A method for calculating total organic carbon content (TOC) of a shale reservoir

Abstract

The application discloses a shale reservoir total organic carbon content TOC calculation method, and relates to the technical field of oil and gas exploration. Including: obtaining a gray scale imaging graph of the shale reservoir, and deriving a depth index column; drawing a TOC index curve through the gray scale imaging graph and the depth index column I_TOC ; resampling the TOC index curve I_TOC according to the precision of measured TOC data to obtain imaging TOC data; drawing a scatter diagram by taking the imaging TOC data as an independent variable and taking the measured TOC data as a dependent variable; performing linear fitting analysis on the scatter diagram to obtain an empirical relationship; and substituting the TOC index curve I_TOC into the empirical relationship to obtain a shale reservoir total organic carbon content TOC curve TOC_FMI . The application can sensitively reflect the subtle, heterogeneous and nonlinear total organic carbon content TOC change of the formation near a well wall.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration technology, and in particular to a method for calculating the total organic carbon (TOC) content of shale reservoirs. Background Technology

[0002] Shale oil and gas is an important unconventional oil and gas resource globally, and improvements in shale reservoir evaluation technology can promote its exploration and development. Because shale oil and gas possess the characteristic of being integrated source and reservoir, its total organic carbon (TOC) content is used not only to assess shale's hydrocarbon generation capacity but also to reveal its ability to store hydrocarbon gases, making it a key indicator for shale oil and gas resource evaluation and "sweet spot" prediction.

[0003] Core / cuttings experiments can provide firsthand data on shale organic carbon content; however, TOC data from limited rock samples cannot fully reflect the variation in organic matter quantity across the entire shale formation. Using well logging data to calculate shale organic carbon content yields continuous TOC curves on the vertical profile, which can be used for quantitative evaluation of shale reservoirs.

[0004] Currently, methods for predicting TOC using well logging data mainly include natural gamma spectroscopy, the classic ΔLog R method and its improvements, multiple linear regression, lithology scanning logging, and the increasingly popular machine learning method. Specifically:

[0005] The ΔLog R method superimposes porosity logging curves (usually sonic transit time) onto resistivity curves at an appropriate scale, making the two types of curves almost completely overlap in argillaceous rock sections (non-source rocks). The logarithmic separation distance after superimposing the two types of logging curves is ΔLog R. Based on the difference in ΔLog R, immature source rocks, mature source rocks, coal seams, oil layers, water layers, etc., can be identified, and then empirical formulas can be used to predict TOC (Total Organic Carbon).

[0006] Natural gamma spectral logging analyzes the energy spectrum based on its characteristic gamma rays to determine the content and ratio of radioactive elements uranium (U), thorium (Th), and potassium (K) in the formation, and then calculates the total organic matter (TOC) of the formation.

[0007] Lithological scanning logging can directly obtain the total carbon content of the formation by interpreting the obtained inelastic spectrum. Then, inorganic carbon correction is performed (removing the carbon components of minerals such as calcite, dolomite, siderite, and ferrodolomite) to calculate the organic carbon content.

[0008] The multiple linear regression prediction method establishes a computational relationship model between various logging parameters and TOC using a large amount of measured TOC data from source rocks.

[0009] Machine learning-based predictive logging of total cost (TOC), exemplified by the application of backpropagation (BP) neural networks, excels at finding and establishing complex nonlinear relationships that are difficult to express using explicit functions.

[0010] The above methods all use traditional conventional logging parameters as input. Traditional conventional logging parameters cannot reflect the subtle heterogeneous changes in the formation around the wellbore, and therefore cannot yield high-precision TOC calculation results. Summary of the Invention

[0011] Therefore, it is necessary to provide a method for calculating the total organic carbon (TOC) content of shale reservoirs based on microresistivity scanning imaging logging to address the aforementioned technical problems.

