CN113239955A - Carbonate reservoir rock classification method - Google Patents
Carbonate reservoir rock classification method Download PDFInfo
- Publication number
- CN113239955A CN113239955A CN202110373691.XA CN202110373691A CN113239955A CN 113239955 A CN113239955 A CN 113239955A CN 202110373691 A CN202110373691 A CN 202110373691A CN 113239955 A CN113239955 A CN 113239955A
- Authority
- CN
- China
- Prior art keywords
- pore
- reservoir
- classified
- parameters
- rock
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
Abstract
The invention provides a carbonate reservoir rock classification method, which comprises the following steps: extracting pore throat radius parameters reflecting reservoir quality from the mercury intrusion capillary pressure curve of each reservoir rock to be classified; extracting pore characteristic parameters reflecting pore types from the casting body slices of each reservoir rock to be classified based on an image processing technology; carrying out principal component analysis on the pore characteristic parameters, and extracting a first principal component parameter; and performing cluster analysis on all pore throat radius parameters and all first principal component parameters to obtain multiple categories of reservoir rocks. The invention integrates parameters extracted by various core scales, such as pore throat radius parameters and pore characteristic parameters, to classify the rock, balances the relationship between geology, rock physics and microscopic slice data, ensures that the classification process of the rock reservoir integrates macroscopic scale and microscopic scale, can reveal the content of reservoir geological causes to achieve the prediction effect, and can also determine the reservoir storage and oil and gas production capacity to guide the development of oil and gas reservoirs.
Description
Technical Field
The invention relates to the field of geological exploration, in particular to a carbonate rock classification method.
Background
The carbonate reservoir rock classification is a process of dividing a reservoir into a plurality of relatively homogeneous categories under the background of strong heterogeneity of the reservoir in an oil reservoir description stage, aims at reservoir permeability research and physical property spatial distribution prediction, serves oil and gas reservoir development, and classifies the reservoir rock, and is not simple lithologic classification. The ideal result of reservoir rock classification is a set of rocks which undergo similar deposition processes and diagenesis processes, have similar pore structures, and have uniform pore-permeability relationship, similar capillary pressure curve distribution and relative permeability under the condition of consistent wettability, and the standard is not uniform in practical application.
The present domestic and foreign classification methods are mostly controlled by single factor, and can belong to three major categories of geology, petrophysics and production dynamics, namely (1) rock classification based on geological causes, namely combining deposition environment and diagenesis, and based on carbonate rock components and structural causes. (2) The classification of rock based on rock physical characteristics depends on rock physical parameter data, such as porosity, permeability, capillary pressure curve parameters, etc. as classification basis. (3) Rock classification based on reservoir production dynamics: and taking oil and gas production development dynamic parameters as classification bases.
At present, there are three reservoir classification methods based on petrophysical characteristics, namely, FZI/RQI classification method: amaefull et al put forward two indexes of rock physical classification, FZI (flow unit index) and RQI (reservoir quality factor), based on the relationship between permeability and effective porosity through a large number of researches, and are used for evaluating the seepage capability of a reservoir. FZI/RQI classification has the advantages that only core porosity and permeability data are needed, testing means such as mercury intrusion and the like are not needed, and similar samples generally have a good pore-permeability relationship. Kharrat et al, Burrows et al, Mirzaei-Paiaman et al applied FZI/RQI to carbonate rock classification in the middle east et al. A method for classifying according to pore throat radius: based on the principle that the capillary pressure curve can directly reflect the pore throat radius of the rock, many scholars propose to use the pore throat radius corresponding to the mercury inlet amount to a certain degree to divide the rock types. Winland finds that R35 (throat radius corresponding to 35% of mercury inlet saturation) has good correlation with porosity and permeability when researching sandstone reservoirs, and Pittman applies the R35 to carbonate reservoirs to expand the application range of the reservoirs. Warren, through analysis of clastic rock samples, believes that there is a good correlation between the median radius R50 and permeability. After analyzing and comparing a plurality of characteristic points on the capillary curve of the carbonate rock, Yan Qin et al thinks that the correlation between the throat radius at the inflection point and the permeability is the best for the carbonate rock with stronger heterogeneity. ③ thomer function classification: the Thomeer function is a mathematical expression provided for a capillary pressure curve, defines a quantitative relation between a pore geometry factor G for representing a pore structure and each parameter of the capillary pressure curve, and takes the two parameters of the pore geometry factor G and the displacement pressure Pd as a basis for distinguishing rock type classification. The method is improved by Clerke and is widely applied to the Gaval oil field. Clerke's study showed that the thomer function has high applicability in bimodal pore throat systems, and has advantages especially in rocks with large amounts of microporosity.
