Method and device for calculating shale content of electro-imaging logging image based on mathematical modeling

Abstract

The application discloses a method and device for calculating shale content of electro-imaging logging image based on mathematical modeling, and relates to the field of well logging geology. In the same sedimentary environment, the application selects reasonable function simulation to fit the mathematical relationship between the shale content and the conductivity by using the response characteristics of the conductivity to the shale content. Meanwhile, the mathematical relationship between the pixel and the conductivity is fitted by using the calibration relationship between the resistivity and the pixel, the simultaneous relationship is derived, the mathematical model between the pixel and the shale content is derived, and then the mathematical model is used to calculate the shale content of the target layer. The application establishes the mathematical relationship between the pixel value and the shale content, realizes the quantitative calculation of the shale content based on the electro-imaging data for the first time, and expands the geological application depth of the electro-imaging logging. By introducing the conductivity as an intermediate variable, the conversion path of the pixel-conductivity-shale content is established, and a new way for calculating the shale content based on the electro-imaging image itself is provided.

Description

Technical Field

[0001] This application relates to the field of well logging geology, specifically to a method and apparatus for calculating the mud content in electrical imaging well logging images based on mathematical modeling. Background Technology

[0002] In the field of well logging geology, electrical imaging logging is a relatively cutting-edge technology. Its principle involves measuring the resistivity values ​​at various points on the wellbore and converting them into pixels to generate logging images. With its intuitive and clear visualization of the wellbore, it can serve as an effective alternative to core observation. Its primary application is in studying the lithology, bedding, and structure of the wellbore surface, operating at a qualitative level. Quantifying physical properties is usually difficult and is typically calculated using other logging methods.

[0003] The clay content is one of the important evaluation indicators of the physical properties of clastic rock reservoirs. The most common way to calculate it is to use conventional logging methods such as natural gamma, neutron, and density to calculate the relative magnitude of the values ​​within a range, and then convert them using empirical formulas.

[0004] The electrical conductivity of clastic rock formations is primarily controlled by the clayey conductive network and pore water; therefore, there is a definite mathematical relationship between clay content and rock conductivity. The concept describing rock conductivity is electrical conductivity, which is the reciprocal of resistivity. To establish a rigorous mathematical model, it is necessary to comprehensively consider uncertain parameters such as porosity, conductivity, and formation factors, making such a mathematical model extremely complex. Therefore, a simplified mathematical model is urgently needed to calculate clay content. Summary of the Invention

[0005] In view of the above problems, this application is made in order to provide a method and apparatus for calculating the mud content of electrical imaging logging images based on mathematical modeling to overcome or at least partially solve the above problems.

[0006] According to one aspect of the embodiments of this application, a method for calculating the mud content of electrical imaging logging images based on mathematical modeling is provided, comprising: Identify reference sections that share the same sedimentary subfacies type or the same sedimentary facies type as the target section; Calculate the clay content and electrical conductivity at each depth point based on the well logging data of the reference layer. Based on the response characteristics of clay content to electrical conductivity, a first mathematical relationship between clay content and electrical conductivity is established. The electrical imaging logging images of the reference layer are processed into grayscale to generate the corresponding first grayscale image; Based on the grayscale pixel values ​​and corresponding conductivity values ​​of the first grayscale image, a second mathematical relationship between pixels and conductivity is established. A mathematical model relating mud content to pixels is constructed using the first and second mathematical relationships. The electrical imaging logging images of the target layer are processed into grayscale to generate a corresponding second grayscale image; The mud content is calculated based on the grayscale pixel values ​​of the second grayscale image and the mathematical model between mud content and pixels.

[0007] Furthermore, calculating the mud content based on the grayscale pixel values ​​of the second grayscale image and the mathematical model relating mud content to pixels further includes: The second grayscale image is divided into multiple rows according to the preset spacing to obtain multiple segmented images; For each segmented image, calculate the average pixel value of the segmented image based on all grayscale pixel values; The average value of each pixel is substituted into the mathematical model between mud content and pixels to calculate the mud content of the corresponding layer.

[0008] Furthermore, based on the response characteristics of clay content to electrical conductivity, the first mathematical relationship between clay content and electrical conductivity is established, which further includes: A scatter plot is generated based on the clay content and electrical conductivity, where the clay content and the corresponding electrical conductivity form a pair of scatter plot data. Based on the response characteristics of clay content to electrical conductivity presented in the scatter plot, the relationship between clay content and electrical conductivity is divided into multiple stages. For each stage, the corresponding fitting method is used to fit the scatter data of the stage to obtain the first mathematical relationship between clay content and electrical conductivity.

[0009] Furthermore, the calculation of the electrical conductivity at each depth point based on the well logging data of the reference layer further includes: Within the preset mud content range, multiple mud content points are selected uniformly at preset mud content intervals; Resistivity at depth points corresponding to multiple clay content points were read from well logging data; Calculate the average resistivity of all resistivities at each depth point within a preset depth range; Calculate conductivity based on the average resistivity.

[0010] Furthermore, establishing a second mathematical relationship between pixels and conductivity based on the grayscale pixel values ​​and corresponding conductivity values ​​of the first grayscale image further includes: Within a preset grayscale value range, select multiple grayscale pixel value points at preset pixel intervals; Calculate the average resistivity of each grayscale pixel at a given depth within a preset depth range; The conductivity is calculated based on the average resistivity, and correlation data between grayscale pixel values ​​and conductivity is generated. An exponential function relationship is established between electrical conductivity and grayscale pixel value. This exponential function relationship includes preset parameters. Specifically, the second mathematical relationship is as follows: σ is the conductivity, g is the grayscale pixel value, and α, β, and γ are preset parameters; By fitting the associated data using a fitting method, the values ​​of preset parameters are determined, and a second mathematical relationship between pixels and conductivity is obtained.

[0011] Furthermore, the first mathematical relationship between clay content and electrical conductivity is as follows:

[0012] Where V1 is the critical clay content of the first segment of the function, in the range of 10%-20%, and σ1 is the conductivity at the end of the first segment. a and b are the parameters of the first function segment; k is the linear growth coefficient, in mS / cm; V2 is the critical clay content of the second function segment, in the range of 40%-60%; c is the attenuation coefficient, dimensionless, with a value range of (0, 1); σ0 and σ max These values ​​were obtained by reading the resistivity of the pure sandstone section and the pure mudstone section, and then converting them into electrical conductivity.

[0013] Furthermore, the mathematical model relating mud content to pixels is as follows:

[0014] Among them, V sh V1 represents the mud content; g represents the grayscale pixel value; g1 is the pixel value corresponding to the mud content of V1; g2 is the pixel value corresponding to the mud content of V2; α, β, and γ are preset parameters.

[0015] Furthermore, identifying reference layers that share the same sedimentary subfacies type or the same sedimentary facies type as the target layer further includes: The sedimentary subfacies type of the target layer is determined by analyzing the electrical imaging logging images, core analysis results, and logging data of adjacent layers in the same well or adjacent layers in adjacent wells. Identify reference layers that share the same sedimentary subfacies type or the same sedimentary facies type as the target layer.

