QT basin logging data processing system and method

Abstract

The invention relates to the technical field of data retrieval, in particular to a QT basin logging data processing system and method, and the system comprises a well section structure analysis module, a lithologic sequence reconstruction module, a reservoir physical property integration module, a sealing cover response extraction module and a combined optimal sequence generation module. According to the method, adjacent standard deviations and variation coefficients are calculated through gamma, density, resistivity and interval transit time, parameter sudden change intensity is quantified, effectiveness of a stratum interface is calibrated, keyword matching is replaced based on resistivity, gamma and density difference vector cosine similarity, and the problem of non-linear correlation misjudgment of lithologic parameters is solved; calculating the product of the porosity difference value and the permeability through a sliding window, recognizing the heterogeneous characteristics of the reservoir, extracting the second derivative peak value of gas logging data to construct an argillaceous rock capping effectiveness model, accumulating and integrating the reservoir stability, the fluorescence intensity and the sealing index, generating a new reservoir cap optimal sequence map, and achieving exploration priority sorting.

Description

Technical Field

[0001] The present invention relates to the technical field of data retrieval, and particularly to a QT basin logging data processing system and method. Background Art

[0002] The technical field of data retrieval encompasses the entire process of classifying, archiving, analyzing, and extracting multi-source, multi-structured, and multi-format data. The core content of this technical field mainly includes data acquisition, data preprocessing, data structuring, data index construction, and data query response. In practical applications, data retrieval technology covers means such as semantic understanding of raw data, metadata extraction, information filtering, and recombination to achieve rapid and accurate positioning of specific target data. In industries such as oil and gas exploration, medical imaging, and geographic information systems, data retrieval technology is widely applied in the automatic processing and retrieval processes of complex heterogeneous data sources due to its high efficiency and systematicness.

[0003] Among them, the QT basin logging data processing system refers to an information technology system dedicated to processing logging and well logging data in the geological exploration area of the QT basin. The technical matters targeted by this patent theme cover the processing of data elements such as logging depth information analysis, well logging lithology parameter classification, stratigraphic sequence division, and oil and gas show feature identification. The specific methods include arranging logging data in a time series through a regularized depth comparison relationship, completing lithology type identification based on keyword extraction from well logging description information, numbering and classifying the stratigraphic sequences identified during the drilling process using formation standard codes, and constructing oil and gas index identification factors by combining cuttings and oil and gas show records. This processing system relies on the above data analysis and identification means to complete the unified and structured integration of multi-round drilling data in the QT basin area.

[0004] Existing logging data processing systems rely on regularized depth comparison and keyword extraction, and it is difficult to capture the co-variation characteristics of parameter mutations in the integration of multi-source heterogeneous data in the QT basin. For example, logging depth analysis only arranges data in a time series, without quantifying the standard deviation and coefficient of variation of parameters such as gamma and resistivity, resulting in blurred interface calibration boundaries. Lithology classification uses keyword matching of well logging descriptions, ignoring the vector space relationship of resistivity and density difference ratios, and having insufficient discrimination for similar lithology sequences. The formation coding mechanism does not introduce a moving average window to analyze the dynamic relationship between porosity and permeability, and ignores the impact of permeability span on heterogeneity when evaluating reservoir stability with static data. Oil and gas show identification does not fuse the second derivative peak of gas logging data and the combined standard deviation of shale parameters, and relies on the qualitative judgment of the fluorescence curve morphology to determine the sealing property of the cap rock, with limited quantification accuracy. Traditional data integration lacks a multi-dimensional parameter multiplication and accumulation operation mechanism, and the optimization order of source-reservoir-cap combinations depends on manual experience weighting, with insufficient decision interpretability and repeatability. Summary of the Invention

[0005] The object of the present invention is to solve the disadvantages existing in the prior art, and to propose a QT basin logging data processing system and method.

[0006] To achieve the above object, the present invention adopts the following technical solutions: A QT basin logging data processing system includes: A well section structure analysis module, which is used to obtain logging data and wellbore trajectory, slice gamma, density, resistivity, and acoustic time difference parameters, calculate the standard deviation and coefficient of variation of adjacent slices, calibrate the effectiveness of the interface, output the boundary coordinates of the parameter mutation zone, and transfer them to the lithology sequence reconstruction module; A lithology sequence reconstruction module, which is used to call the boundary coordinates of the parameter mutation zone, extract the resistivity, natural gamma, and density difference ratio vector group, calculate the included angle between adjacent slices based on the cosine similarity algorithm, determine the attribution to generate lithology sequence numbers, and transfer them to the reservoir physical property integration module; A reservoir physical property integration module, which is used to receive the lithology sequence numbers, obtain density, acoustic, and porosity data, establish a processing window with a width of 3 meters using the moving average algorithm, calculate the product of the porosity difference and the permeability span, output the depth section of the steady-state reservoir window, and transfer it to the combination optimization generation module; A caprock response extraction module, which is used to obtain gas logging data and fluorescence curves, and calculate 、 、 The second derivative peak value, extract the product of the standard deviation of the acoustic impedance of the argillaceous rock section and the inverse resistivity coefficient, output the depth range of the effective caprock zone, and transfer it to the combination optimization generation module.

[0007] As a further solution of the present invention, the boundary coordinates of the parameter mutation zone include gamma mutation point series, density gradient inflection point sets, and resistivity transition intervals. The lithology sequence numbers specifically refer to sandstone sequence identifiers, mudstone attribution codes, and limestone stratification labels. The depth section of the steady-state reservoir window includes a porosity equilibrium section, a permeability stable span, and an acoustic time difference slow change zone. The depth range of the effective caprock zone covers the argillaceous seal top boundary, the gas derivative peak section, and the inverse anomaly closed domain; The porosity equilibrium section is a continuous section with a porosity range ≤ 5%.

[0008] As a further solution of the present invention, the well section structure analysis module includes: The data slice processing sub-module obtains the gamma value, density value, resistivity value, and acoustic time difference value in the logging data, divides the slice unit at a 1-meter vertical depth interval based on the wellbore trajectory, extracts the arithmetic mean of the four parameters in multiple units, and generates a parameter slice data set; The interface validity calibration sub-module calls the parameter slice data set, calculates the gamma mean difference, density mean difference, resistivity mean difference, and acoustic time difference mean difference of adjacent slice units, compares them with the gamma standard deviation threshold, density standard deviation threshold, resistivity standard deviation threshold, and acoustic time difference standard deviation threshold respectively, screens out the over-standard units, calculates the ratio of the multi-parameter standard deviation to the mean, and generates the adjacent parameter fluctuation value and the parameter coefficient of variation; The mutation zone boundary calibration sub-module calls the adjacent parameter fluctuation value and the parameter coefficient of variation, compares the four parameter coefficients of variation with the set variation threshold, extracts the top and bottom boundary coordinates of the vertical depth interval with continuous coefficient of variation exceeding the limit, and generates the parameter mutation zone boundary coordinates.

