Sandstone compaction evaluation method and device

Abstract

The present invention provides a sandstone compaction evaluation method and device, relating to the field of geological exploration technology. The method comprises: acquiring a target image of a sample; determining planar distribution characteristic data and proportion data of each mineral in the sample based on the target image; calculating the sample's compaction intensity factor based on the planar distribution characteristic data and proportion data; and generating a sandstone compaction evaluation result using the compaction intensity factor. The present invention calculates the sample's compaction intensity factor based on the planar distribution characteristic data and proportion data of each mineral in the sample, thereby achieving a quantitative evaluation of compaction intensity. This improves the accuracy and efficiency of sandstone compaction evaluation results, providing technical support for reservoir effectiveness research and inter-reservoir comparative evaluation.

Description

Technical Field

[0001] The present invention relates to the field of geological exploration technology, and in particular to a sandstone compaction evaluation method and device. Background Art

[0002] As a key component of sandstone reservoir evaluation, diagenesis significantly impacts the reservoir's pore structure and storage properties. Compaction, as a key type of diagenesis, has garnered widespread attention from both academia and industry. Extensive research has been conducted on compaction, exploring its impact on reservoir properties in detail. Currently, evaluation of compaction primarily focuses on its impact on reservoir properties, particularly porosity, neglecting the intensity of compaction. Furthermore, current qualitative assessment methods rely heavily on the experience of rock identification experts, which carries significant human influence. This heavy reliance on researcher experience leads to significant variability in experimental results. Different researchers may assess the intensity of compaction differently for the same sample, resulting in insufficient in-depth research on compaction. Manual labor is the primary method, resulting in slow analysis and inefficient operation, leading to low overall evaluation efficiency. Summary of the Invention

[0003] The present invention provides a sandstone compaction effect evaluation method and device, which can realize the quantitative evaluation of the compaction effect strength and improve the accuracy and efficiency of the sandstone compaction effect evaluation results.

[0004] In a first aspect, an embodiment of the present invention provides a method for evaluating the compaction effect of sandstone, the method comprising: acquiring a target image of a sample; determining the planar distribution characteristic data and proportion data of each mineral in the sample based on the target image; calculating the compaction intensity factor of the sample based on the planar distribution characteristic data and the proportion data; and generating a sandstone compaction evaluation result using the compaction intensity factor.

[0005] In a second aspect, an embodiment of the present invention further provides a sandstone compaction effect evaluation device, which includes: an acquisition module for acquiring a target image of a sample; a data module for determining the plane distribution characteristic data and proportion data of each mineral in the sample based on the target image; a calculation module for calculating the compaction effect intensity factor of the sample based on the plane distribution characteristic data and the proportion data; and an evaluation module for generating a sandstone compaction effect evaluation result using the compaction effect intensity factor.

[0006] In a third aspect, an embodiment of the present invention further provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned sandstone compaction effect evaluation method when executing the computer program.

[0007] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium storing a computer program for executing the above-mentioned sandstone compaction effect evaluation method.

[0008] The embodiments of the present invention provide the following beneficial effects: The embodiments of the present invention provide a sandstone compaction evaluation scheme, comprising: acquiring a target image of a sample; determining planar distribution characteristic data and proportion data of each mineral in the sample based on the target image; calculating a compaction intensity factor of the sample based on the planar distribution characteristic data and proportion data; and generating a sandstone compaction evaluation result using the compaction intensity factor. The embodiments of the present invention calculate the compaction intensity factor of the sample based on the planar distribution characteristic data and proportion data of each mineral in the sample, thereby achieving a quantitative evaluation of the compaction intensity. This improves the accuracy and efficiency of the sandstone compaction evaluation results, and provides technical support for reservoir effectiveness research and inter-reservoir comparative evaluation.

[0009] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or understood by practicing the present invention. The purposes and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0010] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0012] Figure 1 A flow chart of a sandstone compaction evaluation method provided in an embodiment of the present invention;

[0013] Figure 2 A schematic diagram of the implementation steps of the sandstone compaction evaluation method provided in an embodiment of the present invention;

[0014] Figure 3 This is a diagram showing the effect of sandstone sample image processing provided by an embodiment of the present invention;

[0015] Figure 4 The effect diagram of the example study provided by the embodiment of the present invention;

[0016] Figure 5A structural block diagram of a sandstone compaction evaluation device provided in an embodiment of the present invention;

[0017] Figure 6 A structural block diagram of another sandstone compaction evaluation device provided in an embodiment of the present invention;

