Data monitoring method, system, medium and program product based on digital oscilloscope

Abstract

A data monitoring method, system, medium, and program product based on a digital oscilloscope, relating to the field of electro-digital data processing, is disclosed. The method includes: receiving raw logging data streams output from a cable logging sensor; substituting the raw logging data streams into a pre-stored instrument response function to obtain formation physical parameter measurements at the current sampling point; calculating the signal-to-noise ratio (SNR) of the raw logging data stream at the current sampling point; generating a numerical deviation interval corresponding to the formation physical parameter measurements based on the SNR and environmental correction parameters; generating a data distribution object containing peak data trajectories and data deviation regions based on the formation physical parameter measurements and the numerical deviation intervals; and performing graphical rendering on the data distribution object, generating a data distribution waveform of the formation physical parameter measurements based on pixel weights. Implementing this application can optimize the display content of cable logging data and avoid cognitive biases caused by information loss.

Description

Technical Field

[0001] This application relates to the field of electronic digital data processing, and in particular to a data monitoring method, system, medium, and program product based on a digital oscilloscope. Background Technology

[0002] Cable logging provides crucial data support for geological interpretation and engineering decisions by lowering sensors into the well to measure formation physical parameters in real time. As exploration of complex reservoirs deepens, the underground geological environment exhibits high heterogeneity. Complex geological phenomena such as thin interbedded layers, fractures, and caverns require field engineers to quickly and accurately identify geological features and make technical judgments based on real-time logging data.

[0003] In this technology, well depth is used as the vertical axis and logging parameter values ​​as the horizontal axis to plot continuous logging curves in real time on the interface. The technology plots newly acquired measurements point by point according to well depth, forming a deterministic numerical curve that varies with depth. Operators identify formation characteristics by observing the curve values, trends, and relationships between multiple curves, while the interface displays the specific values ​​of each parameter within the current well depth.

[0004] However, well logging data is often uncertain due to factors such as instrument accuracy and wellbore environmental conditions. When related technologies select the values ​​with the highest confidence for curve rendering, some cable logging data information is lost, leading to cognitive biases and delays in decision-making by operators. Summary of the Invention

[0005] This application provides a data monitoring method, system, medium, and program product based on a digital oscilloscope, which is used to optimize the display content of cable logging data and avoid cognitive bias caused by information loss.

[0006] In a first aspect, this application provides a data monitoring method based on a digital oscilloscope, applied to a data monitoring system. The method includes: receiving raw logging data streams output from a cable logging sensor; substituting the raw logging data streams into a pre-stored instrument response function to obtain formation physical parameter measurements at the current sampling point; calculating the signal-to-noise ratio (SNR) of the raw logging data streams at the current sampling point; generating a numerical deviation interval corresponding to the formation physical parameter measurements based on the SNR and environmental correction parameters; generating a data distribution object containing a peak data trajectory and a data deviation region based on the formation physical parameter measurements and the numerical deviation interval; the peak data trajectory corresponds to formation physical parameter measurements in a continuous sampling sequence; the coverage width of the data deviation region is determined by the numerical deviation interval; and the pixel weights within the data deviation region decrease from the peak data trajectory towards the edge; and performing graphical rendering on the data distribution object to generate a data distribution waveform of the formation physical parameter measurements based on the pixel weights.

[0007] In the above embodiments, the data monitoring system generates a data distribution waveform that includes the range of numerical deviations, which intuitively shows the uncertainty of the measured values, avoids the cognitive bias caused by relying on a single value, provides operators with a quantitative reference for data quality, and improves the reliability of geological interpretation.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, after the step of rendering the data distribution object graphically and generating the data distribution waveform of the stratigraphic physical parameter measurement values ​​according to the pixel weights, the method further includes: monitoring the frequency of change of the stratigraphic physical parameter measurement values ​​along the depth direction; and increasing the rendering sampling rate of the data distribution object when the frequency of change exceeds a preset change threshold.

[0009] In the above embodiments, the data monitoring system monitors the frequency of data changes and adaptively increases the rendering sampling rate, ensuring that the details of abrupt geological changes (such as thin interlayers) are accurately presented, optimizing the balance between rendering performance and fidelity, and enhancing the ability to identify complex geological structures.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, after the step of monitoring the frequency of change of formation physical parameter measurements along the depth direction, the method further includes: monitoring the depth change direction of the cable logging sensor; within the depth interval where the depth change direction reverses, collecting data distribution objects from multiple sampling points as interval distribution objects; integrating the interval distribution objects in both depth and measurement value dimensions, and rendering to generate a data distribution map that simultaneously represents depth deviation and measurement value deviation.

[0011] In the above embodiments, when the logging instrument depth is reversed, the data monitoring system can simultaneously characterize the uncertainty of depth and measurement value by collecting data and generating a two-dimensional distribution map of depth measurement values.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, the step of graphically rendering the data distribution object and generating a data distribution waveform of the geological physical parameter measurement value according to the pixel weight specifically includes: rendering the data distribution object of the current sampling point to the instantaneous data buffer; calculating the historical hit frequency of the corresponding pixel position in the historical cumulative buffer; updating the historical cumulative buffer according to the historical hit frequency and the preset decay time constant; and color-coding and mixing the instantaneous data buffer and the updated historical cumulative buffer to generate a data distribution waveform.

[0013] In the above embodiments, the data monitoring system achieves a display effect similar to the afterglow of an oscilloscope by separating instantaneous and historical data buffers and introducing an attenuation constant. It can clearly distinguish instantaneous noise from stable formation response and display the recent statistical distribution and trend of data.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, after the step of color-coding and mixing the instantaneous data buffer and the updated historical cumulative buffer to generate a data distribution waveform, the method further includes: setting a first hue range for the data in the instantaneous data buffer to represent the current measurement value; setting a second hue range for the data in the historical cumulative buffer to represent historical trajectory values; mapping historical hit frequencies to brightness or saturation within the second hue range; and superimposing the data in the first hue range onto the data in the second hue range to generate a composite data distribution waveform.

[0015] In the above embodiments, the data monitoring system sets different hues for instantaneous and historical data and maps historical hit frequencies to brightness and saturation, thereby achieving integrated display of information, distinguishing between current values ​​and historical trajectories, and improving data readability and trend interpretation efficiency.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, the step of performing graphic rendering on the data distribution object and generating a data distribution waveform of the geological physical parameter measurement value according to the pixel weight specifically includes: determining the number of data points within the depth range to be rendered and the physical pixel width of the corresponding display area; when the physical pixel width is less than the number of data points, grouping multiple data distribution objects corresponding to the physical pixel width; performing a weighted summation of the probability density function on the multiple data distribution objects in each group to generate an aggregated data distribution object; and performing graphic rendering according to the pixel weight of the aggregated data distribution object.

[0017] In the above embodiments, when there are insufficient display pixels, the data monitoring system generates an aggregated object by performing a weighted summation of the probability density function on the data distribution object and then rendering it. This avoids information distortion caused by data thinning or aliasing and ensures the authenticity of data statistical characteristics at a macro scale.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, after the step of rendering the data distribution object graphically and generating the data distribution waveform of the measured values ​​of the stratigraphic physical parameters according to the pixel weights, the method further includes: receiving another measured value of another stratigraphic physical parameter and generating a second data distribution waveform corresponding to the other measured value; using the numerical deviation range of the data distribution waveform as a confidence index of the first parameter; using the numerical deviation range of the second data distribution waveform as a confidence index of the second parameter; and highlighting the overlapping area of ​​the two data distribution waveforms when both the confidence index of the first parameter and the confidence index of the second parameter simultaneously meet preset conditions.

