A method for analyzing shale facies and texture based on gray phase
By employing grayscale phase analysis, grayscale curves, and cluster analysis techniques, the continuity and accuracy issues in identifying shale facies and structures were resolved, enabling low-cost and efficient shale oil exploration and assessment.
Patent Information
- Application Number
- CN202310112044.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2023-02-08
- Publication Date
- 2025-12-09
- Estimated Expiration
- 2043-02-08
AI Technical Summary
Existing technologies suffer from poor vertical continuity, low accuracy, and high cost in identifying shale facies and structures. In particular, the identification of shale facies and structures is discontinuous in the vertical direction, and the work of laminar flow statistics is cumbersome.
A grayscale facies analysis-based method was adopted to identify shale lithofacies and structures by analyzing absolute grayscale and relative amplitude parameters through grayscale curves. Data processing was performed using Image-J and Matlab software, and cluster analysis was conducted using SPSS software to achieve rapid and continuous lithofacies and structure identification.
It achieves high-precision and low-cost identification of shale facies and structure, and features good continuity and high economy. It can accurately determine the development of shale laminae, providing a favorable interval assessment reference for shale oil exploration.
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Figure CN116070147B_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to unconventional shale oil exploration technology field, especially to a method for analyzing shale facies and structure based on gray phase. BACKGROUND
[0002] In recent years, many scholars at home and abroad have proposed various schemes for the division of shale facies, and the overall trend is to comprehensively consider the mineral composition, color, bedding structure, organic carbon content, etc. of shale. Among them, the mineral composition of shale is the basis for dividing shale facies. Previous studies have mainly based on whole rock diffraction analysis, and divided clay shale, calcareous shale, felsic shale and mixed shale into four facies based on clay minerals, carbonate minerals and felsic mineral components. The identification of shale structure is mainly through core and thin section observation to determine whether the shale bedding structure is developed.
[0003] The above identification of shale facies and structure is carried out by interval sampling method, which does not have continuity, and the sampling interval is generally in meter scale, so there are the following problems: ① The identification of shale facies and lamina in the vertical direction is discontinuous, the shale facies and structure in the interval distance between the sampling points are not clear, and each sample needs to be observed and analyzed. ② To determine the most developed section of lamina in the vertical direction, the number of lamina needs to be counted manually, but the lamina thickness is very small, generally in micrometer scale, and the counting work is very tedious. SUMMARY
[0004] The present application aims to solve the above problems of the prior art, and provides a method for analyzing shale facies and structure based on gray phase. The present application is based on gray curve analysis, and uses two parameters of absolute gray and relative amplitude to quickly identify shale facies and structure in the vertical direction. Compared with the previous method, this method has the characteristics of good continuity, high precision and low cost, and has universal reference value.
[0005] The method for analyzing shale facies and structure based on gray phase comprises the following steps:
[0006] Step S1: Collecting cores, taking photos of the collected cores, and extracting gray values based on the core photos through Image-J software to obtain a gray curve with coordinates of core depth and gray value, calculating the difference between adjacent gray values, and defining the gray value and the difference between adjacent gray values as absolute gray and relative amplitude, respectively;
[0007] Step S2: Select a representative core photo for analysis, the photo color from light to dark, get its absolute gray scale, and determine the mineral composition of each shale corresponding to the core photo to determine its lithofacies, determine the corresponding relationship between the absolute gray scale range and the shale lithofacies, and distinguish the absolute gray scale demarcation value range of each shale lithofacies;
[0008] Step S3: Select the core photos with laminated structure and massive structure for analysis, calculate the relative amplitude of the laminated structure and the massive structure, determine the range of the relative amplitude of the laminated structure and the massive structure, and distinguish the relative amplitude demarcation value range of the laminated structure and the massive structure;
[0009] Step S4: Cluster analysis is performed on the absolute gray scale data sequence and the relative amplitude data sequence obtained in step S1, the cluster category is determined according to the category of shale lithofacies and structure, the absolute gray scale demarcation value X and the relative amplitude demarcation value Y are obtained, and it is judged whether they are between the absolute gray scale demarcation value range and the relative amplitude demarcation value range determined in steps S2 and S3, if they are, the absolute gray scale demarcation value X is used to distinguish the shale lithofacies, and the relative amplitude demarcation value Y is used to distinguish the laminated structure and the massive structure; if not, the cluster category is modified and the cluster analysis is performed again until the absolute gray scale demarcation value and the relative amplitude demarcation value are between the absolute gray scale demarcation value range and the relative amplitude demarcation value range determined in steps S2 and S3;
[0010] Step S5: The sum of absolute gray scale and relative amplitude and its characteristics is defined as gray facies, the absolute gray scale demarcation value X and the relative amplitude demarcation value Y determined in step S4 are used to divide the gray facies, and each gray facies corresponds to a specific lithofacies and structure of shale;
[0011] Step S6: By judging the absolute gray scale and the relative amplitude of each depth in the core photo in which gray facies is located, the lithofacies and structure of the rock layer at each depth can be obtained.