[0016] According to another aspect of the embodiments of this application, an apparatus for calculating the mud content of electrical imaging logging images based on mathematical modeling is provided, comprising: The determination module is suitable for determining reference sections that have the same sedimentary subfacies type or the same sedimentary facies type as the target section; The first calculation module is suitable for calculating the clay content and electrical conductivity at each depth point based on the well logging data of the reference layer. The first module is suitable for establishing a first mathematical relationship between clay content and electrical conductivity based on the response characteristics of clay content to electrical conductivity. The first generation module is adapted to perform grayscale processing on the electrical imaging logging image of the reference layer to generate the corresponding first grayscale image; The second establishment module is adapted to establish a second mathematical relationship between pixels and conductivity based on the grayscale pixel values ​​and corresponding conductivity values ​​of the first grayscale image; The third module is suitable for constructing a mathematical model between mud content and pixels using the first and second mathematical relationships; The second generation module is suitable for performing grayscale processing on the electrical imaging logging images of the target layer to generate the corresponding second grayscale image; The second calculation module is adapted to calculate the mud content based on the grayscale pixel values ​​of the second grayscale image and the mathematical model between the mud content and the pixels.

[0017] According to another aspect of the embodiments of this application, a computing device is provided, including: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other through the communication bus; The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the method described above for calculating the mud content of electrical imaging logging images based on mathematical modeling.

[0018] According to another aspect of the embodiments of this application, a computer storage medium is provided, which stores at least one executable instruction that causes a processor to perform operations corresponding to the method described above for calculating the mud content of electrical imaging logging images based on mathematical modeling.

[0019] According to another aspect of this application, a computer program product is provided, including at least one executable instruction that causes a processor to perform operations corresponding to the method described above for calculating the mud content of electrical imaging logging images based on mathematical modeling.

[0020] According to the technical solution provided in this application, by establishing a mathematical relationship between pixel values ​​and clay content, quantitative calculation of clay content is achieved for the first time based on electro-imaging data, significantly expanding the geological application depth of electro-imaging logging. By introducing electrical conductivity as an intermediate variable, a conversion path from pixel to electrical conductivity to clay content is established, overcoming the limitations of traditional methods that rely on conventional logging data such as natural gamma, neutron, and density, and providing a new approach to calculating clay content based on the electro-imaging image itself. Traditional mathematical models require comprehensive consideration of multiple uncertain parameters such as porosity and formation factors, resulting in complex models and high computational load. In contrast, this application establishes a simplified model using a function fitting method under the same sedimentary environment, significantly reducing computational complexity and making it more suitable for rapid field processing and practical applications.

[0021] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0022] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart illustrating a method for calculating the mud content of electrical imaging logging images based on mathematical modeling according to an embodiment of this application is shown. Figure 2 A scatter plot of clay content versus electrical conductivity is shown. Figure 3 This is a schematic diagram illustrating conductivity and pixel calibration. Figure 4 A schematic diagram of equally spaced grayscale image segmentation; Figure 5 A schematic diagram showing the calculation of clay content and generation of clay content curves based on the second grayscale image of the target layer; Figure 6 A structural block diagram of an apparatus for calculating the mud content of electrical imaging logging images based on mathematical modeling according to an embodiment of this application is shown. Figure 7 A schematic diagram of the structure of a computing device according to an embodiment of this application is shown. Detailed Implementation

[0023] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0024] This application is primarily applicable to the following two scenarios: (1) The original data of a certain well is lost, and only electrical imaging logging images are available. There are also neighboring wells with complete data in the same sedimentary environment. A mathematical model can be established using the data of the neighboring wells, and then the mud content in the electrical imaging logging images of the target layer of the well can be analyzed.

[0025] (2) In a certain well within the same sedimentary environment, conventional logging data is missing for a certain section, but electrical imaging logging images are available for that section, and there are other sections with complete data. A mathematical model can be established using logging data from other sections with complete logging data, and then the mud content in the electrical imaging logging images of the target section of the well can be analyzed.

[0026] Figure 1 A flowchart illustrating a method for calculating the clay content of electrical imaging logging images based on mathematical modeling according to an embodiment of this application is shown, as follows: Figure 1 As shown, the method includes the following steps: Step S101: Determine a reference layer that has the same sedimentary subfacies type or the same sedimentary facies type as the target layer.

[0027] Specifically, this application designates the stratigraphic intervals lacking conventional logging data as the target intervals. Generally, within the same sedimentary environment, clastic strata exhibit similar physical properties, pore structures, and electrical conductivity mechanisms, resulting in a generally consistent mathematical relationship between clay content and electrical conductivity. Therefore, corresponding analyses can be performed based on logging data and electrical imaging logs from reference intervals with the same sedimentary subfacies or sedimentary facies type. Thus, the sedimentary environment of the target interval must first be analyzed.

[0028] Fine interpretation of electrical imaging logging images of the target formation is performed, transforming image features into geological language: geological information such as lithology, bedding, structure, and paleontological remains. Among these, lithology (the physicochemical properties of rocks) is the most direct reflection of the sedimentary environment. Different environments will form specific rock assemblages, and electrical imaging images record this difference through brightness variations (high resistivity appears as bright colors, and low resistivity appears as dark colors). Sandstone: Composed of clastic grains (quartz, feldspar, etc.), with pores mostly filled with high-resistivity formation water or air, thus often appearing as bright, blocky areas in images. Its distribution and grain size (coarse / fine sand) can indicate hydrodynamic intensity—coarse sandstone (such as gravelly sandstone) often forms in strong hydrodynamic environments such as riverbeds and delta fronts; fine sandstone may correspond to weak hydrodynamic environments such as floodplains and shallow lakes. Mudstone: Composed of clay minerals, rich in water and organic matter, with extremely low resistivity, it appears as continuous dark bands or patches in images. Large areas of mudstone (such as thick lacustrine mudstone) usually represent still water environments (such as deep lakes or swamps); if mudstone and sandstone are frequently interbedded (thin dark-light alternation), it may be the product of seasonal floods in floodplains or the action of waves in shallow lakes. Conglomerate: The grains are coarse and poorly sorted, with uneven pore development and drastic changes in resistivity. In images, it appears as a chaotic area of ​​bright coarse grains interspersed with dark muddy infill. It is mostly formed in strong scouring environments such as flash floods, rapid river currents, or glacial fronts. Stratification is a layering phenomenon formed by differences in grain size, composition, and color during sediment deposition. Its type and morphology are directly controlled by hydrodynamic conditions (flow velocity, wave intensity, sediment supply rate, etc.). Electromagnetic imaging can clearly capture the strike, dip angle, and combination patterns of stratification. Cross-bedding: The image shows stripes of different inclinations intersecting and cutting through each other (e.g., unidirectional cross-bedding appears as a set of inclined stripes, while bidirectional cross-bedding appears as a bidirectional inclined herringbone shape). Unidirectional cross-bedding is mostly formed in lateral river migration (bank deposition) or desert dunes, and the inclination direction indicates water flow / wind direction; bidirectional cross-bedding is commonly found in tidal zones such as estuaries and tidal flats, and the bidirectional inclination reflects the alternating water flow of high and low tides. Horizontal bedding: In images, it appears as nearly horizontal parallel stripes with straight and continuous bedding surfaces. It is mostly formed in hydrodynamically stable still water environments (such as deep lakes and deep seas) or in the suspended sediments (mainly muddy) of floodplains. Gradual bedding: From bottom to top, the lithology gradually changes from coarse (light) to fine (dark), with no obvious bedding planes. In the image, it appears as a wedge-shaped light-dark transition zone, which is common in turbidite fans (deep-water gravity flow deposits) and reflects a rapid depositional event (such as the sediment carried by floods gradually settling from coarse to fine). Massive stratification: The image shows no obvious stratification structure and is uniformly bright or dark. It may be the product of rapid deposition (such as debris flow) or strong biological disturbance (destruction of stratification). The former corresponds to a high-energy sudden environment, while the latter may indicate a shallow water environment (with vigorous biological activity). Sedimentary structures are secondary structures formed during sedimentation by physical (e.g., water erosion), chemical (e.g., crystallization), or biological processes. Discontinuous interfaces and uniquely shaped patches in electrochemical imaging often directly reflect these structures and can reveal key sedimentary events. Scouring surface: In the image, it appears as an irregular dark band (muddy infill) cutting through the light-colored sandstone below. Above the interface are coarse-grained sediments (conglomerate or coarse sand), and below are fine-grained sediments that have been scoured. This is a trace of erosion of previous sediments by strong water currents (such as river diversion or floods), and is commonly found in environments with drastic dynamic changes, such as fluvial facies and delta fronts. Ripple marks: These are symmetrical or asymmetrical ridge-like undulations, appearing as continuous arc-shaped bands in images. Symmetrical ripple marks are often formed in the wave action zone of shallow seas / lakes (bidirectional water flow); asymmetrical ripple marks are associated with unidirectional water flow (such as rivers), with the steep slope indicating the direction of water flow. Mud cracks: These appear as polygonal network cracks in images, formed by the drying and shrinkage of exposed muddy sediments. They are commonly found in intermittently exposed environments such as tidal flats and floodplains, and are key evidence for determining whether the sediment was exposed above water during the depositional period.

