A shale full-volume element three-terminal sweet spot evaluation method and device

Abstract

The present application relates to the technical field of shale analysis, and particularly relates to a shale full-volume element three-end-member dessert evaluation method and device. The method comprises the following steps: obtaining total porosity, particle size distribution curves at different positions and X-ray diffraction patterns of each shale sample; according to the distribution characteristics of the particle size distribution curves, candidate positions are screened and particle size score weights of the candidate positions are obtained; the actual diffraction intensity of each mineral component at each candidate position is evaluated by combining the distribution characteristics of the X-ray diffraction patterns of the single candidate position and the particle size score weights; the mass fraction of each mineral component is determined according to the actual diffraction intensity, and the mass of the single mineral component is estimated; the volume fraction of the mineral component is determined based on the mass, density and total porosity, and a dessert classification three-end-member chart is established; and the dessert type is evaluated by using the dessert classification three-end-member chart based on the volume fraction of each mineral component in the shale sample to be evaluated. The present application improves the accuracy of the shale evaluation result.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of shale analysis, and particularly relates to a shale full-volume element three-end-member sweet spot evaluation method and device. BACKGROUND

[0002] Shale oil and gas, as an important unconventional energy, has important significance for guaranteeing energy supply security and promoting energy structure diversification. In the exploration and development process of shale oil and gas, it is necessary to accurately identify engineering sweet spots and geological sweet spots to provide data basis for accurate exploration.

[0003] At present, rock facies division and identification are mainly determined by X-ray diffractometer. When X-rays irradiate shale samples, various mineral components inside produce different diffraction patterns due to different crystal structures and arrangement modes. Qualitative identification of minerals is realized based on the Bragg equation. However, in the actual process, the sample obtained by rock facies processing is a multi-phase mixture, which is easily affected by the matrix effect. The absorption coefficients of each phase in the multi-phase material are different, so that the diffraction intensity and content of each phase do not have a direct linear relationship. This nonlinear relationship is called matrix effect. At the same time, when the multi-phase mixture is ground to prepare an analysis sample, the particle size distribution difference will cause a difference in diffuse reflection, resulting in a large deviation of the actual collected diffraction intensity, affecting the accuracy of three-end-member rock facies division, and further reducing the accuracy of sweet spot identification. SUMMARY

[0004] In order to solve the problem of low accuracy of identification results in the existing method for identifying shale sweet spots, the purpose of the present application is to provide a shale full-volume element three-end-member sweet spot evaluation method and device, and the technical solution adopted is as follows:

[0005] In the first aspect, the present application provides a shale full-volume element three-end-member sweet spot evaluation method, which comprises the following steps:

[0006] Obtaining the total porosity of each shale sample, the particle size distribution curve of different positions in the shale sample, and the X-ray diffraction pattern;

[0007] According to the distribution characteristics of the particle size distribution curve of each position of each shale sample, candidate positions are screened and the particle size score weight of each candidate position of each shale sample is obtained. The actual diffraction intensity of each mineral component of each candidate position is evaluated by combining the distribution characteristics of the X-ray diffraction pattern of the single candidate position and the particle size score weight. The mass fraction of each mineral component is determined according to the actual diffraction intensity;

[0008] The mass of each mineral component is estimated by the mass fraction. The volume fraction of each mineral component in each shale sample is determined based on the mass, density and total porosity;

[0009] A sweet spot classification ternary diagram is established according to the volume fraction of each mineral component in all shale samples; and the sweet spot type of the shale sample to be evaluated is evaluated by using the sweet spot classification ternary diagram based on the volume fraction of each mineral component in the shale sample to be evaluated.

[0010] Preferably, the distribution characteristics of the particle size distribution curve of each position of each shale sample are used to screen candidate positions and obtain the particle size score weight of each candidate position of each shale sample, which includes:

[0011] For any shale sample:

[0012] The consistent deviation of the particle size of each position in the shale sample is evaluated according to the difference between the particle size corresponding to the maximum value of the particle size distribution curve of each position in the shale sample and the standard particle size, and the difference between the particle sizes corresponding to the adjacent minimum values on the left and right sides of the maximum value; the abscissa of the particle size distribution curve is the particle size, and the ordinate is the quantity proportion;

[0013] The range of the consistent deviation of the particle size of all positions in the shale sample and the ratio between the consistent deviations of the particle sizes of each position are determined as the particle size score of each position in the shale sample;

[0014] According to the particle size scores of all positions in the shale sample, the Otsu threshold method is used for classification, and the positions greater than the segmentation threshold are taken as candidate positions;

[0015] The ratio between the particle size score of each candidate position in the shale sample and the cumulative sum of the particle size scores of all candidate positions is determined as the particle size score weight of each candidate position in the shale sample.

[0016] Preferably, the actual diffraction intensity of each mineral component of each candidate position is evaluated by combining the distribution characteristics of the X-ray diffraction spectrum of a single candidate position and the particle size score weight, which includes:

[0017] For any shale sample:

[0018] For any candidate position of the any shale sample, a standard diffraction pattern of each mineral component is obtained based on a standard measurement file of each mineral component; on the standard diffraction pattern of each mineral component, a preset first number of diffraction peaks in the standard diffraction pattern are sequentially selected in descending order of peak value and recorded as candidate peaks; each candidate peak is matched with a diffraction peak in the X-ray diffraction pattern of the any candidate position to obtain a matching peak of each candidate peak; a difference between a diffraction angle of the candidate peak and a diffraction angle of the matching peak is recorded as a diffraction angle deviation; an anti-interference degree of the matching peak is evaluated according to a peak value of the matching peak, the diffraction angle deviation, and a fitting goodness when curve fitting is performed on the matching peak; the matching peak with the largest anti-interference degree is recorded as a target peak; and an integral value of the target peak is determined as an initial diffraction intensity of each mineral component at the any candidate position.

[0019] The initial diffraction intensity of each mineral component at all candidate positions in the any shale sample is weighted and summed by using a particle size score weight to obtain an actual diffraction intensity of each mineral component in the any shale sample.

[0020] Preferably, the determining of the mass fraction of each mineral component according to the actual diffraction intensity comprises:

[0021] The mass fraction of each mineral component in a single shale sample is obtained by using a K value method based on the actual diffraction intensity and a calibration diffraction intensity of each mineral component.