[0012] Step S2 and step S3 have no sequence.
[0013] Further, the number of laminated development is determined according to the relative amplitude, the larger the relative amplitude, the more the number of laminated development.
[0014] Further, in step S2, the average value of all gray scale values in a 1cm window is calculated as the absolute gray scale of the sampling point.
[0015] Further, in step S3, three different size windows are selected: 0.6cm, 1cm, 2cm, and the average value of all relative amplitudes in the window is calculated, which reduces the error caused by the data itself.
[0016] Further, SPSS software clustering analysis is adopted.
[0017] The present application is based on gray scale curve analysis, and the absolute gray scale and relative amplitude are used to identify the shale facies and structure in the longitudinal direction, compared with the previous method, the present application has the characteristics of good continuity, high precision and low cost, and has universal reference value, and the present application can identify the shale facies and structure in the longitudinal direction conveniently and economically, and provide a reference for further evaluating the favorable section of shale oil exploration and development.
[0018] In order to verify the corresponding rationality of the relative amplitude and the development of the shale lamination, the relationship between the relative amplitude of each point and the number of lamination development is quantified, and the results show that the two are generally positively correlated, indicating that the greater the relative amplitude, the more developed the lamination, and in summary, the relative amplitude can not only effectively judge the shale lamination development section, but also further compare the number of lamination development in two intervals. BRIEF DESCRIPTION OF DRAWINGS
[0019] Figure 1 The core scanning photo and the corresponding gray scale curve;
[0020] Figure 2 The facies of the sampling point of the facies photo;
[0021] Figure 3 The absolute gray scale value distribution of calcareous shale and mixed shale;
[0022] Figure 4 The relative amplitude distribution of lamination structure and massive structure;
[0023] Figure 5 The facies triangle of well Luo 69 in Zhanhua Sag;
[0024] Figure 6 The gray scale facies chart;
[0025] Figure 7 The absolute gray scale value and the relationship between the shale mineral components;
[0026] Figure 8 The relationship between the number of lamination and the relative amplitude;
[0027] Figure 9 The longitudinal distribution of the relative amplitude value and the corresponding core photo. DETAILED DESCRIPTION
[0028] The following is a specific embodiment of the present application, and the technical solutions of the present application are further described in combination with the drawings, but the present application is not limited to these embodiments.
[0029] This invention has been successfully applied to the identification of shale facies and structures in the lower sub-member of the Sha-3 formation in the Luojia area of the Zhanhua Depression in the Bohai Bay Basin. Taking Well Luo 69 as an example, detection revealed that the shale facies is mainly composed of migmatitic and calcareous facies. Figure 5 ).
[0030] First, a grayscale database was established by continuously extracting grayscale data from the entire well section. Based on the core scan images from continuous coring, the shale laminar development is mainly concentrated in the lower part of the well section. The absolute grayscale values and relative amplitude values of each sampling point were extracted from the grayscale database to establish core scan images and corresponding grayscale curves, such as... Figure 1 As shown.
[0031] Increased clay mineral content (terrigenous input) favors TOC adsorption, resulting in generally darker-colored shale. Conversely, decreased clay mineral content (terrigenous input) significantly reduces TOC adsorption, generally leading to lighter-colored shale. Therefore, shale color is correlated with the proportion of its various minerals (lithology). Furthermore, in the grayscale values extracted using Image-J software, high grayscale values correspond to lighter-colored shale areas, while low grayscale values correspond to darker-colored shale areas.
[0032] Representative core images with distinct colors were selected for analysis, ranging from light to dark. The absolute grayscale values were obtained (the average of all grayscale values within a 1cm window centered on the sampling point was used as the absolute grayscale value for that sampling point). Mineral composition analysis was performed on the shale corresponding to each core image to determine its lithofacies, clarifying the correspondence between absolute grayscale ranges and shale lithofacies, as well as the absolute grayscale boundary ranges for distinguishing different shale lithofacies. The absolute grayscale distribution ranges for calcareous and mixed lithofacies were also identified.