[0009] As a further solution of the present invention, the lithology sequence reconstruction module includes: The parameter mutation zone extraction sub-module calls the parameter mutation zone boundary coordinates, analyzes the absolute fluctuation amplitude of the resistivity curve, the mean shift of the natural gamma curve, and the standard deviation change of the density curve within the coordinate interval, calculates the absolute value of the numerical difference between adjacent sampling points for the three types of parameters respectively, divides the horizontal slice units according to the mutation zone coordinate range, statistically calculates the weight ratio of the three types of difference data in multiple units to the total fluctuation, constructs a three-dimensional ratio vector, and generates a difference ratio vector group; The sequence included angle calculation sub-module traverses the slice unit order based on the difference ratio vector group, extracts the three-dimensional vector data of adjacent units, projects the vectors to the polar coordinate system, calculates the sum of the products of the radial, tangential, and normal components of the two vectors using the cosine similarity algorithm, divides by the product of the vector modulus lengths, reversely infers the angle value and records the order, and generates an adjacent included angle sequence; The lithology attribution determination sub-module calls the adjacent included angle sequence, compares the angle data with the 45-degree lithology demarcation threshold determined by fitting the core calibration data, statistically calculates the length of the slice interval with continuous included angle values less than the threshold, marks the interval endpoints as the lithology unit segmentation points, and assigns increasing unit numbers in the order of the segmentation points to generate a lithology sequence number.

[0010] As a further solution of the present invention, the reservoir physical property integration module includes: The physical property data integration sub-module receives the lithology sequence number, extracts the density curve sampling point values, acoustic time difference curve amplitude values, and porosity curve discrete values corresponding to the depth segments according to the number, and uses the moving average algorithm to calculate the arithmetic mean of adjacent three sampling points for the three types of data respectively with a fixed window step size to generate a physical property mean window set; The steady state index calculation sub-module calls the physical property mean window set, statistically calculates the difference between the maximum and minimum values of the porosity curve within each window, superimposes the range span of the permeability curve within the window, and takes the product of the porosity difference and the permeability span as the steady state discriminant factor to generate a steady state index sequence; The porosity difference is defined as the range of porosity within the window being ≤5%; The reservoir depth screening sub-module calls the steady-state index sequence, sets the steady-state discrimination threshold to 0.8 times the sequence mean, traverses the discrimination factor values in the sequence, marks the starting and ending point coordinates of the depth segments that continuously exceed the threshold, combines adjacent depth segments with an interval less than the preset distance of 2 meters, and generates the depth segments of the steady-state reservoir window.

[0011] As a further aspect of the present invention, the caprock response extraction module includes: The gas derivative analysis sub-module collects the sampling step of the concentration sequence, the gradient change of the concentration sequence, the extreme value distribution of the concentration sequence and the local amplitude of the fluorescence curve in the gas logging data. For each of the four curves, a differential window is constructed with adjacent sampling points, the mean value of the numerical differences between the middle point and the two side points within the window is calculated, the depth coordinates of the extreme value points where the difference mean exceeds the baseline fluctuation range are marked, the density distribution and amplitude proportion of the extreme value points of multiple gas components are statistically analyzed, and a peak intensity sequence is generated; The parameter fusion calculation sub-module calls the peak intensity sequence, extracts the standard deviation of the acoustic impedance curve and the inverse ratio coefficient of the resistivity curve in the shale section, aligns the standard deviation with the inverse ratio coefficient by depth, calculates the product value of the two and normalizes it to the maximum value of the sequence, and superimposes the normalized weight of the peak intensity sequence to generate the caprock response coefficient; The depth range determination sub-module sets a dynamic threshold based on the distribution characteristics of the caprock response coefficient sequence, traverses the coefficient values within the depth segment, screens the endpoint coordinates of the intervals that continuously exceed the threshold, combines adjacent intervals and eliminates isolated intervals, and generates the depth range of the effective caprock zone; The dynamic threshold is calculated based on the mean and standard deviation of the caprock response coefficient sequence.

[0012] As a further aspect of the present invention, the system further includes: The combined preference generation module is used to receive the depth segments of the steady-state reservoir window and the depth range of the effective caprock zone, call the reservoir physical property stability index, the fluorescence peak intensity, and the caprock sealing index, perform multiplicative cumulative operations to generate a preference value, construct a combined source-reservoir-caprock preference map according to the descending order, and transmit it to an external decision-making terminal; The combined source-reservoir-caprock preference map includes the cumulative index sequence of reservoir physical properties, the preference value column of caprock strength, and the hydrocarbon generation fluorescence response ranking.

[0013] As a further aspect of the present invention, the combined preference generation module includes: The parameter integration sub-module calls the starting and ending coordinates of the steady-state reservoir window depth section and the boundary data of the effective caprock depth range, extracts the sampling point sequence of the reservoir physical property stability index, the discrete distribution values of the fluorescence peak intensity, and the fluctuation curve of the caprock sealing index, and uses the interpolation method to match and align the three types of parameters according to the depth section overlapping area to generate a parameter alignment set; The optimal order calculation sub-module traverses the absolute value of the reservoir physical property stability index, the normalized value of the fluorescence peak intensity, and the weight coefficient of the caprock sealing index at each depth point based on the parameter alignment set, multiplies the three by each depth point in sequence, and accumulates the product results of adjacent three depth points with the weighting coefficients of [0.5, 1, 0.5] as the local optimal order value to generate an optimal order value sequence; The atlas generation sub-module calls the optimal order value sequence, compares the optimal order value of each depth point in the sequence with the values of adjacent depth points, identifies the interval endpoint coordinates where the continuous optimal order value is higher than the sequence mean, and re-organizes the intervals in descending order according to the optimal order value to generate a source-reservoir-caprock combination optimal order atlas.