[0022] The obtaining of the calibration diffraction intensity comprises: averaging the integral values of the candidate peaks in the standard diffraction pattern of each mineral component to obtain the calibration diffraction intensity.

[0023] Preferably, the estimating of the mass of a single mineral component by using the mass fraction comprises:

[0024] For any one shale sample:

[0025] The mass fraction of organic matter is obtained.

[0026] A first difference value between the total mass fraction and the mass fraction of organic matter is calculated, and the total mass of the mineral is obtained by multiplying the total mass of the any one shale sample by the first difference value.

[0027] The mass of each mineral component is determined according to the total mass of the mineral and the mass fraction of each mineral component.

[0028] Preferably, the determining of the volume fraction of each mineral component in each shale sample based on the mass, the density, and the total porosity comprises:

[0029] For any one shale sample:

[0030] According to the mass and density of each mineral component, the volume of each mineral component is obtained;

[0031] A second difference value of the constant 1 and the total porosity is calculated; a volume fraction of each mineral component in each shale sample is obtained by multiplying the volume proportion of each mineral component by the second difference value; the volume proportion of each mineral component is a ratio between the volume of each mineral component and the total volume of all mineral components.

[0032] Preferably, the sweet spot classification ternary diagram is established according to the volume fraction of each mineral component in all shale samples, and includes:

[0033] According to the volume fraction of each mineral component in each shale sample, the total volume fraction of brittle minerals, the total volume fraction of plastic minerals and the combined volume fraction of organic matter and porosity in each shale sample are respectively calculated;

[0034] The total volume fraction of brittle minerals, the total volume fraction of plastic minerals and the combined volume fraction of organic matter and porosity in each shale sample are respectively mapped to three directions of a triangular coordinate system to obtain a sweet spot classification ternary diagram.

[0035] Preferably, after the sweet spot classification ternary diagram is established, the method further includes:

[0036] A brittle mineral threshold value and an organic matter porosity threshold value are obtained, and different shale samples are classified according to the brittle mineral threshold value and the organic matter porosity threshold value as classification boundaries.

[0037] Preferably, the sweet spot type of the shale sample to be evaluated is evaluated by using the sweet spot classification ternary diagram, and the method includes:

[0038] The total volume fraction of brittle minerals, the total volume fraction of plastic minerals and the combined volume fraction of organic matter and porosity in the shale sample to be evaluated are mapped to the sweet spot classification ternary diagram, and an evaluation result of the sweet spot type of the shale sample to be evaluated is obtained according to the mapping result.

[0039] In a second aspect, the present application also provides a shale full-volume element ternary sweet spot evaluation device, which is used to realize the above-mentioned method, and includes:

[0040] A data acquisition module is configured to acquire the total porosity of each shale sample, the particle size distribution curve and the X-ray diffraction spectrum of different positions in the shale sample.

[0041] The mass fraction determination module is used to screen candidate locations and obtain the grain size score weight of each candidate location in each shale sample based on the distribution characteristics of the grain size distribution curves at all locations in each shale sample; combine the distribution characteristics of the X-ray diffraction pattern of a single candidate location with the grain size score weight to evaluate the actual diffraction intensity of each mineral component at each candidate location; and determine the mass fraction of each mineral component based on the actual diffraction intensity.

[0042] The volume fraction determination module is used to estimate the mass of a single mineral component based on the mass fraction; and to determine the volume fraction of each mineral component in each shale sample based on mass, density, and total porosity.

[0043] The dessert evaluation module is used to create a dessert classification triadic diagram based on the volume fraction of each mineral component in all shale samples; based on the volume fraction of each mineral component in the shale sample to be evaluated, the dessert type of the shale sample to be evaluated is evaluated using the dessert classification triadic diagram.

[0044] The present invention has at least the following beneficial effects:

[0045] This invention first analyzes the grain size deviation at different locations in a single shale sample, screening out candidate locations with high reliability. This eliminates the interference of grain size distribution deviation on the measurement results while reducing the computational load. Furthermore, by combining the distribution characteristics of the X-ray diffraction patterns at individual candidate locations with grain size scoring weights, it achieves accurate assessment of the mass fraction of each mineral component, improving the accuracy of the mineral component mass fraction assessment results and providing accurate data support for the subsequent determination of the volume fraction of mineral components. Then, the mass fraction is used to estimate the mass of a single mineral component, and combined with density, the mass of each mineral in a single shale sample is determined. Based on the volume fraction of the constituent elements, a three-terminal graph for sweet spot classification was constructed, transforming complex data into an intuitive three-terminal graph classification model. This graph classification model can be directly used as a quantitative basis. When evaluating the sweet spot type of shale samples, the three-terminal graph for sweet spot classification can be directly used for evaluation, fundamentally overcoming the evaluation limitations caused by traditional methods. This invention can more realistically reflect the complex and heterogeneous geological nature of shale reservoirs, improve the accuracy of sweet spot identification and classification evaluation, and also improve evaluation efficiency, providing a solid data foundation for precision exploration. Attached Figure Description

[0046] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 A flowchart of a three-terminal sweet spot evaluation method for full-volume elements of shale provided in an embodiment of the present invention;

[0048] Figure 2 This is a structural block diagram of a shale full-volume element three-terminal sweet spot evaluation device provided in an embodiment of the present invention. Detailed Implementation

[0049] To further illustrate the technical means and effects adopted by the present invention to achieve the intended purpose, the following detailed description, in conjunction with the accompanying drawings and preferred embodiments, describes a method and apparatus for evaluating the three-terminal sweet spot of shale full-volume elements according to the present invention.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0051] The following description, in conjunction with the accompanying drawings, details the specific scheme of the three-terminal element sweet spot evaluation method and apparatus for shale full-volume elements provided by the present invention.