[0029] Paleontological remains (or traces of their activity) are extremely sensitive to environmental factors (water depth, salinity, oxygen content). While fossil entities are difficult to identify directly from electrophysiological imaging, they can be indirectly determined through bioturbation structures (traces of biological activity altering sediments). If irregular, curved tubular or patchy disturbance zones appear in the image (breaking the original stratification), they are mostly traces of burrowing or foraging by benthic organisms (such as worms and bivalves). These disturbances are most developed in oxygen-rich and light-transmitting environments such as shallow sea tidal flats and lake shores; if disturbances are sparse or absent, they may correspond to deep water (oxygen-deficient) or extreme salinity environments (such as salt lakes). Indirect signals from specific fossils (such as bright spots of ostracod fragments) can further constrain the environment—for example, rock strata containing abundant marine ostracod fragments indicate a marine or saline lacustrine depositional environment.

[0030] Core samples, as direct samples of underground rock, are used to verify the accuracy of well logging image interpretation and supplement microscopic information. By correlating the lithology (e.g., feldspathic sandstone, argillaceous siltstone), grain size (coarse / fine sand), and fossils (e.g., plant fragments, ostracods) observed in the core with the light and dark areas of the well logging images, the lithology identification criteria are calibrated (e.g., a certain type of fine sandstone corresponds to a specific grayscale range in the image). Observe the bedding structure of the core (such as the thickness of cross-bedding and grain orientation) and sedimentary structure (such as the gravel orientation of the scour surface) to correct misjudgments in well logging images caused by resolution limitations (such as identifying blurry bands in the image as bio-disturbed bedding). By analyzing parameters such as clay content and pore structure in core samples, a correlation with well logging response is established (e.g., high clay content corresponds to low resistivity dark areas), providing a quantitative basis for subsequent subfacies division.

[0031] By analyzing the geological information such as lithology, bedding, structure, and paleontological remains displayed on the electrical imaging logging images of the target layer, combined with the core analysis results, logging data of adjacent layers in the same well or adjacent layers in neighboring wells (such as natural gamma, resistivity, sonic transit time, etc.), and comparing them with the typical characteristics of each subfacies in sedimentological theory, the sedimentary subfacies of the target layer are finally classified, and the sedimentary subfacies type of the target layer is comprehensively determined.

[0032] Sedimentary facies are generally widely distributed, especially laterally, resulting in a large area with similar physical properties to the target interval. A reference interval can be selected from intervals in the same or adjacent wells that share the same sedimentary subfacies type as the target interval, where both electrical imaging logging and conventional logging data are complete. The corresponding conventional logging data (gamma, neutron, density, sonic logging, etc.) and electrical imaging logging images from this reference interval can then be extracted for calculating clay content.

[0033] If there is no segment in the same well or adjacent well that has the same sedimentary subfacies type as the target segment, or if the electrical imaging logging images and / or conventional logging data of the segment in the same well or adjacent well that has the same sedimentary subfacies type as the target segment are incomplete, then the scope is expanded to the same sedimentary facies. That is, a segment in the same well or adjacent well that has the same sedimentary facies type as the target segment with complete electrical imaging logging images and conventional logging data is selected as a reference segment, and the logging data and electrical imaging logging images corresponding to the reference segment are extracted.

[0034] Step S102: Calculate the clay content and electrical conductivity at each depth point based on the well logging data of the reference layer.

[0035] Specifically, the mudstone content of the reference layer is calculated from top to bottom based on well logging data. For example, it can be calculated using natural gamma logging curve values. Pure mudstone has the highest radioactivity and gamma value, while sandstone has the lowest radioactivity and gamma value. The proportion ΔGR of the gamma value at a certain point in the well within the gamma range is calculated using the following formula: Then, calculate the clay content V using the following formula. sh : Among them, GR, GR min GR max ΔGR—Gamma value of the reference layer, gamma value of the pure sandstone layer, gamma value of the pure mudstone layer, and relative gamma value; V sh —Mud content; GCUR—Empirical coefficient, 3.7 for new strata and 2.0 for old strata.

[0036] In practice, logging software can be used to generate a clay content curve with one click based on existing logging data, without the need for separate calculation.

[0037] The well logging data records the resistivity at different depths. Since resistivity and conductivity are reciprocals of each other, the corresponding conductivity can be calculated.

[0038] In one optional embodiment of this application, to improve analysis efficiency, multiple clay content points can be selected uniformly at preset clay content intervals within a preset clay content range. For example, at 1% intervals, a series of clay content points from 0% to 100% can be selected uniformly. For instance, clay content points of 0%, 1%, 2%, ..., 99%, 100% can be selected. The preset clay content interval can be flexibly set according to actual needs and is not specifically limited here. The well logging data of the reference layer is complete, recording the resistivity corresponding to different depth points. Therefore, after selecting the above multiple clay content points, it is possible to... The resistivity of multiple clay content points at corresponding depths is read from well logging data. Then, the average resistivity of all resistivities at each corresponding depth point within a preset depth range is calculated. Specifically, all resistivities at each corresponding depth point within the preset depth range are read, and the average of all read resistivities is calculated. For example, the average resistivity of all resistivities within a 0.1m depth range at the corresponding location is calculated. The preset depth range can be flexibly set according to actual needs and is not specifically limited here. The conductivity is calculated based on the average resistivity. Since resistivity and conductivity are reciprocals, conductivity = 1 / average resistivity. This conductivity is the conductivity of the selected corresponding clay content point. Through the above calculation, the conductivity of each clay content point can be obtained, finally yielding point cloud data of clay content and conductivity.