[0018] Figure 7 A structural block diagram of a computing module provided in an embodiment of the present invention;

[0019] Figure 8 This is a structural block diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0021] Existing research on compaction has primarily explored its effects on reservoir properties. For example, Loucks et al. (2012) proposed that compaction causes porosity loss of 83%–88%, and that shale porosity is less than 10% at depths exceeding 2.5 km. Ehrenberg et al. (2005, 2009) showed that the porosity of clastic rocks of the same geological age generally decreases by 1–3% with each additional km of burial depth. Based on existing sandstone compaction response characteristics and porosity evolution curves within sedimentary basins, porosity decreases rapidly from 0 to 2000 m, slowly from 2000 to 3000 m, and minimally at depths greater than 3000 m (Bloch, 1990; Chudi et al., 2015; Meng Yuanlin et al., 2016; Lin Chengyan et al., 2019).

[0022] Regarding the intensity of compaction, qualitative judgments are currently mainly made based on the contact relationship between different particles in rock thin sections or scanning electron microscopes. The compaction effect is mainly divided into three levels: strong, medium, and weak. There is a lack of quantitative evaluation results of different compaction intensities, which also makes it impossible to conduct direct comparisons of the compaction strength of the same type of reservoir in the same region.

[0023] Currently, the evaluation of compaction in sandstone reservoirs is primarily based on the contact relationships between particles. Particle contact relationships primarily include point contact, line contact, concave-convex contact, or suture contact, and different types of contact relationships are closely related to the intensity of compaction. Generally speaking, reservoirs with strong compaction have primarily linear or concave-convex contact; conversely, reservoirs with weaker compaction have primarily point contact. Determination of contact relationships between particles is primarily based on thin-section images or scanning electron microscope images of sandstone samples. The mainstream approach is to visually observe the contact relationships between different particles and empirically define these relationships as point contact, line contact, concave-convex contact, or suture contact. Based on the dominant contact type, the intensity of compaction is then qualitatively described as strong, moderate, weak, or no compaction.

[0024] Based on this, in order to address the current problem that the compaction intensity of sandstone reservoirs cannot be quantitatively evaluated, an embodiment of the present invention provides a sandstone compaction evaluation method and device. This method quantitatively statistics and evaluates the compaction intensity based on the particle contact relationship, and proposes a calculation method and process for the quantitative evaluation parameters of the compaction intensity. Through the constraints and comparisons of expert experience, the quantitative evaluation of the compaction effect of sandstone reservoirs is achieved, the accuracy of reservoir diagenesis research is improved, and important technical support is provided for reservoir comparison.

[0025] To facilitate understanding of this embodiment, a sandstone compaction evaluation method disclosed in an embodiment of the present invention is first introduced in detail.

[0026] The embodiment of the present invention provides a method for evaluating the compaction effect of sandstone. Figure 1 A flow chart of a sandstone compaction evaluation method is shown, the method comprising the following steps:

[0027] Step S202: Acquire a target image of the sample.

[0028] In the embodiment of the present invention, the sample may be a sandstone sample, and the target image is obtained using the sandstone sample. The target image includes image information of various material components in the pre-processed sandstone reservoir sample.

[0029] It should be noted that in sandstone reservoirs, the material composition mainly includes particles and fillings, among which the particles mainly include quartz, feldspar, rock fragments and biotite, and the fillings are mainly carbonate cements and clay mineral matrices.

[0030] Step S204: determining the planar distribution characteristic data and proportion data of each mineral in the sample based on the target image.

[0031] In an embodiment of the present invention, the contact relationship data between particles and minerals can be determined based on the pixel data in the target image. Based on these contact relationship data, the planar distribution characteristic data of each mineral is determined, and then the proportion data of different minerals are obtained.

[0032] It should be noted that the plane distribution characteristic data can be used to determine the positions of different particles and different types of minerals in the target image.

[0033] Step S206: Calculate the compaction intensity factor of the sample based on the plane distribution characteristic data and the proportion data.

[0034] In the present embodiment, compaction primarily refers to the close contact between particles in a sandstone reservoir. The compaction intensity factor is the sum of the contact lengths between a single mineral particle and its surrounding particles divided by the particle's perimeter. Using planar distribution characteristic data and percentage data, the compaction intensity factor of a sample can be calculated. This allows for a quantitative assessment of compaction intensity, improving the accuracy and efficiency of sandstone compaction evaluation results.

[0035] Step S208: Generate sandstone compaction evaluation results using the compaction intensity factor.