[0019] In the above embodiments, the data monitoring system highlights the overlapping area of ​​two parameter waveforms that meet the confidence level conditions, thereby realizing the correlation analysis of multiple parameters. This provides clear visual indications for identifying specific lithologies or fluids (such as gas layers) and improves the efficiency and accuracy of multi-curve comprehensive interpretation.

[0020] In a second aspect, embodiments of this application provide a data monitoring system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, which includes computer instructions, and the one or more processors call the computer instructions to cause the data monitoring system to perform the method described in the first aspect and any possible implementation thereof.

[0021] Thirdly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a data monitoring system, cause the data monitoring system to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, embodiments of this application provide a computer program product containing instructions that, when the computer program product is run on a data monitoring system, cause the data monitoring system to perform the method described in the first aspect and any possible implementation thereof.

[0023] Understandably, the data monitoring system provided in the second aspect, the computer storage medium provided in the third aspect, and the computer program product provided in the fourth aspect are all used to execute the methods provided in the embodiments of this application. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0025] 1. By employing a technical solution that substitutes the raw well logging data stream into the instrument response function to calculate the measured value, and generates numerical deviation intervals based on the signal-to-noise ratio and environmental correction parameters, a data distribution object containing peak data trajectories and data deviation regions is created. This object is then graphically rendered based on pixel weights. Therefore, a single, deterministic well logging curve can be transformed into a data distribution waveform that characterizes data uncertainty. This method not only displays the most probable measured value (peak trajectory) but also intuitively reflects the data quality and confidence level through the width of the deviation region and the pixel weight distribution. It effectively solves the problem in existing technologies where only the single value with the highest confidence level is rendered, resulting in the loss of a large amount of accompanying information. This leads to operators' inability to accurately assess data quality, potentially causing cognitive biases and decision-making delays. This achieves a more comprehensive and realistic display of well logging data, improving the accuracy of on-site geological interpretation and the reliability of decision-making.

[0026] 2. By employing a technical solution that monitors the frequency of changes in the measured values ​​of formation physical parameters along the depth direction and increases the rendering sampling rate when the frequency exceeds a threshold, the data monitoring system can intelligently adjust its resource allocation, performing high-density sampling and high-fidelity rendering in areas with drastic changes in geological features. This adaptive mechanism ensures that critical details are not lost due to a fixed low sampling rate when dealing with geological structures requiring detailed characterization, such as thin interbedded layers and fracture zones. This effectively solves the problem in existing technologies where fixed sampling rate rendering wastes resources in areas with flat data but may lose details in areas with drastic data changes, leading to insufficient ability to identify complex formations. It achieves optimal utilization of computing resources and accurate capture of geological details, enhancing the ability to identify complex reservoirs.

[0027] 3. By employing a technique that renders data distribution objects separately to an instantaneous data buffer and a historical cumulative buffer with decay updates, and then uses color encoding to mix and generate waveforms, this method can simultaneously display the data distribution of the current instantaneous measurement and the cumulative statistical distribution of historical data over a period of time on the display interface. Instantaneous data provides immediate response, while historical cumulative data, through its decay time constant, creates an effect similar to the glow afterglow of an oscilloscope, reflecting the historical hit frequency and stability of the data points. This effectively solves the problem in existing technologies where logging curves only display the current point, lacking historical context, making it difficult for operators to judge the randomness and trend of data points. It achieves dynamic visualization of data in the time (or depth) dimension, providing operators with intuitive insights into data stability and trends, and improving the efficiency of anomaly identification. Attached Figure Description

[0028] Figure 1 This is a flowchart illustrating a data monitoring method based on a digital oscilloscope in an embodiment of this application.

[0029] Figure 2 This is another flowchart illustrating the data monitoring method based on a digital oscilloscope in this application embodiment;

[0030] Figure 3 This is a schematic diagram of the physical device structure of a data monitoring system in an embodiment of this application. Detailed Implementation

[0031] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification of this application, the singular expressions “a,” “an,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0032] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0033] In the specific implementation scenario of this application, cable logging sensors refer to a series of precision measuring instruments, such as resistivity logging tools, sonic logging tools, and gamma logging tools, lowered into the wellbore via cables, used to acquire the physical properties of the formation surrounding the wellbore in real time. The raw logging data stream refers to the continuous electrical signals or digital sequences from these sensors that are unprocessed or only partially processed, directly reflecting the interaction between the sensors and the formation. The instrument response function is a pre-calibrated mathematical model that describes the conversion relationship from receiving raw signals to outputting physical parameters (such as resistivity and sonic transit time) by a specific logging instrument. The measured values ​​of formation physical parameters are physically meaningful values ​​calculated using this function, such as the natural gamma ray count rate (API units) or the formation resistivity (ohm-meters). The signal-to-noise ratio (SNR) is a measure of the ratio of the effective signal strength to the background noise strength in the raw data stream and is a key indicator for evaluating data quality. Environmental correction parameters are a set of data used to compensate for the influence of wellbore conditions (such as well diameter and mud properties) on the measurement results. The numerical deviation interval is a range calculated based on the signal-to-noise ratio and environmental correction, quantifying the uncertainty of formation physical parameter measurements. The peak data trajectory represents the most probable sequence of measurements at continuous depths, while the data deviation region is a band-shaped area around this trajectory defined by the numerical deviation interval. Together, they constitute the data distribution object for subsequent graphical rendering, and the resulting data distribution waveform is an enhanced visualization of traditional well logging curves.

[0034] Furthermore, the digital oscilloscope in this application refers to a software-implemented visualization paradigm that analogizes well logging depth to the oscilloscope's time base axis and formation physical parameter measurements to voltage signal amplitudes. Through specific graphics rendering techniques, it simulates the effect of a digital oscilloscope displaying signal waveforms on a standard computer screen. This paradigm aims to transform the single, deterministic curves in traditional well logging charts into "data distribution waveforms" that dynamically and intuitively display data uncertainty, statistical distribution, and historical trajectories. Its core lies in software algorithms and graphics rendering techniques, rather than relying on specific oscilloscope hardware. This system typically runs on a general-purpose computer hardware platform at a well logging site's data acquisition and processing workstation or a remote monitoring center.

[0035] The following describes the process of the method provided in this implementation. Please refer to [link / reference]. Figure 1 This is a flowchart illustrating a data monitoring method based on a digital oscilloscope in an embodiment of this application.

[0036] S101: Receive the raw logging data stream output by the cable logging sensor, substitute the raw logging data stream into the pre-stored instrument response function, and obtain the formation physical parameter measurement value of the current sampling point.

[0037] In this context, "cable logging sensor" refers to a physical measurement device lowered into the well via a cable, such as a natural gamma ray or resistivity logging tool. The raw logging data stream refers to the sequence of raw electrical signals acquired by the sensor without inversion of formation physical parameters, such as voltage, current, or frequency values. The instrument response function is a pre-defined mathematical transformation model used to convert the raw logging data stream into formation parameters with clear physical meaning. Formation physical parameter measurements refer to the results obtained after the conversion, such as formation resistivity and porosity.