[0039] Step S103: Based on the response characteristics of mud content to electrical conductivity, establish the first mathematical relationship between mud content and electrical conductivity.

[0040] Clay itself has good electrical conductivity, so changes in the clay content in rocks directly affect electrical conductivity: generally, the higher the clay content, the greater the electrical conductivity of the rock (i.e., the stronger the conductivity). However, this relationship may show different trends depending on factors such as clay type and pore fluid properties (e.g., linear growth, exponential growth).

[0041] The response characteristics refer to the specific pattern of "change in clay content → change in electrical conductivity", including the trend of change (positive correlation / negative correlation), sensitivity (whether a small change in clay content will lead to a significant change in electrical conductivity), and whether there is a threshold (such as the change in electrical conductivity trend after the clay content exceeds a certain value).

[0042] Having determined the response characteristics of clay content to electrical conductivity, the intrinsic relationship between clay content and electrical conductivity can be transformed into a quantifiable mathematical expression, that is, establishing the first mathematical relationship between clay content and electrical conductivity.

[0043] In one optional embodiment of this application, Figure 2 A scatter plot of clay content versus electrical conductivity is shown. This plot is generated based on clay content and electrical conductivity, where the horizontal axis represents clay content and the vertical axis represents electrical conductivity. Clay content and its corresponding electrical conductivity form a pair of scatter plot data, as shown below. Figure 2 As shown in the discrete points, the curve in the figure is a scatter plot trend line, which is manually identified and can be divided into three segments according to the pattern. The curve function is simulated by the least squares method using the three segment functions in the text. Based on the response characteristics of clay content to electrical conductivity presented in the scatter plot, the relationship between clay content and electrical conductivity is divided into multiple stages. (1) Low clay content stage: clay is sparse, and the rock mainly conducts electricity through pore water. Clay has little effect on conductivity, so the conductivity increases slowly in this stage. In this application, a power function can be used to simulate the weak nonlinear growth. (2) Medium clay content stage: Clay particles contact each other to form a conductive network. Clay and pore water conduct electricity together in parallel. At this time, the total conductivity is:

[0044] Among them, R 总 R 泥 R 水 These are the total resistivity of the rock formation, the resistivity of the clay, and the resistivity of the formation water, σ 总 σ 泥 σ 水 These are the total electrical conductivity of the rock strata, the electrical conductivity of the clay, and the electrical conductivity of the formation water.

[0045] At this stage, the pore water network is interconnected, and its volume has little effect on conductivity; therefore, the pore water conductivity can be considered constant. An increase in clay content significantly improves clay conductivity due to the increased content of charged particles. The relationship between total conductivity and clay content can be considered a linear growth relationship, and this application can use a linear function for fitting. (3) High clay content stage: Clay particles fill the gaps to form a saturated conductive network. At this time, further increasing the clay content has little effect on the conductivity. This application can use an exponential saturation function to fit the weak nonlinear growth.

[0046] For each stage, the corresponding fitting method is used to fit the scatter data of the stage to obtain the first mathematical relationship between clay content and electrical conductivity.

[0047] The first data relationship is as follows:

[0048] Wherein, endpoints V1 and V2 are obtained through a scatter plot of clay content versus resistivity (e.g., ...). Figure 2The manually identified inflection points divide the scatter plot into three segments. The fitted curve for each segment is simulated using the function in the above formula, where σ is the conductivity in mS / cm; V sh The content is clay content, expressed in percent.

[0049] (1) The first segment of the function σ0 is pure sandstone (V sh The electrical conductivity (approaching 0%) was calculated from the reference layer; a and b are the influence coefficients of the low clay content stage, which are dimensionless, where a affects the slope of the fitted curve and b affects the curvature of the fitted curve. V1 represents the critical mud content of the first segment of the function, which is in the range of 10%-20%. It is manually identified based on the fitted curve and corresponds to the inflection point between the first segment of the fitted curve and the second segment of the straight line.

[0050] (2) The second segment of the function σ1 is the conductivity at the end of the first segment. ; k is the linear growth coefficient, in mS / cm; V2 is the critical clay content of the second segment of the function, in the range of 40%-60%, which is manually identified based on the fitted curve and corresponds to the turning point of the second straight line and the third curve of the fitted curve.

[0051] (3) The third segment of the function σ max Pure mudstone (V) sh The conductivity (approaching 100%) is calculated from the reference layer; c is the attenuation coefficient, dimensionless, with a value range of (0, 1).