The above methods are all deficient in performing petrophysical classification, and the FZI/RQI classification can be true in some clastic or more homogeneous carbonates with weak diagenesis, but in general strongly heterogeneous carbonates, FZI/RQI will not have classification significance. For the classification method according to the pore throat radius, all throats corresponding to the capillary pressure curve play a role in permeability, and the pore throat size corresponding to any point of capillary pressure has certain one-sidedness for distinguishing the rock types. The method for dividing the rock types according to the throat radius is only suitable for samples with similar capillary pressure curves, has certain practical significance for clastic rock and more homogeneous carbonate rock, and is not suitable for multi-mode carbonate rock with high heterogeneity of pore types such as micropores and holes, wherein the capillary pressure curves are complex and various. The thomer function classification method is limited in that geological features cannot be reflected, and evaluation parameters can be obtained only by a mercury intrusion capillary pressure curve within a large pressure range.
Disclosure of Invention
Based on the deficiencies of the classification of reservoir rock in the background art, the present invention provides a method of classifying carbonate reservoir rock that overcomes or at least partially solves the above mentioned problems, comprising: extracting pore throat radius parameters reflecting reservoir quality from the mercury intrusion capillary pressure curve of each reservoir rock to be classified; extracting pore characteristic parameters reflecting pore types from the casting body slices of each reservoir rock to be classified based on an image processing technology; performing principal component analysis on the pore characteristic parameters of each reservoir rock to be classified, and extracting a first principal component parameter; and performing cluster analysis on the pore throat radius parameters of all reservoir rocks to be classified and the first principal component parameters of all reservoir rocks to be classified to obtain a plurality of classes of the reservoir rocks to be classified.
On the basis of the above technical solutions, the embodiments of the present invention may be further improved as follows.
Optionally, the extracting pore throat radius parameters reflecting the reservoir quality from the mercury intrusion capillary pressure curve of each reservoir rock to be classified includes: converting the mercury pressing capillary pressure curve of any reservoir rock to be classified into a pore throat distribution curve; if the pore throat distribution in the pore throat distribution curve is a single peak, extracting a pore throat radius parameter corresponding to the single peak; and if the pore throat distribution in the pore throat distribution curve is double peaks or triple peaks, extracting the pore throat radius parameter corresponding to the peak value with the maximum pore throat radius interval.
Optionally, the extracting pore characteristic parameters reflecting the pore type from each casting slice of the reservoir rock to be classified based on the image processing technology includes: acquiring a casting body slice image of each reservoir rock to be classified, and preprocessing the casting body slice image; and extracting pore characteristic parameters from the preprocessed casting body flake images, wherein the pore characteristic parameters comprise the length-width ratio, the aspect ratio, the tortuosity, the centrifugation degree, the solidity and the shape factor of each pore.
Optionally, the acquiring a casting slice image and preprocessing the casting slice image include: acquiring a plurality of casting body slice images of parallel samples, and performing median filtering, contrast image enhancement and two-dimensional OTSU threshold segmentation pretreatment on the casting body slice images to obtain the pretreated casting body slice images.
Optionally, the extracting pore characteristic parameters from the preprocessed casting slice image includes: extracting the area, the perimeter, the effective length, the equivalent width, the tortuosity length, the major axis and the minor axis of the equivalent ellipse and the convex area of each pore from the preprocessed casting sheet image; calculating the aspect ratio, the tortuosity, the eccentricity, the solidity and the shape factor of each pore based on the extracted surface porosity and the area, the circumference, the effective length, the equivalent width, the tortuosity, the equivalent ellipse major axis and minor axis and the convex area of each pore; wherein the aspect ratio, tortuosity, eccentricity, solidity and shape factor of each pore are pore characteristic parameters.
Optionally, the performing principal component analysis on the pore characteristic parameter to extract a first principal component parameter includes: and performing principal component analysis on each pore characteristic parameter to obtain a plurality of principal component parameters, and extracting a first principal component parameter from the plurality of principal component parameters, wherein the first principal component parameter represents the degree of uniform and regular pore shapes.
Optionally, performing cluster analysis on the pore throat radius parameters of all reservoir rocks to be classified and the first principal component parameters of all reservoir rocks to be classified to obtain multiple categories of the reservoir rocks to be classified, including: and based on a K-means clustering algorithm, taking the distance as a similarity index, and carrying out clustering analysis on the pore throat radius parameters of all reservoir rocks to be classified and the first principal component parameters of all reservoir rocks to be classified to obtain a plurality of classes of the reservoir rocks.