[0038] Specifically, after the cable logging operation begins, the data monitoring system receives continuous data frames from the downhole cable logging sensors in real time through the data acquisition interface. For each sampling depth point, the data monitoring system extracts the corresponding raw logging data stream and calls the instrument response function in memory that matches the current logging project. This function performs a series of corrections and transformations on the input raw data (such as millivolt-level voltage signals) to output a calibrated formation physical parameter measurement value. This process is performed once at each sampling point, forming a sequence of measurement values ​​that vary with depth.

[0039] In some embodiments, this step can be implemented in several ways: Optionally, the data monitoring system uses a lookup table method, that is, a large lookup table (LUT) is pre-made from the raw data measured by the instrument in the calibration well and the standard physical parameter values, and the physical parameter measurement value corresponding to the raw data stream is looked up by interpolation during runtime; Optionally, the data monitoring system uses a formula method, that is, the instrument response function is one or a set of explicit mathematical formulas, and the system substitutes the raw logging data stream as variables into the formula to directly calculate the formation physical parameter measurement value. It is understood that other methods can also be used, such as a response model based on a neural network, which is not limited here.

[0040] It should be noted that the specific implementation of the instrument response function can be a data model. The core of this model is to establish a precise mapping relationship between the raw electrical signals acquired by the sensor and standard formation physical parameters. The training phase is mainly conducted in the laboratory or in dedicated calibration wells. The training data consists of two parts: one part is the raw data stream acquired by the logging instrument under conditions with known physical properties (such as standard cores or formation sections with different porosities, fluid saturation, and lithology) (e.g., induced voltage of different coils, pulse count rate of gamma probes, etc.), which serves as the model's input; the other part is the "true" formation physical parameters corresponding to these environments, obtained through high-precision independent measurements (e.g., core porosity measured in the laboratory, precisely calibrated resistivity values, etc.), which serves as the model's output labels. The goal of training is to adjust the parameters within the model so that the error (e.g., mean square error) between the physical parameter values ​​predicted by the model based on the input raw data stream and the "true" values ​​is minimized. This process utilizes a large number of calibration data points covering the instrument's expected operating range to ensure the model's generalization ability and accuracy.

[0041] In a simpler implementation, the model can be a high-dimensional lookup table (LUT) mapped using a multidimensional interpolation algorithm. In a more complex implementation, it is typically a trained deep neural network (DNN). This network contains multiple hidden layers; the input layer receives a vector of the raw well logging data stream, and the output layer outputs predicted values ​​for one or more formation physical parameters. The weights and biases in the network are model parameters learned during training; together, they form a complex nonlinear function capable of capturing the highly complex relationship between the raw signal and formation physical parameters.

[0042] The model is used during actual wireline logging operations. The data monitoring system takes the raw logging data stream received in real time from downhole sensors as input and feeds it into this pre-trained and fixed instrument response function model. The model performs a single forward propagation calculation, which can solve for the formation physical parameter measurements corresponding to the current sampling depth point in a very short time, providing core data for subsequent steps.

[0043] S102. Calculate the signal-to-noise ratio of the original logging data stream at the current sampling point, and generate the numerical deviation interval corresponding to the formation physical parameter measurement value based on the signal-to-noise ratio and environmental correction parameters.

[0044] The signal-to-noise ratio (SNR) represents the power ratio of the effective signal component to the noise component in the raw data stream, and is a direct measure of data quality. Environmental correction parameters refer to a series of parameters used to correct for the influence of non-formation factors such as wellbore diameter and mud invasion on measurements. The numerical deviation interval refers to a range centered on the measured value of formation physical parameters; its width reflects the degree of uncertainty of that measurement.

[0045] Specifically, after obtaining the formation physical parameter measurements from step S101, the data monitoring system performs parallel signal processing on the original logging data streams of the same batch to assess their quality. The system calculates the signal-to-noise ratio (SNR) of the current sampling point by performing frequency domain analysis or statistical analysis on the data stream. A higher SNR indicates better data quality and lower uncertainty. Simultaneously, the system retrieves environmental correction parameters from the database corresponding to the current depth and logging project. Finally, the system inputs the SNR and environmental correction parameters into a predefined deviation model to calculate the width of the numerical deviation interval.

[0046] It should be noted that the bias model is a key tool for quantifying uncertainty, and its purpose is to provide a reasonable confidence range for each measurement based on real-time data quality and environmental influences.

[0047] The model training phase relies on a large amount of historical logging data or specially designed experimental data. In the training dataset, each sample contains a set of inputs and a corresponding output. Inputs include: the signal-to-noise ratio (SNR) of the raw data stream calculated using signal processing algorithms, a series of environmental correction parameters (such as wellbore diameter, mud temperature, mud resistivity, etc.), and the measured values ​​of formation physical parameters at that time. The output is the actual deviation (or standard deviation) of the measured value relative to the "true value" or the mean of multiple repeated measurements. The "true value" can come from high-precision core analysis or statistical results obtained from multiple repeated measurements in extremely stable formation sections. The training standard is to enable the model to learn the quantitative relationship between the signal-to-noise ratio, environmental parameters, and measurement uncertainty, i.e., minimizing the difference between the model's predicted deviation and the actual observation deviation.

[0048] The model can be a multiple regression model or a small artificial neural network. For example, it can be a multiple polynomial regression function: Deviation width = c0 + c1*SNR + c2*(1 / SNR) + c3*well caliper + c4*mud temperature + ..., where the coefficients c0, c1, ... are obtained by fitting training data. Alternatively, it can be a shallow neural network that takes a vector of signal-to-noise ratio and environmental parameters as input and outputs a scalar (half-width of the deviation interval). This network can learn and express the complex nonlinear coupling effects between these influencing factors; for example, at low signal-to-noise ratios, changes in well caliper exacerbate the impact on deviation.

[0049] After calculating the signal-to-noise ratio (SNR) of the measured formation physical parameters and the raw data stream at the current sampling point, the system retrieves the corresponding environmental correction parameters from the database or real-time sensors. Then, the SNR and these environmental parameters are fed into a pre-trained bias model. The model outputs a specific numerical value, which is the half-width of the numerical bias interval (e.g., ±0.5 ohm·m). The final numerical bias interval is then defined with the current measurement value as the center and this half-width as the radius.

[0050] In some embodiments, this step can be implemented in several ways: Optionally, the deviation can be determined using a preset lookup table. The data monitoring system queries the corresponding standard deviation value in a multidimensional lookup table based on the calculated signal-to-noise ratio level and the current combination of environmental correction parameters, and generates a numerical deviation interval accordingly; alternatively, the deviation can be determined using a dynamic calculation model. The data monitoring system takes the signal-to-noise ratio and environmental correction parameters as inputs, substitutes them into a multivariate function model (such as a multinomial regression model), and calculates the half-width of the numerical deviation interval in real time. It is understood that other methods can also be used, such as an evaluation system based on fuzzy logic, which is not limited here.

[0051] In some embodiments, rapid cable movement or momentary strong interference in the signal transmission link can cause a sudden and drastic drop in the signal-to-noise ratio, resulting in an abnormally widened deviation range in the calculated values. To address this, the data monitoring system sets a maximum allowable width threshold for the deviation range and combines it with a smoothing filter based on the mean signal-to-noise ratio using a sliding window to prevent single-point momentary strong noise from having an excessive and misleading impact on the final visualization results.