[0014] A method for processing logging data in the QT Basin, the method for processing logging data in the QT Basin is executed based on the above-mentioned QT Basin logging data processing system, and includes the following steps: S1: Obtain gamma, density, resistivity, and acoustic time difference parameters through logging instruments, generate vertical depth slices for the wellbore trajectory at preset intervals, calculate the parameter fluctuation amount of adjacent slices using the standard deviation formula, and screen effective mutation interfaces based on the coefficient of variation threshold, and output the boundary coordinates of the parameter mutation zone; The coefficient of variation threshold is optimized and determined based on the gradient descent method; S2: Call the boundary coordinates of the parameter mutation zone, extract the resistivity difference ratio vector, natural gamma difference ratio vector, and density difference ratio vector, input the adjacent slice vectors into the cosine similarity algorithm to calculate the included angle, and determine the lithology attribution category based on the included angle threshold to generate a lithology sequence number; S3: Obtain the density logging value, acoustic time difference value, and porosity curve according to the lithology sequence number, use the moving average algorithm to establish a processing window with a width of 3 meters, calculate the product of the porosity range and the permeability span within the window, and output the steady-state reservoir window depth section where the product is lower than the preset value; S4: Obtain the gas logging total hydrocarbon curve and fluorescence intensity curve through the spectral detection device, calculate 、 、 The second derivative peak of the concentration curve, extract the combined standard deviation and inverse ratio coefficient of gamma, density, and resistivity parameters in the shale section, and output the effective caprock depth range where the inverse ratio coefficient exceeds the threshold; S5: Call the depth section of the steady-state reservoir window and the depth range of the effective caprock zone, input the reservoir physical property stability index, fluorescence peak intensity, and caprock sealing index into the multiplication cumulative operation function to generate an order value sequence, and construct an optimal sequence map of source-reservoir-caprock combinations in descending order.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In the present invention, by calculating the adjacent standard deviation and coefficient of variation through gamma, density, resistivity, and acoustic travel time parameter slices, the mutation intensity of parameters is quantified, the effective boundary of the formation interface is accurately calibrated, and the interference of single-parameter fluctuations is reduced. Based on the cosine similarity calculation of the resistivity, natural gamma, and density difference ratio vector groups, the vector space angle is used to replace keyword matching to solve the problem of sequence misjudgment caused by the non-linear correlation of lithology parameters. The product of the porosity difference and permeability span is calculated using a moving average window to dynamically capture the heterogeneous characteristics of reservoir physical properties and generate the depth section of the steady-state reservoir window. By extracting the peak value of the second derivative of gas logging data and combining the standard deviation and inverse ratio, a mathematical discrimination model for the sealing effectiveness of argillaceous rock sections is constructed to avoid the dependence on the fluorescence curve morphology of artificial experience. The multiplication cumulative operation integrates reservoir stability, fluorescence intensity, and sealing index to generate an optimal sequence map of source-reservoir-caprock combinations with multi-dimensional parameter collaborative optimization, realizing the quantitative priority ranking of exploration decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is the system flowchart of the present invention; Figure 2 is the flowchart of the well section structure analysis module of the present invention; Figure 3 is the flowchart of the lithology sequence reconstruction module of the present invention; Figure 4 is the flowchart of the reservoir physical property integration module of the present invention; Figure 5 is the flowchart of the caprock response extraction module of the present invention; Figure 6 is the flowchart of the combined optimal sequence generation module of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0018] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. In addition, in the description of the present invention, the meaning of "a plurality" is two or more, unless otherwise specifically defined.

[0019] Embodiment 1

[0020] Please refer to Figure 1 , the present invention provides a technical solution: A QT basin logging data processing system includes: A well section structure analysis module, which is used to obtain logging data and wellbore trajectories, slice gamma, density, resistivity, and acoustic time difference parameters, calculate the standard deviation and coefficient of variation of adjacent slices, calibrate the effectiveness of the interface, output the boundary coordinates of the parameter mutation zone, and transfer them to the lithology sequence reconstruction module; A lithology sequence reconstruction module, which is used to call the boundary coordinates of the parameter mutation zone, extract the resistivity, natural gamma, and density difference ratio vector groups, calculate the included angle between adjacent slices based on the cosine similarity algorithm, determine the belonging and generate the lithology sequence number, and transfer it to the reservoir physical property integration module; A reservoir physical property integration module, which is used to receive the lithology sequence number, obtain density, acoustic, and porosity data, establish a processing window with a width of 3 meters using the moving average algorithm, calculate the product of the porosity difference and the permeability span, output the depth section of the steady-state reservoir window, and transfer it to the combined preference generation module; A caprock response extraction module, which is used to obtain gas logging data and fluorescence curves, and calculate , , the peak value of the second derivative, extract the product of the standard deviation of the acoustic impedance of the shale section and the inverse resistivity coefficient, output the depth range of the effective caprock zone, and transfer it to the combined preference generation module; A combined preference generation module, which is used to receive the depth section of the steady-state reservoir window and the depth range of the effective caprock zone, call the reservoir physical property stability index, fluorescence peak intensity, and caprock sealing index, perform multiplication and cumulative operations to generate a preference value, construct a combined preference map of source-reservoir-caprock based on descending order, and transfer it to the external decision-making terminal.

[0021] The boundary coordinates of the parameter mutation zone include the gamma mutation point series, the density gradient inflection point set, and the resistivity transition interval. The lithology sequence number specifically refers to the sandstone sequence identifier, the mudstone attribution code, and the limestone stratification label. The depth section of the steady-state reservoir window includes the porosity equilibrium section, the permeability stability span, and the acoustic travel time slow-varying zone. The depth range of the effective caprock zone covers the shale seal top boundary, the gas derivative peak section, and the inverse anomaly closed domain. The optimal sequence map of the source-reservoir-caprock combination includes the cumulative index sequence of reservoir physical properties, the optimal sequence value column of caprock strength, and the hydrocarbon fluorescence response ranking.

[0022] The porosity equilibrium section is a continuous section where the porosity difference is ≤ 5%.

[0023] Please refer to Figure 2 , and the well section structure analysis module includes: The data slice processing sub-module obtains the gamma value, density value, resistivity value, and acoustic travel time value in the logging data, divides the slice units at 1-meter vertical depth intervals based on the wellbore trajectory, extracts the arithmetic mean of the four parameters within multiple units, and generates a parameter slice data set; The data slice processing sub-module obtains the gamma value, density value, resistivity value, and acoustic travel time value in the logging data, extracts and preprocesses the above four types of parameters from the original logging data in sequence, including removing outliers, smoothing edge data, and uniformly standardizing the data units. For example, for the spike anomaly data in the gamma logging curve, an exclusion threshold needs to be set. If the difference between a certain data point and its adjacent front and back two data points is greater than ±50 API, then this point is removed. After unification, the wellbore trajectory data is segmented at 1-meter intervals in the vertical depth direction. Each segment is called a slice unit. The starting depth of the well section is set to 2500 meters, and the ending depth is 2600 meters, with a total of 100 slice units. In each unit, all the gamma, density, resistivity, and acoustic travel time values within its range are extracted and arithmetic mean calculations are performed respectively. For example, in the interval from 2500 meters to 2501 meters, the sampled gamma measured values are [85, 90, 88, 86] API, then the gamma mean value is (85 + 90 + 88 + 86) / 4 = 87.25 API. The other three parameters are calculated in this way respectively. For example, if the resistivity values are [3.5, 3.6, 3.7] Ω·m, then the mean value is (3.5 + 3.6 + 3.7) / 3 = 3.6 Ω·m. In this way, the mean data of the four parameters within all 100 slice units are respectively summarized to form a new data structure and uniformly constitute a parameter slice data set, which serves as the basic input for subsequent boundary determination and fluctuation analysis.

[0024] The interface validity calibration submodule calls the parameter slice data set, calculates the gamma mean difference, density mean difference, resistivity mean difference, and acoustic time difference mean difference of adjacent slice units, and compares them with the gamma standard deviation threshold, density standard deviation threshold, resistivity standard deviation threshold, and acoustic time difference standard deviation threshold, respectively, screens out the units that exceed the standard, calculates the ratio of the multi-parameter standard deviation to the mean, and generates the adjacent parameter fluctuation value and parameter variation coefficient; The interface validity calibration submodule calls the parameter slice data set formed in the previous section and calculates the mean difference between the four parameters of two adjacent slice units one by one, that is, the gamma mean of the i-th slice unit is , the i+1th unit is , then the gamma difference is , similarly defined , , They represent the mean difference of density, resistivity, and acoustic time difference, respectively. After calculation, they are compared with the preset standard deviation thresholds one by one. The thresholds are set as: gamma 10API, density 0.05g / cm³, resistivity 0.5Ω·m, and acoustic time difference 5μs / m. If any difference is greater than the corresponding threshold, the slice unit is marked as "exceeding the standard". Then, the four differences are divided by the mean of their corresponding parameters to form the coefficient of variation. For example, the coefficient of variation of the gamma of the i-th unit is ,set up , ,but , the mean is 92.925, then , and the other three items are deduced in the same way, forming the adjacent parameter fluctuation value matrix and the corresponding parameter variation coefficient vector.

[0025] The mutation zone boundary calibration submodule calls the adjacent parameter fluctuation values ​​and parameter variation coefficients, compares the four parameter variation coefficients with the set variation threshold, extracts the top and bottom boundary coordinates of the vertical depth interval where the variation coefficient exceeds the limit continuously, and generates the parameter mutation zone boundary coordinates.

[0026] The mutation zone boundary calibration submodule calls the coefficient of variation of the four parameters calculated above, and performs interval screening by setting the coefficient of variation threshold. The threshold setting refers to the variation background value obtained from the previous geological statistical analysis, and takes 1.5 times the maximum background value as the mutation zone calibration threshold. For example, if the gamma background coefficient of variation is 0.08, the threshold is set to 0.12. When three or more consecutive slice units meet the four parameters and at least two coefficients of variation exceed the set threshold, the interval is defined as a potential mutation zone, and the starting depth of the first unit and the ending depth of the last unit that meet the conditions are extracted as the mutation zone boundary coordinates. For example, the 38th to 41st slice units meet the conditions, and the corresponding vertical depth interval is 2538 meters to 2542 meters, then the recorded boundary is [2538,2542]. All interval boundaries that meet the conditions are output and summarized one by one to form a parameter mutation zone boundary coordinate set.

[0027] Table 1 Example Table for Calculating Coefficient of Variation of Adjacent Gamma Value Units

[0028] As shown in Table 1, the gamma variation coefficients between the first three pairs of slice units are listed. According to the set threshold of 0.12, only the first group of slice pairs exceeds the limit.

[0029] This result indicates that by calculating the parameter difference pair by pair and inferring the coefficient of variation therefrom, the position of the mutation boundary can be clearly determined, and it supports the refined characterization of abnormal changes in the well section structure in geotechnical engineering practice.

[0030] Please refer to Figure 3 , the lithology sequence reconstruction module includes: The parameter mutation zone extraction sub-module calls the boundary coordinates of the parameter mutation zone, analyzes the absolute fluctuation amplitude of the resistivity curve, the mean offset of the natural gamma curve, and the standard deviation change of the density curve within the coordinate interval, calculates the absolute value of the numerical difference between adjacent sampling points for the three types of parameters respectively, divides the horizontal slice units according to the mutation zone coordinate range, statistically calculates the weight ratio of the three types of difference data in multiple units to the total fluctuation, constructs a three-dimensional ratio vector, and generates a difference ratio vector group; After the parameter mutation zone extraction sub-module calls the boundary coordinates of the mutation zone, it first extracts the logging data sequences of resistivity, natural gamma, and density according to the boundary interval, and performs the absolute value operation of the numerical difference between adjacent sampling points for each type of parameter respectively. The resistivity sampling points are set once every 0.1 meter. If there are 40 sampling points between 2538 meters and 2542 meters, the resistivity difference between the i-th pair of sampling points is expressed as Taking the actual value as an example, if , , the difference is 0.4. The sum of all differences is accumulated in sequence to obtain the absolute fluctuation amplitude sum. The gamma parameter uses a similar method to statistically calculate the total change in its mean value within the same interval. If the value array is [85, 86, 90, 89, 91], the gamma mean value is 88.2. Compared with the average value of the standard layer, which is 85, the offset is 3.2. For the density parameter, calculate the change in its standard deviation. Suppose the standard deviation of the density in the standard section is 0.06, and the standard deviation of the density data in the current interval is 0.12, then the change is 0.06. Divide the mutation zone interval into several slice units horizontally at 1 meter intervals. For each slice unit, statistically calculate the total resistivity difference, the total gamma offset, and the change in density standard deviation respectively. Divide each value by the sum of the total differences of the three types of parameters in the whole region to form the proportion of the three types of differences. Suppose the three values of a certain unit are 0.85, 5.6, and 0.12. If the overall sum of the mutation zone is resistivity 2.65, gamma 11.6, and density 0.34, then the resistivity weight of this unit is 0.85 / 2.65 = 0.32, the gamma weight is 5.6 / 11.6 = 0.48, and the density weight is 0.12 / 0.34 = 0.35. Finally, form the three-dimensional difference ratio vector [0.32, 0.48, 0.20] of this unit. All slice units are arranged in order to generate a difference ratio vector group.

[0031] Based on the difference ratio vector group, the sequence angle calculation sub-module traverses the order of the slice units, extracts the three-dimensional vector data of adjacent units, projects the vectors to the polar coordinate system, and uses the cosine similarity algorithm to calculate the sum of the products of the radial, tangential, and normal components of the two vectors, divides it by the product of the vector modulus lengths, and calculates and records the angle value in reverse order to generate an adjacent angle sequence; According to the constructed difference ratio vector group, the sequence angle calculation sub-module sequentially extracts the three-dimensional vectors of the current unit and its adjacent unit from the first slice unit. For example, the first and second units are [0.32, 0.48, 0.20] and [0.44, 0.32, 0.24] respectively. Project the two vectors to the polar coordinate system for angle calculation. Suppose the angle between vector a and b is θ, then first calculate the modulus lengths of vector a and b respectively. The modulus length calculation formula is , substituting it in, we get , and similarly, the modulus length of vector b is obtained as , then perform the dot product of the vectors: , finally use the cosine similarity formula , from , finally record this angle into the angle sequence, repeat the calculation for all adjacent slice pairs, and generate an angle value sequence in order.

[0032] The lithology attribution determination sub-module calls the adjacent included angle sequence, compares the angle data with the 45-degree lithology demarcation threshold determined by fitting the core calibration data, counts the length of the slice interval where the continuous included angle values are less than the threshold, marks the interval endpoints as the lithology unit segmentation points, assigns increasing unit numbers in the order of the segmentation points, and generates the lithology sequence numbers.

[0033] Based on the above included angle numerical sequence, the lithology attribution determination sub-module sequentially determines whether each pair of included angles is less than the 45-degree lithology demarcation threshold. This threshold is derived from the core calibration experiment and indicates that there is no significant mutation in the lithology attributes between adjacent units. It is set that if more than 3 consecutive included angle values are less than 45 degrees, it is judged as the same lithology unit, and the starting and ending slice numbers are marked as the boundary endpoints of this unit. For example, if [20.0°, 18.5°, 23.4°, 19.8°] continuously appears in the included angle sequence, it is considered that the corresponding units 1 to 5 are of the same lithology unit, starting from unit 1 and ending at unit 5, and marked as lithology unit 1. Subsequently, the determination of the next lithology unit continues at the next unassigned unit until all slices are traversed. Finally, each lithology unit is numbered in order to generate the lithology sequence numbers. This process is based on the strong correlation between the included angle and lithology, ensuring reasonable segmentation of homologous lithology.

[0034] Please refer to Figure 4 , the reservoir physical property integration module includes: The physical property data integration sub-module receives the lithology sequence numbers, extracts the density curve sampling point values, acoustic time difference curve amplitude values, and porosity curve discrete values according to the depth segments corresponding to the numbers, and uses the moving average algorithm to calculate the arithmetic mean of the adjacent three sampling points for the three types of data respectively with a fixed window step size to generate the physical property mean window set; After receiving the lithology sequence numbers, the physical property data integration sub-module extracts the original data of the density curve, acoustic time difference curve, and porosity curve according to the depth range corresponding to each number. The density curve uses a conventional sampling step size of 0.1 m, with a total of 30 sampling points in the interval from 2500 m to 2503 m. Let the original density data array be [2.42, 2.46, 2.47,…, 2.44] g / cm³. The sampling value unit of the acoustic time difference curve is μs / m, set as [85.3, 86.7, 87.0,…, 84.7], and the porosity is in percentage system, set as [17.8, 18.1, 18.3,…, 17.9]. The sliding window method is applied to the three types of data respectively. The window is set to 3 sampling points and the step size is 1. The entire curve is traversed, and the arithmetic mean operation is performed on the three-point data in each window. For example, the first window of density is [2.42, 2.46, 2.47], and its average value is g / cm³, and the acoustic time difference and porosity are calculated in this way respectively. For example, in window 2, if the acoustic time difference is [86.7, 87.0, 88.6], the average value is Generate a complete set of moving average windows for density, acoustic travel time, and porosity.

[0035] The steady-state index calculation sub-module calls the physical property moving average window set, calculates the difference between the maximum and minimum values of the porosity curve within each window, adds the range span of the permeability curve within the window, and uses the product of the porosity difference and the permeability span as the steady-state discrimination factor to generate a steady-state index sequence. The porosity difference is defined as the porosity range within the window ≤ 5%. The steady-state index calculation sub-module calls the above-mentioned physical property moving average window set, performs the product operation of the porosity range and the permeability span for each window. The porosity range is defined as the difference between the maximum and minimum values within the window. Assume the porosity within window 1 is [17.3, 18.2, 18.5], and the range is , less than the 5% limit, meeting the definition of steady-state porosity. The permeability data is extracted synchronously according to the same window, with the unit of mD. For example, the values are [120, 160, 170], and the range is mD, then the steady-state discrimination factor is , similarly, the porosity range of window 2 is 1.4, the permeability span is 60, then the factor is Record the discrimination factor results of all windows to form a steady-state index sequence for the next step of screening.

[0036] The reservoir depth screening sub-module calls the steady-state index sequence, sets the steady-state discrimination threshold as 0.8 times the sequence mean, traverses the discrimination factor values in the sequence, marks the starting and ending point coordinates of the depth segments that continuously exceed the threshold, and merges the depth segments with an adjacent interval less than the preset spacing of 2 meters to generate the steady-state reservoir window depth segments.

[0037] The reservoir depth screening sub-module uses the aforementioned steady-state index sequence, sets the discrimination threshold as 0.8 times the sequence mean, sets the discrimination factor as [60.0, 84.0, 40.5], and the mean is , the threshold is , based on this, judge whether the discrimination factor of each window is higher than the threshold item by item. If more than 2 consecutive windows are higher than 49.2, such as window 1 and window 2, then map the starting window number and the ending number to the depth coordinates. Assume the window width is 0.2 meters, then the starting coordinate of window 1 is 2500.0 meters, the ending is 2500.6 meters, the starting of window 2 is 2500.2 meters, the ending is 2500.8 meters, and the adjacent spacing between the two is 0.2 meters, less than the preset merging spacing of 2 meters, merge them into a steady-state reservoir segment [2500.0, 2500.8]. After merging all the continuous window segments that meet the conditions, output and form a set of steady-state reservoir window depth segments. This result indicates that when the porosity-permeability combined index meets the set steady-state judgment conditions within the window interval, it can be used to identify the reservoir depth window segments.

[0038] Please refer to Figure 5 , the capping response extraction module includes: The gas derivative analysis sub-module collects the sampling step of the concentration sequence in the gas logging data , the gradient change of the concentration sequence , the extreme value distribution of the concentration sequence and the local amplitude of the fluorescence curve. For the four curves, a difference window is constructed with adjacent sampling points respectively, and the average value of the numerical differences between the middle point and the two side points within the window is calculated. The depth coordinates of the extreme points where the difference average value exceeds the baseline fluctuation range are marked, and the density distribution and amplitude proportion of the extreme points of multiple gas components are statistically analyzed to generate a peak intensity sequence; The gas derivative analysis sub-module collects the concentration in the gas logging data , the concentration , the concentration , the concentration , and the fluorescence curve. First, according to the set sampling step, a difference window is constructed for the four groups of curve data. The window width is three points. After extracting the differences between the middle point and the two side points of each window, the arithmetic mean is taken as the difference average value of the window. For example The window data near 2510.0 meters is [1.12%, 1.26%, 1.18%], the midpoint is 1.26%, the average value of the two sides is (1.12 + 1.18) / 2 = 1.15%, then the difference average value is , and the same processing is carried out Expressed in terms of gradient, if the gradient is [0.08, 0.10, 0.09], the average difference between the midpoint and the adjacent points is , and the same operation is also carried out for the remaining curves. For Taking the extreme value window value such as [1.20%, 1.42%, 1.36%], the midpoint is the maximum value 1.42%, and the difference from the average value of the two sides 1.28% is 0.14%. The fluorescence amplitude is such as [0.80, 1.02, 0.95], then the amplitude difference is , compare all the difference average values with their respective set baseline fluctuation ranges. Let the baseline fluctuation be 0.08%, be 0.05%, CO2 be 0.12%, and fluorescence be 0.10. If any difference average value in a window exceeds the corresponding fluctuation value, record this point as an extreme point, mark its corresponding depth coordinate, and finally count the occurrence frequency of all extreme points in the interval of 2509 to 2511 meters and the proportion of the contribution values of each component, and construct a peak intensity sequence as the input item for subsequent parameter fusion.

[0039] The parameter fusion calculation sub-module calls the peak intensity sequence, extracts the inverse ratio coefficient of the standard deviation of the acoustic impedance curve in the argillaceous rock section and the resistivity curve, aligns the standard deviation with the inverse ratio coefficient by depth, calculates the product value of the two and normalizes it to the maximum value of the sequence, and superimposes the normalized weights of the peak intensity sequence to generate the caprock response coefficient; After the parameter fusion calculation sub-module calls the above peak intensity sequence, it further extracts the data of the acoustic impedance curve and the resistivity curve in the argillaceous rock section. The sampling values of the acoustic impedance curve in the depth interval are set as [12400, 12800, 12650], and the standard deviation is , with the unit of kPa·s / m, and the resistivity curve is [4.0, 5.0, 3.3] Ω·m. Then their reciprocals are [0.25, 0.20, 0.30]. Subsequently, after aligning the two by depth, the product values are calculated respectively. For example, when the standard deviation is 12.5 and the reciprocal is 0.25, the product is , and all product values are normalized. If the maximum value is 3.57 and the current value is 3.13, it is normalized to , and then the peak intensity sequence such as [0.63, 0.75, 0.52] is normalized. The maximum value is 0.75, so the normalized values are [0.84, 1.00, 0.69] in sequence. The two are weighted and averaged. Assuming that the weights of both items are 0.5, the final caprock response coefficient is , and the complete response coefficient sequence is generated in sequence.

[0040] Based on the caprock response coefficient, the depth range determination sub-module sets a dynamic threshold based on the distribution characteristics of the coefficient sequence, traverses the coefficient values in the depth section, screens the endpoint coordinates of the intervals that continuously exceed the threshold, merges adjacent intervals and eliminates isolated intervals to generate the depth range of the effective caprock zone; The dynamic threshold is calculated according to the mean and standard deviation of the caprock response coefficient sequence.

[0041] Based on the caprock response coefficient sequence generated above, the depth range determination sub-module sets a dynamic threshold. The threshold is obtained by adding a certain multiple of the standard deviation to the mean of the sequence. Assuming that the response coefficient is [0.858, 0.74, 1.00], the mean is , and the standard deviation is , then the dynamic threshold is the mean plus 0.5 times the standard deviation, that is , screen all depth points with values greater than 0.931. Only 1.00 satisfies. Take the depth intervals before and after it as the caprock response candidate segments. If the adjacent segments are within 2 meters, they are merged. Otherwise, the isolated values are eliminated. Finally, the complete depth range of the effective caprock zone is output. This result indicates that this section shows caprock response characteristics under the superimposed discrimination of various gas responses and geophysical properties.

[0042] Please refer to Figure 6, the combination preference generation module includes: The parameter integration sub-module calls the starting and ending coordinates of the steady-state reservoir window depth segment and the boundary data of the effective caprock zone depth range, extracts the sampling point sequence of the reservoir physical property stability index, the discrete distribution value of the fluorescence peak intensity, and the fluctuation curve of the caprock sealing index, and uses the interpolation method to match and align the three types of parameters according to the depth segment overlapping area to generate a parameter alignment set; The parameter integration sub-module calls the depth segment information corresponding to the previously identified steady-state reservoir window and the effective caprock zone. Suppose the start and end of the reservoir are [2510.0, 2511.0] meters, the caprock segment is [2510.2, 2511.4] meters, and the overlapping interval is [2510.2, 2511.0] meters. Extract the three types of parameter curve data within this range, namely the reservoir physical property stability index sequence, the fluorescence peak intensity discrete value, and the caprock sealing index fluctuation value. Suppose the depth interval is 0.2 meters to obtain the three data at each depth point. For example, at 2510.2 meters, the three values are 0.85, 0.72, and 0.60. Then, use the linear interpolation method to fill in the data for any missing depth point. If there is no fluorescence value at 2510.6 meters but the values at 2510.4 and 2510.8 meters are 0.65 and 0.75 respectively, the interpolation result is , after alignment, a complete three-value structure is formed at each depth point, and finally a parameter alignment set sorted by the overlapping depth segment is formed as the input structure for subsequent preference calculation.

[0043] Based on the parameter alignment set, the preference calculation sub-module traverses the absolute value of the reservoir physical property stability index, the normalized value of the fluorescence peak intensity, and the weight coefficient of the caprock sealing index at each depth point, multiplies the three in sequence according to the depth order, and accumulates the product results of three adjacent depth points with the weighting coefficients of [0.5, 1, 0.5] as the local preference value to generate a preference value sequence; The preference calculation sub-module calls the above parameter alignment set, traverses each depth point in sequence, extracts the three types of index data, namely the absolute value of the reservoir physical property stability index, the normalized fluorescence intensity value, and the sealing index weight coefficient. According to the depth point data example in Table 2, the three values at the 2510.2-meter point are 0.85, 0.72, and 0.60, and their product is , suppose the weight coefficient vector is [0.5, 1.0, 0.5], and calculate with three adjacent depth points as a window. For example, the depth product values at 2510.0, 2510.2, and 2510.4 meters are 0.292, 0.367, and 0.579 respectively, then their local preference value is , Repeat this window moving calculation operation to record the preference value sequence for all intermediate depth points.

[0044] Table 2 Example table for preference value calculation

[0045] Table 2 lists the values of three types of parameters at the actual depth points and their dot product results, and calculates the local preference value with a central window of 2510.2 meters.

[0046] The map generation sub-module calls the preference value sequence, compares the preference value of each depth point in the sequence with the values of adjacent depth points, identifies the coordinate of the interval endpoints where the continuous preference value is higher than the mean value of the sequence, re-organizes the intervals in descending order according to the preference value, and generates the preference map of the source-reservoir-caprock combination.

[0047] The map generation sub-module uses the aforementioned preference value sequence to calculate the average value of the whole sequence. Suppose the sequence is [0.25, 0.31, 0.35, 0.30, 0.45], then the mean value is , filters all continuous intervals where the preference value is greater than the mean value. If the preference values in the depth section from 2510.2 to 2510.6 meters are [0.35, 0.37, 0.45], then mark the starting endpoint of 2510.2 meters and the ending endpoint of 2510.6 meters, and take it as the preference section. If there are multiple sections, sort them according to the maximum preference value within the section, and give priority to displaying the higher one. Finally, combine and generate the preference map of the source-reservoir-caprock. This map can clearly show the distribution structure of the combined preference within the depth interval. The result shows that the intervals of the preference map can be constructed by the product and weighted average between parameters.

[0048] A method for processing logging data in the QT Basin. The method for processing logging data in the QT Basin is executed based on the above-mentioned logging data processing system in the QT Basin, and includes the following steps: S1: Obtain gamma, density, resistivity, and acoustic travel time difference parameters through logging instruments, generate vertical depth slices at preset intervals for the wellbore trajectory, calculate the parameter fluctuation amount between adjacent slices using the standard deviation formula, and screen effective mutation interfaces based on the coefficient of variation threshold, and output the boundary coordinates of the parameter mutation zone; The coefficient of variation threshold is optimized and determined based on the gradient descent method; S2: Call the boundary coordinates of the parameter mutation zone, extract the resistivity difference ratio vector, natural gamma difference ratio vector, and density difference ratio vector, input the adjacent slice vectors into the cosine similarity algorithm to calculate the included angle, and determine the lithology attribution category based on the included angle threshold, and generate the lithology sequence number; S3: Obtain the density logging value, acoustic travel time difference value, and porosity curve according to the lithology sequence number, establish a processing window with a width of 3 meters using the moving average algorithm, calculate the product of the porosity range and the permeability span within the window, and output the depth section of the stable reservoir window where the product is lower than the preset value; S4: Obtain the gas logging total hydrocarbon curve and fluorescence intensity curve through the spectral detection device, and calculate , , The peak value of the second derivative of the concentration curve, extract the combined standard deviation and inverse ratio coefficient of gamma, density, and resistivity parameters in the argillaceous rock section, and output the depth range of the effective cap zone where the inverse ratio coefficient exceeds the threshold value. S5: Call the depth section of the steady-state reservoir window and the depth range of the effective cap zone, input the reservoir physical property stability index, fluorescence peak intensity, and cap layer sealing index into the multiplication cumulative operation function to generate an order value sequence, and construct an order map of the source-reservoir-cap combination in descending order.

[0049] The above is only a preferred embodiment of the present invention, and does not limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution content of the present invention still belong to the protection scope of the technical solution of the present invention.

Claims

1. A data processing system for logging while drilling in the QT Basin, characterized in that, The system includes: A well section structure analysis module, which is used to obtain logging data and wellbore trajectory, slice gamma, density, resistivity, and acoustic travel time parameters, calculate the standard deviation and coefficient of variation of adjacent slices, calibrate the validity of the interface, output the boundary coordinates of the parameter mutation zone, and transfer them to the lithology sequence reconstruction module; A lithology sequence reconstruction module, which is used to call the boundary coordinates of the parameter mutation zone, extract the resistivity, natural gamma, and density difference ratio vector groups, calculate the included angle between adjacent slices based on the cosine similarity algorithm, determine the attribution to generate lithology sequence numbers, and transfer them to the reservoir physical property integration module; A reservoir physical property integration module, which is used to receive the lithology sequence numbers, obtain density, acoustic, and porosity data, establish a processing window with a width of 3 meters using the moving average algorithm, calculate the product of the porosity difference and the permeability span, output the depth section of the steady-state reservoir window, and transfer it to the combination preference generation module; The capping response extraction module is used to obtain gas logging data and fluorescence curves, and calculate , , the peak value of the second derivative, extract the product of the standard deviation of the acoustic impedance and the inverse resistivity coefficient in the shale section, output the depth range of the effective capping zone, and transfer it to the combined optimal order generation module.

2. The QT basin logging data processing system according to claim 1, wherein The boundary coordinates of the parameter mutation zone include gamma mutation point columns, density gradient inflection point sets, and resistivity transition intervals. The lithology sequence numbers specifically refer to sandstone sequence identifiers, mudstone attribution codes, and limestone layer labels. The depth section of the steady-state reservoir window includes a porosity equilibrium section, a permeability stable span, and an acoustic travel time slow change zone. The depth range of the effective capping zone covers the shale seal top boundary, the gas derivative peak section, and the inverse anomaly closed domain; The porosity equilibrium section is a continuous section where the porosity range is ≤5%.

3. The QT basin logging data processing system according to claim 2, characterized in that, The well section structure analysis module includes: A data slice processing sub-module obtains the gamma value, density value, resistivity value, and acoustic travel time value in the logging data, divides the slice units at intervals of 1 meter in vertical depth based on the wellbore trajectory, extracts the arithmetic mean values of the four parameters in multiple units, and generates a parameter slice data set; An interface validity calibration sub-module calls the parameter slice data set, calculates the gamma mean difference, density mean difference, resistivity mean difference, and acoustic travel time mean difference between adjacent slice units, compares them with the gamma standard deviation threshold, density standard deviation threshold, resistivity standard deviation threshold, and acoustic travel time standard deviation threshold respectively, screens out the over-standard units, calculates the ratio of the multi-parameter standard deviation to the mean value, and generates the adjacent parameter fluctuation value and the parameter coefficient of variation; A mutation zone boundary calibration sub-module calls the adjacent parameter fluctuation value and the parameter coefficient of variation, compares the four parameter coefficients of variation with the set variation threshold, extracts the top and bottom boundary coordinates of the vertical depth interval where the coefficient of variation is continuously exceeded, and generates the boundary coordinates of the parameter mutation zone.

4. The QT basin logging data processing system according to claim 3, characterized in that, The lithology sequence reconstruction module includes: A parameter mutation zone extraction sub-module calls the boundary coordinates of the parameter mutation zone, analyzes the absolute fluctuation amplitude of the resistivity curve, the mean shift of the natural gamma curve, and the standard deviation change of the density curve within the coordinate interval, calculates the absolute value of the numerical difference between adjacent sampling points for the three types of parameters respectively, divides the horizontal slice units according to the mutation zone coordinate range, statistically calculates the weight ratio of the three types of difference data in multiple units to the total fluctuation, constructs a three-dimensional ratio vector, and generates a difference ratio vector group; The sequence included angle calculation sub-module traverses the order of slice units based on the difference ratio vector group, extracts the three-dimensional vector data of adjacent units, projects the vectors to the polar coordinate system, calculates the sum of the products of the radial, tangential, and normal components of the two vectors using the cosine similarity algorithm, divides it by the product of the vector modulus lengths, inversely calculates the angle value and records the order, and generates an adjacent included angle sequence; The lithology attribution determination sub-module calls the adjacent included angle sequence, compares the angle data with the 45-degree lithology demarcation threshold determined by fitting based on core calibration data, counts the length of the slice interval where the continuous included angle values are less than the threshold, marks the interval endpoints as lithology unit segmentation points, assigns increasing unit numbers in the order of the segmentation points, and generates a lithology sequence number.