The carbonate reservoir rock classification method provided by the invention can integrate parameters extracted by various core scales, such as pore throat radius parameters and pore characteristic parameters, to classify rocks, balance the relationship between geology, rock physics and microscopic slice data, enable the rock reservoir classification process to take macroscopic scale and microscopic scale into account, can reveal the content of reservoir geological causes to achieve the prediction effect, and can also clarify the reservoir storage and oil and gas production capacity to guide the development of oil and gas reservoirs.
Drawings
FIG. 1 is a flow chart of a carbonate reservoir rock classification method according to an embodiment of the present invention;
FIG. 2-a is a pressure curve diagram of a mercury injection capillary with a single peak;
FIG. 2-b is a plot of pore throat distribution after transformation of FIG. 2-a;
FIG. 3-a is a bimodal mercury intrusion capillary pressure curve;
FIG. 3-b is a plot of pore throat distribution after conversion of FIG. 3-a;
FIG. 4-a is a three-peak mercury intrusion capillary pressure plot;
FIG. 4-b is a plot of pore throat distribution after conversion of FIG. 4-a;
FIG. 5 is a schematic representation of four reservoir classifications;
FIG. 6 is a schematic diagram of the porosity versus permeability for four reservoir classifications.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
Fig. 1 is a flow chart of a carbonate reservoir rock classification method according to an embodiment of the present invention, as shown in fig. 1, the method includes: 101. extracting pore throat radius parameters reflecting reservoir quality from the mercury intrusion capillary pressure curve of each reservoir rock to be classified; 102. extracting pore characteristic parameters reflecting pore types from the casting body slices of each reservoir rock to be classified based on an image processing technology; 103. performing principal component analysis on the pore characteristic parameters of each reservoir rock to be classified, and extracting a first principal component parameter; 104. and performing cluster analysis on the pore throat radius parameters of all reservoir rocks to be classified and the first principal component parameters of all reservoir rocks to obtain a plurality of categories of the reservoir rocks to be classified.
It can be understood that the embodiment of the invention relates to two important concepts, and the capillary pressure curve is a relation curve of the capillary pressure, the pore throat radius and the mercury saturation after calculating the mercury saturation and the pore throat radius according to the actually measured mercury injection pressure and the corresponding mercury-containing volume of the rock sample; the cast body slice is a rock slice which is prepared by injecting colored liquid glue into a rock pore space under vacuum pressurization and grinding after the liquid glue is solidified.
Based on the defects of reservoir rock classification used in the background art, the embodiment of the invention provides a reservoir rock classification method combining mercury intrusion and slices, firstly, a new pore throat radius parameter reflecting reservoir quality is provided from a mercury intrusion capillary pressure curve of each reservoir rock to be classified, and finally, performing cluster analysis on the pore throat radius parameters of all the reservoir rocks to be classified and the first principal component parameters of all the reservoir rocks to be classified so as to achieve the purpose of reservoir rock classification.
The embodiment of the invention integrates various core scale extracted parameters, including pore throat radius parameters and pore characteristic parameters, and adopts various different parameter combinations to classify the rocks, and the classification has the advantages that the relationship between geology, rock physics and microscopic slice data is balanced, so that the reservoir classification process has both macroscopic scale and microscopic scale, the reservoir geological cause content can be revealed to achieve the prediction effect, and the reservoir storage and oil and gas production capacity can be determined to guide the development of oil and gas reservoirs.
In one possible embodiment, extracting pore throat radius parameters reflecting reservoir quality from the mercury intrusion capillary pressure curve of each reservoir rock to be classified comprises: converting the mercury pressing capillary pressure curve of each reservoir rock to be classified into a pore throat distribution curve; if the pore throat distribution in the pore throat distribution curve is a single peak, extracting a pore throat radius parameter corresponding to the single peak; and if the pore throat distribution in the pore throat distribution curve is double peaks or triple peaks, extracting the pore throat radius parameter corresponding to the peak value with the maximum pore throat radius interval.