[0052] S103. Based on the measured values ​​of formation physical parameters and the numerical deviation range, generate a data distribution object containing peak data trajectory and data deviation area.

[0053] The peak data trajectory refers to a linear trajectory formed by the measured values ​​of stratigraphic physical parameters from a continuously sampled sequence, representing the most probable value at each depth. The data deviation region refers to the band-shaped area surrounding the peak data trajectory, its boundaries defined by the numerical deviation interval. Pixel weight refers to a numerical value assigned to each pixel within the data deviation region, used to determine its color or brightness during rendering. The data distribution object is a data structure containing all the above information, used to guide subsequent graphics rendering.

[0054] Specifically, based on the measured values ​​obtained in step S101 and the deviation interval obtained in step S102, the data monitoring system constructs a data structure for graphic rendering for the current sampling point, namely a data distribution object. This object logically defines a probability density distribution centered on the measured values ​​of the geological physical parameters. The peak data trajectory corresponds to the peak value of the distribution, and the data deviation region corresponds to the main range of the distribution. The pixel weights within each region are set to decrease smoothly from the peak data trajectory (highest weight) to the region edge (lowest weight), for example, following a Gaussian or triangular distribution.

[0055] In some embodiments, this step can be implemented in several ways: Optionally, a one-dimensional Gaussian function can be used to generate weights. The data monitoring system maps the width of the numerical deviation interval to the standard deviation (σ) of the Gaussian function, and generates a Gaussian distribution with the measured value as the mean (μ). The pixel weight of any numerical point within the region is the function value of that point on the Gaussian curve. Optionally, a piecewise linear function can be used to generate weights. The data monitoring system sets the weight at the peak data trajectory to 1, the weight at the edge of the data deviation region to 0, and the weight in the middle part to be calculated through linear interpolation. It is understood that other methods can also be used, such as using other types of window functions to define the weight distribution, which is not limited here.

[0056] S104. Render the data distribution object graphically and generate the data distribution waveform of the geological physical parameter measurement values ​​according to the pixel weight.

[0057] Graphics rendering refers to the process of transforming the abstract data structure of data distribution objects into a visible image on the screen. Data distribution waveform refers to the glow-like banded waveform that is ultimately presented on the display interface, reflecting the characteristics of data distribution; it is an enhancement of the traditional single-line logging curve.

[0058] Specifically, the data monitoring system calls the graphics processing unit (GPU) or utilizes the graphics library of the central processing unit (CPU) to draw the series of depth-varying data distribution objects generated in step S103 onto the designated logging channel on the display interface. For each pixel in the display area, the system determines whether it falls within the data deviation area of ​​a certain data distribution object. If so, it assigns a corresponding color and opacity based on the pixel weight corresponding to the pixel position. Generally, pixels with higher weights (closer to the peak trajectory) have higher brightness and higher opacity, creating a visual effect of a bright center and gradually darkening edges.

[0059] In some embodiments, this step can be implemented in several ways: Optionally, a forward ray casting method can be used. For each data distribution object, the system calculates its coverage area on the screen and actively fills the pixels within that area, calculating the color value of each pixel according to its weight; Optionally, a fragment shader-based method can be used. The system passes the peak trajectory and deviation range as geometric information to the GPU. In the fragment shader, the pixel weight of each fragment (pixel) is calculated in real time based on its position relative to the peak trajectory, and the final color is output. It is understood that other methods can also be used, such as texture mapping-based techniques, which are not limited here.

[0060] In some embodiments, rapid fluctuations in stratigraphic parameters can cause multiple data distribution objects to overlap on the same pixel column of the screen. To address this, the data monitoring system performs a blending process on the multiple weight values ​​falling on the same pixel during rendering, for example, using additive blending or maximum value blending. That is, the final brightness of a pixel depends on the sum or maximum value of the weights of all the data distribution objects covering it. This process ensures that even with drastic data fluctuations, the overall brightness of the waveform accurately reflects the data density at that pixel location.

[0061] The following provides a more detailed description of the process of the method provided in this implementation. Please refer to [link / reference]. Figure 2 This is another flowchart illustrating the data monitoring method based on a digital oscilloscope in this application embodiment.

[0062] S201: Receive the raw logging data stream output by the cable logging sensor, substitute the raw logging data stream into the pre-stored instrument response function, and obtain the formation physical parameter measurement value of the current sampling point.

[0063] Refer to step S101, which will not be repeated here.

[0064] S202. Calculate the signal-to-noise ratio of the original logging data stream at the current sampling point, and generate the numerical deviation range corresponding to the formation physical parameter measurement value based on the signal-to-noise ratio and environmental correction parameters.

[0065] Refer to step S102, which will not be repeated here.

[0066] S203. Based on the measured values ​​of formation physical parameters and the numerical deviation range, generate a data distribution object containing peak data trajectory and data deviation area.

[0067] Refer to step S103, which will not be repeated here.

[0068] S204. Render the data distribution object of the current sampling point to the instantaneous data buffer.

[0069] The instantaneous data buffer is a dedicated memory or video memory area used to store the rendering result of the data distribution object of the latest sampling point. It represents the logging instrument's immediate response at the current depth.

[0070] Specifically, after executing step S203, the data monitoring system does not directly render the generated data distribution object onto the final display screen. Instead, it first renders it into an off-screen instantaneous data buffer of the same size as the well logging display area. This process is similar to the rendering process described in step S104, which maps the pixel weights of the data distribution object to the color and transparency values ​​of the corresponding pixels in the buffer. The contents of this buffer are completely rewritten each time a sampling point arrives, so it only contains the latest data information.

[0071] In some embodiments, this rendering step can be implemented in several ways: Optionally, the data monitoring system allocates an independent framebuffer object (FBO) for the transient data buffer and draws the data distribution object as a texture onto a rectangle bound to the FBO; alternatively, the data monitoring system creates a two-dimensional array on the CPU side as a buffer, traverses the pixel range covered by the data distribution object, calculates the color value of each pixel, and stores it in the array. It is understood that other methods can also be used to implement this step, and no limitation is made here.

[0072] In some embodiments, there may be situations where special marking is required to highlight the current measurement value. To address this, when rendering the data distribution object to the instantaneous data buffer, the data monitoring system, in addition to generating a glow effect based on pixel weights, can also render an extra bright, vividly colored thin line at the location of the peak data trajectory to clearly indicate the most likely measurement value, ensuring it remains clearly distinguishable in subsequent mixed displays with historical data.

[0073] S205. Calculate the historical hit frequency of the corresponding pixel position in the historical cumulative buffer.

[0074] The historical accumulation buffer is another dedicated memory or video memory area used to record the cumulative frequency of occurrence of all data points at each pixel location over a past period. Historical hit frequency refers to the cumulative weight or number of times a pixel location is covered by a data distribution object within the historical accumulation buffer.

[0075] Specifically, this step involves reading and analyzing the historical accumulation buffer. Before updating historical data, the data monitoring system needs to know the historical state of each pixel at the previous moment. Therefore, the system accesses the historical accumulation buffer and reads the value stored in each pixel. This value directly represents or indirectly reflects the historical hit frequency of that pixel location. For example, this value could be the weighted sum of all past sampling points at that pixel, or a glow intensity value after attenuation calculation.

