained.

In some embodiments of the present description, it may also be advantageous to further improve the accuracy of the carbonate reservoir pore structure prediction by using a weighted sum of the first pore structure factor and the second pore structure factor as the third pore structure factor. When the first pore structure factor and the second pore structure factor are subjected to weighted summation, the weighting coefficients of the first pore structure factor and the second pore structure factor can be set as required. For example, in an exemplary embodiment of the present description, the weighting factors of the first pore structure factor and the second pore structure factor may each be 0.5.

In an exemplary embodiment of the present specification, the porosity data volume of the target block shown in fig. 18 may be obtained by inputting the longitudinal wave velocity data volume shown in fig. 16 and the density data volume shown in fig. 17 into the third relation model.

In some embodiments of the present disclosure, the intersecting the longitudinal wave velocity data volume and the porosity data volume to obtain the pore structure data volume of the target block means intersecting the longitudinal wave velocity data volume and the porosity data volume on a plane by using an intersection map method, so as to determine a value or a range of the pore structure according to coordinates of an intersection point.

As shown in fig. 19, in an exemplary embodiment of the present disclosure, a porosity data volume of a target block is intersected with a longitudinal wave velocity data volume based on seismic prestack inversion, so that types of pore structures corresponding to different intersections can be predicted, and a corresponding pore structure data volume can be obtained (for example, as shown in fig. 20). And calculating the pore structures of all measuring lines of the three-dimensional longitudinal wave velocity data body to obtain the pore structure data body of the whole block.

Referring to fig. 21, in correspondence with the above carbonate reservoir pore structure prediction method, a carbonate reservoir pore structure prediction apparatus according to some embodiments of the present specification may include:

the first data volume acquisition module 211 may be configured to acquire a longitudinal wave velocity data volume and a density data volume of a target block based on seismic prestack inversion;

the first model obtaining module 212 may be configured to determine a first relation model between porosity and longitudinal wave velocity and density according to the logging data of the target block, and determine a second relation model between porosity and longitudinal wave velocity and density according to the digital core data of the target block;

a second model obtaining module 213, configured to use a weighted sum of the first relation model and the second relation model as a third relation model;

a first factor obtaining module 214, configured to obtain a first pore structure factor of the target block based on the logging data, and obtain a second pore structure factor of the target block based on the digital core data;

a second factor acquisition module 215 operable to take a weighted sum of the first pore structure factor and the second pore structure factor as a third pore structure factor;

a second data volume obtaining module 216, configured to input the longitudinal wave velocity data volume and the density data volume into the third relation model, so as to obtain a porosity data volume of the target block;

the pore structure obtaining module 217 may be configured to intersect the longitudinal wave velocity data volume with the porosity data volume to obtain a pore structure data volume of the target block.

In some embodiments of the present disclosure, in the carbonate reservoir pore structure prediction apparatus, the determining a first relationship model between porosity and compressional velocity and density according to the log data of the target block may include:

acquiring logging data of the target block; the well logging data comprises porosity, compressional velocity and density;

and training the logging data based on a preset machine learning model to obtain a first relation model of porosity, longitudinal wave speed and density.

In some embodiments of the present disclosure, in the carbonate reservoir pore structure prediction apparatus, the determining a second relationship model between porosity and compressional velocity and density according to the digital core data of the target block includes:

acquiring rock physical parameters of the designated rock sample of the target block;

constructing a digital core based on the petrophysical parameters and the CT scanning image of the specified rock sample;

reading specified data of the digital core; the specified data comprises porosity, longitudinal wave velocity and density;

and training the designated data based on a preset machine learning model to obtain a second relation model of porosity, longitudinal wave velocity and density.

In some embodiments of the present description, the acquiring a first pore structure factor of the target block based on the well log data in a carbonate reservoir pore structure prediction apparatus includes:

according to the formula

Determining a second pore structure factor for the target block;

wherein rho is the density of the target block based on the logging data, rho_mDensity, ρ, of solid phase mineral framework in pore phase based on well log data for a target block_fDensity of fluid or gas filled in pore phase of target block based on logging data, phi is porosity of target block based on logging data, K is bulk modulus of target block based on logging data, V_PFor the longitudinal wave velocity V of the target block based on the logging data and changing with the confining pressure under the specified frequency band_SThe transverse wave speed of the target block based on the logging data and changing with the confining pressure under the specified frequency band is shown, mu is the shear modulus of the target block based on the logging data, F_kIs an intermediate variable, K_mBulk modulus, K, of solid phase mineral framework in pore phase based on well log data for target block_fThe volume modulus of the fluid or gas filled in the pore phase of the target block based on the logging data, f is an intermediate variable, and gamma is the target block based on the logging dataPore structure factor of well data.

In some embodiments of the present description, in the carbonate reservoir pore structure prediction apparatus, the obtaining a second pore structure factor of the target block based on the digital core data includes:

according to the formula

Determining a second pore structure factor for the target block;

wherein rho is the density of the target block based on the digital core, rho_mDensity, rho, of solid phase mineral framework in pore phase based on digital core for target block_fDensity of fluid or gas filled in the digital core-based pore phase for the target block, [ phi ] is porosity of the target block based on the digital core, [ K ] is bulk modulus of the target block based on the digital core, V_PFor a target block based on the longitudinal wave velocity V of the digital core changing with the confining pressure under the specified frequency band_SThe transverse wave speed of the target block based on the digital core and changing with the confining pressure under the designated frequency band, mu is the shear modulus of the target block based on the digital core, F_kIs an intermediate variable, K_mBulk modulus, K, of solid phase mineral framework in pore phase based on digital core for target block_fAnd f is the bulk modulus of the fluid or gas filled in the pore phase of the digital core based on the target block, f is an intermediate variable, and gamma is the pore structure factor of the digital core based on the target block.

For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functions of the various elements may be implemented in the same one or more software and/or hardware implementations of the present description.

Referring to fig. 22, corresponding to the carbonate reservoir pore structure prediction method described above, a computer device of some embodiments of the present description may include a memory, a processor, and a computer program stored on the memory, the computer program when executed by the processor performing the steps of:

While the process flows described above include operations that occur in a particular order, it should be appreciated that the processes may include more or less operations that are performed sequentially or in parallel (e.g., using parallel processors or a multi-threaded environment).

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method or apparatus that comprises the element.

As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, the description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

This description 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 specification 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.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

The above description is only an example of the present specification, and is not intended to limit the present specification. Various modifications and alterations to this description will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification should be included in the scope of the claims of the present specification.

Claims

1. A carbonate reservoir pore structure prediction method is characterized by comprising the following steps:

intersecting the longitudinal wave velocity data volume with the porosity data volume to obtain a pore structure data volume of the target block;

wherein the obtaining a first pore structure factor for the target block based on the well log data comprises:

according to the formula

Determining a first pore structure factor for the target block;

where ρ is₁Density, ρ, based on well log data for a target block_m1Density, ρ, of solid phase mineral framework in pore phase based on well log data for a target block_f1Density of fluid or gas filled in pore phase based on well log data for target block₁Porosity, K, based on well log data for a target block₁Bulk modulus, V, based on well log data for a target block_P1For the longitudinal wave velocity V of the target block based on the logging data and changing with the confining pressure under the specified frequency band_S1For the target block, based on the logging data, the shear wave velocity (mu) varying with the confining pressure in the specified frequency band₁Shear modulus based on well log data for target block, F_k1Is a first intermediate variable, K_m1Bulk modulus, K, of solid phase mineral framework in pore phase based on well log data for target block_f1Bulk modulus of fluid or gas filled in pore phase based on well log data for target block, f₁Is a second intermediate variable, γ₁A pore structure factor based on well log data for the target block;

the obtaining a second pore structure factor for the target block based on the digital core data comprises:

according to the formula

Determining a second pore structure factor for the target block;

where ρ is₂Density, rho, based on digital core for target block_m2Density, rho, of solid phase mineral framework in pore phase based on digital core for target block_f2Density of fluid or gas filled in pore phase of digital core based on target block₂Porosity based on digital core, K, for target block₂Is based on the target blockVolume modulus of digital core, V_P2For a target block based on the longitudinal wave velocity V of the digital core changing with the confining pressure under the specified frequency band_S2For a target block based on the transverse wave velocity mu of the digital core changing with confining pressure under a specified frequency band₂Shear modulus based on digital core for target block, F_k2Is a third intermediate variable, K_m2Bulk modulus, K, of solid phase mineral framework in pore phase based on digital core for target block_f2Bulk modulus of fluid or gas filled in pore phase of digital core based on target block, f₂Is a fourth intermediate variable, γ₂A pore structure factor based on the digital core for the target block.