[0052] An example of a three-terminal sweet spot evaluation method for full-volume elements of shale:

[0053] This embodiment proposes a three-terminal sweet spot evaluation method for full-volume elements of shale, such as... Figure 1 As shown, the method for evaluating the three-terminal sweet spot of shale full-volume elements in this embodiment includes the following steps:

[0054] The specific scenario addressed in this embodiment is as follows: When evaluating the sweet spot of the three-terminal element of the total volume element of shale, the volume fraction of various mineral components is first determined. Based on the total volume fraction of brittle minerals, the total volume fraction of plastic minerals, and the combined volume fraction of organic matter and porosity in different shale samples, a sweet spot classification three-terminal element chart is constructed. The total volume fraction of brittle minerals, the total volume fraction of plastic minerals, and the combined volume fraction of organic matter and porosity in the shale sample to be evaluated are mapped to the sweet spot classification three-terminal element chart. The sweet spot evaluation result of the shale sample to be evaluated is obtained by comparing it with the corresponding threshold.

[0055] Step S1: Obtain the total porosity, grain size distribution curves at different locations in the shale sample, and X-ray diffraction patterns for each shale sample.

[0056] First, shale samples are collected from the oil reservoir using a sampling device. This device selects multiple representative core or cuttings samples from the target formation to ensure they accurately reflect the reservoir's mineral, organic matter, and porosity characteristics. After obtaining multiple samples, each sample undergoes pretreatment. This pretreatment includes washing to remove surface mud and contaminants, followed by low-temperature drying to protect the original organic matter and pore structure. Then, based on the specific requirements of subsequent experiments, such as rock property measurements and geochemical analysis, the samples are divided, crushed, ground, and sieved to prepare specimens that meet the standards for each analytical item. Each pretreated sample is designated as a shale sample, resulting in multiple shale samples. The number of shale samples can be set by the implementer according to specific circumstances, for example, up to 30 samples.

[0057] Taking the Q9 oil-bearing layer of the Gulong shale oil formation in the Qingshankou Formation of the Songliao Basin as an example, 30 shale core samples from different development effect units within this layer were selected, each weighing approximately 300 grams. First, the samples were ultrasonically cleaned with deionized water and alcohol to remove drilling fluid and adhering impurities. Then, they were dried in a 60℃ constant-temperature vacuum drying oven for 48 hours to ensure thorough moisture removal and avoid pyrolysis of organic matter. After drying, the samples were evenly divided into three portions, and further processed according to the sample specifications required for different experiments, such as total organic carbon content determination, whole-rock X-ray diffraction analysis, and helium porosity measurement.

[0058] For a single shale sample, in order to facilitate X-ray diffraction, the single shale sample was crushed and ground to 200 mesh. The X-ray diffraction instrument was used to sample and analyze multiple locations in the shale sample to obtain X-ray diffraction patterns at different locations.

[0059] As a specific example, for each shale sample, X-ray diffraction sampling was performed at 10 locations to obtain X-ray diffraction patterns at different locations within a single shale sample. Simultaneously, a baseline correction algorithm was used to correct the X-ray diffraction patterns, thereby reducing the impact of baseline deviation on the measurements. It should be noted that the X-ray diffraction patterns mentioned below are all corrected X-ray diffraction patterns. In practical applications, the implementer determines the number of locations selected in a single shale sample based on specific circumstances.

[0060] For a single shale sample, particle size was collected at each location using a particle size analyzer. The distribution characteristics of particle size were statistically analyzed to obtain the proportion of each particle size at a single location. Curve fitting was then performed on the proportion of all particle sizes at a single location to obtain a particle size distribution curve. The x-axis of the particle size distribution curve represents particle size, and the y-axis represents the proportion of different particle sizes. It should be noted that multiple particles exist at each location in a single shale sample; therefore, each location has a corresponding particle size distribution curve. Curve fitting is a prior art technique and will not be elaborated upon further here.

[0061] For samples from the same batch (with the same label), the total porosity of blocky samples was measured using a helium porosimeter. In this embodiment, 30 samples from the Q9 oil layer of the Gulong Shale were used. For samples with a single label, one sample was selected, and the total porosity of each sample was determined using an AP-121-003-1 helium porosimeter according to GB / T34533-2017 standard. Another sample was pulverized and ground to 200 mesh and thoroughly mixed to ensure the representativeness and consistency of subsequent tests. An appropriate amount of powder sample was selected, and the total organic carbon content was determined using a carbon-sulfur analyzer. In this embodiment, for 30 samples from the Q9 oil layer of the Gulong Shale, samples with the same label were pulverized and ground to 200 mesh, and half of them were selected. The total organic carbon content (TOC) was determined using a CS-230 carbon-sulfur analyzer according to GB / T 19145-2022 standard.

[0062] Step S2: Based on the distribution characteristics of the grain size distribution curves at all locations of each shale sample, candidate locations are screened and the grain size score weight of each candidate location in each shale sample is obtained; combining the distribution characteristics of the X-ray diffraction pattern of a single candidate location and the grain size score weight, the actual diffraction intensity of each mineral component at each candidate location is evaluated; the mass fraction of each mineral component is determined based on the actual diffraction intensity.

[0063] To achieve three-end-member sweet spot analysis of the entire volume of shale, it is necessary to determine the mass fraction of various mineral components in the shale sample. Considering that the diffraction intensity of each component may be affected by matrix effects and differences in grain size distribution during the determination process, the diffraction peak intensity and the corresponding component content do not directly show a linear relationship in practice. Therefore, it is necessary to determine and correct the mass fraction of various mineral components based on the distribution characteristics of the measured X-ray diffraction pattern. This includes the following three sub-steps:

[0064] The first sub-step involves screening candidate locations and determining the grain size score weight for each candidate location based on the distribution characteristics of the grain size distribution curves at all locations in each shale sample.

[0065] Grinding processes resulted in shale samples with a particle size distribution generally around 200 mesh (75 μm). However, due to sieve leakage, the final shale sample may exhibit uneven particle size distribution. Larger particle sizes lead to greater interparticle spacing, resulting in fewer crystal planes participating in Bragg diffraction under the same irradiation conditions. This results in poorer peak superposition and thus lower diffraction intensity with larger particle sizes. Conversely, smaller particle sizes result in more crystals participating in diffraction within the same diffraction range, leading to higher diffraction intensity. Furthermore, smaller particle sizes reduce diffuse reflection, making the diffraction intensity more distinct from the background. Therefore, particle size distribution variation is a key factor affecting quantitative analysis using X-ray diffraction.