[0076] In some embodiments, this step of statistics can be implemented in several ways: Optionally, the historical accumulation buffer directly stores the floating-point accumulated weight value, which the system can directly read; alternatively, the historical accumulation buffer stores the hit count in integer form, which the system reads as a frequency metric. It is understood that other methods can also be used, such as encoding the historical hit frequency into a specific color channel of the pixel, which is not limited here.

[0077] In some embodiments, the historical accumulation buffer may need to be reset, such as when a logging project is switched or a user requests to clear historical tracks. To address this, the data monitoring system provides a reset interface. Upon receiving a reset command, the system will clear all pixel values ​​in the historical accumulation buffer to zero, erase all historical track information, and begin a completely new data accumulation process.

[0078] S206. Update the historical cumulative buffer based on the historical hit frequency and the preset decay time constant.

[0079] The decay time constant is a preset parameter that controls the rate at which historical data is "forgotten." The larger the constant, the slower the historical trajectory disappears, and vice versa.

[0080] Specifically, the data monitoring system executes a decay algorithm to update the historical accumulation buffer. The system iterates through each pixel in the historical accumulation buffer, multiplying its current value (i.e., the historical hit frequency calculated in step S205) by a decay coefficient less than 1. This decay coefficient is typically derived from the decay time constant and the sampling time interval, for example, decay coefficient = exp(-Δt / τ), where Δt is the sampling time interval and τ is the decay time constant. This process simulates the natural decay of glow on a physical fluorescent screen, causing distant data trajectories to gradually darken and eventually disappear.

[0081] In some embodiments, this update step can be implemented in several ways: Optionally, it can be done in the GPU using a full-screen shader program. This shader reads the old texture from the history accumulation buffer, multiplies the color value of each pixel by a decay factor, writes the result into a new texture, and finally replaces the old texture with the new texture; alternatively, it can be done on the CPU by traversing a two-dimensional array representing the history buffer and performing a multiplication operation on each element. It is understood that other methods can also be used, such as using a non-linear decay function, which is not limited here.

[0082] In some embodiments, formations at different depths exhibit varying stability, requiring different decay rates for observation. To address this, the data monitoring system allows users to dynamically adjust the decay time constant. For example, when observing stable, homogeneous formations, users can increase the overview time constant to observe the long-term statistical characteristics of the data; when analyzing rapidly changing thin interbedded layers, the constant can be decreased to refresh historical trajectories more quickly, focusing on recent changes.

[0083] S207. Color-encoded and mixed instantaneous data buffer and updated historical cumulative buffer to generate data distribution waveform.

[0084] Color coding mixing refers to the process of merging two or more image buffers into a final image according to specific rules (such as different colors and transparency).

[0085] Specifically, the data monitoring system merges the instantaneous data buffer containing the latest data in step S204 with the historical cumulative buffer after decay update in step S206. The merging method is typically pixel-level additive mixing. That is, the color of each pixel ultimately displayed on the screen is the sum of the color value of the corresponding pixel in the instantaneous data buffer and the color value of the corresponding pixel in the historical cumulative buffer. In this way, the current data point is superimposed in its brightest form on the gradually darkening historical trajectory, forming a clear dynamic waveform.

[0086] In some embodiments, this blending step can be implemented in several ways: Optionally, the blending function provided by the graphics API can be utilized, for example, by setting the blending mode to GL_FUNC_ADD and then sequentially drawing the contents of the history buffer and the transient buffer to the screen; alternatively, a dedicated blending shader can be written that simultaneously samples both buffers as input textures, adds their color values ​​internally, and outputs the final pixel color. It is understood that other methods can also be used, such as maximum value blending or other more complex compositing algorithms, which are not limited here.

[0087] In some embodiments, the mixed pixel brightness values ​​may exceed the maximum brightness that the display can represent (i.e., oversaturation). To address this, the data monitoring system applies a tone mapping algorithm after mixing. This algorithm non-linearly compresses high dynamic range brightness values ​​to a standard display range (e.g., 0-255), preserving the sense of detail in high-brightness areas while avoiding the appearance of pure white "burnt" areas, thus ensuring the visual quality of the final waveform.

[0088] In some embodiments, based on step S207, in order to further enhance the readability of the waveform, the data monitoring system sets a first hue range for the data in the instantaneous data buffer to represent the current measurement value; sets a second hue range for the data in the historical cumulative buffer to represent the historical trajectory value; maps the historical hit frequency to the brightness or saturation in the second hue range; and superimposes the data in the first hue range onto the data in the second hue range to generate a composite data distribution waveform.

[0089] Here, the first and second hue ranges refer to two preset color systems, such as green and blue tones. Historical trajectory values ​​refer to the data stored in the historical accumulation buffer. The composite data distribution waveform refers to the final display waveform that combines the two hues, providing richer information.

[0090] Specifically, the data monitoring system no longer simply adds the monochrome brightness of the instantaneous and historical buffers, but instead employs a more complex color encoding scheme. In step S204, when the data distribution object is rendered to the instantaneous data buffer, the system colors it using a first hue range (e.g., bright green). After updating the historical accumulation buffer in step S206, the system maps the historical hit frequencies (a normalized value from 0 to 1) in that buffer to specific colors in a second hue range (e.g., a gradient from dark blue to cyan) during rendering. The mapping method can be that the higher the frequency, the brighter the color or the higher the saturation. Finally, in step S207, the system renders the colored instantaneous data buffer (green foreground) on top of the colored historical accumulation buffer (blue background) with a transparent overlay, generating the final composite data distribution waveform.

[0091] In some embodiments, color encoding for this step can be implemented in several ways: Optionally, the HSV / HSL color space can be used. The data monitoring system fixes the hue (e.g., blue) of the second hue range and linearly maps historical hit frequencies to the saturation or value / lightness channels, generating a smooth gradient from dark blue to light blue; alternatively, a color look-up table (CLUT) can be used. The system predefines a color gradient bar from black to light blue, and historical hit frequencies are used as indexes to look up the corresponding color value in the lookup table. It is understood that other methods can also be used, such as using more complex color mixing modes, which are not limited here.

[0092] In some embodiments, when instantaneous values ​​highly overlap with historical trajectories, the foreground green may completely obscure the background blue, making it impossible to determine the stability of the historical data. To address this, the data monitoring system can employ a non-linear transparency blending algorithm during overlay and mixing. For example, the transparency of an instantaneous data distribution object is determined not only by its own pixel weights but also by the brightness of the historical data beneath it. When the historical data brightness is high, the instantaneous high-brightness green is used to ensure that even when the data is stable, the user can still perceive the presence of the high-density historical trajectory underneath.

[0093] In some embodiments, after completing the waveform generation described above, the data monitoring system will monitor the direction of depth change of the cable logging sensor in order to handle common situations such as retesting or encountering resistance during logging. In the depth interval where the direction of depth change reverses, the data distribution object of multiple sampling points will be collected as the interval distribution object. The interval distribution object will be integrated in both the depth and measurement dimensions to render and generate a data distribution map that simultaneously represents the depth deviation and the measurement deviation.

[0094] The direction of depth change refers to whether the cable logging sensor is measuring downwards or upwards. The depth interval where the direction of depth change reverses refers to the section of the wellbore through which the instrument changes from downward to upward (or vice versa). The interval distribution object is a two-dimensional data structure used to accumulate the distribution information of all measurement data within that depth interval. The data distribution map is a two-dimensional color map or density map with depth as one axis and measurement values ​​as the other.

