Rock type classification methods, equipment, media, and terminals based on fractal dimension

Abstract

This invention belongs to the field of rock type classification technology, and discloses a method, device, medium, and terminal for rock type classification based on fractal dimension. It includes establishing a fractal dimension-based model and parameter Dn, comparing the correlation between Dn and mercury intrusion porosimetry (MIP) curve characteristics, and using parameter Dn to reflect the differences in pore throat distribution curves based on the R35 classification method. The fractal dimension-based model and parameter Dn established in this invention are correlated with pore throat distribution images, and comparing the magnitude of this parameter can clarify the differences in image features. Based on the combination of the absolute pore throat diameter method and R35 for pre-classification of rock types, the Dn parameter is used for more detailed classification. When mercury intrusion porosimetry pore throat distribution curves are similar, higher peak values ​​and larger pore throat radii lead to larger Dn parameters; therefore, curve difference characteristics can be obtained by comparing Dn.

Description

Technical Field

[0001] This invention belongs to the field of rock type classification technology, and particularly relates to a method, device, medium and terminal for rock type classification based on fractal dimension. Background Technology

[0002] Currently, rock type classification is an important tool for geological reservoir evaluation, frequently used in carbonate formations. Specific methods include classification based on differences in geological facies, pore type, rock physical parameters from cores or well logging, diagenesis, and well logging facies. These classification methods often require different research data. For rock physical classification at the carbonate core scale, a common approach is to study the rock's rock physical characteristics using methods such as mercury intrusion porosimetry, classifying the rock based on pore size distribution and the relationship between porosity and permeability. Commonly used derived methods include the absolute pore throat value comparison method and the R35 pore throat radius method.

[0003] The absolute pore throat diameter method analyzes the pore throat radius of rocks using mercury intrusion porosimetry (MIP). Based on curve morphology and existing technology, it defines macropores, mesopores, and micropores, calculates the percentage of pores occupied by each type, and then analyzes the relationship between this percentage and porosity / permeability. However, this technique faces challenges due to inconsistent pore size classification standards, increasing the difficulty of comparative analysis across different formations. Furthermore, it fails to characterize the pore size distribution features obtained from MIP data.

[0004] The R35 method defines the pore throat radius corresponding to 35% mercury penetration and classifies rocks based on the magnitude of this parameter. While this method divides images into three broad categories, it fails to capture the detailed differences within each subcategory. Curve features within the same category cannot be reflected in the porosity-permeability correlation.

[0005] However, both of these methods can only reflect the average parameters of pore throat size and cannot reflect the curve characteristics of pore size distribution. Therefore, this patent establishes a fractal dimension-based model based on the concept of fractal dimension, analyzes the correlation between this model and the differences in pore size distribution, and proposes a new rock type classification method for carbonate rocks using Middle Eastern carbonate reservoir cores as an example, in order to make up for the technical deficiency that pore size distribution characteristics cannot be used in rock classification evaluation.

[0006] Based on the above analysis, the problems and shortcomings of the existing technology are as follows:

[0007] Existing rock classification methods often require different data, using pore size distribution and the relationship between porosity and permeability for classification. Commonly used derived methods include the absolute pore throat diameter comparison method and the R35 pore throat radius method. However, these two methods only reflect the average parameter of pore throat size and cannot reflect the curve characteristics of pore size distribution. In actual production research and analysis, the pore size distribution curve is crucial, as it is the most intuitive parameter representing the physical properties of rocks. The inadequacy of existing methods leads to the underutilization of rock physics experimental data, resulting in relatively one-sided conclusions. Summary of the Invention

[0008] To address the problems existing in the prior art, this invention provides a method, device, medium, and terminal for classifying rock types based on fractal dimension.

[0009] This invention is implemented as follows: a rock type classification method based on fractal dimension includes:

[0010] A model and parameter Dn based on fractal dimension are established, and the correlation between Dn and the characteristics of mercury intrusion curves are compared. Based on the R35 classification method, the parameter Dn is used to reflect the differences in pore throat distribution curves.

[0011] Furthermore, the specific steps of the rock type classification method based on fractal dimension include:

[0012] 1) Analyze the basic rock physical parameters of the test samples; obtain the basic physical properties that can be used for horizontal comparison.