[0036] In an embodiment of the present invention, after obtaining the compaction intensity factor, the calculated compaction intensity factor is verified with the compaction intensity determined by expert experience to clarify the critical value of the compaction intensity factor corresponding to different compaction intensity levels; the rock strength of the sandstone reservoir is evaluated based on the compaction intensity factor, and the larger the value of the compaction intensity factor, the greater the compaction intensity of the reservoir.

[0037] An embodiment of the present invention provides a sandstone compaction evaluation scheme, comprising: acquiring a target image of a sample; determining planar distribution characteristic data and proportion data for each mineral in the sample based on the target image; calculating the sample's compaction intensity factor based on the planar distribution characteristic data and proportion data; and generating a sandstone compaction evaluation result using the compaction intensity factor. By calculating the sample's compaction intensity factor based on the planar distribution characteristic data and proportion data for each mineral in the sample, the embodiment of the present invention achieves a quantitative evaluation of compaction intensity, improving the accuracy and efficiency of sandstone compaction evaluation results and providing technical support for reservoir effectiveness research and inter-reservoir comparative evaluation.

[0038] To improve the accuracy and efficiency of the evaluation results, the following steps can be performed before obtaining the target image of the sample:

[0039] Acquire a sandstone sample image; perform binarization processing on the sandstone sample image to obtain a target image.

[0040] In the embodiment of the present invention, the binarization process is to set the grayscale value of the pixel on the image to 0 or 255, that is, to make the entire image appear in obvious black and white. Figure 3 The SEM image of the sample shown in A in the figure is converted into a black and white image to obtain the SEM image binary segmentation result, which can effectively distinguish the particles from the filling materials (cement and matrix). Figure 3 In B, in this figure, the particles are displayed in white and the gap fillers are displayed in black, and the binarization result is used as the target image.

[0041] In order to expand the scope of application, in this solution, the image type of the sandstone sample image is a rock thin section image, a scanning electron microscope image, or a CT (Computed Tomography) image.

[0042] In the embodiment of the present invention, the image type of the sandstone sample image can be any one of a rock thin section image, a scanning electron microscope image, and a CT image. It should be noted that the embodiment of the present invention can also use other image types, which are not specifically limited here.

[0043] Taking scanning electron microscope images as an example, a sample that meets the requirements of scanning electron microscope research is prepared, and after the surface is carbon-coated or gold-sprayed, it is placed in a scanning electron microscope to collect images and obtain sandstone sample images.

[0044] It should be noted that regardless of the image type used to capture sandstone samples, the observation field must be 15 to 20 times the sample's primary particle size and contain at least 200 particles. This is primarily to ensure representativeness and minimize the impact of heterogeneity on experimental results. The quantitative evaluation process and methods remain identical regardless of the image type used.

[0045] In one embodiment, determining the planar distribution characteristic data and proportion data of each mineral in the sample based on the target image can be performed according to the following steps:

[0046] The image information of the interstitial objects in the target image is eliminated, and the image information of the mineral particles in the target image is separated and classified to obtain the plane distribution characteristic data of each mineral; the proportion data of each mineral is determined based on the plane distribution characteristic data.

[0047] In the embodiment of the present invention, the energy dispersive spectrometer equipped in the scanning electron microscope is used to determine the mineral composition characteristics within the observation field, and to clarify the plane distribution characteristics and percentage content of each mineral. Image processing software is used to remove the interstitial objects within the field of view, separate and distinguish different mineral particles, and normalize the content of the mineral particles, which are respectively marked as quartz content (C Quartz ), feldspar content (CFeldspar ), rock debris content (C Rock-fragment ), mica content (C Muscovite ) and other particle content (C Other-particles ).

[0048] In the embodiment of the present invention, see Figure 3 In B, after removing the black interstitial image information, the particle information in the target image is separated and classified according to the type of mineral particles, and the following is obtained: Figure 3 In the image shown in C, different shades of color can be used to represent different types of particles, or different colors can be used to represent different types of particles. The area occupied by the same type of mineral particles in the image is divided by the area occupied by all particles in the image to obtain the percentage data.

[0049] It should be noted that Figure 3 Figures C and D in the figure are the planar distribution of particles marked with mineral components. The black boxes in the figure indicate the mutual contact relationship between particles. The solid line boxes are generally defined as point contacts in traditional methods, while the dotted line boxes are mostly defined as line contacts in traditional studies.

[0050] The embodiment of the present invention obtains the compaction intensity factor by calculating the ratio of the length of each particle contact portion to the particle perimeter, obtains a quantitative result through statistical analysis of a large amount of quantitative data, and ultimately determines the sandstone compaction intensity factor.