[0095] Specifically, the data monitoring system continuously monitors the depth sensor readings to determine whether the depth value is increasing (downward) or decreasing (upward). When a reversal of the depth change direction is detected (e.g., after encountering an obstacle, the instrument is raised and then lowered again), the system marks the depth point where the reversal occurred. When the instrument retraces through that depth interval, the system aggregates the data distribution objects from all sampling points within that interval (including measurements from different directions and times). The system creates a two-dimensional grid (or buffer) with depth and measurement values ​​as coordinate axes, projects each data distribution object onto this grid, and accumulates it into the corresponding grid cell according to its pixel weight, forming a two-dimensional interval distribution object. Finally, this two-dimensional grid is rendered into a heatmap or density map, where the shade or hue of the color in the image represents the probability density of the data occurring under that combination of depth and measurement value.

[0096] In some embodiments, this step of integration and rendering can be implemented in several ways: Optionally, the data monitoring system constructs a two-dimensional histogram. The depth axis and the measurement value axis are divided into several bins. The system traverses all the aggregated data distribution objects, assigns their energy (weights) to the corresponding two-dimensional bins, and finally maps the histogram count values ​​to colors for display. Optionally, the data monitoring system uses kernel density estimation (KDE). Each data distribution object is treated as a two-dimensional kernel function (e.g., a two-dimensional Gaussian function). The system sums all kernel functions to generate a smooth two-dimensional probability density surface, and then renders this surface as a color contour map or heatmap. It is understood that other methods can also be used, such as directly overlaying and rendering all data distribution objects in a semi-transparent manner; this is not limited here.

[0097] In some embodiments, errors in the depth measurement system itself can lead to a mismatch between the uplink and downlink depth measurements (i.e., depth drift). To address this, the data monitoring system performs a depth correction and alignment preprocessing step before integrating the data. The system can employ algorithms such as cross-correlation to automatically find the optimal depth offset between the uplink and downlink measurement curves, correct the depth of one measurement, and then aggregate and integrate the data distribution objects to ensure that the generated two-dimensional data distribution map accurately reflects the consistency of data from multiple measurements of the same geological body.

[0098] S208. Monitor the frequency of changes in the measured values ​​of formation physical parameters along the depth direction.

[0099] Among them, the frequency of change in the depth direction refers to the degree of drastic change in the value of formation physical parameters with depth, which can be understood as a measure of the "slope" or "curvature" of the logging curve on the depth axis.

[0100] Specifically, the data monitoring system maintains a sliding window containing measurements of formation physical parameters from the N most recent sampling points. Upon the arrival of each new sampling point, the system analyzes the data sequence within the window to calculate its frequency of change. This can be achieved by calculating the average or root mean square of the absolute values ​​of the first-order differences (approximate derivatives), or by performing a short-time Fourier transform (STFT) on the data and analyzing the energy of its high-frequency components. The calculated frequency values ​​are used to determine whether the formation is undergoing rapid changes.

[0101] In some embodiments, this step of monitoring can be implemented in several ways: Optionally, the difference can be calculated. The data monitoring system calculates the difference between the current measurement and the previous measurement, takes its absolute value as the instantaneous rate of change, and performs a smooth average of the rates of change over the most recent several points; Optionally, the local variance can be calculated. The data monitoring system calculates the variance of the measurements within a sliding window. The larger the variance, the more drastic the data fluctuation and the higher the frequency of change. It is understood that other methods can also be used, such as using wavelet transform to analyze the change characteristics at different scales, which is not limited here.

[0102] In some embodiments, high-frequency noise in the measurements may interfere with the calculation of the frequency variation. To address this, the data monitoring system applies a low-pass filter (such as a moving average or Gaussian filter) to the data within the sliding window before calculating the frequency variation to remove random noise interference, ensuring that the calculated frequency variation accurately reflects the trend of formation properties rather than measurement noise.

[0103] S209. When the frequency of change exceeds the preset change threshold, increase the rendering sampling rate of the data distribution object.

[0104] The preset change threshold is a critical value used to determine whether changes in the geological formation are drastic. The rendering sampling rate refers to the number of points used to generate and render data distribution objects per unit depth.

[0105] Specifically, the data monitoring system compares the frequency of change calculated in step S208 with a preset threshold. If the frequency of change is below the threshold, it indicates that the formation is stable, and the system can use a standard, lower rendering sampling rate (e.g., rendering one point every 10 centimeters) to save computational resources. Once the frequency of change exceeds the threshold, the system determines that it has entered a region with complex geological features (such as thin interlayer interfaces) and immediately increases the rendering sampling rate. Increasing the sampling rate means that the system will perform S203 and subsequent rendering steps at a denser depth (e.g., increasing to rendering one point every 2 centimeters), drawing more detailed and realistic waveforms on the screen to capture subtle changes in the formation.

[0106] In some embodiments, this adjustment can be implemented in several ways: Optionally, dynamic interpolation. Between the original sampling points, the data monitoring system dynamically increases interpolation points according to the frequency of change, and generates and renders a data distribution object for each interpolation point; Optionally, adjusting the rendering step size. During the rendering loop, the data monitoring system dynamically adjusts the depth step value according to the frequency of change; the step value decreases when the change is drastic and returns to normal when the change is gradual. It is understood that other methods can also be used, such as establishing a continuous functional relationship between the frequency of change and the rendering sampling rate to achieve stepless smooth adjustment of the sampling rate, which is not limited here.

[0107] In some embodiments, frequent oscillations around a threshold can cause the rendering sampling rate to switch continuously, leading to screen flickering or performance instability. To address this, the data monitoring system introduces a hysteresis mechanism. This involves setting two thresholds: a higher "entry" threshold and a lower "exit" threshold. The sampling rate is increased only when the frequency of change exceeds the "entry" threshold, and the normal sampling rate is restored only when the frequency drops below the "exit" threshold. This effectively avoids unstable switching in critical states.

[0108] In some embodiments, when a large-scale display of an entire well section or multiple well sections is required, in order to address the issue that the number of data points far exceeds the number of screen pixels, the data monitoring system determines the number of data points within the depth range to be rendered and the physical pixel width of the corresponding display area; when the physical pixel width is less than the number of data points, it groups the multiple data distribution objects corresponding to the physical pixel width; it performs a weighted summation of the probability density function on the multiple data distribution objects in each group to generate an aggregated data distribution object; and it performs graphics rendering based on the pixel weights of the aggregated data distribution object.

[0109] The physical pixel width refers to the number of horizontal pixels occupied by the logging channel on the screen. An aggregated data distribution object is a new composite distribution object formed by merging multiple original data distribution objects. The weighted summation of the probability density function refers to mathematically superimposing multiple probability distributions to form a new probability distribution.

[0110] Specifically, when a user zooms in on a well logging map to a very large depth range (e.g., displaying formations thousands of meters deep), a single pixel column may need to represent tens or even hundreds of raw data sampling points. To avoid information loss, the data monitoring system first determines the pixel width of the current display area and the total number of data points to be rendered. When the number of data points exceeds the pixel width, the system groups all data points according to their horizontal pixel positions on the screen. For each pixel position (i.e., each group), the system performs a weighted summation of all data distribution objects within the group (which are essentially probability density functions, such as Gaussian functions). This summation process generates a new, more complex probability density function, namely the aggregated data distribution object. This aggregated object accurately reflects the statistical distribution characteristics of all the raw data points represented by that pixel column. Finally, the system performs a one-time graphical rendering on that pixel column based on the pixel weights of this aggregated object.