[0013] 2) Classify according to R35 pore throat radius and its correlation with sample porosity and permeability; using the relationship between R35 and porosity and permeability, the coarser rocks are divided into 3 categories.

[0014] 3) The Dn parameter is derived through a fractal dimension-based model to reflect the PTD information of each sample;

[0015] Based on the Dn model based on fractal dimension, the parameter Dn reflecting the sample pore size distribution curve is derived for subsequent data analysis.

[0016] 4) Analyze the differences between samples with similar PTD and find that the higher the PTD peak, the larger the pore throat radius and the smaller the Dn.

[0017] Sensitivity analysis was performed on Dn to clarify the correlation between Dn and specific characteristics of pore size distribution, and the correlation was used to analyze the porosity and permeability characteristics of the rock.

[0018] Furthermore, the model based on fractal dimension is as follows:

[0019]

[0020] In the formula, V pLet r be the total volume of the pores, r be the pore radius, and Dn be a fractal-based parameter.

[0021] Furthermore, in the fractal dimension-based model, the total volume of pores is represented by the total porosity, which is obtained through MICP experiments:

[0022]

[0023] Where Vp1 is the minimum pore radius and Vpn is the maximum pore radius.

[0024] Furthermore, in the fractal dimension-based model, the pore volume V with radius r... pi for:

[0025]

[0026] Number of pores with radius greater than r, Nr:

[0027] N r ∝r -Df

[0028] Df is the fractal dimension.

[0029] Furthermore, in the fractal dimension-based model, the pore volume is expressed as:

[0030]

[0031] In the formula, j = i + 1.

[0032] Furthermore, the classification based on R35 pore throat radius and its correlation with sample porosity and permeability are used to characterize the pore size distribution characteristics of the R35 rock classification using the Dn parameter. The rule is that the smaller the Dn parameter, the larger the pore size and the higher the peak pore size of the sample.

[0033] Based on the above technical solutions and the technical problems solved, please analyze the advantages and positive effects of the technical solution to be protected by this invention from the following aspects:

[0034] First, addressing the technical problems existing in the prior art and the difficulty of solving them, this paper closely analyzes, in conjunction with the technical solution to be protected by this invention and the results and data obtained during the research and development process, how the technical solution of this invention solves the technical problems, and the inventive technical effects brought about by solving these problems. The specific description is as follows:

[0035] This invention establishes a model based on fractal dimension and a parameter Dn, the magnitude of which is correlated with pore throat distribution images. Comparing the magnitude of this parameter can clarify differences in image features. Based on the absolute pore throat diameter method and R35 for pre-classification of rock types, the Dn parameter is used for more detailed classification.

[0036] When the pore throat distribution curves of mercury porosimetry are similar, a higher peak value and a larger pore throat radius will result in a larger Dn parameter. Therefore, the difference characteristics of the curves can be obtained by comparing Dn.

[0037] Second, considering the technical solution as a whole or from a product perspective, the technical effects and advantages of the technical solution to be protected by this invention are specifically described as follows:

[0038] This invention can highlight the characteristic differences in pore throat distribution curves, thereby improving the accuracy and precision of rock classification.

[0039] Third, as supplementary evidence of the inventive step of the claims of this invention, it is also reflected in the following important aspects:

[0040] (1) The expected benefits and commercial value of the technical solution of the present invention after transformation are: it can classify rocks more meticulously and improve the accuracy of reservoir evaluation.

[0041] (2) The technical solution of the present invention fills the technical gap in the domestic and foreign industry: fractal dimension is often used in rock analysis, but it has not yet been used in rock classification, and existing models require artificial segmentation of the porosity-permeability distribution curve for analysis, while this study overcomes this shortcoming. Attached Figure Description

[0042] Figure 1 This is a flowchart of a rock type classification method based on fractal dimension provided in an embodiment of the present invention;

[0043] Figure 2 This is a diagram showing the relationship between Dn and the pore throat distribution provided in an embodiment of the present invention. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0045] I. Explanatory and Illustrative Embodiments. To enable those skilled in the art to fully understand how the present invention is specifically implemented, this section provides an explanatory and illustrative description of the embodiments described in the claims.