[0066] This embodiment utilizes the K-value method to achieve quantitative analysis of various mineral components in shale samples. This requires maintaining consistent particle size in the standard materials used for measurement. The analysis of particle size interference is based on particle size distribution curves at multiple locations within the shale sample. During the standard material measurement process, a particle size of 200 mesh (75 μm) is strictly required. For a single location's particle size distribution curve, a smaller deviation from the required peak size indicates greater consistency between the particle size distribution at that location and the standard material measurement. Furthermore, for a single location's particle size distribution curve, if most of the particle size is concentrated at the peak, it indicates a high degree of concentration in the particle size distribution curve, resulting in a smaller particle size difference between the peak bases.

[0067] The following example uses a shale sample for illustration. Other shale samples can be processed using the method provided in this embodiment.

[0068] Specifically, for any given shale sample:

[0069] The particle size consistency deviation at each location in the shale sample is evaluated based on the difference between the particle size corresponding to the maximum value of the particle size distribution curve at each location and the standard particle size, as well as the difference between the particle size corresponding to the minimum value closest to the maximum value on both sides of it (horizontal distance).

[0070] Grain size consistency deviation is used to measure the deviation of the grain size distribution at various locations in a shale sample from the standard requirements. It is mainly measured from two aspects: first, center deviation, which is the difference between the grain size corresponding to the peak value and the standard grain size, assessing the deviation of most grain sizes at the scanned location from the standard grain size; and second, concentration, which is the range of grain size deviation at the troughs, used to assess the concentration of most grain sizes at the scanned location. As a specific example, the absolute difference between the grain size corresponding to the maximum value of the grain size distribution curve at each location in the shale sample and the standard grain size is recorded as the first difference at each location, where the standard grain size is 75 μm. The absolute difference between the grain sizes corresponding to the two nearest minimum values ​​on either side of the maximum value of the grain size distribution curve at each location is recorded as the second difference at each location. It should be noted that the distance here is the horizontal distance, i.e., the difference in grain size. The second difference represents the difference in grain size between the left and right sides of the maximum value and the trough adjacent to the maximum value. The more concentrated the grain size distribution, the smaller the second difference. Specifically, if there are not minima on both sides of the maximum value, the point on the grain size distribution curve that is furthest horizontally from the maximum value on the side without a minima is considered the minimum point for the above calculation. For each location in the shale sample, the sum of the first and second differences at that location is taken as the grain size consistency deviation at that location. The smaller the difference between the grain size corresponding to the maximum value and the standard grain size, and the smaller the grain size difference between the left and right sides of the maximum value and the adjacent trough, the smaller the grain size difference of the particles at the corresponding location, i.e., the smaller the grain size consistency deviation. The less the location is affected by grain size, and the more accurate the measured diffraction intensity value will be.

[0071] The above steps yielded the grain size consistency deviation at various locations within a single shale sample. This deviation reflects the interference of grain size on the diffraction intensity at a single location. Next, a grain size score is calculated based on the consistency deviation; locations with less interference will have higher corresponding grain size scores. Specifically, the range of grain size consistency deviations at all locations within the shale sample is obtained, and the ratio of this range to the grain size consistency deviation at each location is used as the grain size score for that location. During the grain size scoring process, the range of grain size consistency deviations at all locations within the shale sample measures the distribution interference of grain size at each location. The greater the distribution difference, the larger the corresponding range value. Therefore, locations whose grain size is closer to the standard will have a higher corresponding grain size score. The purpose of grain size scoring is to convert the dimensional quantity of grain size consistency deviation into a dimensionless quantity, while simultaneously achieving a positive correlation with the consistency of the deviation; that is, locations whose grain size distribution is closer to the standard requirements will have higher grain size scores.

[0072] The above method can be used to obtain the grain size score at each location in the shale sample. It should be noted that the location mentioned here is the location detected in step S1.

[0073] As one implementation method, locations with high grain size scores are first selected from all positions for subsequent analysis, based on the grain size score. This means eliminating interfering locations with low grain size scores. Specifically, the elimination process involves classifying locations based on the grain size scores of all positions in the shale sample using the Otsu thresholding method. Locations with scores greater than the segmentation threshold are designated as candidate locations, which are then included in the subsequent determination of the mass fraction of each mineral component in the shale. Other locations are treated as interference points and eliminated, i.e., not analyzed. This reduces the interference of grain size distribution differences on diffraction intensity measurements and improves the accuracy of subsequent measurement results. Obtaining the segmentation threshold using the Otsu thresholding method is an existing technique and will not be elaborated upon further here.

[0074] Calculate the sum of the grain size scores of all candidate locations in the shale sample. The ratio between the grain size score of each candidate location in the shale sample and the sum is determined as the grain size score weight of each candidate location in the shale sample. That is, the sum of the grain size score weights of all candidate locations in the shale sample is 1.

[0075] The second sub-step involves combining the distribution characteristics of the X-ray diffraction patterns at individual candidate locations with the grain size scoring weights to evaluate the actual diffraction intensity of each mineral component at each candidate location.

[0076] For a single candidate location, X-ray diffraction patterns are obtained for each mineral component according to the standard determination document for that single mineral component; the standard determination document can be SY / T 5163-2018. In this embodiment, the shale sample contains quartz, potassium feldspar, plagioclase, calcite, dolomite, and clay minerals, totaling six mineral components.

[0077] When using X-ray diffraction to obtain diffraction patterns for quantitative analysis of mineral components, diffraction peaks are often selected, and their peak areas are used to represent diffraction intensity. However, for a single mineral component, there are often multiple diffraction peaks. Traditional methods often calculate diffraction intensity based only on the highest diffraction peak or the top three diffraction peaks for the current component. However, due to matrix effects, the diffraction intensity of the diffraction peaks does not directly show a linear relationship with the component content. This leads to significant deviations in the results of quantitative analysis using traditional methods, requiring further analysis.

[0078] For any shale sample:

[0079] For any candidate location in the shale sample, a standard diffraction pattern of each mineral component is obtained based on the standard determination document of each mineral component. On the standard diffraction pattern of each mineral component, a preset first number of diffraction peaks are selected in descending order of peak value in the standard diffraction pattern and recorded as candidate peaks. The preset first number is 1 to 5. In this embodiment, the preset first number is 3. In specific applications, the implementer can set it according to the specific situation.