[0051] In order to obtain more accurate quantitative evaluation results, the compaction intensity factor of the sample can be calculated based on the plane distribution characteristic data and proportion data. The following steps can be performed:

[0052] Calculate the individual mineral particle strength factor based on the plane distribution characteristic data; calculate the category mineral particle strength factor based on the individual mineral particle strength factor; calculate the viewfield strength factor based on the category mineral particle strength factor and proportion data; calculate the compaction intensity factor of the sample based on multiple viewfield strength factors.

[0053] In an embodiment of the present invention, the length of the contact edge between each type of mineral particles and the surrounding mineral particles is counted to calculate the perimeter of the mineral particles; the compaction intensity factor of each particle is defined as the sum of the lengths of the contact edges between the particle and the surrounding particles divided by the perimeter of the mineral; after calculating the individual mineral particle intensity factors of all particles of each type of mineral, the sum is added and the average value is taken to obtain the category mineral particle intensity factor.

[0054] The mineral proportion data, i.e. the percentage content of mineral particles and the intensity factor of mineral particles of different categories, are used to obtain the field intensity factor of the sandstone reservoir within the observation field by weighted average method.

[0055] The view intensity factors of other view areas are calculated and replaced respectively. Then, the average value of all the view intensity factors is taken to obtain the compaction intensity factor of the sample.

[0056] It should be noted that the single mineral particle intensity factor is used to describe the compaction intensity of a mineral particle, the category mineral particle intensity factor is used to describe the compaction intensity of a category of mineral particles, the field of view intensity factor is used to describe the compaction intensity of mineral particles within a field of view, and the sample compaction intensity factor is used to describe the compaction intensity of mineral particles in the sample that forms the target image.

[0057] In the embodiment of the present invention, the interstitial material is first separated from the particles, and then the evaluation factor reflecting the contact relationship between different particles is quantitatively calculated. Finally, the weighted average method is used to comprehensively evaluate the compaction effect of the entire sandstone reservoir.

[0058] In one embodiment, the strength factor of a single mineral particle is calculated based on the plane distribution characteristic data according to the following formula: Quartz-1 =Sum(L Quartz-1-1 , L Quartz-1-2 ,……,L Quartz-1-n ) / G Quartz-1 Among them, CM Quartz-1 Represents the strength factor of a single mineral particle, G Quartz-1 represents the perimeter of a single mineral particle, n represents the number of particles that a single mineral particle contacts, and the length of the contact edge between a single mineral particle and each particle is L Quartz-1-1 , L Quartz-1-2 ,……,L Quartz-1-n .

[0059] In one embodiment, the class mineral particle strength factor is calculated based on the individual mineral particle strength factor according to the following formula: Quartz =Sum(CM Quartz-1 , CM Quartz-2 ,……,CM Quartz-n ) / nAmong them, CM Quartz Indicates the mineral particle strength factor, CM Quartz-1 , CM Quartz-2 ,……,CM Quartz-n represents the strength factor of a single mineral particle, and n represents the number of single mineral particles.

[0060] In one embodiment, the visual field intensity factor is calculated based on the mineral particle intensity factor and the proportion data according to the following formula: CM1 = CM Quartz ×C Quartz +CM Feldspar ×C Feldspar +CM Rock-fragment ×CRock-fragment +CM Muscovite ×C Muscovite +CM Other-particles ×C Other-particles Among them, CM1 represents the viewing intensity factor, CM Quartz represents the quartz grain strength factor, CM Feldspar represents the feldspar grain strength factor, CM Rock-fragment represents the rock fragment strength factor, CM Muscovite represents the mica particle strength factor, CM Other-particles Indicates other particle strength factors, C Quartz 、C Feldspar 、C Rock-fragment 、C Muscovite and C Other-particles Indicates percentage data.

[0061] In one embodiment, the compaction intensity factor of the sample is calculated based on multiple visual intensity factors according to the following formula: CM=Sum(CM1, CM2, ..., CM n ) / n, where CM represents the compaction intensity factor of the sample, and n represents the number of field intensity factors, CM1, CM2, ..., CM n Represents the viewshed intensity factor.

[0062] See also Figure 2 S01-S06 shown, the implementation process of the method is described below with a specific embodiment.

[0063] (1) Select a representative sandstone sample from an area of ​​interest for research and prepare a SEM sample. It is recommended that the sample surface be polished with 2000 mesh or higher corundum or argon ions to avoid errors caused by surface undulation or obstruction. Then, carbonize or gold-spray the sample surface. After completion, place the sample in a SEM for observation.