[0033] Figure 2 This shows the lithofacies corresponding to the sampling points in the lithofacies photographs. Light-colored areas generally represent calcareous shale, while dark-colored areas generally represent mixed shale.
[0034] Figure 3 This diagram shows the absolute grayscale value distribution of calcareous shale and mixed shale. It quantitatively characterizes the absolute grayscale values of calcareous shale and mixed shale, and clarifies the range of their absolute grayscale boundaries.
[0035] like Figure 3 As shown, the absolute gray values of the mixed rock facies are distributed in the range [40, 50], and the absolute gray values of the calcareous rock facies are distributed in the range [60, 80]. Therefore, the absolute gray value boundary range is [50, 60].
[0036] The core photos with lamellar structure and massive structure were selected for analysis. With the sampling point as the center, three different size windows were selected: 0.6 cm, 1 cm, and 2 cm. The average value of the relative amplitude in the three windows was calculated to reduce the error caused by the data itself. Figure 4 The relative amplitude distribution of lamellar structure and massive structure was calculated. The relative amplitude of lamellar structure and massive structure was calculated to determine the range of the relative amplitude of lamellar structure and massive structure. The large value of the relative amplitude corresponds to lamellar shale, and the small value of the relative amplitude corresponds to massive shale. The relative amplitude of lamellar structure and massive structure is [1, 2].
[0037] Considering the distance between the "cracks" on the core photo and the sampling point, as well as the existence of data error, the window size should not be too large or too small. Finally, the absolute gray level and relative amplitude level of the sampling point were measured by the gray level average in the window of 1 cm. This process can be quickly realized by programming in Matlab software.
[0038] The absolute gray level sequence data and the relative amplitude data sequence were analyzed by clustering analysis. Since it has been determined that the shale facies are mainly mixed facies and calcareous facies, and the lamellar structure and massive structure are also two structures, the clustering class is selected as 2. The clustering centers of the absolute gray level value data sequence are 47 and 65, and the clustering centers of the relative amplitude data sequence are 1.39 and 3.99. After reordering the two data series, the dividing values are 56 and 2.3. According to the rationality of the absolute gray level dividing value range and the relative amplitude dividing value range obtained above, the absolute gray level dividing value is within the absolute gray level dividing value range, while the relative amplitude dividing value is not within the relative amplitude dividing value range, and it is obviously too high. Therefore, the relative amplitude data series is re-clustered into three categories, with dividing values of 1.7 and 3.4. The value of 1.7 is interpreted as the dividing value of lamellar structure and massive structure.
[0039] The sum of the absolute gray scale and the relative amplitude and their characteristics is defined as a gray scale phase, and the absolute gray scale and the relative amplitude in the same gray scale phase have the same or similar characteristics. The above-mentioned determined absolute gray scale boundary value 56 and relative amplitude boundary value 1.7 divide the gray scale phase, each gray scale phase corresponds to a specific lithofacies and structure of shale, and the absolute gray scale less than 56 and the relative amplitude less than 1.7 are defined as a low value small amplitude phase, the absolute gray scale less than 56 and the relative amplitude greater than 1.7 are defined as a low value large amplitude phase, the absolute gray scale greater than 56 and the relative amplitude less than 1.7 are defined as a high value small amplitude phase, and the absolute gray scale greater than 56 and the relative amplitude greater than 1.7 are defined as a high value large amplitude phase, wherein the low value small amplitude phase corresponds to massive mixed shale, the low value large amplitude phase corresponds to laminated mixed shale, the high value small amplitude phase corresponds to massive calcareous shale, and the high value large amplitude phase corresponds to laminated calcareous shale.
[0040] Finally, a gray scale phase map is established based on the data sequence of the two parameters and the boundary values corresponding thereto, and the lithofacies and structure of shale are divided according to the distribution of the absolute gray scale value and the relative amplitude in the vertical direction Figure 6 ).
[0041] In order to verify the rationality of the corresponding relationship between the absolute gray scale value and the lithofacies of shale, the variation trend of each component of shale with the absolute gray scale value is further analyzed, and the absolute gray scale value is negatively correlated with TOC, felsic minerals and clay minerals, and positively correlated with carbonate mineral components Figure 7 ), which conforms to the general geological law.