It can be understood that carbonate reservoirs are complex in pore structure, various in pore morphology, and commonly develop erosion pores, cast mold pores (connected or not), fossil inner pores, inter-granular pores, matrix micropores and the like. The mercury-pressing capillary pressure curve can represent the pore structure of reservoir rock, and rocks with different pore structures have difference in the form of the mercury-pressing capillary pressure curve. For carbonate rocks with complex pore structures and pore types, the mercury intrusion capillary pressure curve is generally in three typical forms, the corresponding pore throat radius distribution is in a single-peak form, a double-peak form and a triple-peak form, and the mercury intrusion capillary pressure curve of each reservoir rock to be classified is converted into a pore throat distribution curve, which can be seen in fig. 2, 3 and 4. Wherein, fig. 2-a is a single-peak mercury injection capillary pressure curve chart, fig. 2-b is a pore throat distribution curve after conversion in fig. 2-a, fig. 3-a is a double-peak mercury injection capillary pressure curve chart, fig. 3-b is a pore throat distribution curve after conversion, fig. 4-a is a triple-peak mercury injection capillary pressure curve chart, and fig. 4-b is a pore throat distribution curve after conversion.
For rock samples with a unimodal, bimodal or trimodal pore throat radius distribution, each peak represents a type of pore throat system unit, and the contribution of each pore throat system unit to the rock permeability is different. By arranging 378 pieces of rock sample mercury intrusion data with pore throat distribution in a bimodal form and 36 pieces of rock sample mercury intrusion data with pore throat distribution in a trimodal form, the contribution ratio of each pore throat system unit to the permeability of the whole rock is calculated respectively, the influence of each pore throat system unit on the permeability of the rock is analyzed, and the pore throat system unit with the largest pore throat radius interval mainly contributes to the permeability of the rock sample no matter whether the pore throat distribution is bimodal or trimodal.
Because the reservoir quality depends on the pore throat system unit with the largest pore throat radius interval and corresponds to the peak No. 1 in the graph 2-b, the graph 3-b and the graph 4-b, the pore throat radius (Mode) corresponding to the spectrum peak in the peak No. 1 is extracted to represent the quality parameter of the rock reservoir in the embodiment of the invention, and the pore throat radius parameter of each reservoir rock to be classified can be extracted by the method.
In one possible embodiment, extracting pore characteristic parameters reflecting pore types from casting body slices of each reservoir rock to be classified based on an image processing technology comprises the following steps: acquiring a casting body slice image of each reservoir rock to be classified, and preprocessing the casting body slice image; and extracting pore characteristic parameters from the preprocessed casting body flake images, wherein the pore characteristic parameters comprise the length-width ratio, the aspect ratio, the tortuosity, the centrifugation degree, the solidity and the shape factor of each pore.
Wherein, the cast body slice image is preprocessed, which comprises the following steps: acquiring a plurality of casting body slice images of parallel samples, and performing median filtering, contrast image enhancement and two-dimensional OTSU threshold segmentation pretreatment on the casting body slice images to obtain the pretreated casting body slice images.
It can be understood that the casting slice can be used for researching the pore structure of reservoir rocks, and for the obtained casting slice image corresponding to each reservoir rock to be classified, the pore characteristic parameters can be automatically picked from the casting slice image through an image processing technology. And preprocessing the acquired casting body slice image before extracting the pore characteristic parameters of the casting body slice.
The method mainly comprises the steps of image median filtering, image enhancement processing and threshold segmentation, wherein the median filtering is a typical nonlinear filtering technology, and the basic idea is to replace the gray value of a pixel point by the median of the gray value of the neighborhood of the pixel point so as to eliminate an isolated noise point. The image enhancement mainly solves the problem of low contrast caused by a small gray level range of an image, and aims to enlarge the gray level of an output image to a specified degree so that details in the image are seen to be increased in definition.
After median filtering and image enhancement processing are carried out on the casting sheet image, threshold segmentation is carried out on the image, and the threshold segmentation algorithm comprises self-adaptive threshold segmentation, particle swarm algorithm-based two-dimensional entropy threshold segmentation, OTSU threshold segmentation and other algorithms. The self-adaptive threshold segmentation algorithm calculates local thresholds of the images according to the brightness distribution of different regions of the images, does not calculate the threshold of a global image, and is suitable for some images with uneven illumination. The two-dimensional entropy threshold segmentation based on the particle swarm optimization is to find an optimal threshold according to the entropy maximum principle by using a two-dimensional histogram of point gray and area gray mean values. The algorithm is easy to be trapped in local self-optimization and is not suitable for massive data operation. The OTSU threshold segmentation is an efficient algorithm for carrying out binarization on an image, is also called as an Otsu threshold segmentation method, and is the optimal segmentation in the least square sense. According to the embodiment of the invention, an OTSU threshold segmentation algorithm is selected to perform threshold segmentation on the casting sheet image.