[0064] (2) Set reasonable SEM experimental parameters, including accelerating voltage, beam current, and magnification; select a representative field of view for research, requiring the outline of the particles in the field of view to be clearly visible, the entire field of view should be 15 to 20 times the main particle diameter of the particles, and the field of view should contain at least 200 particles;

[0065] (3) Scanning electron microscopy is performed on the selected field of view to obtain an image of the overall distribution of particles; an energy spectrum scanner is used to mark the elemental composition of the minerals in the observed field of view, and then the mineral type is determined.

[0066] (4) Based on the mineral type results determined by energy spectrum analysis, two-dimensional image processing software is used to complete the labeling of different minerals and remove the filling materials, mainly including the dolomite, calcite carbonate cements and clay minerals mentioned above; then the mineral particles in the image are separated, mainly including quartz, feldspar, rock fragments, mica and other special particles;

[0067] (5) Using the two-dimensional image processing software, calculate the percentage of different mineral particles and mark them as quartz content (C Quartz ), feldspar content (C Feldspar ), rock debris content (C Rock-fragment ), mica content (C Muscovite ) and other particle content (C Other-particles ); Considering that the compaction strength is mainly studied for particles, the content of all particles in the sample is normalized, requiring the sum of the percentage of the above mineral particles to be 100%, that is: Sum(C Quartz , C Feldspar , C Rock-fragment , C Muscovite , C Other-particles )=100%;

[0068] (6) Calculate the length of the contact edge between each mineral particle and the surrounding mineral particles, as well as the perimeter of the mineral particle. This is mainly accomplished by counting the number of pixels. The following uses quartz particles as an example to illustrate the calculation process.

[0069] Assume that the first quartz grain is in contact with n grains and its perimeter is G Quartz-1 , and the length of the contact edge with each particle is L Quartz-1-1 , L Quartz-1-2 ,……,L Quartz-1-n , then the compaction intensity factor of the quartz particle is calculated according to formula 1:

[0070] CM Quartz-1 =Sum(L Quartz-1-1 , L Quartz-1-2 ,……,L Quartz-1-n ) / G Quartz-1 (Formula 1)

[0071] By analogy, the compaction intensity factor CM of all n quartz particles in the field of view is calculated as Quartz , see formula 2.

[0072] CM Quartz =Sum(CM Quartz-1 , CM Quartz-2 ,……,CM Quartz-n ) / n (Formula 2)

[0073] (7) Repeat step (6) to calculate the feldspar particle compaction intensity factor CM Feldspar , rock debris particle compaction intensity factor CM Rock-fragment , mica particle compaction intensity factor CM Muscovite and other particle compaction intensity factors CM Other-particles The compaction intensity factor CM1 of this field is obtained by weighted average based on the percentage of different mineral particles, as shown in Formula 3:

[0074] CM1=CM Quartz ×C Quartz +CM Feldspar ×C Feldspar +CM Rock-fragment ×C Rock-fragment +CM Muscovite ×C Muscovite +

[0075] CM Other-particles ×C Other-particles (Formula 3)

[0076] (8) Change the view area and repeat steps (2) to (7) to calculate the compaction intensity factors CM2, CM3, ..., CM n It is required to calculate at least 5 fields of view and the number of particles calculated should be no less than 1000. The main purpose is to minimize the influence of rock heterogeneity on the experimental results and further improve the accuracy and representativeness. The average value of the compaction intensity factor in different fields of view is calculated as the compaction intensity factor of the sandstone sample, as shown in Formula 4:

[0077] CM=Sum(CM1,CM2,…,CM n ) / n (Formula 4)

[0078] (9) Invite well-known experts in the industry to determine the compaction intensity type of the samples. Through the analysis of a large number of samples, the relationship between the compaction intensity factor CM and the compaction intensity is established, and the critical factor value of CM corresponding to different compaction intensities is clarified.

[0079] See also Figure 4 The effect diagram of the case study is shown, in which the lithology is feldspathic sandstone. Figure 4 Figure A shows the mineral composition and distribution determined by scanning electron microscopy (SEM), with different colors representing different mineral types. Figure 4 Figure B is based on Figure 4 The planar distribution of feldspar particles separated in Figure A; Figure 4 Figure C in the figure is based on Figure 4 The plane distribution of the separated quartz grains in Figure A; Figure 4 Figure D shows a statistical histogram of the calculated compaction intensity factor between each quartz grain and the surrounding mineral particles. It can be seen that particles with a compaction intensity factor exceeding 0.1 account for over 70% of all quartz grains. The calculated compaction intensity factor for the sample is 0.18, indicating high overall compaction intensity. This evaluation result is well consistent with the physical property tests, with the sample having a porosity of 5.8% and a permeability of 0.05 mD.