[0046] like Figure 1 As shown, the rock type classification method based on fractal dimension provided in this embodiment of the invention includes:

[0047] S101, Analyze the basic rock physical parameters of the test sample;

[0048] S102, classified according to R35 pore throat radius and its correlation with sample porosity and permeability;

[0049] S103, the Dn parameter is derived through a fractal dimension-based model, reflecting the pore throat distribution (PTD) information of each sample;

[0050] S104, analysis of the differences between samples with similar PTD peaks showed that the higher the PTD peak, the larger the pore throat radius and the smaller the Dn.

[0051] Fractal dimension is a parameter describing the regularity of pores. Starting from the most basic formula, this invention establishes a model and parameter Dn based on fractal dimension, compares the correlation between Dn and the characteristics of mercury intrusion porosimetry curves, and uses parameter Dn to reflect the differences in pore throat distribution curves that cannot be reflected by the R35 method based on R35 classification.

[0052] Derivation of the fractal dimension model:

[0053] Fractal dimension is an important characteristic that describes the self-affinity of fractal objects. This fractal structure can be mathematically explained using a power-law relationship (Pfeifer, Avnir, and Farin 1983):

[0054] N r ∝r -Df (2)

[0055] Where r is the pore radius, Nr is the number of pores with radii greater than r, and Df is the fractal dimension. Different methods exist for calculating the number of pores Nr based on different assumptions. In the fractal model, the total volume of pores (Vp as a percentage) is represented as the total porosity, which can be obtained through MICP experiments.

[0056]

[0057] Where Vp1 corresponds to the minimum pore radius and Vpn corresponds to the maximum pore radius. In this study, to relate incremental intrusion (%) to pore radius, we considered the Kozeny-Carman rock model (Kozeny 1927) by treating the pore space as a capillary bundle. The pore volume Vpi with radius r along the capillary length can be written as:

[0058]

[0059] For simplicity, let's assume that equations 2 and 3 are combined with r = l, and the pore volume can be expressed by equation 5:

[0060]

[0061] j = i + 1. In this case, we use the total volume percentage increment (Vp) greater than radius r to represent Vpi. Then Equation 5 can be expressed as:

[0062]

[0063]

[0064] Equation 5 shows that the number of pores greater than r (Nr) is proportional to the pore radius (r). By transforming Equation 5 into a double logarithmic harmonic (logarithm (Nr) - logarithm (r)) expressed by Equation 6, the fractal dimension Df can be determined by the negative slope of the log-to-logarithmic plot. For Equation 7, Dn is named as a new fractal-based parameter that has a similar rock-physical meaning to the ratio of the fractal dimension Df to analyze the PTD differences between samples.

[0065] II. Application Examples. To demonstrate the inventiveness and technical value of the technical solution of this invention, this section provides application examples of the technical solution of the claims on specific products or related technologies.

[0066] In practical reservoir analysis and evaluation, rock classification is a fundamental and indispensable task regardless of lithology or geographical location. Utilizing parameters Dn based on a fractal dimension model allows for more detailed differentiation of rock types within the target layer, thereby improving classification accuracy.

[0067] The computer device provided by the present invention includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor performs the steps of the rock type classification method based on fractal dimension.

[0068] The present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the rock type classification method based on fractal dimension.

[0069] The information data processing terminal provided by this invention is used to implement the steps of the rock type classification method based on fractal dimension.

[0070] III. Evidence of the Relevant Effects of the Embodiments. The embodiments of the present invention have achieved some positive effects during research and development or use, and indeed possess significant advantages compared to existing technologies. The following description, in conjunction with data, charts, and other materials from the experimental process, illustrates these advantages.

[0071] like Figure 2 As shown, this is the PTD and sensitivity analysis of Dn for the two groups of samples. Figure 2 middle:

[0072] a) S13 and S15 have similar PTD, but different peak values;

[0073] b) The peak value increases by parameter Dn, which is the area of ​​the red circle;

[0074] c) S14 and S3 have similar peak values, but S14 has a higher pore throat radius than S3;

[0075] d) The larger the radius of the pore throat, the smaller Dn is.