In one possible embodiment, extracting the pore characteristic parameters from the preprocessed casting body slice image comprises: extracting the area, the perimeter, the effective length, the equivalent width, the tortuosity length, the major axis and the minor axis of the equivalent ellipse and the convex area of each pore from the preprocessed casting sheet image; calculating the aspect ratio, the tortuosity, the eccentricity, the solidity and the shape factor of each pore based on the extracted surface porosity and the area, the circumference, the effective length, the equivalent width, the tortuosity, the equivalent ellipse major axis and minor axis and the convex area of each pore; wherein the aspect ratio, tortuosity, eccentricity, solidity and shape factor of each pore are pore characteristic parameters.
It can be understood that after the cast body slice image is preprocessed, the pore characteristic parameters are extracted from the preprocessed cast body slice image, firstly, the fine pores in the image are wiped off, then, the surface porosity, the area, the perimeter, the effective length, the equivalent width, the tortuosity length, the equivalent ellipse long axis, the short axis and the convex surface area in the slice image are automatically extracted, then, the aspect ratio, the tortuosity, the centrifugation degree, the solidity degree and the shape factor of each pore are calculated according to the extracted parameters, the pore characteristic parameters of each reservoir rock to be classified can be extracted, and the extracted pore characteristic parameters are shown in table 1.
TABLE 1 pore characteristics parameters extracted from single cast body slices
Among the pore characteristic parameters, the aspect ratio and the aspect ratio are important parameters for evaluating the development degree of the strip-shaped morphology of the pore space, wherein the aspect ratio is equal to the ratio of the tortuosity length to the equivalent width; the aspect ratio is equal to the ratio of the effective length to the equivalent width. The tortuosity is mainly used for evaluating the bending degree of the centre shaft skeleton of the pore space, wherein the tortuosity is equal to the ratio of the tortuosity length to the effective length. The eccentricity is an important parameter for evaluating the shape characteristics of the pore space, and the value is equal to the ratio of the elliptical area to the pore area. The shape factor is mainly used for describing the smoothness and regularity of pore edges, the numerical value of the shape factor is used for representing the uniform and regular degree of pore shapes, the shape factor is equal to 4 pi S/C2, S is the pore area, and C is the pore perimeter. The solidity can reflect the degree of shape regularity and the development condition of internal holes, and provides important parameters for subsequent crack and hole identification, and the value of the solidity is equal to the ratio of the pore area to the convex area.
In a possible embodiment, the principal component analysis is performed on the pore characteristic parameters, and the first principal component parameter is extracted, including: and performing principal component analysis on each pore characteristic parameter to obtain a plurality of principal component parameters, and extracting a first principal component parameter from the plurality of principal component parameters, wherein the first principal component parameter represents the degree of uniform and regular pore shapes.
It can be understood that for each reservoir rock to be classified, after pore characteristic parameters are extracted from a casting body slice image, the pore characteristic parameters are subjected to principal component analysis, the principal component analysis is a common method for reducing the dimension of high-dimensional data, and the method is used for reducing the dimension of the pore characteristic parameters and solving the problem of common linearity among the parameters.
In the embodiment of the invention, the pore characteristic parameters such as the eccentricity, the pore shape factor, the aspect ratio, the tortuosity, the solidity, the surface porosity and the like are extracted from 161 cast body slice images, then the main component analysis is carried out, and 7 main components are formed after the main component analysis is carried out on each pore characteristic parameter. Table 2 shows the principal component analysis results, and table 3 shows the principal component coefficient matrix. From the results of tables 2 and 3, 7 main components can be calculated in order, for example: the calculation formula of the principal component 1 is:
TABLE 2 principal Components analysis results
TABLE 3 principal component coefficient matrix
The principal component analysis is mainly used for data compression, collinearity among data is eliminated, factor rotation analysis can be conducted on the extracted factors through factor analysis based on a parameter matrix, and therefore the relation between the factors and components is redistributed, and therefore the pore characteristics of the sheet are easier to explain. Table 4 is a factor analysis rotational component matrix:
TABLE 4 factorial analysis rotational component matrix
From table 4, it can be seen that: the factor scores of the main component 1 of the pore shape factor and the solidity are 0.803 and 0.946 respectively, which shows that the main component 1 mainly represents the degree of uniform and regular pore shapes, and is an important parameter for subsequently judging the types of cracks and holes. The pore shape factor and solidity values are between 0 and 1, with the smaller these two parameters, the closer the pore morphology is to the fracture, and the larger these two parameters, the closer the pore morphology is to the pore.