[0080] The embodiment of the present invention provides a sandstone compaction evaluation method and device, which can be used for quantitative evaluation of the compaction strength of sandstone reservoirs. By defining the particle compaction strength factor, the contact edge length and particle perimeter between different minerals are quantitatively counted using image analysis. By separating different particles in the rock according to the mineral components and quantitatively calculating the content, the compaction strength factor of the entire sandstone sample is calculated after weighted averaging, and a relationship between the compaction strength factor and the compaction strength determined by expert experience is established, and the critical factor value is clarified. The present invention establishes a quantitative evaluation method for compaction for the first time and determines the critical threshold values ​​of different compaction strengths. The present invention directly changes the current situation where compaction evaluation mainly relies on expert experience and qualitative evaluation, fills the gap in the quantitative evaluation method of compaction strength, provides a basic model for reservoir effectiveness evaluation and improving oil and gas recovery research, and further promotes basic laboratory research.

[0081] The present invention also provides a device for evaluating the compaction effect of sandstone, as described in the following embodiments. Since the principle of the device to solve the problem is similar to that of the sandstone compaction effect evaluation method, the implementation of the device can refer to the implementation of the sandstone compaction effect evaluation method, and the repeated parts will not be repeated. Figure 5 The structure block diagram of a sandstone compaction evaluation device is shown, and the device includes:

[0082] An acquisition module 61 is used to acquire a target image of a sample; a data module 62 is used to determine the planar distribution characteristic data and proportion data of each mineral in the sample based on the target image; a calculation module 63 is used to calculate the compaction intensity factor of the sample based on the planar distribution characteristic data and the proportion data; and an evaluation module 64 is used to generate a sandstone compaction evaluation result using the compaction intensity factor.

[0083] See also Figure 6 Another structural block diagram of a sandstone compaction effect evaluation device is shown. In one embodiment, the device further includes an image module 65 for: acquiring a sandstone sample image; and performing binarization processing on the sandstone sample image to obtain a target image.

[0084] In one embodiment, the image type of the sandstone sample image is a rock thin section image, a scanning electron microscope image, or a CT image.

[0085] In one embodiment, the data module is specifically used to: eliminate the image information of the fillers in the target image, separate and classify the image information of the mineral particles in the target image, and obtain the plane distribution characteristic data of each mineral; determine the proportion data of each mineral based on the plane distribution characteristic data.

[0086] In one embodiment, see Figure 7 The calculation module structure block diagram shown in the figure comprises: a first calculation unit 71, used to calculate the single mineral particle strength factor according to the plane distribution characteristic data; a second calculation unit 72, used to calculate the category mineral particle strength factor according to the single mineral particle strength factor; a third calculation unit 73, used to calculate the field strength factor according to the category mineral particle strength factor and the proportion data; a fourth calculation unit 74, used to calculate the compaction intensity factor of the sample according to a plurality of the field strength factors.

[0087] In one embodiment, the first calculation unit is specifically configured to calculate the strength factor of a single mineral particle according to the plane distribution characteristic data according to the following formula: CM Quartz-1 =Sum(L Quartz-1-1 , L Quartz-1-2 ,……,L Quartz-1-n ) / G Quartz-1 Among them, CM Quartz-1 Represents the strength factor of a single mineral particle, G Quartz-1 represents the perimeter of a single mineral particle, n represents the number of particles that a single mineral particle contacts, and the length of the contact edge between a single mineral particle and each particle is L Quartz-1-1 , L Quartz-1-2 ,……,L Quartz-1-n .

[0088] In one embodiment, the second calculation unit is specifically configured to calculate the category mineral particle strength factor according to the individual mineral particle strength factor according to the following formula: CM Quartz =Sum(CM Quartz-1 , CM Quartz-2 ,……,CM Quartz-n ) / nAmong them, CM Quartz Indicates the mineral particle strength factor, CM Quartz-1 , CM Quartz-2 ,……,CM Quartz-n represents the strength factor of a single mineral particle, and n represents the number of single mineral particles.

