Pixel-level high-fidelity real-time longitudinal curve thinning algorithm

Abstract

The invention provides a pixel-level high-fidelity real-time longitudinal curve thinning algorithm, and the algorithm comprises the steps: 1, selecting a target well, creating a well logging histogram, and adding a data channel in the histogram; step 2, acquiring drawing parameters of a well logging curve channel in the well logging histogram; 3, when thinning needs to be carried out, traversing is started from the uppermost pixel of the logging curve channel at the current viewport, and logging curve thinning of each pixel distance is processed downwards in the longitudinal direction; 4, adding the data points into the output curve data; 5, all data points are processed, logging curve data of the current viewport are created, and a curve is drawn according to output data. According to the pixel-level high-fidelity real-time longitudinal curve thinning algorithm, the drawing speed is remarkably increased, the curve form is effectively guaranteed, the thought is simple, the operation speed is high, and the operation speed requirement of real-time drawing can be completely met.

Description

Technical Field

[0001] The present invention relates to the technical field of oilfield development, and in particular to a pixel-level high-fidelity real-time longitudinal curve thinning algorithm. Background Art

[0002] Single-well histograms are an important tool for comprehensive analysis of single-well data. Histogram content is displayed in "tracks," with well logging curve tracks being an extremely important type of data track. Displaying well logging curves in well logging curve tracks often requires plotting a large number of data points, and the intervals between logging curves are often fixed. Traditionally, plotting all points significantly reduces software performance in curve plotting scenarios with massive amounts of data, resulting in slow drawing and responsiveness, and a reduced user experience.

[0003] In fact, when a map is scaled down to increase the visible area, the coordinate points do not need to be drawn with great precision. Often, a few key points can be used to draw the curve trajectory. This is a common thinning algorithm. Fast and high-fidelity thinning is the key to drawing curves with large amounts of data.

[0004] There are two common thinning algorithms for efficiently drawing large amounts of data. One is uniformly spaced thinning, which involves setting a fixed interval and extracting data points at fixed intervals. This approach is simple and fast, making it suitable for real-time extraction. However, its major drawback is that the curve's sample features may be lost, meaning high fidelity cannot be achieved. The other is a thinning scheme based on feature value extraction, the most common of which is the Douglas-Peucker thinning algorithm. Douglas-Peucker thinning is widely used to compress a large number of redundant graphical data points to extract the necessary data points. This algorithm implements thinning by first connecting the first and last points of a curve with a straight line. The distance from each remaining point to the line is calculated, and the maximum value is compared with a specified threshold. If the distance is less than the threshold, all points between the two ends of the line are discarded. Otherwise, the point with the largest distance from the line is retained. The original line is then split into two parts, and the thinning process is repeated for each part until the end. The number of points in the thinning result decreases as the selected tolerance threshold increases. When applying this algorithm, the tolerance threshold should be selected based on accuracy to achieve the best results. The advantage of this algorithm is its high fidelity, as the eigenvalues ​​are fully preserved at the target accuracy. However, its disadvantage is that when using large sample sizes, the computational speed cannot keep up with the requirements of real-time rendering.

[0005] In the Chinese patent application with application number: CN202110921800.7, a method for drawing a terrain profile line based on a three-dimensional model of a geological outcrop is involved. The basic steps are to select two points from the three-dimensional model of the geological outcrop, perform equidistant interpolation between the two points, and obtain the elevation information of each point through the three-dimensional model of the geological outcrop; extract the extreme points of the target profile curve, and group the elevation points according to the extreme points; perform a first thinning process on the elevation points used to draw the target curve; perform a second thinning process in each group of elevation points, delete the elevation points that do not meet the conditions, and use the remaining elevation points to draw the profile curve. The method provided by this invention has the advantages of wide applicability, scientific and reasonable, easy to implement, from the whole to the part, high precision, and high generation efficiency. It improves the accuracy and efficiency of drawing terrain profile lines by encrypting interpolated elevation points and accurately thinning elevation points.