Therefore, the embodiment of the invention performs principal component analysis on the pore characteristic parameters, extracts the first principal component parameter of the pore characteristic parameters, and can represent the degree of uniformity and regularity of the pore shape. And extracting the first principal component parameter of the pore characteristic parameter for the pore characteristic parameter obtained from each reservoir rock to be classified, thus obtaining the first principal component parameter of each reservoir rock to be classified.
In a possible embodiment, performing cluster analysis on the pore throat radius parameters of all reservoir rocks to be classified and the first principal component parameters of all reservoir rocks to be classified to obtain a plurality of classes of the reservoir rocks to be classified, including: based on a K-means clustering algorithm, and taking the distance as a similarity index, carrying out clustering analysis on the pore throat radius parameters of all reservoir rocks to be classified and the first principal component parameters of all reservoir rocks to be classified to obtain a plurality of classes of the reservoir rocks to be classified.
It can be understood that, in the above embodiment, the pore throat radius parameters of all reservoir rocks to be classified and the pore characteristic parameters of all reservoir rocks to be classified, which are acquired based on the image processing technology, are extracted from the mercury intrusion capillary pressure curve, and the pore characteristic parameters are subjected to principal component analysis to extract the first principal component parameter of each pore characteristic parameter. And then introducing K-means clustering, wherein the algorithm is a simple iterative clustering algorithm, the distance is used as a similarity index, so that K classes in a given data set are found, the center of each class is obtained according to the mean value of all numerical values in the class, and the center of each class is described by a clustering center. And according to the K-means clustering result, classifying all reservoir rock types to be classified into 4 types.
The carbonate reservoir rock classification method provided by the embodiment of the invention is explained by taking a specific example, and mainly comprises the following steps:
the first step is as follows: and (3) converting the pressure curve of the 161 mercury intrusion capillary into a pore throat distribution curve, extracting the pore throat radius corresponding to the peak value if the pore throat distribution is a single peak, and extracting the pore throat radius corresponding to the peak value with the largest pore throat radius interval if the pore throat distribution is a double peak or a triple peak.
The second step is that: selecting 161 parallel sample casting body slice images, carrying out median filtering, contrast image enhancement and two-dimensional OTSU threshold segmentation, automatically extracting the surface porosity, the area, the perimeter, the effective length, the equivalent width, the meandering length, the equivalent ellipse long axis, the equivalent ellipse short axis and the convex area from the 161 slice images, and then calculating the length-width ratio, the aspect ratio, the meandering degree, the eccentricity, the solidity and the shape factor of each pore according to the extracted parameters.
The third step: and (3) performing principal component and factor analysis on the extracted 161 sheet pore characteristic parameters (length-width ratio, aspect ratio, tortuosity, centrifugation degree, solidity and shape factor), calculating a first principal component, performing K-means clustering with the extracted pore throat radius, and finally dividing the reservoir into 4 classes according to a clustering result, wherein the obtained four storage classification result schematic diagrams can be shown in FIG. 5.
According to the clustering analysis result, the pore-permeability relations corresponding to four different categories can be intuitively reflected in the pore-permeability relation graph (figure 6) corresponding to the rock core. The porosity and permeability of the I-type reservoir are large, the pore morphology is regular, the pore throat radius is large, and pores develop. The II type reservoir permeability is intensively distributed at 10-100Md, most of the porosity is more than 20%, the throat radius of the II type pore structure is large, and pores generally develop; the permeability of the III-type reservoir is distributed between 1 and 10mD, the porosity is between 15 and 30 percent, and the pore characteristics mainly include small pore throat radius, reservoir fracture development and irregular pore form; the porosity and the permeability of IV are low, the porosity is lower than 20%, the permeability is mainly distributed in the range of 0.1-1Md, the IV belongs to a low-porosity and low-permeability reservoir, and neither pores nor cracks develop.
The carbonate reservoir rock classification method provided by the embodiment of the invention can integrate parameters extracted by various core scales, such as pore throat radius parameters and pore characteristic parameters, to classify rocks, balance the relationship between geology, petrophysics and microscopic slice data, enable the rock reservoir classification process to take macroscopic scale and microscopic scale into account, reveal the reservoir geological cause content to achieve the prediction effect, and clarify the reservoir and oil and gas production capacity to guide the development of oil and gas reservoirs.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (7)
1. A carbonate reservoir rock classification method, comprising:
extracting pore throat radius parameters reflecting reservoir quality from the mercury intrusion capillary pressure curve of each reservoir rock to be classified;
extracting pore characteristic parameters reflecting pore types from the casting body slices of each reservoir rock to be classified based on an image processing technology;
performing principal component analysis on the pore characteristic parameters of each reservoir rock to be classified, and extracting a first principal component parameter;
and performing cluster analysis on the pore throat radius parameters of all reservoir rocks to be classified and the first principal component parameters of all reservoir rocks to be classified to obtain a plurality of classes of the reservoir rocks to be classified.