2. The method for predicting carbonate reservoir pore structure according to claim 1, wherein the determining a first relation model of porosity, compressional velocity and density according to the log data of the target block comprises:

3. The method for predicting carbonate reservoir pore structure according to claim 1, wherein the determining the second relational model of porosity, longitudinal wave velocity and density according to the digital core data of the target block comprises:

4. A carbonate reservoir pore structure prediction device, comprising:

the pore structure acquisition module is used for intersecting the longitudinal wave velocity data volume and the porosity data volume to acquire a pore structure data volume of the target block;

according to the formula

Determining a first pore structure factor for the target block;

where ρ is₁Based on the density of the well log data for the target block,ρ_m1density, ρ, of solid phase mineral framework in pore phase based on well log data for a target block_f1Density of fluid or gas filled in pore phase based on well log data for target block₁Porosity, K, based on well log data for a target block₁Bulk modulus, V, based on well log data for a target block_P1For the longitudinal wave velocity V of the target block based on the logging data and changing with the confining pressure under the specified frequency band_S1For the target block, based on the logging data, the shear wave velocity (mu) varying with the confining pressure in the specified frequency band₁Shear modulus based on well log data for target block, F_k1Is a first intermediate variable, K_m1Bulk modulus, K, of solid phase mineral framework in pore phase based on well log data for target block_f1Bulk modulus of fluid or gas filled in pore phase based on well log data for target block, f₁Is a second intermediate variable, γ₁A pore structure factor based on well log data for the target block;

according to the formula

Determining a second pore structure factor for the target block;

where ρ is₂Density, rho, based on digital core for target block_m2Density, rho, of solid phase mineral framework in pore phase based on digital core for target block_f2Density of fluid or gas filled in pore phase of digital core based on target block₂Porosity based on digital core, K, for target block₂Volume modulus, V, based on digital core for target block_P2For a target block based on the longitudinal wave velocity V of the digital core changing with the confining pressure under the specified frequency band_S2For a target block based on the transverse wave velocity mu of the digital core changing with confining pressure under a specified frequency band₂Shear modulus based on digital core for target block, F_k2Is a third intermediate variable, K_m2Bulk modulus, K, of solid phase mineral framework in pore phase based on digital core for target block_f2Bulk modulus of fluid or gas filled in pore phase of digital core based on target block, f₂Is a fourth intermediate variable, γ₂A pore structure factor based on the digital core for the target block.

5. The carbonate reservoir pore structure prediction device according to claim 4, wherein the determining a first relation model of porosity and compressional velocity and density according to the log data of the target block comprises:

6. The carbonate reservoir pore structure prediction device according to claim 4, wherein the determining the second relational model of porosity and compressional velocity and density according to the digital core data of the target block comprises:

7. A computer device comprising a memory, a processor, and a computer program stored on the memory, wherein the computer program when executed by the processor performs the steps of:

according to the formula

Determining a first pore structure factor for the target block;

where ρ is₁Density, ρ, based on well log data for a target block_m1Density, ρ, of solid phase mineral framework in pore phase based on well log data for a target block_f1Density of fluid or gas filled in pore phase based on well log data for target block₁Porosity, K, based on well log data for a target block₁Bulk modulus, V, based on well log data for a target block_P1For the longitudinal wave velocity V of the target block based on the logging data and changing with the confining pressure under the specified frequency band_S1Basing well log data for target blockTransverse wave velocity, mu, varying with confining pressure in a given frequency band₁Shear modulus based on well log data for target block, F_k1Is a first intermediate variable, K_m1Bulk modulus, K, of solid phase mineral framework in pore phase based on well log data for target block_f1Bulk modulus of fluid or gas filled in pore phase based on well log data for target block, f₁Is a second intermediate variable, γ₁A pore structure factor based on well log data for the target block;

according to the formula

Determining a second pore structure factor for the target block;

8. A computer storage medium having a computer program stored thereon, the computer program, when executed by a processor, performing the steps of:

according to the formula

Determining a first pore structure factor for the target block;

according to the formula

Determining a second pore structure factor for the target block;