[0012] This invention provides a method for calculating the total organic carbon (TOC) content of shale reservoirs, including:

[0013] Obtain the grayscale image of the shale reservoir and the corresponding depth index column;

[0014] Within the neighborhood centered on the target pixel, the segmentation threshold for each pixel is determined based on the grayscale values ​​of all pixels. T ; Compare the pixel value of the target pixel with the segmentation threshold T The comparison is performed to obtain the classification result of the target pixel, and the binarization image segmentation of the grayscale image is completed based on the classification result;

[0015] On the segmented grayscale image, a fixed interval between target pixels is used as a statistical window, the depth interval between two adjacent statistical windows is used as the step size, and each depth index point in the depth index column is used as the statistical center. A point-by-point sliding statistical process is then performed within the vertical depth range to obtain the TOC exponential curve. I_TOC ;

[0016] Obtain measured TOC data from the logging section corresponding to the grayscale imaging image; analyze the TOC index curve based on the accuracy of the measured TOC data. I_TOC The depth interval is adjusted, and the TOC exponential curve after depth interval adjustment is displayed. I_TOC Read the TOC data of the logging section corresponding to the grayscale image;

[0017] Using the imaged TOC data as the independent variable and the measured TOC data as the dependent variable, a scatter plot was drawn; linear fitting analysis was performed on the scatter plot to obtain the empirical relationship; and the TOC exponential curve was then plotted. I_TOC Substituting into the empirical formula, we obtain the total organic carbon (TOC) curve for shale reservoirs. TOC_FMI .

[0018] Optionally, obtain a grayscale image of the shale reservoir and a depth index column corresponding to the grayscale image, specifically including:

[0019] Micro-resistivity scanning imaging logging technology was used to measure shale reservoirs and obtain raw images of the shale reservoirs;

[0020] Preprocessing operations such as electrical snap alignment, bad electrical snap correction, equalization, and acceleration correction are performed on the original images of shale reservoirs using professional processing software.

[0021] The preprocessed data is dynamically enhanced with histograms; a dynamic imaging image is generated by matching colors with common color scales; and a grayscale image is obtained by matching colors with 8-bit linear grayscale color scales.

[0022] Export the grayscale image as a lossless compressed PNG image, and export the depth index column corresponding to the grayscale image.

[0023] Optionally, within the neighborhood centered on the target pixel, the average gray value of all pixels, the standard deviation of the gray values ​​of all pixels, and the correction coefficient are obtained.

[0024] The formula for calculating the average grayscale value of all pixels is as follows:

[0025] ;

[0026] The formula for calculating the standard deviation of the gray values ​​of all pixels is:

[0027] ;

[0028] The segmentation threshold is determined based on the average gray value of all pixels, the standard deviation of the gray values ​​of all pixels, and the correction coefficient. T The expression is:

[0029] ;

[0030] in, m Centered on the target pixel w×w The average grayscale value of all pixels within the region. s Centered on the target pixel w×w The standard deviation of the gray values ​​of all pixels within the region. k and C The correction factor is... N P for w ×w The number of all pixels in the region P i For the first i The grayscale value of each pixel.

[0031] Optionally, the pixel value of the target pixel is compared with the segmentation threshold. TThe comparisons include:

[0032] If the pixel value of the target pixel P 0 Greater than the segmentation threshold T If so, the target pixel is assigned a value of 0, which is black;

[0033] If the pixel value of the target pixel P 0 Less than or equal to the segmentation threshold T If so, the target pixel is assigned a value of 255, which is white.

[0034] Optionally, a point-by-point sliding statistics process is performed sequentially within the vertical depth range, specifically including:

[0035] For each statistical window, the content of the target pixel within that window is the TOC index. I TOC The calculation formula is as follows:

[0036] ;

[0037] In the formula, N 0 represents the count of the target pixels within the current statistics window. N t This represents the total number of valid pixels within the current statistics window.

[0038] The statistical results of each window length are recorded at the depth position of the statistical center, forming a point-by-point continuous TOC exponential curve in the vertical direction.

[0039] Optionally, we obtain an empirical relation, which is expressed as follows:

[0040] Y = aX + b ;

[0041] in, X This indicates that imaging logging TOC data is used as the independent variable. Y This indicates that the measured TOC data is used as the dependent variable. a and b The fitting coefficients are denoted as .

[0042] The method for calculating the total organic carbon (TOC) content of shale reservoirs provided in this invention has the following advantages compared with the prior art:

[0043] This invention, based on a grayscale image, plots a TOC (Total Cost of Carry) exponent curve using the grayscale image and the depth index column. I_TOCScatter plots were created using measured TOC data and imaged TOC data. Linear fitting analysis was then performed on the scatter plots to derive empirical relationships. The TOC exponent curves were then analyzed. I_TOC Substituting into the empirical formula, we obtain the total organic carbon (TOC) curve for shale reservoirs. TOC_FMI .

[0044] Compared to other existing technologies, using grayscale images as the processing object can comprehensively, intuitively and accurately present the rich features of the formation near the wellbore and its subtle heterogeneous changes.

[0045] More importantly, the TOC exponent curve obtained by processing the grayscale image. I_TOC Substituting into the empirical formula, the total organic carbon (TOC) curve of the shale reservoir is obtained. TOC_FMI It can not only sensitively reflect the subtle, heterogeneous, and nonlinear changes in total organic carbon (TOC) content in the formation near the wellbore, but also has strong adaptability in the processing, fewer model parameters, and is easy to promote and apply. Attached Figure Description

[0046] Figure 1 This is a flowchart illustrating a method for calculating the total organic carbon (TOC) content of a shale reservoir, as provided in one embodiment.

[0047] Figure 2 This is a microresistivity scanning imaging well logging image of a method for calculating the total organic carbon (TOC) content of a shale reservoir provided in one embodiment. Figure 2 (a) in the image is a dynamic image. Figure 2 (b) in the image is a grayscale image. Figure 2 (c) in the image is the segmented grayscale image;

[0048] Figure 3 An imaging logging TOC index and calculated TOC curve are provided for a method of calculating the total organic carbon (TOC) content of a shale reservoir in one embodiment.

[0049] Figure 4 This is a graph showing the fitting relationship between the TOC index from imaging logging and the TOC from core analysis of a method for calculating the total organic carbon (TOC) content of a shale reservoir, as provided in one embodiment. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0051] Among all physical logging methods, microresistivity scanning imaging logging has the highest resolution (both longitudinal and lateral resolutions are around 5 mm), coverage (over 80% wellbore coverage in an 8-inch borehole), sensitivity (responding to micron-sized ultrathin geological bodies (such as fractures) and geological features with extremely low resistivity contrast), and dynamic range (from below 10⁻¹ Ωm to above 10⁴ Ωm). It can comprehensively, intuitively, and accurately present the rich features of the formation near the wellbore and its subtle heterogeneous changes.

[0052] In one embodiment, a method for calculating the total organic carbon (TOC) content of a shale reservoir is provided, such as... Figure 1 As shown, the method includes:

[0053] Obtain the grayscale image of the shale reservoir and the corresponding depth index column.

[0054] Within the neighborhood centered on the target pixel, the segmentation threshold for each pixel is determined based on the grayscale values ​​of all pixels. T ; Compare the pixel value of the target pixel with the segmentation threshold T The comparison is performed to obtain the classification result of the target pixel, and the binarization image segmentation of the grayscale image is completed based on the classification result.

[0055] On the segmented grayscale image, a fixed interval between target pixels is used as a statistical window, the depth interval between two adjacent statistical windows is used as the step size, and each depth index point in the depth index column is used as the statistical center. A point-by-point sliding statistical process is then performed within the vertical depth range to obtain the TOC exponential curve. I_TOC .

[0056] Obtain measured TOC data from the logging section corresponding to the grayscale imaging image; analyze the TOC index curve based on the accuracy of the measured TOC data. I_TOC The depth interval is adjusted, and the TOC exponential curve after depth interval adjustment is displayed. I_TOC The imaging TOC data of the logging section corresponding to the grayscale imaging image is read.