2. The rock classification method as claimed in claim 1, wherein the extracting pore throat radius parameters reflecting reservoir quality from the mercury intrusion capillary pressure curve of each reservoir rock to be classified comprises:
converting the mercury pressing capillary pressure curve of any reservoir rock to be classified into a pore throat distribution curve;
if the pore throat distribution in the pore throat distribution curve is a single peak, extracting a pore throat radius parameter corresponding to the single peak;
and if the pore throat distribution in the pore throat distribution curve is double peaks or triple peaks, extracting the pore throat radius parameter corresponding to the peak value with the maximum pore throat radius interval.
3. The rock classification method according to claim 1, wherein the extracting pore characteristic parameters reflecting pore types from the casting body slices of each reservoir rock to be classified based on the image processing technology comprises:
acquiring a casting body slice image of each reservoir rock to be classified, and preprocessing the casting body slice image;
and extracting pore characteristic parameters from the preprocessed casting body flake images, wherein the pore characteristic parameters comprise the length-width ratio, the aspect ratio, the tortuosity, the centrifugation degree, the solidity and the shape factor of each pore.
4. The method of claim 3, wherein the acquiring of the casting slice image and the preprocessing of the casting slice image comprise:
acquiring a plurality of casting body slice images of parallel samples, and performing median filtering, contrast image enhancement and two-dimensional OTSU threshold segmentation pretreatment on the casting body slice images to obtain the pretreated casting body slice images.
5. The rock classification method according to claim 3 or 4, wherein the extracting pore characteristic parameters from the preprocessed casting body slice images comprises:
extracting the area, the perimeter, the effective length, the equivalent width, the tortuosity length, the major axis and the minor axis of the equivalent ellipse and the convex area of each pore from the preprocessed casting sheet image;
calculating the aspect ratio, the tortuosity, the eccentricity, the solidity and the shape factor of each pore based on the extracted surface porosity and the area, the circumference, the effective length, the equivalent width, the tortuosity, the equivalent ellipse major axis and minor axis and the convex area of each pore;
wherein the aspect ratio, tortuosity, eccentricity, solidity and shape factor of each pore are pore characteristic parameters.
6. The method for classifying rocks according to claim 5, wherein the performing principal component analysis on the pore characteristic parameter to extract a first principal component parameter comprises:
and performing principal component analysis on each pore characteristic parameter to obtain a plurality of principal component parameters, and extracting a first principal component parameter from the plurality of principal component parameters, wherein the first principal component parameter represents the degree of uniform and regular pore shapes.
7. The rock classification method according to claim 6, wherein the clustering analysis is performed on the pore throat radius parameters of all reservoir rocks to be classified and the first principal component parameters of all reservoir rocks to be classified to obtain a plurality of classes of the reservoir rocks to be classified, and the method comprises the following steps:
and based on a K-means clustering algorithm, taking the distance as a similarity index, and carrying out clustering analysis on the pore throat radius parameters of all reservoir rocks to be classified and the first principal component parameters of all reservoir rocks to be classified to obtain a plurality of classes of all reservoir rocks to be classified.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110373691.XA CN113239955A (en) | 2021-04-07 | 2021-04-07 | Carbonate reservoir rock classification method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110373691.XA CN113239955A (en) | 2021-04-07 | 2021-04-07 | Carbonate reservoir rock classification method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113239955A true CN113239955A (en) | 2021-08-10 |
Family
ID=77131154
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110373691.XA Pending CN113239955A (en) | 2021-04-07 | 2021-04-07 | Carbonate reservoir rock classification method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113239955A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116879129A (en) * | 2023-07-11 | 2023-10-13 | 中国矿业大学 | Rock-soil material effective seepage path characterization method based on three-dimensional microscopic image |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102704924A (en) * | 2012-06-05 | 2012-10-03 | 中国石油天然气股份有限公司 | Effective dry layer determining method and device |