[0080] Each candidate peak is matched with the diffraction peaks in the X-ray diffraction pattern at that candidate location to obtain a matching peak. As a specific example, the matching peak can be obtained as follows: The diffraction peak corresponding to the diffraction angle of the candidate peak in the X-ray diffraction pattern at that candidate location is obtained, and that diffraction peak is taken as the matching peak. For example, if the diffraction angle of a candidate peak is 26.6°, then the diffraction peak corresponding to 26.6° in the X-ray diffraction pattern at that candidate location is taken as the matching peak.

[0081] The absolute difference between the diffraction angle of a candidate peak and its matching peak is denoted as the diffraction angle deviation. The anti-interference level of each matching peak is evaluated based on its peak value, the diffraction angle deviation corresponding to each matching peak, and the goodness of fit when curve fitting. Specifically, for each matching peak, the product of the normalized peak value, the normalized diffraction angle deviation corresponding to each matching peak, and the normalized goodness of fit when curve fitting is used as the anti-interference level of that diffraction peak. The normalization of peak value, diffraction angle deviation, and goodness of fit is performed using the minimum-maximum normalization method to eliminate the influence of dimensions. The minimum-maximum normalization method is existing technology and will not be elaborated further here.

[0082] The degree of anti-interference is mainly determined by mapping the diffraction angles of diffraction peaks in the standard diffraction pattern to the sampled X-ray diffraction pattern. Higher peak values ​​in the X-ray diffraction pattern at each sampled location indicate stronger anti-interference capability. Furthermore, the closer adjacent diffraction peaks are, the more easily they are interfered with by adjacent peaks from a diffraction angle perspective. The matching peak with the highest anti-interference capability is designated as the target peak. The integral value of the target peak is determined as the initial diffraction intensity of each mineral component at that candidate location. In this embodiment, the integral value is the integral value on the horizontal axis.

[0083] The initial diffraction intensities of each mineral component at all candidate locations in the shale sample are weighted and summed using the grain size score weights of each candidate location. This weighted sum is taken as the actual diffraction intensity of each mineral component in the shale sample. Taking quartz from a single shale sample as an example, assuming there are two candidate locations with grain size score weights of 0.2 and 0.8 respectively, the initial diffraction intensity of quartz at the first candidate location is 4.5, and the initial diffraction intensity of quartz at the second candidate location is 6.8. A linear weighting is then performed to obtain the actual diffraction intensity of quartz in the shale sample. .

[0084] The calibrated diffraction intensity is obtained by averaging the integral values ​​of candidate peaks in the standard diffraction patterns of each mineral component.

[0085] The third sub-step involves determining the mass fraction of each mineral component based on the actual diffraction intensity.

[0086] The actual and calibrated diffraction intensities of each mineral component are substituted into the K-value method to obtain the mass fraction of each mineral component in a single shale sample, thereby achieving the determination of the mass fraction of each mineral component in each shale sample. The K-value method is an existing algorithm and will not be elaborated further here.

[0087] Through the above steps, the mass fraction of each mineral component at each location in a single shale sample was determined, and the accurate mass percentage of the mineral components was obtained through analysis and correction.

[0088] Step S3: Estimate the mass of a single mineral component using the mass fraction; determine the volume fraction of each mineral component in each shale sample based on mass, density, and total porosity.

[0089] After determining the mass fraction of each mineral component, in order to perform sweet spot analysis on the three end-members, it is also necessary to test the total porosity and total organic carbon content of the shale sample.

[0090] The three-phase parameters in the shale sample were determined through steps S1 and S2. All measurement results are recorded in Table 1, providing a data basis for subsequent volume calculation and sweet spot evaluation.

[0091] Table 1. Measurement results of whole-rock minerals, total organic carbon content, and total porosity of shale samples.

[0092]

[0093] Traditional shale sweet spot analysis relies on the mass fractions of clay minerals, quartz + feldspar, and carbonate minerals in shale samples, analyzing the sweet spot distribution from actual core and cuttings samples. However, this method fails to comprehensively reflect the complex lithology and fluid characteristics of shale reservoirs, limiting the accuracy of sweet spot identification. Furthermore, traditional three-end-member analysis depends on mass fractions or pore types, but the volumetric composition of shale is fundamental to understanding its physical properties, mechanical behavior, and hydrocarbon occurrence. Ignoring volumetric analysis leads to insufficient understanding of the essential characteristics of shale, consequently affecting the ability to deeply understand and predict the formation, accumulation, and production mechanisms of shale hydrocarbons.

[0094] To address the issues of limited evaluation dimensions and lack of volumetric analysis perspective in traditional three-dimensional dessert analysis, this embodiment combines the measured mineral content fractions, total organic carbon content, and total porosity. Based on the density parameters of various minerals and organic matter, the volume is calculated through density conversion, and then the volume fraction is obtained based on the total volume. This provides a standardized data foundation for subsequent three-dimensional charts.

[0095] It should be noted that, since the mass fraction of each mineral is determined by X-ray diffraction (XRD) in a more detailed manner, potassium feldspar and plagioclase are combined into feldspar for calculation, and calcite and dolomite are combined into carbonate rock minerals for calculation. In addition, the densities of various minerals and organic matter are obtained through prior measurement or reference.

[0096] In step S1, the minerals are pretreated to remove surface mud and contaminants. The pretreated shale sample is then mainly composed of two types of minerals (including various minerals) and organic matter. Therefore, the total mineral content can be obtained based on the mass fraction of organic matter and the mass of the shale sample.

[0097] The following explanation will use a shale sample as an example.

[0098] Specifically, for any given shale sample:

[0099] The difference between the total mass fraction and the organic matter mass fraction is recorded as the first difference. Since the shale sample contains only minerals and organic matter after pretreatment, the total mass fraction is 1, which is the difference between the constant 1 and the organic matter mass fraction. Then, the total mass of the minerals is obtained by multiplying the total mass of the shale sample by the first difference, which can be expressed as follows: ,in, Indicates the total mass of the minerals. This indicates the total mass of the shale sample. This indicates the mass fraction of organic matter.