[0111] It's important to note that the weighted summation of all data distribution objects within a group essentially merges the probability distributions of all discrete data points into a continuous, overall probability distribution within the depth range represented by a single pixel. Specifically, suppose a vertical column of pixels on the screen needs to display N data points from depth D_start to D_end. Each data point i (at depth d_i) has a geological physical parameter measurement value μ_i and a numerical deviation interval, which can be represented as a probability density function (PDF), such as a Gaussian distribution G(x; μ_i, σ_i) with mean μ_i and standard deviation σ_i corresponding to the width of the deviation interval. Then, the aggregated data distribution object P_agg(x) for this pixel column is a linear superposition of these N Gaussian distributions, i.e., P_agg(x) = (1 / N) * Σ[G(x; μ_i, σ_i)], where x represents the value of the geological physical parameter. This P_agg(x) function describes the relative probability of any measurement value x occurring within the entire depth range from D_start to D_end. For example, if this depth segment happens to cross an interface from a low-resistivity layer to a high-resistivity layer, then the μ_i values ​​of these N data points will exhibit two clustering centers. Therefore, the aggregated P_agg(x) function will show two distinct peaks (i.e., a bimodal distribution), rather than a blurred single peak resulting from averaging. During final rendering, the brightness or color of each pixel in this pixel column is determined by the value of its represented measurement x on the P_agg(x) function. In this way, even on a macroscopic scale, a pixel column on the screen can accurately reflect the complex statistical characteristics of the data within its covered depth segment through the distribution of brightness, avoiding information distortion caused by simple sampling or averaging.

[0112] In some embodiments, this aggregation step can be implemented in several ways: Optionally, discrete summation can be used. The data monitoring system discretizes the measurement range into a one-dimensional array, traverses each data distribution object within the group, accumulates its probability density distribution into the array, and finally uses this array as the pixel weight distribution of the aggregation object; Optionally, parametric summation of a Gaussian Mixture Model (GMM) can be used. If the original data distribution objects are all Gaussian functions, the system directly aggregates the parameters (mean, variance, weights) of these Gaussian functions to form a GMM, and calculates directly based on the GMM during rendering. It is understood that other methods can also be used, such as using Fast Fourier Transform to perform convolution summation in the frequency domain, which is not limited here.

[0113] In some embodiments, when the number of data points within a pixel column is extremely large, performing an exact summation of the probability density function for all data distribution objects can be very time-consuming, affecting the smoothness of scaling operations. To address this, the data monitoring system employs a Level of Detail (LOD) aggregation strategy. For regions with very high data density, the system no longer performs an exact PDF summation but instead uses a faster approximation method. For example, it calculates only the mean and variance of all data points within a group and then approximates the aggregated data distribution object with a single, equivalent Gaussian distribution. While this approximation loses some detail (such as bimodal distribution features), it improves rendering performance at large scales.

[0114] In some embodiments, when multiple channels are displayed, the data monitoring system, in order to assist in multi-parameter cross-sectional analysis, receives another measurement value of another stratigraphic physical parameter and generates a second data distribution waveform corresponding to the other measurement value; the numerical deviation range of the data distribution waveform is used as the confidence index of the first parameter; the numerical deviation range of the second data distribution waveform is used as the confidence index of the second parameter; when the confidence index of the first parameter and the confidence index of the second parameter simultaneously meet preset conditions, the overlapping area of ​​the two data distribution waveforms is highlighted.

[0115] The confidence index is a numerical value that quantifies the reliability of data, and is usually inversely proportional to the range of numerical deviation. Preset conditions refer to a set of multi-parameter threshold combinations predefined by geological interpretation experts to identify specific geological bodies (such as reservoirs and gas layers). The overlapping region refers to the portion of the deviation areas of two data distribution waveforms at the same depth that overlap on the measurement axis.

[0116] Specifically, the data monitoring system simultaneously processes two or more formation physical parameters (e.g., resistivity and neutron porosity). For each parameter, the system generates its data distribution waveform according to the method described in this application. The system uses the width (or its reciprocal) of the numerical deviation interval for each parameter as a confidence index for that parameter at the current depth. Simultaneously, the system loads a preset rule base, such as "when resistivity is greater than 100 ohm-meters and neutron porosity is less than 5%, it may be gas-bearing sandstone." During rendering, for each depth point, the system checks whether the confidence indices for both the first parameter (resistivity) and the second parameter (neutron porosity) are higher than a certain threshold (indicating that both data are reliable), and whether their measured values ​​simultaneously fall within the range of the preset rules. If all conditions are met, the system performs special highlighting rendering on the overlapping area of ​​the data distribution waveforms of these two parameters at that depth, for example, highlighting it with bright yellow or a flashing effect.

[0117] In some embodiments, this step of judgment and rendering can be implemented in several ways: Optionally, a stencil buffer can be used. The system first renders the regions that meet the conditions of the first parameter to the stencil buffer according to preset conditions, then renders the regions that meet the conditions of the second parameter. Drawing is only performed on pixels that have been marked in the stencil buffer, and finally, the regions that pass the test are highlighted. Optionally, logical judgment can be performed in the fragment shader. When drawing each pixel, the shader simultaneously obtains the measured values ​​and deviation ranges of the two parameters at that depth, performs logical judgment internally, and outputs a highlight color if the conditions are met. It is understood that other methods can also be used, such as pre-calculating the depth segments that meet the conditions and rendering them as an independent layer, which is not limited here.

[0118] In some embodiments, preset conditions may be hard thresholds, potentially missing some critical but equally valuable geological anomalies. To address this, the data monitoring system can employ fuzzy logic-based judgment rules. The preset conditions are no longer simple "greater than / less than," but defined as membership functions. The system calculates the membership degree of each parameter's measured value to its target interval and performs a fuzzy logic operation (e.g., fuzzy AND) on the membership degrees of the two parameters along with their confidence indices to obtain a "probability" score. Ultimately, the brightness or color saturation of the highlighted area is proportional to this "probability" score, achieving a smooth transition from "not satisfied" to "fully satisfied," providing interpreters with richer decision-making information.