[0052] Using a computer program, the three segments of the function are fitted using the least squares method, and the code is as follows: To facilitate code differentiation, V sh If we abbreviate it as variable V, then the first segment of the function is: Where σ0 and V1 are constants, and a and b are the parameters to be fitted, the least squares method is used for fitting using Python: import numpy as np import matplotlib.pyplot as plt from scipy.optimize import curve_fit # ---------------------- 1. Known constant settings---------------------- sigma0 = 2.0 # Given a constant σ0, modify as needed V1 = 10.0 # Given a constant V1, modify as needed # ---------------------- 2. Constructing the Model Function ---------------------- def model(V, a, b): This formula only applies if V<=V1. return sigma0 (1 + a (V b)) # ---------------------- 3. Simulate / Import Experimental Data---------------------- # Example: Generate noisy simulation data and replace it with your real data. np.random.seed(42) V_data = np.linspace(0.1, V1, 30) # V ranges from [0.1, V1] true_a, true_b = 0.8, 1.5 # Real parameters (used only for simulation data) sigma_true = model(V_data, true_a, true_b) sigma_noisy = sigma_true + np.random.normal(0, 0.15, size=len(V_data)) # Add noise # ---------------------- 4. Nonlinear Least Squares Fitting ---------------------- # Initial guess values ​​(a0, b0), adjusted based on business experience. p0_guess = [1.0, 1.0] params_opt, params_cov = curve_fit(model, V_data, sigma_noisy, p0=p0_guess) a_fit, b_fit = params_opt # Calculate the standard deviation of the parameters param_err = np.sqrt(np.diag(params_cov)) a_err, b_err = param_err # ---------------------- 5. Output Fitting Results---------------------- print("===== Fitting Results=====") print(f"The fitted result is a = {a_fit:.4f} ± {a_err:.4f}") print(f"The fitted result is b = {b_fit:.4f} ± {b_err:.4f}") print(f"Fixed constants σ0 = {sigma0}, V1 = {V1}") # ---------------------- 6. Graphical Comparison---------------------- V_fit_line = np.linspace(0.1, V1, 200) sigma_fit_line = model(V_fit_line, a_fit, b_fit) plt.figure(figsize=(8, 5)) plt.scatter(V_data, sigma_noisy, label="Noisy Experimental Data", color="gray", alpha=0.7) plt.plot(V_fit_line, sigma_fit_line, "r-", linewidth=2, label=f"Fitted curve S\\sigma=\\sigma_0(1+{a_fit:.3f}V^{b_fit:.3f})S") plt.xlabel("SVS") plt.ylabel("S\\sigmaS") plt.legend() plt.grid(True, alpha=0.3) plt.title("Nonlinear Least Squares Fitting S\\sigma = \\sigma_0(1+aV^b)S") plt.show() Similarly, the second function is Where σ1, V1, and V2 are constants, k is the parameter to be fitted, and V is a variable. The least squares method is used for simulation in Python. import numpy as np import matplotlib.pyplot as plt from scipy.optimize import curve_fit # ====================== 1. Set known constants ====================== sigma1 = 5.0 # Constant σ1, modify as needed V1 = 2.0# Left boundary of the interval V2 = 10.0# Right boundary of the interval # ====================== 2. Constructing the Model Function ====================== def linear_model(V, k): return sigma1 + k (V - V1) # ====================== 3. Generate noisy simulated data (replace with your real data) ====================== np.random.seed(10) # Only take data points within the interval (V1, V2] V_data = np.linspace(V1+ 0.1, V2, 40) true_k = 1.8 # True slope (used only to generate simulation data) sigma_true = linear_model(V_data, true_k) # Add Gaussian noise to simulate experimental errors sigma_noisy = sigma_true + np.random.normal(loc=0, scale=0.4, size=len(V_data)) # ====================== Method 1: scipy curve_fit Least Squares Fitting ====================== p0 = [1.0]# Initial guess value of k k_opt, k_cov = curve_fit(linear_model, V_data, sigma_noisy, p0=p0) k_fit = k_opt[0] k_err = np.sqrt(k_cov[0][0]) # Standard deviation of parameters print("===== Fitting results (curve_fit) =====") print(f"Fit slope k = {k_fit:.4f} ± {k_err:.4f}") print(f"Given fixed constants σ1={sigma1}, V1={V1}, V2={V2}") # ====================== Method 2: Linear Analytic Least Squares (No dependency on SciPy, pure NumPy) ====================== # Transformation: σ - σ1 = k (V - V1), let y=σ-σ1, x=V-V1, k = (x·y) / (x·x) x = V_data - V1 y = sigma_noisy - sigma1 k_analytic = np.sum(x y) / np.sum(x 2) print(f"\nAnalytical solution least squares k = {k_analytic:.4f}") # ====================== Drawing Comparison ===================== V_plot = np.linspace(V1, V2, 200) sigma_fit_curve = linear_model(V_plot, k_fit) plt.figure(figsize=(8, 5)) plt.scatter(V_data, sigma_noisy, label="Simulation Noisy Experimental Data", color="gray", alpha=0.7) plt.plot(V_plot, sigma_fit_curve, "r-", linewidth=2, label=f"Fitted line S\sigma={sigma1:.1f} + {k_fit:.3f}(V-{V1:.1f})S") plt.axvspan(V1, V2, alpha=0.15, color="blue", label="Valid interval SV_1") <V\le V_2S") plt.xlabel("SVS") plt.ylabel("S\sigmaS") plt.legend() plt.grid(True, alpha=0.3) plt.title("Linear least squares fitting S\sigma = \sigma_1 + k(V-V_1)S") plt.show() Similarly, the third function is , where σ max V² is a constant, V is a variable, and c is the parameter to be determined. The least squares method is used for fitting in Python. import numpy as np import matplotlib.pyplot as plt from scipy.optimize import curve_fit # ========== 1. Set a known constant (modify to your actual value) ========== sigma_max = 20.0# Maximum saturation stress, constant value V2 = 10.0# Segmented interval boundary value, constant value # ========== 2. Define the fitting model function ========== def model(V, c): return sigma_max (1 - np.exp(-c (V - V2))) # ========== 3. Generate simulated noisy data (subsequently replaced with your real data) ========== np.random.seed(20) # Only take data points where V > V2 V_data = np.linspace(V2+ 0.2, V2+ 10, 50) true_c = 0.35 # Real parameters used to generate simulation data sigma_true = model(V_data, true_c) # Add Gaussian noise to simulate experimental errors sigma_noisy = sigma_true + np.random.normal(loc=0, scale=0.3, size=len(V_data)) # ========== 4. Nonlinear Least Squares Fitting ========== c_guess = [0.2] # Initial guess value for c, which can be adjusted if convergence fails. params_opt, params_cov = curve_fit(model, V_data, sigma_noisy, p0=c_guess) c_fit = params_opt[0] c_err = np.sqrt(params_cov[0, 0]) # Standard deviation of the parameters, representing the fitting error # Output fitting results print("==== Fitting Results====") print(f"Parameter to be determined c = {c_fit:.4f} ± {c_err:.4f}") print(f"Fixed constant σ_max = {sigma_max}, V2= {V2}") # ========== 5. Plotting the Fitting Results ========== V_plot = np.linspace(V2, V2+ 10, 300) sigma_fit_curve = model(V_plot, c_fit) plt.figure(figsize=(9, 5)) plt.scatter(V_data, sigma_noisy, color="gray", alpha=0.7, label="Experimental simulation data") plt.plot(V_plot, sigma_fit_curve, "r-", linewidth=2, label=f"Fitted curve S\\sigma=\\sigma_{{max}}(1-e^{{-{c_fit:.3f}(V-V_2)}})S") plt.axvline(x=V2, linestyle="--", color="orange", label=f"Boundary SV_2={V2}S") plt.xlabel("SVS") plt.ylabel("S\\sigmaS") plt.legend() plt.grid(alpha=0.3) plt.title("Less squares fitting of exponential saturation model") plt.show() Step S104: Perform grayscale processing on the electrical imaging logging image of the reference layer to generate the corresponding first grayscale image.

[0053] Electrical imaging logging images are color images. Here, it's necessary to convert them to grayscale images, that is, to transform RGB-calibrated color images into grayscale images. The formula for calculating the grayscale pixel value (Gray) is Gray = 0.299R + 0.587G + 0.144B, resulting in Gray = R = G = B after conversion. This step can be done with a single click using image processing software.

[0054] Step S105: Based on the grayscale pixel values ​​and corresponding conductivity values ​​of the first grayscale image, establish a second mathematical relationship between pixels and conductivity.

[0055] Electrical resistivity values ​​vary widely in electrical imaging, while pixel values ​​have a much smaller range, with grayscale images only ranging from [0, 255]. Low pixel values ​​are light in color, representing sandstone, which has high resistivity and low conductivity; high pixel values ​​are dark in color, representing mudstone, which has low resistivity and high conductivity.

[0056] The change in resistivity and the change in pixel value are generally not linearly related, but rather exponentially correlated. That is, as the pixel value increases uniformly, the resistivity decreases exponentially, while the conductivity increases exponentially. (Calibration logic reference...) Figure 3In the figure, each electrical clip is used to record the conductivity value at the corresponding location. The electrical clip data is the conductivity value collected by the electrical clip.

[0057] If the calibration logic used in electrical imaging logging can be directly found, the mathematical relationship between pixels and conductivity can be directly derived.

[0058] If the query cannot be completed, an exponential relationship between conductivity and pixel value is fitted. Within a preset grayscale value range, multiple grayscale pixel values ​​are selected at preset pixel intervals. For example, pixels with grayscale values ​​Gray=5, 15, 25, ..., 255 are selected sequentially at intervals of 10. The average resistivity of the corresponding depth point within a preset depth range is calculated for each grayscale pixel value. Specifically, all resistivities of the corresponding depth point within the preset depth range are read, and then the average of all read resistivities is calculated. For example, the average resistivity within a depth range of 0.1m for these points is calculated. Conductivity is calculated based on the average resistivity. Since resistivity and conductivity are reciprocals of each other, conductivity = 1 / average resistivity. This generates correlation data between grayscale pixel values ​​and conductivity. An exponential function relationship between conductivity and grayscale pixel values ​​is set. The exponential function relationship includes preset parameters. Specifically, the exponential function relationship is as follows: σ represents electrical conductivity, g represents the grayscale pixel value, and α, β, and γ are preset parameters. A fitting method is used to fit the associated data, determine the values ​​of the preset parameters, and obtain a second mathematical relationship between pixels and electrical conductivity. For example, the least squares method or computer software can be used for fitting.