[0057] Using the imaged TOC data as the independent variable and the measured TOC data as the dependent variable, a scatter plot was drawn; linear fitting analysis was performed on the scatter plot to obtain the empirical relationship; and the TOC exponential curve was then plotted. I_TOC Substituting into the empirical formula, we obtain the total organic carbon (TOC) curve for shale reservoirs. TOC_FMI .

[0058] The specific implementation process includes:

[0059] I. Preprocessing of Microresistivity Scanning Imaging Logging Data

[0060] Microresistivity scanning imaging logging technology was used to measure shale reservoirs and obtain raw images of the shale reservoirs.

[0061] Preprocessing operations such as electrical snap alignment, bad electrical snap correction, equalization, and acceleration correction are performed on the raw images of shale reservoirs using software such as Techlog or CifLog.

[0062] II. Image Generation

[0063] Histogram enhancement is applied to the preprocessed data; dynamic imaging is generated using common color schemes (also known as color palettes or color tables, such as Heated and Yellow). Figure 2 (a) uses an 8-bit linear grayscale color scale (containing 256 progressive colors from 0 to 255) to generate a grayscale image. Figure 2 (b) of the diagram. The depth index is used as the vertical axis for drawing, and the horizontal drawing width is determined according to the well diameter; the horizontal and vertical drawing scales are both 1:10.

[0064] Export the grayscale image as a lossless compressed PNG image for subsequent processing. Also, export the depth index column corresponding to the grayscale image.

[0065] III. Adaptive Image Segmentation Based on Local Thresholding

[0066] For grayscale images, the NiBlack method is used for binarization and image segmentation. That is, within the neighborhood centered on the target pixel, the segmentation threshold for each pixel is determined based on the grayscale values ​​of all pixels. T :

[0067] ;

[0068] in, m Centered on the target pixel w×w The average grayscale value of all pixels within the region is calculated using the following formula:

[0069] ;

[0070] s Centered on the target pixel w×w The standard deviation of the gray values ​​of all pixels within the region is calculated using the following formula:

[0071] ;

[0072] N P for w×w The number of all pixels in the region P i For the firsti The grayscale value of each pixel k and C This is the correction factor.

[0073] Compare the pixel value of the target pixel with the segmentation threshold T The comparison is performed to obtain the classification result of the target pixel, and the binarization image segmentation of the grayscale image is completed based on the classification result.

[0074] If the pixel value of this pixel P 0> T If it is a target, then it is considered a target and a value is assigned to it. P 1 = 0 (black); otherwise, it is considered background and assigned a value. P 1 = 255 (white), that is

[0075] P 1 = (P0>T)? Target (0): Background (255);

[0076] For this invention, based on experience, the neighborhood side length is taken. w =30 pixels K =-0.5, C =0, that is

[0077] ;

[0078] IV. Sliding process, calculating the TOC index

[0079] The TOC exponent curve is obtained by sliding the segmented grayscale image based on the depth index column.

[0080] The specific process is as follows: For the segmented grayscale image ( Figure 2 In (c), with a window length of 0.1 meters (0.5 meters above and below the center point as the local statistical range of that point) and a step size of 1 sampling point (the depth interval between two adjacent statistical windows), the micro resistivity scanning imaging logging image is sequentially subjected to point-by-point sliding statistics within the vertical depth range, with each depth index point as the statistical center.

[0081] For each statistical window, the content (a decimal between 0 and 1) of the target pixel (a pixel with a grayscale value of 0) within that window is the TOC index. I TOC :

[0082] ;

[0083] In the formula, N 0 represents the count of the target pixels within the current statistics window. N tThis represents the total number of valid pixels within the current statistics window.

[0084] Because of the gaps between the electrodes in the micro-resistivity scanning imaging logging instrument during the measurement process, meaningless blanks (which are measurement blind spots, not background values) appear in its image. N t The count is based on the actual number of validly measured data points. That is, for each row in the horizontal direction, the count is the number of electrical measurements taken by the instrument, not the total number of pixels. Taking Schlumberger's FMI instrument as an example, if each horizontal row has 192 electrical measurements and the vertical sampling interval is 0.00254 meters, then within a 1-meter window... N t =192×(1 / 0.00254)≈75590.

[0085] After sliding statistical processing, the statistical results of each window length are recorded at the depth position of the statistical center, forming a point-by-point continuous, high-resolution TOC exponential curve in the vertical direction. Figure 3 curves in I_TOC ).

[0086] V. Data Query and Value Retrieval

[0087] In the logging interval corresponding to the grayscale imaging map of the shale reservoir, core samples were taken and experimental analysis was conducted to obtain measured TOC data. Based on the accuracy of the measured TOC data, the TOC index curve was resampled to obtain imaging TOC data.

[0088] The specific process is as follows: In the target well section, select certain depth points to sample the drilling core, and measure the measured TOC data of the rock samples in the laboratory. Based on the accuracy of the measured TOC data (usually retaining two decimal places, i.e., accurate to 0.01 meters), resample the TOC index curve (e.g., resample the initial 0.00254-meter depth interval to 0.01-meter intervals) to obtain the imaging TOC data of the logging section corresponding to the grayscale imaging map.

[0089] In one embodiment, a linear interpolation method is used to resample (thin out) the TOC exponential curves at 0.00254-meter depth intervals to 0.01-meter depth intervals.

[0090] ① If a depth point with an accuracy of 0.01 meters can be accurately read from the original data, then use the value of the original data as the result of resampling at that depth point; otherwise, perform the following operations:

[0091] ② If the desired depth cannot be accurately retrieved from the original data i X (Accuracy is 0.01 meters), but adjacent depth sampling points can be retrieved.i A and i B ,satisfy i A < i X < i B ,like i A and i B The values ​​of the original data in terms of depth are respectively C A and C B ,but i X Values ​​in depth C X Calculate using the following formula:

[0092] ;

[0093] If at the depth point i X The nearest upper bound point can only be retrieved. i A or lower bound i B (This situation generally only exists at the top and bottom depths of the data), then let or .

[0094] VI. Determine the quantitative relationship between imaging TOC data and measured TOC data

[0095] Using the imaging TOC data as the independent variable and the measured TOC data as the dependent variable, a scatter plot was created. Linear fitting analysis was performed on the scatter plot to obtain an empirical relationship.

[0096] The TOC index value read at the corresponding depth is used as the independent variable. X The measured TOC data of the rock samples are the dependent variable. Y A scatter plot was drawn to characterize the fitting relationship between total organic carbon content and measured TOC data. Linear fitting analysis was performed on the scatter plot to obtain the empirical relationship:

[0097] Y = aX + b ;

[0098] in, a and b These are the fitting coefficients, obtained by fitting the data points.

[0099] Taking a certain shale reservoir as an example, the two exhibit the following relationship:

[0100] Y = 4.4005 X + 0.2995;

[0101] Its multiple correlation coefficient is R ² = 0.8009, as Figure 4 As shown.

[0102] VII. Calculate the high-resolution TOC curve

[0103] Substituting the TOC exponent curve obtained from microresistivity scanning imaging logging into the above fitting relationship, a continuous, high-resolution TOC curve for that well section can be obtained. Figure 3 curves in TOC_FMI ).