[0042] In order to verify the rationality of the corresponding relationship between the relative amplitude and the development of shale lamina, the relationship between the relative amplitude of each point and the number of lamina development is quantified, and the results show that the two are generally positively correlated Figure 8 ), indicating that the greater the relative amplitude, the more developed the lamina. Subsequently, the corresponding results of the relative amplitude size of 315 point positions in the whole well section and the development of lamina on the core photos are counted, and it is found that there are 15 abnormal values, 300 values correspond well, and the accuracy is 95.24%, Figure 9 Some core photos near the sampling points (16) and the corresponding relative amplitudes are shown, and the development of lamina on the core photos near the sampling points and the size of the relative amplitude correspond well. This figure shows that this parameter has a good indication effect on the shale lamina development section.
[0043] Due to the existence of "scratches" in the coring process, it also causes artificial errors of "light and dark" interval distribution without lamina development. Therefore, theoretically, the accuracy will be improved accordingly as long as the surface of the core is clean. In summary: the relative amplitude can not only effectively judge the shale lamina development section, but also further compare the number of lamina development in two intervals.
[0044] The above-mentioned matters not covered are applicable to the prior art.
[0045] Although some specific embodiments of the present application have been described in detail by way of example with reference to the drawings, it is to be understood that the above examples are intended to be illustrative only and are not intended to limit the scope of the present application, which is defined in the appended claims. Various modifications and changes can be made to the described embodiments by those skilled in the art which pertain to the technical field of the application without departing from the scope of the present application, which is defined in the appended claims. It is to be understood that any modifications, equivalents or improvements made to the above embodiments in accordance with the technical spirit of the present application should be included in the scope of the present application.
Claims
1. A method of analyzing shale facies and texture based on gray-scale phase, characterized in that: Comprising the following steps: Step S1: Collecting cores, taking photos of the collected cores, and extracting gray values based on the core photos through Image-J software to obtain a gray curve with coordinates of core depth and gray value, calculating the difference between adjacent gray values, and defining the gray value and the difference between adjacent gray values as absolute gray and relative amplitude, respectively; Step S2: Selecting a core photo with representative photo color for analysis, obtaining the absolute gray from light to dark photo color, and determining the lithofacies of the shale corresponding to each core photo through mineral composition analysis, determining the corresponding relationship between the absolute gray range and the shale lithofacies, and distinguishing the absolute gray demarcation value range of each shale lithofacies; Step S3: Selecting core photos with laminated structure and massive structure for analysis, calculating the relative amplitude of the laminated structure and the massive structure, determining the relative amplitude range of the laminated structure and the massive structure, and distinguishing the relative amplitude demarcation value range of the laminated structure and the massive structure; Step S4: Performing cluster analysis on the absolute gray data sequence and the relative amplitude data sequence obtained in step S1, determining the cluster category according to the category of the shale lithofacies and structure, obtaining the absolute gray demarcation value X and the relative amplitude demarcation value Y, and determining whether they are between the absolute gray demarcation value range and the relative amplitude demarcation value range determined in steps S2 and S3, if so, using the absolute gray demarcation value X to distinguish the shale lithofacies, and using the relative amplitude demarcation value Y to distinguish the laminated structure and the massive structure; if not, modifying the cluster category and performing cluster analysis again until the absolute gray demarcation value and the relative amplitude demarcation value are between the absolute gray demarcation value range and the relative amplitude demarcation value range determined in steps S2 and S3; Step S5: Defining the sum of the absolute gray and the relative amplitude and their characteristics as a gray facies, and dividing the gray facies according to the absolute gray demarcation value X and the relative amplitude demarcation value Y determined in step S4, each gray facies corresponding to a specific lithofacies and structure of the shale; Step S6: Determining the lithofacies and structure of the rock layer at each depth by judging the absolute gray and the relative amplitude of each depth in the core photo in the gray facies divided in step S5; Steps S2 and S3 have no sequence.
2. A method of analyzing shale facies and texture based on gray-scale phase according to claim 1, characterized in that: The relative amplitude is used to determine the number of laminations, and the greater the relative amplitude, the more the number of laminations.
3. A method of analyzing shale facies and texture based on gray-scale phase according to claim 1, characterized in that: In step S2, the average of all gray values in a 1cm window is calculated as the absolute gray of the sampling point.
4. A method of analyzing shale facies and texture based on gray-scale phase according to claim 1, characterized in that: In step S3, three different size windows, 0.6cm, 1cm and 2cm, are selected as the center of the sampling point, and the average of all relative amplitudes in the window is calculated to reduce the error caused by the data itself.
5. A method of analyzing shale facies and texture based on gray-scale phase, as claimed in claim 1, characterized in that: SPSS software is used for cluster analysis.
Citation Information
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