[0059] Step S106: Construct a mathematical model between mud content and pixels using the first mathematical relationship and the second mathematical relationship.

[0060] By combining the first mathematical relationship between clay content and electrical conductivity, and the second mathematical relationship between pixels and electrical conductivity, we obtain:

[0061] This leads to the conclusion that:

[0062] Among them, V sh The mud content is represented by g, the pixel value is g, and V is V. sh 'g' and 'g' are variables, where g1 is the pixel value corresponding to a clay content of V1, g2 is the pixel value corresponding to a clay content of V2, and α, β, and γ are preset parameters. The endpoints V1 and V2 are obtained from a scatter plot of clay content versus resistivity. Figure 2 The inflection points identified by humans are used to divide the scatter plot into three segments. The fitted curve of each segment is simulated using the function in the above formula.

[0063] Step S107: Perform grayscale processing on the electrical imaging logging image of the target layer to generate the corresponding second grayscale image.

[0064] The specific processing procedure is similar to step S104, and will not be repeated here.

[0065] Step S108: Calculate the mud content based on the grayscale pixel values ​​of the second grayscale image and the mathematical model between mud content and pixels.

[0066] The mathematical model is a model between clay content and pixels. Therefore, after converting the electrical imaging logging image of the target layer into a grayscale image, the grayscale pixel value of each pixel in the grayscale image can be determined. Then, the grayscale pixel value is substituted into the mathematical model between clay content and pixels to solve for the clay content.

[0067] In one optional embodiment of this application, the second grayscale image can be divided into multiple rows according to a preset interval to obtain multiple segmented images; then, for each segmented image, the average pixel value of the segmented image is calculated based on all grayscale pixel values; finally, the average pixel value is substituted into the mathematical model between mud content and pixels to calculate the mud content of the corresponding layer.

[0068] Specifically, after converting the electrical imaging logging images of the target layer into grayscale images, the images are divided into n equal rows at 0.1m intervals, numbered from 1 to n from top to bottom. Each row is then used to generate a separate image, which are all saved in a single folder. The image cutting process is as follows: Figure 4 Install the PIL (Python Imaging Library) library and run the following Python program: import Image from PIL import os def split_image_into_rows(image_path, output_dir, rows): # Ensure the output directory exists if not os.path.exists(output_dir): os.makedirs(output_dir) # Open the original image with Image.open(image_path) as img: # Get the width and height of the image width, height = img.size # Calculate the height of each row (round up to ensure the last row is not missing pixels). row_height = (height + rows - 1) / / rows # Iterate through each row and save it as a separate image for i in range(rows): # Calculate the start and end positions of the current line start_y = i row_height end_y = min((i + 1) row_height, height) # Crop the image portion of the current row row_img = img.crop((0, start_y, width, end_y)) # Generate output filenames and save images output_file = os.path.join(output_dir, f"row_{i+1}.jpg") row_img.save(output_file) Calculate the average pixel value for each segmented image and output the data in numerical order as a column, generating an Excel spreadsheet. Run the following Python program: import os import Image from PIL import pandas as pd # Folder path where the images are located folder_path = 'path_to_your_images' # Replace with the path to your image folder # Get all grayscale image filenames in the folder image_files = [f for f in os.listdir(folder_path) if f.endswith(('.png', '.jpg', '.jpeg', '.bmp', '.tiff'))] # Initialize an empty list to store the average pixel values average_pixel_values ​​= [ ] # Iterate through the image filenames and calculate the average pixel value for each image. for image_file in image_files: image_path = os.path.join(folder_path, image_file) try: with Image.open(image_path).convert('L') as img: # Calculate average pixel value pixels = img.getdata() average_pixel_value = sum(pixels) / len(pixels) average_pixel_values.append(average_pixel_value) except Exception as e: print(f"Error processing image {image_file}: {e}") # Convert the list to a pandas Series object series = pd.Series(average_pixel_values, name='Average Pixel Value') # Save Series as an Excel file containing only one column excel_path = 'average_pixel_values.xlsx' series.to_excel(excel_path, header=True) print(f"Average pixel values ​​have been saved to {excel_path}") The obtained series of average pixel values ​​are successively substituted into the mathematical model between the above mud content and pixels for solution, and the mud content of the corresponding layer is obtained respectively.

[0069] Optionally, the maximum and minimum pixel values ​​of the original image after grayscale processing are calculated. Considering the presence of pure white bands in the well logging image, pure white pixel values ​​with Gray=255 should be removed. Run the following Phython program: import Image from PIL def get_min_max_pixel_values_gray_excluding_white(image_path): # Open the image and convert it to grayscale mode. with Image.open(image_path).convert('L') as img: # Get the pixel data of the image pixels = img.getdata() # Initialize minimum and maximum pixel values min_pixel_value = 255 # Initialize to a number larger than the smallest possible non-white value max_pixel_value = 0 # Initialize to 0 because the minimum value of a non-white pixel cannot be negative. # Iterate through all pixels to find the minimum and maximum values, ignoring pixels with a value of 255 (white). for pixel in pixels: if pixel != 255: # Ignore white pixels min_pixel_value = min(min_pixel_value, pixel) max_pixel_value = max(max_pixel_value, pixel) # Returns the minimum and maximum pixel values return min_pixel_value, max_pixel_value Importing the depth value and clay content into the logging software will generate a clay content curve, such as... Figure 5 As shown.

[0070] This embodiment does not strictly limit the execution order of some steps, and the execution order of steps can be flexibly adjusted, or some steps can be executed in parallel.

[0071] In summary, this application utilizes the response characteristics of electrical conductivity to clay content within the same sedimentary environment, selects a reasonable function for simulation, and fits the mathematical relationship between clay content and electrical conductivity. Simultaneously, it uses the calibration relationship between resistivity and pixels to fit the mathematical relationship between pixels and electrical conductivity. By combining these relationships, the mathematical relationship between pixels and clay content is derived, thus constructing a mathematical model. This mathematical model is then used to calculate the clay content of the target layer.

[0072] The method for calculating clay content in electro-imaging logging images based on mathematical modeling, as provided in this application, establishes a mathematical relationship between pixel values ​​and clay content. For the first time, it achieves quantitative calculation of clay content based on electro-imaging data, significantly expanding the geological application depth of electro-imaging logging. By introducing electrical conductivity as an intermediate variable, a conversion path from pixel to electrical conductivity to clay content is established, overcoming the limitations of traditional methods that rely on conventional logging data such as natural gamma, neutron, and density. This provides a new approach to calculating clay content based on the electro-imaging image itself. Traditional mathematical models require comprehensive consideration of multiple uncertain parameters such as porosity and formation factors, resulting in complex models and high computational loads. This application, however, establishes a simplified model using function fitting under the same sedimentary environment, significantly reducing computational complexity and making it more suitable for rapid field processing and practical applications.