[0104] The embodiments described above are merely examples of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

Claims

1. A method for calculating the total organic carbon (TOC) content of shale reservoirs, characterized in that, include: Obtain the grayscale image of the shale reservoir and the corresponding depth index column; Within the neighborhood centered on the target pixel, the segmentation threshold for each pixel is determined based on the grayscale values ​​of all pixels. T ; Compare the pixel value of the target pixel with the segmentation threshold T The comparison is performed to obtain the classification result of the target pixel, and the binarization image segmentation of the grayscale image is completed based on the classification result; On the segmented grayscale image, a fixed interval between target pixels is used as a statistical window, the depth interval between two adjacent statistical windows is used as the step size, and each depth index point in the depth index column is used as the statistical center. A point-by-point sliding statistical process is then performed within the vertical depth range to obtain the TOC exponential curve. I_TOC ; Obtain measured TOC data in the logging section corresponding to the grayscale imaging image; Based on the accuracy of measured TOC data, the TOC index curve was analyzed. I_TOC The depth interval is adjusted, and the TOC exponential curve after depth interval adjustment is displayed. I_TOC Read the TOC data of the logging section corresponding to the grayscale image; Using the imaged TOC data as the independent variable and the measured TOC data as the dependent variable, a scatter plot was drawn; linear fitting analysis was performed on the scatter plot to obtain the empirical relationship; and the TOC exponential curve was then plotted. I_TOC Substituting into the empirical formula, we obtain the total organic carbon (TOC) curve for shale reservoirs. TOC_FMI .

2. The method for calculating the total organic carbon (TOC) content of shale reservoirs as described in claim 1, characterized in that, The acquisition of the grayscale image of the shale reservoir and the corresponding depth index column specifically includes: Micro-resistivity scanning imaging logging technology was used to measure shale reservoirs and obtain raw images of the shale reservoirs; The raw images of shale reservoirs were preprocessed using processing software, including electrical snap alignment, bad electrical snap correction, equalization, and acceleration correction. The preprocessed data is dynamically enhanced with histograms; a dynamic imaging image is generated by matching colors with common color scales; and a grayscale image is obtained by matching colors with 8-bit linear grayscale color scales. Export the grayscale image as a lossless compressed PNG image, and export the depth index column corresponding to the grayscale image.

3. The method for calculating the total organic carbon (TOC) content of shale reservoirs as described in claim 1, characterized in that, The segmentation threshold for each pixel is determined based on the grayscale values ​​of all pixels. T Specifically, it includes: Within the neighborhood centered on the target pixel, obtain the average gray value of all pixels, the standard deviation of the gray values ​​of all pixels, and the correction coefficient; The formula for calculating the average grayscale value of all pixels is as follows: ； The formula for calculating the standard deviation of the gray values ​​of all pixels is: ； The segmentation threshold is determined based on the average gray value of all pixels, the standard deviation of the gray values ​​of all pixels, and the correction coefficient. T The expression is: ； in, m Centered on the target pixel w×w The average grayscale value of all pixels within the region. s Centered on the target pixel w×w The standard deviation of the gray values ​​of all pixels within the region. k and C The correction factor is... N P for w×w The number of all pixels in the region P i For the first i The grayscale value of each pixel.

4. The method for calculating the total organic carbon (TOC) content of shale reservoirs as described in claim 1, characterized in that, The pixel value of the target pixel is compared with the segmentation threshold. T The comparisons include: If the pixel value of the target pixel P 0 is greater than the segmentation threshold T If so, the target pixel is assigned a value of 0, which is black; If the pixel value of the target pixel P 0 is less than or equal to the segmentation threshold T If so, the target pixel is assigned a value of 255, which is white.

5. The method for calculating the total organic carbon (TOC) content of shale reservoirs as described in claim 1, characterized in that, The step-by-step sliding statistics within the vertical depth range specifically include: For each statistical window, the content of the target pixel within that window is the TOC index. I TOC The calculation formula is as follows: ； In the formula, N 0 represents the count of the target pixels within the current statistics window. N t This represents the total number of valid pixels within the current statistics window. The statistical results of each window length are recorded at the depth position of the statistical center, forming a point-by-point continuous TOC exponential curve in the vertical direction.

6. The method for calculating the total organic carbon (TOC) content of shale reservoirs as described in claim 1, characterized in that, The obtained empirical relation is expressed in the following form: Y = aX + b ； in, X This indicates that imaging logging TOC data is used as the independent variable. Y This indicates that the measured TOC data is used as the dependent variable. a and b The fitting coefficients are denoted as .

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