[0100] Furthermore, based on the total mass of the minerals and the mass fraction of each mineral component, the mass of each mineral component is determined. The specific calculation formula can be expressed as follows:

[0101]

[0102] in, This represents the mass of the i-th mineral component. Indicates the total mass of the minerals. This represents the mass fraction of the i-th mineral component. This indicates the number of different types of mineral components.

[0103] The shale samples contain a variety of mineral components, such as quartz, feldspar, carbonate rocks, and clay. This is used to reflect the mass percentage of the i-th mineral component, aiming to eliminate the possibility that the measured mass percentage of each mineral component might not be equal due to minor deviations during the measurement process. Therefore, the mass of each mineral component is obtained by multiplying the total mass of the minerals by the mass percentage of each mineral component. The mass of each mineral component in each shale sample is recorded in a table, as shown in Table 2:

[0104] Table 2. Mass of organic matter and mineral components in shale samples (unit: g)

[0105]

[0106] After obtaining the mass of each mineral component through the above steps, the volume of each mineral component is calculated using the density formula. The specific calculation formula is as follows: In the formula, This represents the volume of the i-th mineral component. This represents the mass of the i-th mineral component in the shale sample. This represents the density of the i-th mineral component. The density of each mineral component can be obtained from reference materials; for example, the densities of quartz, potassium feldspar, plagioclase, calcite, dolomite, clay minerals, and organic matter are 2.65 g / cm³, respectively. 3 2.55g / cm 3 2.7g / cm 3 2.7g / cm 3 3.0g / cm 3 2.75g / cm 3 1.35g / cm 3 The density formula above is an existing formula and will not be elaborated further here. The volume of each mineral component in each shale sample is recorded in a table, as shown in Table 3:

[0107] Table 3. Volume of organic matter and mineral components in shale samples (unit: cm³)3 )

[0108]

[0109] A single shale sample contains minerals and organic matter. Due to the small pore size and relatively light mass of gas, these can be ignored. However, the volume composition of shale consists of mineral volume, organic matter volume, and pores. Therefore, the total volume fraction of minerals and organic matter and the total porosity of the whole are equal to 1.

[0110] The difference obtained by subtracting the total porosity from the constant 1 is denoted as the second difference. The volume fraction of each mineral component in the shale sample is obtained by multiplying the second difference by the volume percentage of each mineral component. The volume percentage of each mineral component is the ratio between the volume of each mineral component and the total volume of all mineral components. The volume fraction of each mineral component can be expressed as:

[0111]

[0112] in, This represents the volume fraction of the i-th mineral component. Indicates total porosity. Indicates the number of mineral components. This represents the volume of the i-th mineral component.

[0113] When calculating the volume fraction of various mineral components, the difference between 1 and the total porosity is multiplied by the volume percentage of the individual mineral component to obtain the volume fraction of each mineral component. The volume fraction of each mineral component in each shale sample is recorded in a table, as shown in Table 4:

[0114] Table 4. Volume fractions of total porosity, organic matter, and mineral components in shale samples (unit: %)

[0115]

[0116] Step S4: Establish a dessert classification triad based on the volume fraction of each mineral component in all shale samples; evaluate the dessert type of the shale sample to be evaluated based on the volume fraction of each mineral component in the shale sample to be evaluated using the dessert classification triad.

[0117] After determining the volume fraction of each mineral component in all shale samples, a sweet spot classification triadic chart was constructed based on the obtained volume fractions.

[0118] Before constructing the three-dimensional graph for classifying shale minerals, the total volume fraction of brittle minerals, the total volume fraction of plastic minerals, and the combined volume fraction of organic matter and porosity in each shale sample were calculated based on the volume fraction of each mineral component in each sample. Brittle minerals include quartz, feldspar, and carbonate rocks, while plastic minerals are clay. Specifically, the total volume fraction of brittle minerals in each shale sample was calculated by summing the volume fractions of quartz, feldspar, and carbonate rocks, the total volume fraction of clay in each shale sample was calculated by summing the volume fraction of plastic minerals in each shale sample, and the combined volume fraction of organic matter and total porosity in each shale sample was calculated by summing the volume fraction of organic matter and total porosity in each shale sample.

[0119] The total volume fraction of brittle minerals, the total volume fraction of plastic minerals, and the combined volume fraction of organic matter and porosity in each shale sample were mapped to the three directions of a triangular coordinate system, thus mapping all shale samples and obtaining a sweet spot classification three-terminal graph.

[0120] The distribution patterns of different shale samples can be clearly observed in the three-dimensional chart of the dessert classification, which intuitively presents the distribution characteristics of shale in terms of brittleness, plasticity and reservoir performance, providing a visualization tool for classification and evaluation.

[0121] Based on the known distribution boundaries of sample clusters with excellent development results, the thresholds for brittle minerals and organic matter porosity were determined. Using these thresholds as classification boundaries, different shale samples were categorized into the following four types:

[0122] The first category is a region with high porosity, high brittleness, and low plasticity. For this category, the combined volume fraction of organic matter and porosity of shale samples is greater than the organic matter porosity threshold, and the total volume fraction of brittle minerals is greater than the brittle mineral threshold. Shale samples of this category have strong hydrocarbon generation and oil storage capacity, as well as good compressibility, making them a double sweet spot for shale oil geology and engineering.

[0123] The second category is the high-porosity, low-brittleness, and low-plasticity region. For this category, the combined volume fraction of organic matter and porosity of shale samples is greater than the organic matter porosity threshold, and the total volume fraction of brittle minerals is less than or equal to the brittle mineral threshold. This type of shale sample has strong hydrocarbon generation and oil storage capacity, but poor compressibility, and is a sweet spot for shale oil geology.

[0124] The third category is the low-porosity, high-brittleness, and low-plasticity region. For this category, the combined volume fraction of organic matter and porosity of shale samples is less than or equal to the organic matter porosity threshold, and the total volume fraction of brittle minerals is greater than the brittle mineral threshold. Shale samples of this category have poor hydrocarbon generation and oil storage capacity, but good compressibility, making them a sweet spot for shale oil engineering.

[0125] The fourth category is the low-porosity, low-brittleness, and high-plasticity region. For this category, the combined volume fraction of organic matter and porosity of shale samples is less than or equal to the organic matter porosity threshold, and the total volume fraction of brittle minerals is less than or equal to the brittle mineral threshold. Shale samples of this category have weak hydrocarbon generation, oil storage capacity, and compressibility, and are non-sweet spots for shale oil.

[0126] When evaluating the sweet spot type of the shale sample to be evaluated, the method provided in this embodiment is used to calculate the total volume fraction of brittle minerals, the total volume fraction of plastic minerals, and the combined volume fraction of organic matter and porosity in the shale sample to be evaluated. The total volume fraction of brittle minerals, the total volume fraction of plastic minerals, and the combined volume fraction of organic matter and porosity in the shale sample to be evaluated are mapped onto the sweet spot classification three-terminal graph. The evaluation result of the sweet spot type of the shale sample to be evaluated is obtained based on the mapping result.

[0127] Taking the three-end-member sweet spot analysis of the Gulong Shale of the Qingshankou Formation in the Songliao Basin as an example, the analysis was conducted on shale sample No. 20. Based on the determination of whole-rock minerals, total porosity, and TOC values, the corresponding three-end-member values ​​were obtained through the above method. The total volume fraction of brittle minerals was 0.51, the total volume fraction of plastic minerals was 0.358, and the combined volume fraction of organic matter and porosity was 0.132. According to the current geological background and known production data of the region, the threshold for brittle minerals was 0.50, and the threshold for organic matter porosity was 0.15. According to the classification, this sample falls into the third category, and is therefore evaluated as an engineering sweet spot. This indicates that the lithology is brittle and easy to be fractured, but the organic matter content and porosity are low, and the oil storage capacity is relatively insufficient.

[0128] Thus, the method provided in this embodiment has been used to evaluate the dessert type of shale.

[0129] This embodiment first analyzes the grain size deviation at different locations in a single shale sample, screening out candidate locations with high reliability. This eliminates the interference of grain size distribution deviation on the measurement results while reducing the computational load. Furthermore, by combining the distribution characteristics of the X-ray diffraction patterns at individual candidate locations and the grain size scoring weight, the mass fraction of each mineral component is accurately assessed, improving the accuracy of the mineral component mass fraction assessment results and providing accurate data support for the subsequent determination of the volume fraction of mineral components. Then, the mass fraction is used to estimate the mass of a single mineral component, and combined with density, the volume fraction of each mineral component in a single shale sample is determined. Based on the obtained volume fractions, a three-terminal graph for sweet spot classification was constructed, transforming complex data into an intuitive three-terminal graph classification model. This graph classification model can be directly used as a quantitative basis. When evaluating the sweet spot type of shale samples to be evaluated, the three-terminal graph for sweet spot classification can be directly used for evaluation, fundamentally overcoming the evaluation limitations caused by the reliance on single or few parameters in traditional methods. The evaluation method provided in this embodiment can more realistically reflect the complex and heterogeneous geological nature of shale reservoirs, improve the accuracy of sweet spot identification and classification evaluation, and also improve evaluation efficiency, providing a solid data foundation for precise exploration.

[0130] An embodiment of a three-terminal sweet spot evaluation device for full-volume elements of shale:

[0131] See Figure 2 The diagram illustrates a structural block diagram of a three-terminal sweet spot evaluation device for full volume elements of shale provided by an embodiment of the present invention. The device may include a data acquisition module, a mass fraction determination module, a volume fraction determination module, and a sweet spot evaluation module.

[0132] The data acquisition module is used to acquire the total porosity of each shale sample, the grain size distribution curves at different locations in the shale sample, and the X-ray diffraction pattern.

[0133] The mass fraction determination module is used to screen candidate locations and obtain the grain size score weight of each candidate location in each shale sample based on the distribution characteristics of the grain size distribution curves at all locations in each shale sample; combine the distribution characteristics of the X-ray diffraction pattern of a single candidate location with the grain size score weight to evaluate the actual diffraction intensity of each mineral component at each candidate location; and determine the mass fraction of each mineral component based on the actual diffraction intensity.

[0134] The volume fraction determination module is used to estimate the mass of a single mineral component based on the mass fraction; and to determine the volume fraction of each mineral component in each shale sample based on mass, density, and total porosity.

[0135] The dessert evaluation module is used to create a dessert classification triadic diagram based on the volume fraction of each mineral component in all shale samples; based on the volume fraction of each mineral component in the shale sample to be evaluated, the dessert type of the shale sample to be evaluated is evaluated using the dessert classification triadic diagram.

[0136] It should be understood that Figure 2 The structural block diagram and modules of the shale full-volume element three-terminal sweet spot evaluation device shown can be implemented in various ways. For example, in some embodiments, the device and its modules can be implemented by hardware, software, or a combination of software and hardware. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by a suitable instruction execution device, such as a microprocessor or dedicated hardware. Those skilled in the art will understand that the above-described methods and devices 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 this specification can be implemented not only by hardware circuits 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., but also by software executed by various types of processors, or by a combination of the above-described hardware circuits and software (e.g., firmware).

[0137] For more details about the above modules, please refer to other parts of this manual; they will not be repeated here.

[0138] The provided device is used to execute the corresponding method provided above. Therefore, the beneficial effects it can achieve can be referred to the beneficial effects of the corresponding method provided above, and will not be repeated here.