[0073] Figure 6 A structural block diagram of an apparatus for calculating the mud content of electrical imaging logging images based on mathematical modeling, according to an embodiment of this application, is shown. Figure 6 As shown, the device includes: The determination module 601 is suitable for determining a reference layer that has the same sedimentary subfacies type or the same sedimentary facies type as the target layer; The first calculation module 602 is adapted to calculate the clay content and corresponding electrical conductivity at each depth point based on the well logging data of the reference layer. The first module 603 is adapted to establish a first mathematical relationship between clay content and electrical conductivity based on the response characteristics of clay content to electrical conductivity. The first generation module 604 is adapted to perform grayscale processing on the electrical imaging logging image of the reference layer to generate a corresponding first grayscale image; The second establishment module 605 is adapted to establish a second mathematical relationship between pixels and conductivity based on the grayscale pixel values ​​and corresponding conductivity values ​​of the first grayscale image. The third module 606 is suitable for constructing a mathematical model between mud content and pixels using the first mathematical relationship and the second mathematical relationship; The second generation module 607 is adapted to perform grayscale processing on the electrical imaging logging image of the target layer to generate a corresponding second grayscale image; The second calculation module 608 is adapted to calculate the mud content based on the grayscale pixel values ​​of the second grayscale image and the mathematical model between the mud content and the pixels.

[0074] Optionally, the second calculation module is further adapted to: divide the second grayscale image into multiple rows according to a preset interval to obtain multiple segmented images; For each segmented image, calculate the average pixel value of the segmented image based on all grayscale pixel values; The average value of each pixel is substituted into the mathematical model between mud content and pixels to calculate the mud content of the corresponding layer.

[0075] Optionally, the first establishing module is further adapted to: generate a scatter plot based on the clay content and electrical conductivity, wherein the clay content and the corresponding electrical conductivity constitute a pair of scatter plot data; Based on the response characteristics of clay content to electrical conductivity presented in the scatter plot, the relationship between clay content and electrical conductivity is divided into multiple stages. For each stage, the corresponding fitting method is used to fit the scatter data of the stage to obtain the first mathematical relationship between clay content and electrical conductivity.

[0076] Optionally, the first calculation module is further adapted to: select multiple mud content points in a uniform manner at preset mud content intervals within a preset mud content range; Resistivity at depth points corresponding to multiple clay content points were read from well logging data; Calculate the average resistivity of all resistivities at each depth point within a preset depth range; Calculate conductivity based on the average resistivity.

[0077] Optionally, the second establishing module is further adapted to: select multiple grayscale pixel value points at preset pixel intervals within a preset grayscale value range; Calculate the average resistivity of each grayscale pixel at a given depth within a preset depth range; The conductivity is calculated based on the average resistivity, and correlation data between grayscale pixel values ​​and conductivity is generated. An exponential function relationship is established between electrical conductivity and grayscale pixel value. This exponential function relationship includes preset parameters. Specifically, the second mathematical relationship is as follows: σ is the conductivity, g is the grayscale pixel value, and α, β, and γ are preset parameters; By fitting the associated data using a fitting method, the values ​​of preset parameters are determined, and a second mathematical relationship between pixels and conductivity is obtained.

[0078] Alternatively, the first mathematical relationship between clay content and electrical conductivity is as follows:

[0079] Where V1 is the critical clay content of the first segment of the function, in the range of 10%-20%, and σ1 is the conductivity at the end of the first segment. a and b are the parameters of the first function segment; k is the linear growth coefficient, in mS / cm; V2 is the critical clay content of the second function segment, in the range of 40%-60%; c is the attenuation coefficient, dimensionless, with a value range of (0, 1); σ0 and σ maxThese values ​​were obtained by reading the resistivity of the pure sandstone section and the pure mudstone section, and then converting them into electrical conductivity.

[0080] Optionally, the mathematical model between mud content and pixels is as follows:

[0081] Among them, V sh V1 represents the mud content; g represents the grayscale pixel value; g1 is the pixel value corresponding to the mud content of V1; g2 is the pixel value corresponding to the mud content of V2; α, β, and γ are preset parameters.

[0082] Optionally, the determination module is further adapted to: determine the sedimentary subfacies type of the target layer based on the electrical imaging logging images of the target layer, core analysis results, and logging data of adjacent layers in the same well or the same layer in adjacent wells; Determine the logging data of reference sections that have the same sedimentary subfacies type or the same sedimentary facies type as the target section.

[0083] The apparatus for calculating clay content in electro-imaging logging images based on mathematical modeling, as provided in the embodiments of this application, establishes a mathematical relationship between pixel values ​​and clay content, achieving quantitative calculation of clay content for the first time based on electro-imaging data, significantly expanding the geological application depth of electro-imaging logging. By introducing electrical conductivity as an intermediate variable, a conversion path from pixel to electrical conductivity to clay content is established, overcoming the limitations of traditional methods that rely on conventional logging data such as natural gamma, neutron, and density, and providing a new approach to calculating clay content based on the electro-imaging image itself. Traditional mathematical modeling requires comprehensive consideration of multiple uncertain parameters such as porosity and formation factors, resulting in complex models and high computational load. In contrast, this application establishes a simplified model using a function fitting method under the same sedimentary environment, significantly reducing computational complexity and making it more suitable for rapid field processing and practical applications.

[0084] This application provides a non-volatile computer storage medium storing at least one executable instruction or computer program that enables a processor to perform the operation corresponding to the method for calculating the mud content of electrical imaging logging images based on mathematical modeling in any of the above method embodiments.

[0085] This application provides a computer program product, which includes at least one executable instruction or computer program that enables a processor to perform the operation corresponding to the method for calculating the mud content of electrical imaging logging images based on mathematical modeling in any of the above method embodiments.

[0086] Figure 7The diagram shows a structural schematic of a computing device according to one embodiment of the present application. The specific embodiments of the present application do not limit the specific implementation of the computing device.

[0087] like Figure 7 As shown, the computing device may include: a processor 702, a communication interface 704, a memory 706, and a communication bus 708.

[0088] The processor 702, communication interface 704, and memory 706 communicate with each other via communication bus 708.

[0089] The communication interface 704 is used to communicate with other network elements such as clients or other servers.

[0090] The processor 702 is used to execute program 710, which can specifically execute the relevant steps in the above-described method embodiment for calculating the mud content of electrical imaging logging images based on mathematical modeling.

[0091] Specifically, program 710 may include program code that includes computer operation instructions.

[0092] The processor 702 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application. The computing device includes one or more processors, which may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0093] Memory 706 is used to store program 710. Memory 706 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0094] Specifically, program 710 can be used to cause processor 702 to execute the method for calculating the mud content of electrical imaging logging images based on mathematical modeling in any of the above method embodiments. The specific implementation of each step in program 710 can be found in the corresponding steps and units described in the above method embodiments for calculating the mud content of electrical imaging logging images based on mathematical modeling, and will not be repeated here. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described equipment and modules can be referred to the corresponding process descriptions in the foregoing method embodiments, and will not be repeated here.

[0095] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, this application is not directed to any particular programming language. It should be understood that the content of this application described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of this application.

[0096] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0097] Similarly, it should be understood that, in order to streamline this disclosure and aid in understanding one or more of the various aspects of the invention, in the above description of exemplary embodiments of the present application, various features of the present application are sometimes grouped together into a single embodiment, figure, or description thereof.