5. The QT basin logging data processing system according to claim 4, characterized in that, The reservoir physical property integration module includes: The physical property data integration sub-module receives the lithology sequence number, extracts the density curve sampling point values, acoustic wave transit time curve amplitude values, and porosity curve discrete values corresponding to the depth segments according to the number, and calculates the arithmetic mean of the adjacent three sampling points for each of the three types of data using the moving average algorithm with a fixed window step size to generate a physical property mean window set; The steady-state index calculation sub-module calls the physical property mean window set, counts the difference between the maximum and minimum values of the porosity curve within each window, superimposes the range span of the permeability curve within the window, takes the product of the porosity difference and the permeability span as the steady-state discrimination factor, and generates a steady-state index sequence; The porosity difference is defined as the porosity range within the window ≤ 5%; The reservoir depth screening sub-module calls the steady-state index sequence, sets the steady-state discrimination threshold to 0.8 times the sequence mean, traverses the discrimination factor values in the sequence, marks the starting and ending point coordinates of the depth segments that continuously exceed the threshold, merges the depth segments with an adjacent interval less than the preset spacing of 2 meters, and generates the steady-state reservoir window depth segments.

6. The QT basin logging data processing system according to claim 5, wherein The caprock response extraction module includes: The gas derivative analysis sub-module collects the sampling step of the concentration sequence, gradient change of the concentration sequence, extreme value distribution of the concentration sequence and the local amplitude of the fluorescence curve. For the four curves, a difference window is constructed with adjacent sampling points respectively, and the average value of the numerical differences between the middle point and the two side points within the window is calculated. The depth coordinates of the extreme value points where the difference average value exceeds the baseline fluctuation range are marked, and the density distribution and amplitude proportion of the extreme value points of multiple gas components are statistically analyzed to generate a peak intensity sequence; The parameter fusion calculation sub-module calls the peak intensity sequence, extracts the standard deviation of the acoustic impedance curve and the inverse ratio coefficient of the resistivity curve in the shale section, aligns the standard deviation with the inverse ratio coefficient by depth, calculates the product value of the two and normalizes it to the sequence maximum value, and superimposes the normalized weights of the peak intensity sequence to generate a caprock response coefficient; The depth range determination sub-module sets a dynamic threshold based on the distribution characteristics of the caprock response coefficient sequence, traverses the coefficient values within the depth segment, screens the interval endpoint coordinates that continuously exceed the threshold, merges adjacent intervals and eliminates isolated intervals, and generates the depth range of the effective caprock zone; The dynamic threshold is calculated based on the mean and standard deviation of the caprock response coefficient sequence.

7. The QT basin logging data processing system according to claim 6, characterized in that, The system further includes: The combined priority generation module is used to receive the steady-state reservoir window depth segments and the depth range of the effective caprock zone, call the reservoir physical property stability index, fluorescence peak intensity, and caprock sealing index, perform multiplicative cumulative operations to generate a priority value, construct a source-reservoir-caprock combination priority map according to the descending order, and transmit it to an external decision-making terminal; The source-reservoir-caprock combination priority map includes a reservoir physical property cumulative index sequence, a caprock strength priority value column, and a hydrocarbon generation fluorescence response ranking.

8. The QT basin logging data processing system according to claim 7, characterized in that, The combined priority generation module includes: The parameter integration sub-module calls the starting and ending coordinates of the steady-state reservoir window depth segment and the boundary data of the effective cap rock zone depth range, extracts the sampling point sequence of the reservoir physical property stability index, the discrete distribution values of the fluorescence peak intensity, and the fluctuation curve of the cap rock sealing index, and uses the interpolation method to match and align the three types of parameters in the depth segment overlapping area to generate a parameter alignment set; The preference order calculation sub-module traverses the absolute value of the reservoir physical property stability index, the normalized value of the fluorescence peak intensity, and the weight coefficient of the cap rock sealing index at each depth point based on the parameter alignment set, multiplies the three in sequence by depth point, and accumulates the product results of three adjacent depth points with weighting coefficients of [0.5, 1, 0.5] as the local preference order value to generate a preference order value sequence; The map generation sub-module calls the preference order value sequence, compares the preference order value of each depth point in the sequence with the adjacent depth point value, identifies the interval endpoint coordinates where the continuous preference order value is higher than the sequence mean, and re-organizes the intervals in descending order according to the preference order value to generate a source-reservoir-cap combination preference order map.

9. A method for processing logging data in the QT Basin, characterized in that, The method is used to implement the QT basin logging data processing system described in any one of claims 1-8, and includes the following steps: S1: Obtain gamma, density, resistivity, and acoustic wave transit time parameters through logging instruments, generate vertical depth slices for the wellbore trajectory at a preset interval, calculate the parameter fluctuation amount between adjacent slices using the standard deviation formula, and screen out effective mutation interfaces based on the coefficient of variation threshold to output the boundary coordinates of the parameter mutation zone; The coefficient of variation threshold is optimized and determined based on the gradient descent method; S2: Call the boundary coordinates of the parameter mutation zone, extract the resistivity difference ratio vector, natural gamma difference ratio vector, and density difference ratio vector, input the adjacent slice vectors into the cosine similarity algorithm to calculate the included angle, and determine the lithology attribution category based on the included angle threshold to generate a lithology sequence number; S3: Obtain the density logging value, acoustic wave transit time value, and porosity curve according to the lithology sequence number, use the moving average algorithm to establish a processing window with a width of 3 meters, calculate the product of the porosity range and the permeability span within the window, and output the steady-state reservoir window depth segment where the product is lower than the preset value; S4: Obtain the gas logging total hydrocarbon curve and the fluorescence intensity curve through a spectral detection device, calculate , , the peak value of the second derivative of the concentration curve, extract the combined standard deviation and inverse ratio coefficient of the gamma, density, and resistivity parameters in the shale section, and output the depth range of the effective cap rock zone where the inverse ratio coefficient exceeds the threshold; S5: Call the steady-state reservoir window depth segment and the effective cap rock zone depth range, input the reservoir physical property stability index, fluorescence peak intensity, and cap rock sealing index into the multiplication and accumulation operation function to generate a preference order value sequence, and construct a source-reservoir-cap combination preference order map in descending order.

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