[0089] In one embodiment, the third calculation unit is specifically configured to calculate the visual field intensity factor according to the mineral particle intensity factor and the proportion data according to the following formula: CM1 = CMQuartz ×C Quartz +CM Feldspar ×C Feldspar +CM Rock-fragment ×C Rock-fragment +CM Muscovite ×C Muscovite +CM Other-particles ×C Other-particles Among them, CM1 represents the viewing intensity factor, CM Quartz represents the quartz grain strength factor, CM Feldspar represents the feldspar grain strength factor, CM Rock-fragment represents the rock fragment strength factor, CM Muscovite represents the mica particle strength factor, CM Other-particles Indicates other particle strength factors, C Quartz 、C Feldspar 、C Rock-fragment 、C Muscovite and C Other-particles Indicates percentage data.

[0090] In one embodiment, the fourth calculation unit is specifically configured to calculate the compaction intensity factor of the sample according to the plurality of visual intensity factors according to the following formula: CM=Sum(CM1, CM2, ..., CM n ) / n, where CM represents the compaction intensity factor of the sample, and n represents the number of field intensity factors, CM1, CM2, ..., CM n Represents the viewshed intensity factor.

[0091] The embodiment of the present invention also provides a computer device, see Figure 8 The computer device shown in the figure is a schematic block diagram of the structure, which includes a memory 81, a processor 82 and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of any one of the above-mentioned sandstone compaction effect evaluation methods are implemented.

[0092] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working process of the computer device described above can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0093] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program for executing any of the above-mentioned sandstone compaction effect evaluation methods.

[0094] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0095] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0096] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0097] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0098] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. Such modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A method for evaluating sandstone compaction, characterized in that: include: Acquire a target image of the sample; Determine planar distribution characteristic data and proportion data of each mineral in the sample according to the target image; Calculating a compaction intensity factor of the sample according to the plane distribution characteristic data and the proportion data; generating a sandstone compaction evaluation result using the compaction intensity factor; Calculating the compaction intensity factor of the sample according to the plane distribution characteristic data and the proportion data includes: Calculating the strength factor of a single mineral particle based on the plane distribution characteristic data; Calculating a category mineral particle strength factor based on the individual mineral particle strength factor; Calculate the viewshed intensity factor according to the category mineral particle intensity factor and the proportion data; Calculating a compaction intensity factor of the sample based on a plurality of said view intensity factors; The strength factor of a single mineral particle is calculated based on the plane distribution characteristic data according to the following formula: CM Quartz-1 =Sum(L Quartz-1-1 ,L Quartz-1-2 ,……,L Quartz-1-n ) / G Quartz-1 Among them, CM Quartz-1 Represents the strength factor of a single mineral particle, G Quartz-1 represents the perimeter of a single mineral particle, n represents the number of particles that a single mineral particle contacts, and the length of the contact edge between a single mineral particle and each particle is L Quartz-1-1 , L Quartz-1-2 ,……,L Quartz-1-n .

2. The method according to claim 1, characterized in that Before acquiring the target image of the sample, it also includes: Acquire images of sandstone samples; The sandstone sample image is binarized to obtain a target image.

3. The method according to claim 2, characterized in that The image type of the sandstone sample image is a rock thin section image, a scanning electron microscope image or a CT image.

4. The method according to claim 1, wherein Determining planar distribution characteristic data and proportion data of each mineral in the sample according to the target image includes: Eliminating image information of interstitial objects in the target image, separating and classifying image information of mineral particles in the target image, and obtaining plane distribution characteristic data of each mineral; The proportion data of each mineral is determined based on the plane distribution characteristic data.

5. The method according to claim 1, characterized in that The method includes calculating the category mineral particle strength factor based on the individual mineral particle strength factor according to the following formula: CM Quartz =Sum(CM Quartz-1 ,CM Quartz-2 ,……,CM Quartz-n ) / n Among them, CM Quartz Indicates the mineral particle strength factor, CM Quartz-1 , CM Quartz-2 ,……,CM Quartz-n represents the strength factor of a single mineral particle, and n represents the number of single mineral particles.

6. The method according to claim 1, characterized in that The method includes calculating the viewshed intensity factor according to the mineral particle intensity factor of the category and the proportion data according to the following formula: CM1=CM Quartz ×C Quartz +CM Feldspar ×C Feldspar +CM Rock-fragment ×C Rock-fragment +CM Muscovite ×C Muscovite + CM Other-particles ×C Other-particles Among them, CM1 represents the viewing intensity factor, CM Quartz represents the quartz grain strength factor, CM Feldspar represents the feldspar grain strength factor, CM Rock-fragment represents the rock fragment strength factor, CM Muscovite represents the mica particle strength factor, CM Other-particles Indicates other particle strength factors, C Quartz 、C Feldspar 、C Rock-fragment 、C Muscovite and C Other-particles Indicates percentage data.