[0139] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for evaluating the sweet spot of three end-member elements of shale full-volume elements, characterized in that, The method includes the following steps: Obtain the total porosity, grain size distribution curves at different locations in the shale sample, and X-ray diffraction patterns for each shale sample; Based on the distribution characteristics of the grain size distribution curves at all locations of each shale sample, candidate locations are screened and the grain size score weight of each candidate location in each shale sample is obtained; combining the distribution characteristics of the X-ray diffraction pattern of a single candidate location and the grain size score weight, the actual diffraction intensity of each mineral component at each candidate location is evaluated; and the mass fraction of each mineral component is determined based on the actual diffraction intensity. The mass of a single mineral component is estimated using the mass fraction; the volume fraction of each mineral component in each shale sample is determined based on mass, density, and total porosity. A sweet spot classification triadic chart was established based on the volume fraction of each mineral component in all shale samples. Based on the volume fraction of each mineral component in the shale sample to be evaluated, the sweet spot type of the shale sample to be evaluated was evaluated using the sweet spot classification triadic chart. The process of screening candidate locations and obtaining the grain size score weights for each candidate location in each shale sample includes: For any shale sample: The particle size consistency deviation at each location in any shale sample is evaluated based on the difference between the particle size corresponding to the maximum value of the particle size distribution curve at each location and the standard particle size, as well as the difference between the particle sizes corresponding to the minimum values ​​adjacent to the maximum value on both sides. The horizontal axis of the particle size distribution curve represents the particle size, and the vertical axis represents the proportion of the number of particles. The ratio between the range of the uniformity deviation of grain size at all locations in any shale sample and the uniformity deviation of grain size at each location is determined as the grain size score at each location in any shale sample. Based on the grain size scores of all locations in any shale sample, the Otsu threshold method is used for classification, and locations with a size greater than the segmentation threshold are selected as candidate locations. The ratio between the grain size score of each candidate location in any shale sample and the sum of the grain size scores of all candidate locations is determined as the grain size score weight of each candidate location in any shale sample. The evaluation of the actual diffraction intensity of each mineral component at each candidate location includes: For any shale sample: For any candidate location of any shale sample, standard diffraction patterns of each mineral component are obtained based on standard measurement documents for each mineral component. On the standard diffraction patterns of each mineral component, a predetermined number of diffraction peaks are selected sequentially in descending order of peak value and recorded as candidate peaks. Each candidate peak is matched with the diffraction peaks in the X-ray diffraction pattern of any candidate location to obtain a matching peak for each candidate peak. The difference between the diffraction angle of a candidate peak and the diffraction angle of its matching peak is recorded as the diffraction angle deviation. The anti-interference degree of the matching peak is evaluated based on the peak value of the matching peak, the diffraction angle deviation, and the goodness of fit when curve fitting the matching peak. The matching peak with the highest anti-interference degree is recorded as the target peak. The integral value of the target peak is determined as the initial diffraction intensity of each mineral component at any candidate location. The initial diffraction intensities of each mineral component at all candidate locations in any shale sample are weighted and summed using the grain size scoring weights to obtain the actual diffraction intensity of each mineral component in any shale sample.

2. The method for evaluating the three-terminal sweet spot of shale full-volume elements according to claim 1, characterized in that, The determination of the mass fraction of each mineral component based on the actual diffraction intensity includes: Based on the actual and calibrated diffraction intensities of each mineral component, the mass fraction of each mineral component in a single shale sample is obtained using the K-value method. The determination of the diffraction intensity is obtained by averaging the integral values ​​of candidate peaks in the standard diffraction patterns of each mineral component to obtain the determination of the diffraction intensity.

3. The method for evaluating the three-terminal sweet spot of shale full-volume elements according to claim 1, characterized in that, The estimation of the mass of a single mineral component using the mass fraction includes: For any given shale sample: Obtain the mass fraction of organic matter; Calculate the first difference between the total mass fraction and the organic matter mass fraction, and obtain the total mass of the minerals by multiplying the total mass of any shale sample by the first difference. The mass of each mineral component is determined based on the total mass of the minerals and the mass fraction of each mineral component.

4. The method for evaluating the three-terminal sweet spot of shale full-volume elements according to claim 1, characterized in that, The determination of the volume fraction of each mineral component in each shale sample based on mass, density, and total porosity includes: For any given shale sample: The volume of each mineral component is obtained based on its mass and density. Calculate the second difference between constant 1 and total porosity; multiply the second difference by the volume fraction of each mineral component to obtain the volume fraction of each mineral component in each shale sample; the volume fraction of each mineral component is the ratio between the volume of each mineral component and the total volume of all mineral components.

5. The method for evaluating the three-terminal sweet spot of shale full-volume elements according to claim 1, characterized in that, The method for establishing a sweet spot classification triadic plate based on the volume fraction of each mineral component in all shale samples includes: Based on the volume fraction of each mineral component in each shale sample, calculate the total volume fraction of brittle minerals, the total volume fraction of plastic minerals, and the combined volume fraction of organic matter and porosity in each shale sample. The total volume fraction of brittle minerals, the total volume fraction of plastic minerals, and the combined volume fraction of organic matter and porosity in each shale sample were plotted on three directions of a triangular coordinate system to obtain a three-terminal graph for sweet spot classification.

6. The method for evaluating the three-terminal sweet spot of shale full-volume elements according to claim 1, characterized in that, After establishing the three-dimensional meta-graphics for dessert classification, it also includes: The threshold values ​​for brittle minerals and organic matter porosity were obtained, and different shale samples were classified using these threshold values ​​as classification boundaries.

7. The method for evaluating the three-terminal sweet spot of shale full-volume elements according to claim 6, characterized in that, The evaluation of the dessert type of the shale sample to be evaluated using the dessert classification three-terminal graph includes: The total volume fraction of brittle minerals, the total volume fraction of plastic minerals, and the combined volume fraction of organic matter and porosity in the shale sample to be evaluated are mapped onto a dessert classification three-terminal graph. The evaluation result of the dessert type of the shale sample to be evaluated is obtained based on the mapping result.

8. A shale full-volume element three-terminal sweet spot evaluation device, the device being used to implement the method of claim 1, the device comprising: The data acquisition module is used to acquire the total porosity of each shale sample, the grain size distribution curves at different locations in the shale sample, and the X-ray diffraction pattern. The mass score determination module is used to screen candidate locations and obtain the grain size score weight of each candidate location in each shale sample based on the distribution characteristics of the grain size distribution curves at all locations in each shale sample. By combining the distribution characteristics of the X-ray diffraction patterns of individual candidate locations with the grain size scoring weight, the actual diffraction intensity of each mineral component at each candidate location is evaluated. The mass fraction of each mineral component is determined based on the actual diffraction intensity. The volume fraction determination module is used to estimate the mass of a single mineral component based on the mass fraction; and to determine the volume fraction of each mineral component in each shale sample based on mass, density, and total porosity. The dessert evaluation module is used to create a dessert classification triadic chart based on the volume fraction of each mineral component in all shale samples. Based on the volume fraction of each mineral component in the shale sample to be evaluated, the dessert type of the shale sample to be evaluated is evaluated using a dessert classification three-terminal graph.

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