[0119] It should be noted that the judgment rules based on fuzzy logic transform the experiential knowledge of geological experts into a series of continuous mathematical functions to achieve more refined and robust pattern recognition. In specific implementation, it is first necessary to define the membership function of each parameter used for cross-hatching analysis (such as resistivity and neutron porosity) for the target geological body (such as a "gas layer"). For example, for the resistivity parameter, its "high resistivity" membership function μ_res(x) can be a sigmoid function (such as a sigmoid function) or a trapezoidal function. This function maps the input resistivity value x to a membership degree in the interval [0, 1]. For example, the membership degree is 0 when the resistivity is below 50 ohm-meters, smoothly increases between 50 and 150, and is 1 when it is above 150. Similarly, a "low porosity" membership function μ_neu(y) is defined for neutron porosity. When determining whether a point at a certain depth is a "gas layer," the system first obtains the resistivity measurement value *x* and the neutron porosity measurement value *y* for that point, and calculates *μ_res(x)* and *μ_neu(y)* respectively. Then, a "fuzzy AND" operation is performed to combine these two conditions. A common "fuzzy AND" operator is to take the minimum of the two values, i.e., *μ_gas* = min(*μ_res(x)*, *μ_neu(y)*). This *μ_gas* value represents the probability that the point "conforms to gas layer characteristics." To incorporate data quality, this probability score can be further multiplied by confidence indices for the two parameters (e.g., normalized values ​​*C_res* and *C_neu*, which are inversely proportional to the width of the deviation interval) to obtain the final composite score: Score = *μ_gas* *C_res* *C_neu*. The score value in the [0, 1] range is ultimately used to control the rendering effect of overlapping areas, such as directly mapping it to the transparency or brightness of the highlight color, so as to visually present a smooth transition from "most likely" to "most unlikely", providing more layers of explanatory information than the hard thresholding method.

[0120] In this embodiment, a data monitoring method that quantifies the uncertainty of measured values ​​and integrates it into the graphic rendering process is adopted. This method can upgrade traditional deterministic logging curves into probabilistic data distribution waveforms. Combined with techniques such as instantaneous / historical data separation, adaptive sampling rate, and multi-parameter confidence correlation, it effectively solves the problems of information loss, cognitive bias, and decision delay caused by the single data visualization method in the prior art. This enables a more comprehensive, dynamic, and intuitive display of cable logging data, improving the ability of field engineers to identify geological features and their decision-making efficiency.

[0121] The data monitoring system in the embodiments of this invention is described below from the perspective of hardware processing. Please refer to [link / reference]. Figure 3 This is a schematic diagram of the physical device structure of a data monitoring system in an embodiment of this application.

[0122] It should be noted that, Figure 3 The structure of the data monitoring system shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0123] like Figure 3 As shown, the data monitoring system includes a CPU 301, which can perform various appropriate actions and processes according to a program stored in ROM 302 or a program loaded from storage section 308 into RAM 303, such as executing the methods described in the above embodiments. RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via bus 304. I / O interface 305 is also connected to bus 304.

[0124] The following components are connected to I / O interface 305: input section 306 including audio input devices, push-button switches, etc.; output section 307 including liquid crystal display (LCD) and audio output devices, indicator lights, etc.; storage section 308 including hard disks, etc.; and communication section 309 including network interface cards such as LAN (Local Area Network) cards, modems, etc. Communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as disks, optical disks, magneto-optical disks, semiconductor memories, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0125] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by CPU 301, it performs the various functions defined in the present invention.

[0126] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0127] Specifically, the data monitoring system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, it implements the data monitoring method based on a digital oscilloscope provided in the above embodiment.

[0128] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the data monitoring system described in the above embodiments; or it may exist independently and not be assembled into the data monitoring system. The storage medium carries one or more computer programs that, when executed by a processor of the data monitoring system, cause the data monitoring system to implement the digital oscilloscope-based data monitoring method provided in the above embodiments.

[0129] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0130] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

Claims

1. A data monitoring method based on a digital oscilloscope, characterized in that, The method, applied to a data monitoring system, includes: The system receives the raw logging data stream output from the cable logging sensor, substitutes the raw logging data stream into the pre-stored instrument response function, and obtains the formation physical parameter measurement value of the current sampling point. Calculate the signal-to-noise ratio of the original logging data stream at the current sampling point, and generate the numerical deviation range corresponding to the measured value of the formation physical parameter based on the signal-to-noise ratio and environmental correction parameters; Based on the measured values ​​of the formation physical parameters and the numerical deviation interval, a data distribution object containing peak data trajectories and data deviation regions is generated; the peak data trajectory corresponds to the measured values ​​of the formation physical parameters in a continuous sampling sequence, the coverage width of the data deviation region is determined by the numerical deviation interval, and the pixel weight within the data deviation region decreases from the peak data trajectory towards the edge; The data distribution object is graphically rendered, and the data distribution waveform of the measured values ​​of the formation physical parameters is generated according to the pixel weights.

2. The method according to claim 1, characterized in that, After the step of graphically rendering the data distribution object and generating the data distribution waveform of the stratigraphic physical parameter measurements based on the pixel weights, the method further includes: Monitor the frequency of variation of the measured values ​​of the formation physical parameters along the depth direction; When the change frequency exceeds a preset change threshold, the rendering sampling rate of the data distribution object is increased.

3. The method according to claim 2, characterized in that, After the step of monitoring the frequency of variation of the measured values ​​of the formation physical parameters along the depth direction, the method further includes: Monitor the direction of depth change of the cable logging sensor; Within the depth interval where the direction of depth change reverses, a data distribution object that aggregates multiple sampling points is used as an interval distribution object; The interval distribution objects are integrated in two dimensions: depth and measurement value, and a data distribution map that simultaneously represents depth deviation and measurement value deviation is generated by rendering.

4. The method according to claim 1, characterized in that, The step of rendering the data distribution object graphically and generating the data distribution waveform of the measured values ​​of the formation physical parameters according to the pixel weights specifically includes: Render the data distribution object of the current sampling point to the instantaneous data buffer; Statistically calculate the historical hit frequency of the corresponding pixel position in the historical cumulative buffer; The historical cumulative buffer is updated based on the historical hit frequency and the preset decay time constant. The instantaneous data buffer and the updated historical cumulative buffer are color-coded and mixed to generate the data distribution waveform.

5. The method according to claim 4, characterized in that, After the step of color-coding and mixing the instantaneous data buffer and the updated historical cumulative buffer to generate the data distribution waveform, the method further includes: A first hue range is set for the data in the instantaneous data buffer to characterize the current measurement value; A second hue range is set for the data in the historical cumulative buffer to represent historical trajectory values; Map the historical hit frequencies to brightness or saturation within the second hue range; The data from the first hue range is superimposed on the data from the second hue range to generate a composite data distribution waveform.

6. The method according to claim 1, characterized in that, The step of rendering the data distribution object graphically and generating the data distribution waveform of the measured values ​​of the formation physical parameters according to the pixel weights specifically includes: Determine the number of data points within the depth range to be rendered and the physical pixel width of the corresponding display area; When the physical pixel width is less than the number of data points, the multiple data distribution objects corresponding to the physical pixel width are grouped. The probability density function is weighted and summed for multiple data distribution objects within each group to generate an aggregated data distribution object; Graphics rendering is performed based on the pixel weights of the aggregated data distribution object.

7. The method according to claim 1, characterized in that, After the step of graphically rendering the data distribution object and generating the data distribution waveform of the stratigraphic physical parameter measurements based on the pixel weights, the method further includes: Receive another measurement value of another stratigraphic physical parameter, and generate a second data distribution waveform corresponding to the other measurement value; The numerical deviation range of the data distribution waveform is used as the confidence index of the first parameter; The numerical deviation range of the second data distribution waveform is used as the confidence index of the second parameter; When the confidence index of the first parameter and the confidence index of the second parameter both meet the preset conditions, the overlapping area of ​​the two data distribution waveforms is highlighted.

8. A data monitoring system, characterized in that, The data monitoring system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the data monitoring system to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the data monitoring system, it causes the data monitoring system to perform the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on the data monitoring system, it causes the data monitoring system to perform the method as described in any one of claims 1-7.

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