7. The method according to claim 1, characterized in that The method comprises calculating a compaction intensity factor of the sample based on a plurality of the visual field intensity factors according to the following formula: CM=Sum(CM1,CM2,……,CM n ) / n Where CM represents the compaction intensity factor of the sample, n represents the number of field intensity factors, CM1, CM2, ..., CM n Represents the viewshed intensity factor.

8. A sandstone compaction evaluation device, characterized in that: include: An acquisition module, used for acquiring a target image of a sample; A data module, configured to determine planar distribution characteristic data and proportion data of each mineral in the sample based on the target image; a calculation module, configured to calculate a compaction intensity factor of the sample based on the plane distribution characteristic data and the proportion data; an evaluation module for generating a sandstone compaction evaluation result using the compaction intensity factor; The calculation module includes: A first calculation unit is used to calculate the strength factor of a single mineral particle according to the plane distribution characteristic data; A second calculation unit is used to calculate the category mineral particle strength factor according to the single mineral particle strength factor; A third calculation unit is used to calculate a view intensity factor according to the category mineral particle intensity factor and the proportion data; a fourth calculation unit, configured to calculate a compaction intensity factor of the sample based on the plurality of viewing intensity factors; The first computing unit is specifically configured to: The strength factor of a single mineral particle is calculated based on the plane distribution characteristic data according to the following formula: CM Quartz-1 =Sum(L Quartz-1-1 ,L Quartz-1-2 ,……,L Quartz-1-n ) / G Quartz-1 Among them, CM Quartz-1 Represents the strength factor of a single mineral particle, G Quartz-1 represents the perimeter of a single mineral particle, n represents the number of particles that a single mineral particle contacts, and the length of the contact edge between a single mineral particle and each particle is L Quartz-1-1 , L Quartz-1-2 ,……,L Quartz-1-n .

9. The device according to claim 8, characterized in that The device further includes an image module, configured to: Acquire images of sandstone samples; The sandstone sample image is binarized to obtain a target image.

10. The device according to claim 9, characterized in that The image type of the sandstone sample image is a rock thin section image, a scanning electron microscope image or a CT image.

11. The device according to claim 8, characterized in that The data module is specifically used to: Eliminating image information of interstitial objects in the target image, separating and classifying image information of mineral particles in the target image, and obtaining plane distribution characteristic data of each mineral; The proportion data of each mineral is determined based on the plane distribution characteristic data.

12. The device according to claim 8, characterized in that The second computing unit is specifically configured to: The category mineral particle strength factor is calculated based on the individual mineral particle strength factor according to the following formula: CM Quartz =Sum(CM Quartz-1 ,CM Quartz-2 ,……,CM Quartz-n ) / n Among them, CM Quartz Indicates the mineral particle strength factor, CM Quartz-1 , CM Quartz-2 ,……,CM Quartz-n represents the strength factor of a single mineral particle, and n represents the number of single mineral particles.

13. The device according to claim 8, characterized in that The third computing unit is specifically configured to: The visual field intensity factor is calculated based on the mineral particle intensity factor of the category and the proportion data according to the following formula: CM1=CM Quartz ×C Quartz +CM Feldspar ×C Feldspar +CM Rock-fragment ×C Rock-fragment +CM Muscovite ×C Muscovite + CM Other-particles ×C Other-particles Among them, CM1 represents the viewing intensity factor, CM Quartz represents the quartz grain strength factor, CM Feldspar represents the feldspar grain strength factor, CM Rock-fragment represents the rock fragment strength factor, CM Muscovite represents the mica particle strength factor, CM Other-particles Indicates other particle strength factors, C Quartz 、C Feldspar 、C Rock-fragment 、C Muscovite and C Other-particles Indicates percentage data.

14. The device according to claim 8, characterized in that The fourth computing unit is specifically configured to: The compaction intensity factor of the sample is calculated based on the multiple viewing intensity factors according to the following formula: CM=Sum(CM1,CM2,……,CM n ) / n Where CM represents the compaction intensity factor of the sample, n represents the number of field intensity factors, CM1, CM2, ..., CM n Represents the viewshed intensity factor.

15. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the sandstone compaction effect evaluation method according to any one of claims 1 to 7 is implemented.

16. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the sandstone compaction effect evaluation method according to any one of claims 1 to 7 is implemented.

Images