[0006] In the Chinese patent application with application number: CN201811023996.2, a method for vectorizing a color curve bitmap is involved, which belongs to the field of oil and gas geophysical exploration engineering. The method includes: obtaining the color code of the target logging curve in the color curve bitmap; obtaining the binary image corresponding to the target logging curve based on the matching relationship between the color code of each pixel point within the extension range of the curve and the color code of the target logging curve; obtaining the logging data corresponding to the target pixel point in each preset pixel point group based on the coordinates of the boundary pixel points of the curve extension range and the logging data corresponding to the boundary pixel points of the curve extension range, and completing the vectorization processing of the color curve bitmap. This application embodiment directly obtains the target curve by collecting the color code of the target curve, avoiding the problem of difficulty in contour extraction of the color curve image in the related art, making the method more flexible in application in the color bitmap vectorization of the logging curve and higher in picking efficiency.

[0007] Chinese patent application number CN201710452169.4 discloses a method and application for identifying light oil and condensate gas layers in a formation. The method comprises: Step 1: Obtaining a test well and measuring the gas composition of layers at different depths within the test well, including the percentages of C1, C2, C3, iC4, nC4, and optionally iC5 and nC5 in each layer; Step 2: Normalizing the measured gas composition values ​​and calculating the relative content of each gas component; Step 3: Producing a graph based on the relative content distribution data obtained after the normalization in Step 2. Step 4: Constructing a gas line curve from the graph in Step 3. Step 5: Based on the comparison of the relative content distribution data curve of each component in the gas logging log for each layer with the gas line curve, light oil or condensate gas layers in the formation are identified. This method can effectively identify light oil and condensate gas layers in a formation, thereby improving the accuracy of interpretation of oil and gas layers.

[0008] The Chinese patent application, application number CN201811563050.5, describes a method for superimposing and analyzing horizontal well fracturing pressure. The method includes the following steps: data acquisition → data thinning → data segment selection → dimensionless time processing → elimination of abnormal operation segments → cubic spline interpolation → curve superimposition analysis → curve normalization analysis → curve analysis. Through this analysis, normalized fracturing pressure curves for horizontal well fracturing in different regions can be obtained, thus providing a new means and method for evaluating the post-fracturing effect of shale gas reservoirs. This method has been applied to the production of multiple wells in the first phase of the Fuling production capacity construction project, providing a scientific basis for evaluating the post-fracturing effect of these wells. This demonstrates that analyzing fracturing pressure is a necessary technical means for studying fracture extension patterns, evaluating fracturing effects, improving production stimulation, and increasing post-fracturing production capacity.

[0009] The above existing technologies are significantly different from the present invention and fail to solve the technical problem we want to solve. Therefore, we have invented a new pixel-level high-fidelity real-time longitudinal curve thinning algorithm. Summary of the Invention

[0010] The purpose of the present invention is to provide a pixel-level high-fidelity real-time longitudinal curve thinning algorithm that meets the demand for fast and high-fidelity drawing of longitudinal curves of large amounts of well logging curves.

[0011] The object of the present invention can be achieved by the following technical measures: a pixel-level high-fidelity real-time longitudinal curve thinning algorithm, which includes:

[0012] Step 1: Select the target well, create a logging histogram, and add data channels to the histogram;

[0013] Step 2, obtaining the drawing parameters of the logging curve in the logging histogram;

[0014] Step 3: When thinning is required, start traversing from the top pixel of the logging curve in the current viewport and thin out the logging curve at each pixel distance vertically downwards;

[0015] Step 4, add the data points to the output curve data;

[0016] Step 5: After all data points are processed, the logging curve data of the current viewport is created and the curve is drawn according to the output data.

[0017] The purpose of the present invention can also be achieved by the following technical measures:

[0018] In step 1, select the target well, create a logging histogram, add data channels to the histogram, and the channel that displays the logging curve in the data channel is called the curve channel. Select the logging curve of the current well, construct the logging curve channel, and draw the logging histogram of the target well.

[0019] In step 2, in the single-well histogram window, perform graphic display viewing operations such as panning, zooming in, and zooming out on the current well log histogram, and obtain the drawing parameters of the well log curves in the well log histogram under the current view display state, including the scale size and the current view range.

[0020] In step 3, obtain the pixel distance IntervalY in the Y direction of the current viewport and the sampling interval pixelLengthY of the curve. If IntervalY >= pixelLengthY * 0.25, it means that thinning is not required, the process ends, and the position of the drawn curve data is calculated according to the original well log curve data, that is, the current curve data is not thinned.

[0021] In step 3, if the pixel distance IntervalY < pixelLengthY * 0.25, it means that thinning is required.

[0022] In step 3, use curStartY to represent the vertical coordinate of the first data point within the current pixel. Starting from the second data point, traverse. For the current point PT(x, y), if y < curStartY + pixelLengthY, it means that the current point and the first data point are within the same pixel, and the current point is pushed onto the current set. If the distance between the next point of the current point and the first point exceeds the pixel distance, it means that the current set is created; calculate the number of data points intervalDataCount in the current set; calculate the minimum value minX and the maximum value maxX of the data points in the X direction.

[0023] In step 4, if intervalDataCount = 1, it means that there is only one point within the current pixel, and it is directly added to the output curve data.

[0024] In step 4, if intervalDataCount >= 2, add the minimum position sample point and the maximum position sample point to the output curve data.

[0025] In step 4, if the vertical distance DisY between the first data point in the current set and its previous data point is > pixelLengthY, then insert a fidelity data point before the minimum position sample point in the output curve data.

[0026] In step 3, if the vertical distance DisY between the last data point and its next data point is > pixelLengthY, then add a fidelity data point after the maximum position sample point in the output curve data.

[0027] The pixel-level high-fidelity real-time longitudinal curve thinning algorithm further includes, after step 5, step 6, refreshing and drawing the longitudinal curve in real time.

[0028] In step 6, in the single well histogram window, the current logging histogram is translated, zoomed in, and zoomed out, and high-fidelity thinning of the curve is performed in real time according to the current drawing environment, and the vertical curve is refreshed and drawn in real time.

[0029] The pixel-level, high-fidelity, real-time longitudinal curve thinning algorithm of this invention performs rapid, high-fidelity thinning of curve data based on pixel distance in real time. This method for drawing thinned curves in real time meets the demand for fast, high-fidelity drawing of longitudinal curves for large amounts of well logging curves. First, because the amount of thinned curve data is greatly reduced, drawing speed is significantly improved. Second, the algorithm employs a pixel-level, high-fidelity thinning approach, effectively preserving curve morphology. Third, this thinning algorithm is simple in concept and fast in computational speed, fully meeting the computational speed requirements for real-time drawing. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 A schematic diagram of creating and drawing a bar graph in a specific embodiment of the present invention;

[0031] Figure 2 This is a schematic diagram of a non-thinning state in a specific embodiment of the present invention;

[0032] Figure 3 A schematic diagram of a high-fidelity thinning algorithm in a specific embodiment of the present invention;

[0033] Figure 4 A schematic diagram of inserting minimum sample points and maximum sample points in a specific embodiment of the present invention;

[0034] Figure 5 A schematic diagram of inserting a high-fidelity point in a specific embodiment of the present invention;

[0035] Figure 6 A schematic diagram of inserting a lower high-fidelity point in a specific embodiment of the present invention;

[0036] Figure 7 A schematic diagram showing that characteristic values ​​before and after puffing remain unchanged and the curve drawing time is reduced by 80% in one embodiment of the present invention;

[0037] Figure 8 A schematic diagram showing that the characteristic values ​​before and after pumping remain unchanged and the seismic profile drawing time is reduced by 90% in a specific embodiment of the present invention;

[0038] Figure 9 Schematic diagram of a high-fidelity thinning algorithm in another specific embodiment 2 of the present invention;

[0039] Figure 10 It is a schematic diagram of the high - fidelity thinning algorithm in the third specific embodiment of the present invention;

[0040] Figure 11 It is a flowchart of a specific embodiment of the pixel - level high - fidelity real - time longitudinal curve thinning algorithm of the present invention. Specific embodiments

[0041] It should be noted that the following detailed description is exemplary and is intended to provide further illustration of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0042] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they specify the presence of features, steps, operations, and / or combinations thereof.

[0043] As Figure 9 shown, Figure 9 It is a flowchart of the pixel - level high - fidelity real - time longitudinal curve thinning algorithm of the present invention. The pixel - level high - fidelity real - time longitudinal curve thinning algorithm includes:

[0044] Step 101. Select a target well, create a logging histogram according to the histogram module, and add a data track in the histogram. The track that displays the logging curve in the data track is called the curve track. Select the logging curve of the current well and construct a logging curve track. Draw the logging histogram of the target well ( Figure 1 ).

[0045] Step 102. In the single - well histogram window, perform graphic display viewing operations such as translation, zooming in, and zooming out on the current logging histogram. Obtain the drawing parameters of the logging curve track in the logging histogram in the current view display state, such as the scale size, and the current view range, etc.

[0046] Step 103. Obtain the pixel distance IntervalY in the Y - direction of the current viewport and the sampling interval pixelLengthY of the curve. If IntervalY >= pixelLengthY * 0.25, it means that thinning is not required, exit the algorithm ( Figure 2 ), and calculate the position of the drawn curve data according to the original logging curve data, that is, the current curve data is not thinned. If the pixel distance IntervalY < pixelLengthY * 0.25, then proceed to step 4.

[0047] Step 104. Start traversing from the topmost pixel of the well logging curve track in the current viewport, and process the thinning of the well logging curve for each pixel distance vertically downwards.

[0048] Use curStartY to represent the vertical coordinate of the first data point within the current pixel. Start traversing from the second data point. For the current point PT(x, y), if y < curStartY + pixelLengthY, it means the current point and the first data point are within the same pixel, and the current point is pushed onto the current set. If the distance between the next point of the current point and the first point exceeds the pixel distance, it means the current set is created. Calculate the number of data points intervalDataCount in the current set. Calculate the minimum value minX and the maximum value maxX of the data points in the X direction.

[0049] Step 105. If intervalDataCount = 1, it means there is only one point within the current pixel, and it is directly added to the output curve data.

[0050] If intervalDataCount >= 2, add the sample points at the minimum position and the maximum position to the output curve data. If the vertical distance DisY between the first data point in the current set and its previous data point > pixelLengthY, then insert a fidelity data point before the sample point at the minimum position in the output curve data. If the vertical distance DisY between the last data point and its next data point > pixelLengthY, then add a fidelity data point after the sample point at the maximum position in the output curve data ( Figure 3 )

[0051] Step 106. Until all data points are processed. The well logging curve data of the current viewport is created. Draw the curve according to the output data.

[0052] Step 107. In the single - well histogram window, perform graphic display viewing operations such as translation, zoom - in, and zoom - out on the current well logging histogram, and perform high - fidelity thinning of the curve in real - time according to the current drawing environment, and refresh the drawn vertical curve in real - time.

[0053] The following are several specific embodiments of applying the present invention

[0054] Embodiment 1

[0055] In a specific embodiment 1 of applying the present invention, the pixel - level high - fidelity real - time vertical curve thinning algorithm includes:

[0056] 1. Select the target well, create a logging histogram according to the histogram module, and add a data track to the histogram. The track that displays the logging curve in the data track is called the curve track. Select the logging curves of the current well and construct the logging curve tracks. Draw the logging histogram of the target well( Figure 1 ).

[0057] 2. In the single-well histogram window, perform graphic display viewing operations such as panning, zooming in, and zooming out on the current logging histogram. Obtain the drawing parameters of the logging curve tracks in the logging histogram under the current view display state, such as the scale size, and the current view range, etc.

[0058] 3. Obtain the pixel distance pixelLengthY in the Y direction of the longitudinal curve curveLine represented by each pixel in the current viewport. Obtain the sampling interval IntervalY in the Y direction of the longitudinal curve data points. If IntervalY >= pixelLengthY * 0.25, it means that thinning is not required, and the algorithm exits. The output curve data is equal to the original longitudinal curve data( Figure 2 ).

[0059] 4. Use curStartY to represent the longitudinal coordinate of the first data point within the current pixel, denoted as the start depth of the current pixel, and curStartY + pixelLengthY represents the end depth of the current pixel range. curStartY is initialized to the longitudinal coordinate Y of the first data point of the longitudinal curve.

[0060] 5. Start traversing from the second data point. For the current point PT(x, y), if y < curStartY + pixelLengthY, it means that the current point and the first data point are within the same pixel, and the current point is pushed onto the current set. If the distance between the next point of the current point and the first point exceeds the pixel distance, it means that the current set is created.

[0061] 6. Calculate the number of data points intervalDataCount within the current pixel range (that is, the data points in the current set). Calculate the minimum value minX and the maximum value maxX in the X direction of the data points within the current pixel range.

[0062] 7. If intervalDataCount = 1, it means that there is only one point within the current pixel, and it is directly added to the output curve data.

[0063] 8. If intervalDataCount>=2, add two points to the output curve data: the minimum sample point CPoint(minX,curStartY*keyEpsilon*0.25) and the maximum sample point CPoint(maxX,curStartY*keyEpsilon*0.75), such as Figure 4 .

[0064] 9. Based on step 6, if the vertical distance DisY between the first data point (X, Y) in the current set and the previous data point in the current set is greater than pixelLengthY, it means that the distance between the two points exceeds the pixel distance. To ensure high fidelity of the morphology, it is necessary to insert a high-fidelity data point CPoint (X, curStartY*keyEpsilon*0.2) before the minimum sample point in the output curve data, that is, insert a high-fidelity point, such as Figure 5 .

[0065] 10. Based on step 6, if the vertical distance DisY between the last data point (X, Y) in the current set and the next data point is greater than pixelLengthY, it means that the distance between the two points exceeds the pixel distance. To ensure high fidelity of the morphology, it is necessary to add a fidelity data point CPoint (X, curStartY*keyEpsilon*0.8) after the maximum sample point in the output curve data, that is, insert a high-fidelity point, such as Figure 6 .

[0066] 11. Steps 7 and 8 are completed, that is, the data within the current pixel is thinned out, and the effect is as follows Figure 3 After the current set is processed, clear the current set, continue to traverse the data points, push the current point into the current set, and repeat steps 2, 3, 4, 5, 6, 7, and 8.

[0067] 12. Continue until all data points have been processed. The output curve data is created. Draw the curve based on the output data.

[0068] 13. In the single well histogram window, perform graphic display and viewing operations such as translation, zooming in and out on the current logging histogram, perform high-fidelity thinning of the curve according to the current drawing environment in real time, and refresh the drawing of the vertical curve in real time.

[0069] The object of the present invention is to provide a method for high-fidelity thinning of curve data based on pixel distance in real time during the process of drawing a longitudinal curve, and for real-time drawing of the thinned curve, so as to meet the requirements of fast and high-fidelity drawing of longitudinal curves with a large amount of data. On the one hand, due to the significant reduction in the amount of thinned curve data, the drawing speed is significantly improved. On the other hand, the pixel-level high-fidelity thinning adopted by the algorithm effectively ensures the curve shape. In the actual drawing environment, the larger the pixel distance, the smaller the view ratio, the higher the suction ratio, and the more obvious the speed improvement. In a single-well histogram, when applying the idea of the present invention in drawing logging curves with a large amount of data, the curve track can be quickly drawn, greatly accelerating the response speed of the software (such as Figure 7 , the characteristic values before and after suction remain unchanged. The drawing time of the curve track is reduced by 80%). In a small-layer plan view, it can meet the curve drawing requirements of a large number of wells (more than 10,000 wells). When applying the idea of the present invention in a seismic profile, the seismic profile can also be quickly drawn, accelerating the drawing speed and response speed of the seismic profile. In the example test, the real-time drawing time of a large-scale seismic profile is reduced by 90%( Figure 8 ).

[0070] Example 2

[0071] In a specific Example 2 of applying the present invention, as Figure 9 shown, the pixel-level high-fidelity real-time longitudinal curve thinning algorithm includes:

[0072] 1. Calculate the number intervalDataCount of data points within the current pixel range (i.e., the current set of data points). Calculate the minimum value minX and the maximum value maxX of the data points in the X direction within the current pixel range.

[0073] 2. Since intervalDataCount = six, exceeding two data points, add two points to the output curve data: the minimum sample point CPoint(minX, curStartY * keyEpsilon * 0.25) and the maximum sample point CPoint(maxX, curStartY * keyEpsilon * 0.75).

[0074] 3. The vertical distance DisY between the first data point (X, Y) in the current set and the previous data point in the current set <= pixelLengthY, so the high-fidelity point is not inserted above. The vertical distance DisY between the last data point (X, Y) in the current set and the next data point < pixelLengthY, so the high-fidelity point is not inserted below.

[0075] 4. Only two points need to be inserted at the end within the current pixel.

[0076] Example 3

[0077] In a specific embodiment 3 of the present invention, as Figure 10 As shown in FIG, the pixel-level high-fidelity real-time longitudinal curve thinning algorithm includes:

[0078] 1. Calculate the number of data points (intervalDataCount) within the current pixel range (that is, the current set of data points). Calculate the minimum X value minX and maximum X value maxX of the data points within the current pixel range.

[0079] 2. Since intervalDataCount=8, there are more than 2 data points, so two points are added to the output curve data: the minimum sample point CPoint(minX,curStartY*keyEpsilon*0.25) and the maximum sample point CPoint(maxX,curStartY*keyEpsilon*0.75).

[0080] 3. The vertical distance DisY between the first data point (X, Y) in the current set and the previous data point in the current set is less than pixelLengthY, so the upper high-fidelity point is not inserted. The vertical distance DisY between the last data point (X, Y) in the current set and the next data point is greater than pixelLengthY, so the lower high-fidelity point is inserted.

[0081] 4. Finally insert three points into the current pixel.

[0082] The pixel-level, high-fidelity, real-time longitudinal curve thinning algorithm of this invention performs rapid, high-fidelity thinning of curve data based on pixel distance during longitudinal curve drawing. This method for real-time thinned curve drawing meets the demand for fast, high-fidelity drawing of longitudinal curves with large data volumes. First, because the amount of thinned curve data is significantly reduced, drawing speed is significantly improved. Second, the algorithm employs a pixel-level, high-fidelity thinning approach, effectively preserving curve morphology. Third, this thinning algorithm is simple in concept and fast in computation, fully meeting the computational speed requirements of real-time drawing.

[0083] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art may modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features therein. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

[0084] Except for the technical features described in the specification, all other technical features are known technologies to those skilled in the art.

Claims

1. A pixel-level high-fidelity real-time longitudinal curve thinning algorithm, characterized by: The pixel-level high-fidelity real-time longitudinal curve thinning algorithm includes: Step 1: Select a target well, create a logging histogram, and add a data track to the histogram; Step 2: Obtain the drawing parameters of the logging curve track in the logging histogram; Step 3: When thinning is required, start traversing from the topmost pixel of the logging curve track in the current viewport, and process the thinning of the logging curve for each pixel distance vertically downward; Step 4: Add the data points to the output curve data; Step 5: When all data points are processed, the logging curve data for the current viewport is created, and a curve is drawn according to the output data.

2. The pixel-level high-fidelity real-time longitudinal curve thinning algorithm according to claim 1, characterized in that: In Step 1, select a target well, create a logging histogram, and add a data track to the histogram. The track that displays the logging curve in the data track is called the curve track. Select the logging curve of the current well, construct the logging curve track, and draw the logging histogram of the target well.

3. The pixel-level high-fidelity real-time longitudinal curve thinning algorithm according to claim 1, characterized in that: In Step 2, in the single-well histogram window, perform graphic display viewing operations such as panning, zooming in, and zooming out on the current logging histogram, and obtain the drawing parameters of the logging curve track in the logging histogram in the current view display state, including the scale size and the current view range.

4. The pixel-level high-fidelity real-time longitudinal curve thinning algorithm according to claim 1, characterized in that: In Step 3, obtain the pixel distance IntervalY in the Y direction of the current viewport and the sampling interval pixelLengthY of the curve. If IntervalY >= pixelLengthY * 0.25, it means that thinning is not required, and the process ends. The position of the drawn curve data is calculated according to the original logging curve data, that is, the current curve data is not thinned.

5. The pixel-level high-fidelity real-time longitudinal curve thinning algorithm according to claim 4, characterized in that: In Step 3, if the pixel distance IntervalY < pixelLengthY * 0.25, it means that thinning is required.

6. The pixel-level high-fidelity real-time longitudinal curve thinning algorithm according to claim 5, characterized in that: In Step 3, use curStartY to represent the longitudinal coordinate of the first data point within the current pixel. Start traversing from the second data point. For the current point PT(x, y), if y < curStartY + pixelLengthY, it means that the current point and the first data point are within the same pixel, and the current point is pushed onto the current set. If the distance between the next point of the current point and the first point exceeds the pixel distance, it means that the current set is created; calculate the number of data points intervalDataCount in the current set; calculate the minimum value minX and the maximum value maxX of the data points in the X direction.

7. The pixel-level high-fidelity real-time longitudinal curve thinning algorithm according to claim 6, characterized in that: In Step 4, if intervalDataCount = 1, it means that there is only one point within the current pixel, and it is directly added to the output curve data.

8. The pixel-level high-fidelity real-time longitudinal curve thinning algorithm according to claim 6, characterized in that: In Step 4, if intervalDataCount >= 2, add the minimum position sample point and the maximum position sample point to the output curve data.

9. The pixel-level high-fidelity real-time longitudinal curve thinning algorithm according to claim 8, characterized in that: In Step 4, if the longitudinal distance DisY between the first data point in the current set and its previous data point is > pixelLengthY, then insert a fidelity data point before the minimum position sample point in the output curve data.

10. The pixel-level high-fidelity real-time longitudinal curve thinning algorithm according to claim 8, characterized in that: In step 3, if the longitudinal distance DisY between the last data point and the next data point is greater than pixelLengthY, a fidelity data point is added after the maximum position sample point in the output curve data.

11. The pixel-level high-fidelity real-time longitudinal curve thinning algorithm according to claim 1, characterized in that: The pixel-level high-fidelity real-time longitudinal curve thinning algorithm further includes, after step 5, step 6, refreshing and drawing the longitudinal curve in real time.

12. The pixel-level high-fidelity real-time longitudinal curve thinning algorithm according to claim 11, characterized in that: In step 6, in the single well histogram window, the current logging histogram is translated, zoomed in, and reduced, and these graphic display viewing operations are performed, and high-fidelity thinning of the curve is performed in real time according to the current drawing environment, and the vertical curve is refreshed and drawn in real time.

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