[0098] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying abstract and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying abstract and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0099] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features included in other embodiments but not others, combinations of features from different embodiments are meant to be within the scope of this application and form different embodiments.

[0100] The various component embodiments of this application can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components according to the embodiments of this application. This application can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such an implementation of this application can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

Claims

1. A method for calculating the clay content in electrical imaging logging images based on mathematical modeling, characterized in that, The method includes: Identify reference sections that share the same sedimentary subfacies type or the same sedimentary facies type as the target section; Calculate the clay content and electrical conductivity at each depth point based on the well logging data of the reference layer. Based on the response characteristics of clay content to electrical conductivity, a first mathematical relationship between clay content and electrical conductivity is established. The electrical imaging logging images of the reference layer are processed into grayscale to generate a corresponding first grayscale image; Based on the grayscale pixel values ​​and corresponding conductivity values ​​of the first grayscale image, a second mathematical relationship between pixels and conductivity is established. A mathematical model relating mud content to pixels is constructed using the first and second mathematical relationships. The electrical imaging logging image of the target layer is processed into grayscale to generate a corresponding second grayscale image; The mud content is calculated based on the grayscale pixel values ​​of the second grayscale image and the mathematical model between mud content and pixels.

2. The method for calculating the clay content of electrical imaging logging images based on mathematical modeling according to claim 1, characterized in that, The step of calculating the mud content based on the grayscale pixel values ​​of the second grayscale image and the mathematical model between mud content and pixels further includes: According to the preset spacing, the second grayscale image is divided into multiple rows to obtain multiple segmented images; For each segmented image, calculate the average pixel value of the segmented image based on all grayscale pixel values; The average value of each pixel is substituted into the mathematical model between mud content and pixels to calculate the mud content of the corresponding layer.

3. The method for calculating the clay content of electrical imaging logging images based on mathematical modeling according to claim 1 or 2, characterized in that, The step of establishing a first mathematical relationship between clay content and electrical conductivity based on the response characteristics of clay content to electrical conductivity further includes: A scatter plot is generated based on the clay content and the electrical conductivity, wherein the clay content and the corresponding electrical conductivity constitute a pair of scatter plot data. Based on the response characteristics of mud content to electrical conductivity presented in the scatter plot, the relationship between mud content and electrical conductivity is divided into multiple stages. For each stage, the corresponding fitting method is used to fit the scatter data of that stage to obtain the first mathematical relationship between clay content and electrical conductivity.

4. The method for calculating the mud content of electrical imaging logging images based on mathematical modeling according to claim 1 or 2, characterized in that, The calculation of the electrical conductivity at each depth point based on the well logging data of the reference layer further includes: Within the preset mud content range, multiple mud content points are selected uniformly at preset mud content intervals; Read the resistivity at the depth points corresponding to the multiple clay content points from the well logging data; Calculate the average resistivity of all resistivities at the depth point corresponding to each resistivity within a preset depth range; The conductivity is calculated based on the average resistivity.

5. The method for calculating the clay content of electrical imaging logging images based on mathematical modeling according to claim 1 or 2, characterized in that, The step of establishing a second mathematical relationship between pixels and conductivity based on the grayscale pixel values ​​and corresponding conductivity values ​​of the first grayscale image further includes: Within a preset grayscale value range, select multiple grayscale pixel value points at preset pixel intervals; Calculate the average resistivity of each grayscale pixel value at a corresponding depth point within a preset depth range; The conductivity is calculated based on the average resistivity, and correlation data between grayscale pixel values ​​and conductivity is generated. An exponential function relationship is established between electrical conductivity and grayscale pixel value. This exponential function relationship includes preset parameters, wherein the second mathematical relationship is specifically as follows: σ is the conductivity, g is the grayscale pixel value, and α, β, and γ are preset parameters; The correlation data is fitted using a fitting method to determine the value of the preset parameter, thereby obtaining a second mathematical relationship between pixels and conductivity.

6. The method for calculating the clay content of electrical imaging logging images based on mathematical modeling according to claim 5, characterized in that, The first mathematical relationship between the clay content and electrical conductivity is as follows: Where V1 is the critical clay content of the first segment of the function, in the range of 10%-20%, and σ1 is the conductivity at the end of the first segment. a and b are the parameters of the first function segment; k is the linear growth coefficient, in mS / cm; V2 is the critical clay content of the second function segment, in the range of 40%-60%; c is the attenuation coefficient, dimensionless, with a value range of (0, 1); σ0 and σ max These values ​​were obtained by reading the resistivity of the pure sandstone section and the pure mudstone section, and then converting them into electrical conductivity.

7. The method for calculating the clay content of electrical imaging logging images based on mathematical modeling according to claim 6, characterized in that, The mathematical model relating mud content to pixels is as follows: Among them, V sh V1 represents the mud content; g represents the grayscale pixel value; g1 is the pixel value corresponding to the mud content of V1; g2 is the pixel value corresponding to the mud content of V2; α, β, and γ are preset parameters.

8. The method for calculating the clay content of electrical imaging logging images based on mathematical modeling according to claim 1 or 2, characterized in that, The determination of the reference layer having the same sedimentary subfacies type or the same sedimentary facies type as the target layer further includes: The sedimentary subfacies type of the target layer is determined by analyzing the electrical imaging logging images, core analysis results, and logging data of adjacent layers in the same well or adjacent layers in adjacent wells. Identify reference layers that share the same sedimentary subfacies type or the same sedimentary facies type as the target layer.

9. A device for calculating the clay content of electrical imaging logging images based on mathematical modeling, characterized in that, The device includes: The determination module is suitable for determining reference sections that have the same sedimentary subfacies type or the same sedimentary facies type as the target section; The first calculation module is adapted to calculate the clay content and electrical conductivity at each depth point based on the well logging data of the reference layer. The first module is suitable for establishing a first mathematical relationship between clay content and electrical conductivity based on the response characteristics of clay content to electrical conductivity. The first generation module is adapted to perform grayscale processing on the electrical imaging logging image of the reference layer to generate a corresponding first grayscale image; The second establishment module is adapted to establish a second mathematical relationship between pixels and conductivity based on the grayscale pixel values ​​and corresponding conductivity values ​​of the first grayscale image; The third module is adapted to construct a mathematical model between mud content and pixels using the first mathematical relationship and the second mathematical relationship; The second generation module is adapted to perform grayscale processing on the electrical imaging logging image of the target layer to generate a corresponding second grayscale image; The second calculation module is adapted to calculate the mud content based on the grayscale pixel values ​​of the second grayscale image and the mathematical model between the mud content and the pixels.

10. A computing device, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction that causes the processor to perform the operation corresponding to the method for calculating the mud content of electrical imaging logging images based on mathematical modeling as described in any one of claims 1-8.

11. A computer storage medium, characterized in that, The computer storage medium stores at least one executable instruction that causes the processor to perform the operation corresponding to the method for calculating the mud content of electrical imaging logging images based on mathematical modeling as described in any one of claims 1-8.

12. A computer program product, characterized in that, It includes at least one executable instruction that causes the processor to perform the operation corresponding to the method for calculating the mud content of electrical imaging logging images based on mathematical modeling as described in any one of claims 1-8.

Images