[0076] Figure 2 The controlling factors of Dn were analyzed from the log-log plot. Figure 2 The ptd values ​​of S13 and S15 were compared. Their pore throat widths were similar, but the peak value of S13 was higher than that of S15. Using the fractal sedimentation method, the slope of the fitted negative slope (Dn) of S15 (2.67) was higher than that of S13. This is because... Figure 2 The high incremental Hg value (2.59) of S13 in the a-type instrument corresponds to a relatively high Log (V value, p / r3) in the a-type instrument. Figure 2 In the diagram, b (the area marked in red). This phenomenon alters the slope of the fit, leading to a change in Dn. Another typical case is... Figure 2 As shown in c, the throat radius of S14 is larger than that of S3, but they have similar peaks (marked in red). For Figure 2 In the diagram, the black dashed line for S14 indicates a higher Log(Vp / r3) value than S3 (gray dashed line). Based on the marked area separation of the dashed lines, within a pore throat radius of 0.1 μm or less, mercury intrusion in S3 is higher than in S14 (…). Figure 2 c) in the equation corresponds to a higher value of Log(Vp / r3) that is lower than... Figure 2 In the figure, Log(r) = -1 for d. Furthermore, outside the pore throat radius of 1 μm (Log(r) = 0), the negative slope (gray) of the fitted dashed line value for sample S3 is higher than that for S14 (black). Therefore, we can conclude that for samples with similar ptd, Dn increases with increasing pore throat radius and increasing mercury intrusion.

[0077] When the pore throat distribution curves of mercury porosimetry are similar, a higher peak value and a larger pore throat radius will result in a larger Dn parameter. Therefore, the difference characteristics of the curves can be obtained by comparing Dn.

[0078] It should be noted that embodiments of the present invention can be implemented in hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by a suitable instruction execution system, such as a microprocessor or dedicated-design hardware. Those skilled in the art will understand that the above-described devices and methods can be implemented using computer-executable instructions and / or included in processor control code, for example, such code provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented by hardware circuitry such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field-programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of the above-described hardware circuitry and software, such as firmware.

[0079] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention, and within the spirit and principles of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for classifying rock types based on fractal dimension, characterized in that, The rock type classification method based on fractal dimension includes: A model and parameter Dn based on fractal dimension are established, and the correlation between Dn and the characteristics of mercury intrusion curves are compared. Based on the R35 classification method, the parameter Dn is used to reflect the differences in pore throat distribution curves. The specific steps of the rock type classification method based on fractal dimension include: 1) Analyze the basic rock physical parameters of the test samples; 2) Classification by R35 pore throat radius and its correlation with sample porosity and permeability; 3) The Dn parameter is derived through a fractal dimension-based model to reflect the PTD information of each sample; 4) Analyzing the differences between samples with similar PTD peaks, it was found that the higher the PTD peak, the larger the pore throat radius and the smaller the Dn. The model based on fractal dimension is as follows: ; In the formula, V p Let r be the total volume of the pores, r be the pore radius, and Dn be a fractal-based parameter.

2. The rock type classification method based on fractal dimension as described in claim 1, characterized in that, In the fractal dimension-based model, the total volume of pores is represented by the total porosity, which is obtained through MICP experiments: ; Where Vp1 is the minimum pore radius and Vpn is the maximum pore radius.

3. The rock type classification method based on fractal dimension as described in claim 1, characterized in that, In the fractal dimension-based model, the pore volume with radius r is... V pi for: ; Number of pores with radius greater than r, Nr: ; Df is the fractal dimension.

4. The rock type classification method based on fractal dimension as described in claim 3, characterized in that, In the fractal dimension-based model, the pore volume is expressed as: ; In the formula, j = i + 1.

5. The rock type classification method based on fractal dimension as described in claim 1, characterized in that, Based on the classification of R35 pore throat radius and its correlation with sample porosity and permeability, the Dn parameter is used to characterize the pore size distribution characteristics of R35 rock categories. The rule is that the smaller the Dn parameter, the larger the pore size and the higher the peak pore size of the sample. Based on this, the pore size distribution characteristics of different rock categories can be determined.

6. A computer device, characterized in that, The computer device includes a memory and a processor. The memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the rock type classification method based on fractal dimension as described in any one of claims 1 to 5.

7. A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the rock type classification method based on fractal dimension as described in any one of claims 1 to 5.

8. An information data processing terminal, characterized in that, The information data processing terminal is used to implement the steps of the rock type classification method based on fractal dimension as described in any one of claims 1 to 5.

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