| CN106156452A (en) * | 2015-03-24 | 2016-11-23 | 中国石油化工股份有限公司 | A kind of Reservoir Analysis method |
| CN107133630A (en) * | 2016-02-29 | 2017-09-05 | 中国石油化工股份有限公司 | A kind of method that carbonate porosity type is judged based on scan image |
| CN109711429A (en) * | 2018-11-22 | 2019-05-03 | 中国石油天然气股份有限公司 | A kind of evaluating reservoir classification method and device |
| CN110222981A (en) * | 2019-06-05 | 2019-09-10 | 中国石油大港油田勘探开发研究院 | A kind of reservoir classification evaluation method based on the secondary selection of parameter |
| CN110618082A (en) * | 2019-10-29 | 2019-12-27 | 中国石油大学(北京) | Reservoir micro-pore structure evaluation method and device based on neural network |
| CN112362553A (en) * | 2020-11-06 | 2021-02-12 | 西南石油大学 | Compact sandstone micro-pore structure characterization method |
-
2021
- 2021-04-07 CN CN202110373691.XA patent/CN113239955A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102704924A (en) * | 2012-06-05 | 2012-10-03 | 中国石油天然气股份有限公司 | Effective dry layer determining method and device |
| CN106156452A (en) * | 2015-03-24 | 2016-11-23 | 中国石油化工股份有限公司 | A kind of Reservoir Analysis method |
| CN107133630A (en) * | 2016-02-29 | 2017-09-05 | 中国石油化工股份有限公司 | A kind of method that carbonate porosity type is judged based on scan image |
| CN109711429A (en) * | 2018-11-22 | 2019-05-03 | 中国石油天然气股份有限公司 | A kind of evaluating reservoir classification method and device |
| CN110222981A (en) * | 2019-06-05 | 2019-09-10 | 中国石油大港油田勘探开发研究院 | A kind of reservoir classification evaluation method based on the secondary selection of parameter |
| CN110618082A (en) * | 2019-10-29 | 2019-12-27 | 中国石油大学(北京) | Reservoir micro-pore structure evaluation method and device based on neural network |
| CN112362553A (en) * | 2020-11-06 | 2021-02-12 | 西南石油大学 | Compact sandstone micro-pore structure characterization method |
Non-Patent Citations (1)
| Title |
|---|
| 徐永强: "鄂尔多斯盆地陇东地区长7致密砂岩储层微观孔喉特征及分类评价研究", 《中国博士学位论文全文数据库 基础科学辑》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116879129A (en) * | 2023-07-11 | 2023-10-13 | 中国矿业大学 | Rock-soil material effective seepage path characterization method based on three-dimensional microscopic image |
| CN116879129B (en) * | 2023-07-11 | 2024-03-22 | 中国矿业大学 | Rock-soil material effective seepage path characterization method based on three-dimensional microscopic image |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107817199B (en) | A kind of construction method of tight sand multi-scale porosity model and application | |
| CN104036239B (en) | Fast high-resolution SAR (synthetic aperture radar) image ship detection method based on feature fusion and clustering | |
| CN106053315B (en) | A kind of biocalcarenite RESERVOIR PORE STRUCTURE classification method | |
| CN103034838B (en) | A kind of special vehicle instrument type identification based on characteristics of image and scaling method | |
| CN103065149A (en) | Netted melon fruit phenotype extraction and quantization method | |
| CN104182763A (en) | Plant type identification system based on flower characteristics | |
| CN111062425A (en) | Unbalanced data set processing method based on C-K-SMOTE algorithm | |
| CN110047079A (en) | A kind of optimum segmentation scale selection method based on objects similarity | |
| CN113239955A (en) | Carbonate reservoir rock classification method | |
| CN108061697B (en) | Method for calculating three-dimensional porosity of soil body | |
| CN113379256A (en) | Reservoir evaluation prediction method for Kmeans-random forest | |
| CN106447686A (en) | Method for detecting image edges based on fast finite shearlet transformation | |
| CN111046838A (en) | Method and device for identifying wetland remote sensing information | |
| CN113034471B (en) | SAR image change detection method based on FINCH clustering | |
| CN111523587A (en) | Woody plant species spectrum identification method based on machine learning | |
| CN109670408A (en) | A kind of object-based remote sensing images Clean water withdraw method | |
| CN116341932B (en) | Tidal flat change monitoring method and system based on double remote sensing time sequence indexes | |
| CN112991370A (en) | Rock core CT crack identification and segmentation method | |
| CN107818341A (en) | A kind of color extraction method based on improvement K means algorithms | |
| CN107977968A (en) | The building layer detection method excavated based on buildings shadow information | |
| CN115393733B (en) | Automatic water body identification method and system based on deep learning | |
| CN113640244B (en) | Fruit tree variety identification method based on visible near infrared spectrum | |
| CN102750547A (en) | Fruit size grading method based on compressed sensing | |
| CN115640546A (en) | Lithology identification method based on fusion of image and feature information | |
| CN110287992A (en) | Agricultural features information extracting method based on big data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |