A QT Basin logging data processing system and method

Abstract

The present invention relates to the field of data retrieval technology, specifically a QT basin logging data processing system and method. The system includes: a well section structure analysis module, a lithologic sequence reconstruction module, a reservoir physical property integration module, a capping response extraction module, and a combined priority generation module. In the present invention, adjacent standard deviations and coefficients of variation are calculated using gamma, density, resistivity, and acoustic time difference to quantify parameter mutation intensity and calibrate formation interface effectiveness. Keyword matching is replaced by cosine similarity of resistivity, gamma, and density difference vectors to resolve misjudgments of nonlinear associations of lithologic parameters. A sliding window is used to calculate the product of porosity difference and permeability to identify reservoir heterogeneity. The second-order derivative peak of gas logging data is extracted to construct a mudstone capping effectiveness model. Reservoir stability, fluorescence intensity, and sealing index are cumulatively integrated to generate a source-reservoir-capping priority map, achieving exploration priority sorting.

Description

Technical Field

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

[0002] The field of data retrieval technology 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 primarily includes data acquisition, data preprocessing, data structuring, data indexing, and data query response. In practical applications, data retrieval technology encompasses semantic understanding of raw data, metadata extraction, information filtering, and reorganization, enabling the rapid and accurate location of specific target data. In industries such as oil and gas exploration, medical imaging, and geographic information systems, data retrieval technology, due to its efficiency and systematic nature, is widely used in the automated processing and retrieval of complex and heterogeneous data sources.

[0003] Among them, the QT Basin logging and mud logging data processing system refers to an information technology system dedicated to logging and mud logging data processing in the QT Basin geological exploration area. The technical matters targeted by this patent subject cover the processing of data elements such as logging depth information analysis, mud logging lithologic parameter classification, stratigraphic sequence division, and oil and gas display feature identification. Its specific methods include arranging logging data in time series through regularized depth comparison relationships, completing lithologic type identification based on keyword extraction of mud logging description information, numbering and classifying stratigraphic sequences identified during the drilling process using stratigraphic standard coding, and constructing oil and gas indicator identification factors in combination with rock cuttings and oil and gas display records. The processing system relies on the above-mentioned data analysis and identification methods to complete the unification and structured integration of multiple rounds of drilling data in the QT Basin area.

[0004] Existing logging data processing systems rely on regularized depth comparison and keyword extraction, making it difficult to capture the synergistic characteristics of parameter mutations when integrating multi-source heterogeneous data from the QT Basin. For example, logging depth analysis only arranges data in time series, without quantifying the standard deviations and coefficients of variation of parameters such as gamma and resistivity, resulting in blurred interface demarcation boundaries. Lithologic classification relies on keyword matching of logging descriptions, ignoring the spatial relationship between resistivity and density difference ratio vectors, resulting in insufficient differentiation between similar lithologic sequences. Formation coding mechanisms fail to incorporate a sliding average window to analyze the dynamic correlation between porosity and permeability, and when assessing reservoir stability using static data, the impact of permeability span on heterogeneity is ignored. Oil and gas show identification relies on the combined standard deviation of the second-order derivative peak and shale parameters in unfused gas logging data, relying on fluorescence curve morphology to qualitatively determine the sealability of the caprock, resulting in limited quantification accuracy. Traditional data integration lacks a multidimensional parameter multiplication and accumulation mechanism, and the prioritization of source-reservoir-caprock assemblages relies on manual empirical weighting, resulting in insufficient interpretability and repeatability of decisions. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a QT basin logging data processing system and method.

[0006] In order to achieve the above-mentioned object, the present invention adopts the following technical solution: A QT basin logging data processing system comprises:

[0007] The well section structure analysis module 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 interface effectiveness, output the boundary coordinates of the parameter mutation zone, and transmit them to the lithologic sequence reconstruction module;

[0008] The lithologic sequence reconstruction module 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 angle between adjacent slices based on the cosine similarity algorithm, determine the attribution, generate the lithologic sequence number, and pass it to the reservoir physical property integration module;

[0009] The reservoir physical property integration module is used to receive the lithologic sequence number, obtain density, acoustic wave, and porosity data, establish a 3-meter-wide processing window using a sliding average algorithm, calculate the product of the porosity difference and the permeability span, output the steady-state reservoir window depth segment, and pass it to the combined priority generation module;

[0010] Capping response extraction module, used to obtain gas measurement data and fluorescence curve, calculate 、 、 The second-order derivative peak is used to extract the product of the standard deviation of the acoustic impedance and the inverse resistivity coefficient of the argillaceous rock section, and the depth range of the effective sealing zone is output and passed to the combination priority generation module.

[0011] As a further solution of the present invention, the parameter mutation zone boundary coordinates include a gamma mutation point series, a density gradient inflection point set, and a resistivity transition interval; the lithologic sequence number specifically refers to a sandstone sequence identifier, a mudstone attribution code, and a limestone layer label; the steady-state reservoir window depth segment includes a porosity equilibrium segment, a permeability stable span, and a sonic time difference slow-changing zone; and the effective sealing zone depth range covers the top boundary of the mud isolation, the gas derivative peak segment, and the inverse anomaly sealing domain;

[0012] The porosity equalization section is a continuous section with a porosity extreme difference of ≤5%.

[0013] As a further solution of the present invention, the well section structure analysis module includes:

[0014] The data slicing submodule obtains the gamma value, density value, resistivity value, and acoustic wave delay value from the logging data, divides the slice unit into 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;

[0015] 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, 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 units that exceed the standard, calculates the ratio of the multi-parameter standard deviation to the mean, and generates adjacent parameter fluctuation values ​​and parameter variation coefficients;

[0016] 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 thresholds, extracts the top and bottom boundary coordinates of the vertical depth interval where the variation coefficient continuously exceeds the limit, and generates the parameter mutation zone boundary coordinates.

[0017] As a further solution of the present invention, the lithologic sequence reconstruction module includes:

[0018] The parameter mutation zone extraction submodule 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, divides the transverse slice units according to the mutation zone coordinate range, calculates the weight ratio of the three types of difference data in multiple units to the total fluctuation, constructs a three-dimensional proportion vector, and generates a difference proportion vector group;

[0019] The sequence angle calculation submodule traverses the slice unit sequence based on the difference ratio vector group, extracts the three-dimensional vector data of adjacent units, projects the vectors into the polar coordinate system, 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, reversely deduces the angle value and records the order to generate a sequence of adjacent angles;

[0020] The lithologic attribution determination submodule calls the adjacent angle sequence, compares the angle data with the 45-degree lithologic boundary threshold determined based on the core calibration data fitting, counts the length of the slice interval with continuous angle values ​​less than the threshold, marks the interval endpoints as the lithologic unit division points, assigns increasing unit numbers in the order of the division points, and generates a lithologic sequence number.

[0021] As a further solution of the present invention, the reservoir property integration module includes:

[0022] The physical property data integration submodule receives the lithologic sequence number, extracts density curve sampling point values, acoustic wave transit time curve amplitude values, and porosity curve discrete values ​​according to the depth segment corresponding to the number, and uses a sliding average algorithm with a fixed window step size to calculate the arithmetic mean of three adjacent sampling points for the three types of data to generate a physical property mean window set;

[0023] The steady-state index calculation submodule calls the physical property mean window set, calculates the difference between the maximum and minimum values ​​of the porosity curve in each window, superimposes the range span of the permeability curve in the window, and uses the product of the porosity difference and the permeability span as a steady-state discriminant factor to generate a steady-state index sequence;

[0024] The porosity difference is defined as the porosity range within the window ≤ 5%;

[0025] The reservoir depth screening submodule calls the steady-state indicator sequence, sets the steady-state discrimination threshold to 0.8 times the sequence mean, traverses the discrimination factor values ​​in the sequence, marks the coordinates of the starting and ending points of the depth segments that continuously exceed the threshold, merges adjacent depth segments with a preset spacing of less than 2 meters, and generates a steady-state reservoir window depth segment.

[0026] As a further solution of the present invention, the capping response extraction module includes:

[0027] The gas derivative analysis submodule collects gas measurement data The sampling step of the concentration series, The gradient change of the concentration sequence, The extreme value distribution of the concentration sequence and the local amplitude of the fluorescence curve are analyzed. For each of the four curves, a differential window is constructed using adjacent sampling points. The mean difference between the values ​​of the middle point and the points on both sides of the window is calculated. The depth coordinates of the extreme points where the mean difference exceeds the baseline fluctuation range are marked. The density distribution and amplitude ratio of the extreme points of multiple gas components are statistically analyzed to generate a peak intensity sequence.

[0028] The parameter fusion calculation submodule calls the peak intensity sequence, extracts the standard deviation of the acoustic impedance curve of the argillaceous rock section and the inverse coefficient of the resistivity curve, aligns the standard deviation with the inverse coefficient according to depth, calculates the product 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 capping response coefficient;

[0029] The depth range determination submodule sets a dynamic threshold based on the coefficient sequence distribution characteristics based on the capping response coefficient, traverses the coefficient values ​​in the depth segment, screens the interval endpoint coordinates that continuously exceed the threshold, merges adjacent intervals and eliminates isolated interval segments to generate the capping effective zone depth range;

[0030] The dynamic threshold is calculated based on the mean and standard deviation of the capping response coefficient sequence.

[0031] As a further embodiment of the present invention, the system further comprises:

[0032] A combination priority generation module is configured to receive the steady-state reservoir window depth segment and the capping effective zone depth range, call the reservoir physical property stability index, fluorescence peak intensity, and capping layer closure index, perform multiplication and accumulation operations to generate priority values, construct a source-reservoir-capping combination priority map based on descending order, and transmit the map to an external decision-making terminal;

[0033] The source-reservoir-cap combination priority map includes a reservoir physical property accumulation index sequence, a capping strength priority value sequence, and a hydrocarbon generation fluorescence response sequence.

[0034] As a further solution of the present invention, the combination priority generation module includes:

[0035] The parameter integration submodule calls the starting and ending coordinates of the steady-state reservoir window depth segment and the boundary data of the capping effective zone depth range, extracts the sampling point sequence of the reservoir physical stability index, the discrete distribution value of the fluorescence peak intensity, and the fluctuation curve of the capping layer closure index, and uses the interpolation method to match and align the three types of parameters according to the overlapping areas of the depth segments to generate a parameter alignment set;

[0036] Based on the parameter alignment set, the priority calculation submodule traverses the absolute value of the reservoir physical stability index, the normalized value of the fluorescence peak intensity, and the weight coefficient of the capping layer sealing index at each depth point, multiplies the three points point by point in depth order, and accumulates the product results of three adjacent depth points with weighted coefficients of [0.5, 1, 0.5] as the local priority value to generate a priority value sequence;

[0037] The map generation submodule calls the priority value sequence, compares the priority value of each depth point in the sequence with the values ​​of adjacent depth points, identifies the coordinates of the endpoints of intervals whose continuous priority values ​​are higher than the sequence mean, reorganizes the intervals in descending order according to the size of the priority values, and generates a priority map of the source-reservoir-caprock combination.

[0038] A QT basin mud logging data processing method is provided. The QT basin mud logging data processing method is performed based on the QT basin mud logging data processing system, and includes the following steps:

[0039] S1: Gamma, density, resistivity, and acoustic travel time parameters are acquired through logging instruments. Vertical depth slices are generated at preset intervals for the wellbore trajectory. The standard deviation formula is used to calculate the fluctuation of parameters in adjacent slices. Effective mutation interfaces are screened based on the coefficient of variation threshold, and the boundary coordinates of the parameter mutation zone are output.

[0040] The coefficient of variation threshold is determined based on gradient descent optimization;

[0041] S2: calling the boundary coordinates of the parameter mutation zone, extracting the resistivity difference ratio vector, the natural gamma difference ratio vector, and the density difference ratio vector, inputting the adjacent slice vectors into the cosine similarity algorithm to calculate the angle, determining the lithology category based on the angle threshold, and generating a lithology sequence number;

[0042] S3: Obtain density logging values, acoustic transit time values, and porosity curves based on the lithologic sequence numbers, establish a processing window with a width of 3 meters using a sliding average algorithm, 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 a preset value;

[0043] S4: Obtain the gas hydrocarbon curve and fluorescence intensity curve through the spectrum detection device, and calculate 、 、 The second derivative peak of the concentration curve is used to extract the joint standard deviation and inverse coefficient of the gamma, density, and resistivity parameters of the argillaceous rock section, and the depth range of the effective sealing zone where the inverse coefficient exceeds the threshold is output;

[0044] S5: Call the steady-state reservoir window depth segment and the capping effective zone depth range, input the reservoir physical property stability index, fluorescence peak intensity, and capping layer sealing index into the multiplication and accumulation operation function, generate a priority value sequence, and construct a source-reservoir-capping combination priority map in descending order.

[0045] Compared with the prior art, the advantages and positive effects of the present invention are:

[0046] In the present invention, adjacent standard deviations and coefficients of variation are calculated by slicing gamma, density, resistivity, and acoustic time difference parameters to quantify parameter mutation intensity, accurately calibrate the effectiveness boundary of the formation interface, and reduce the interference of single parameter fluctuations. Based on the cosine similarity calculation of the resistivity, natural gamma, and density difference ratio vector group, the vector space angle is used instead of keyword matching to solve the problem of sequence misjudgment caused by nonlinear correlation of lithologic parameters. A sliding average window is used to calculate the product of porosity difference and permeability span to dynamically capture the heterogeneous characteristics of reservoir physical properties and generate a steady-state reservoir window depth segment. By extracting the combined standard deviation and inverse ratio of the second-order derivative peak of gas logging data, a mathematical discrimination model for the sealing effectiveness of mudstone sections is constructed to avoid the dependence of manual experience on the morphology of the fluorescence curve. The multiplication and accumulation operation integrates reservoir stability, fluorescence intensity, and closure index to generate a priority map of source-reservoir-cap combination with multi-dimensional parameter collaborative optimization, thereby realizing quantitative priority sorting of exploration decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is a system flow chart of the present invention;

[0048] Figure 2 This is a flow chart of the well section structure analysis module of the present invention;

[0049] Figure 3 This is a flow chart of the lithologic sequence reconstruction module of the present invention;

[0050] Figure 4 This is a flow chart of the reservoir property integration module of the present invention;

[0051] Figure 5 This is a flow chart of the capping response extraction module of the present invention;

[0052] Figure 6 This is a flow chart of the module for generating the combined priority sequence of the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, 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 intended to limit the present invention.

[0054] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0055] Example 1

[0056] See also Figure 1 The present invention provides a technical solution: a QT basin logging data processing system comprising:

[0057] The well section structure analysis module 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 interface effectiveness, output the boundary coordinates of the parameter mutation zone, and transmit them to the lithologic sequence reconstruction module;

[0058] The lithologic sequence reconstruction module 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 angle between adjacent slices based on the cosine similarity algorithm, determine the attribution, generate the lithologic sequence number, and pass it to the reservoir physical property integration module;

[0059] The reservoir physical property integration module receives the lithologic sequence number, acquires density, acoustic wave, and porosity data, uses a sliding average algorithm to establish a 3-meter-wide processing window, calculates the product of the porosity difference and the permeability span, outputs the steady-state reservoir window depth segment, and transmits it to the combined priority generation module;

[0060] Capping response extraction module, used to obtain gas measurement data and fluorescence curve, calculate 、 、 The second-order derivative peak value is used to extract the product of the standard deviation of the acoustic impedance of the argillaceous rock section and the inverse resistivity coefficient, and the depth range of the effective sealing zone is output and transmitted to the combination priority generation module;

[0061] The combination priority generation module is used to receive the steady-state reservoir window depth segment and the capping effective zone depth range, call the reservoir physical property stability index, fluorescence peak intensity, and capping layer sealing index, perform multiplication and accumulation operations to generate priority values, construct the source-reservoir-cap combination priority map based on descending order, and transmit it to the external decision terminal.

[0062] 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 lithologic sequence number specifically refers to the sandstone sequence identifier, the mudstone attribution code, and the limestone layer label. The steady-state reservoir window depth segment includes the porosity equilibrium segment, the permeability stable span, and the sonic time difference slow-changing zone. The depth range of the effective sealing zone covers the muddy isolation top boundary, the gas derivative peak segment, and the inverse anomaly sealing domain. The source-reservoir-cap combination priority map includes the reservoir physical property cumulative index sequence, the sealing strength priority value sequence, and the hydrocarbon generation fluorescence response ranking.

[0063] The porosity equilibrium section is a continuous section with a porosity extreme difference of ≤5%.

[0064] See also Figure 2 , the well section structure analysis module includes:

[0065] The data slicing submodule obtains the gamma value, density value, resistivity value, and acoustic wave delay value from the logging data, divides the slice unit into 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;

[0066] The data slicing processing submodule obtains the gamma value, density value, resistivity value, and acoustic time difference value in the logging data, and extracts and pre-processes the above four types of parameters from the original logging data in turn, including removing outliers, smoothing edge data, and standardizing the data units. For example, an exclusion threshold value needs to be set for the spike abnormal data in the gamma logging curve. If the difference between a data point and its two adjacent data points is greater than ±50API, the point will be eliminated. After unification, the wellbore trajectory data is segmented at intervals of 1 meter 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 set to 2600 meters, for a total of 100 slice units. In each unit, the entire range is sliced. The gamma, density, resistivity, and acoustic time difference values ​​are extracted and arithmetic averages are calculated. For example, in the interval from 2500 m to 2501 m, the sampled gamma values ​​are [85, 90, 88, 86] API, then the gamma mean is (85+90+88+86) / 4=87.25 API. The other three parameters are calculated in the same way. For example, if the resistivity values ​​are [3.5, 3.6, 3.7] Ω·m, then the mean is (3.5+3.6+3.7) / 3=3.6 Ω·m. In this way, the mean data of the four parameters in all 100 slice units are summarized to form a new data structure and a unified parameter slice dataset, which serves as the basic input for subsequent boundary determination and fluctuation analysis.

[0067] The interface effectiveness 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, to screen out the units that exceed the standard, calculate the ratio of the multi-parameter standard deviation to the mean, and generate the adjacent parameter fluctuation value and parameter variation coefficient;

[0068] 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 value of unit i 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.

[0069] 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 continuously exceeds the limit, and generates the parameter mutation zone boundary coordinates.

[0070] 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 requirement that at least two of the coefficients of variation of the four parameters exceed the set threshold, the interval is defined as a potential mutation zone. The starting depth of the first unit and the ending depth of the last unit that meet the conditions are extracted as the boundary coordinates of the mutation zone. For example, if the 38th to 41st slice units meet the conditions, the corresponding vertical depth range is 2538 meters to 2542 meters, and the boundary is recorded as [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.

[0071] Table 1 Example of calculation of coefficient of variation of adjacent units of gamma value

[0072]

[0073] As shown in Table 1 , the gamma coefficients of variation 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 exceeded the limit.

[0074] The results show that by calculating the parameter differences pair by pair and inferring the coefficient of variation based on them, the location of the mutation boundary can be clearly determined, and it supports the refined characterization of abnormal changes in well section structure in geological engineering practice.

[0075] See also Figure 3 , the lithologic sequence reconstruction module includes:

[0076] The parameter mutation zone extraction submodule 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 each of the three types of parameters, divides the transverse slice units according to the mutation zone coordinate range, calculates the weight ratio of the three types of difference data in multiple units to the total fluctuation, constructs a three-dimensional scale vector, and generates a difference scale vector group;

[0077] After calling the mutation zone boundary coordinates, the parameter mutation zone extraction submodule first extracts the resistivity, natural gamma, and density logging data sequences according to the boundary interval, and performs the absolute value calculation of the numerical difference between adjacent sampling points for each type of parameter. The resistivity sampling point is set to once every 0.1 meter. If there are 40 sampling points between 2538 meters and 2542 meters, the resistivity difference of the i-th pair of sampling points is expressed as , taking the actual value as an example, if , , then the difference is 0.4, and all the differences are accumulated in sequence to obtain the absolute fluctuation amplitude and. The gamma parameter uses a similar method to count the total amount of its mean change in the same interval. If the value array is [85,86,90,89,91], the gamma mean is 88.2, compared with the standard layer average value of 85, the offset is 3.2. The density parameter calculates its standard deviation change. Suppose the standard deviation of the standard section density is 0.06, and the standard deviation of the density data in the current interval is 0.12, then the change is 0.06. The mutation zone interval is divided into several slice units according to the horizontal direction of 1 meter. For each slice unit, the total resistivity difference and gamma offset are counted separately. The total amount and density standard deviation change are divided by the total difference of the three types of parameters in the whole area to form the proportion of the three types of differences. Suppose the three values ​​of a unit are 0.85, 5.6, and 0.12 respectively. If the overall sum of the mutation zone is resistivity 2.65, gamma 11.6, and density 0.34, then the resistivity weight of the 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, and finally the three-dimensional difference ratio vector [0.32, 0.48, 0.20] of the unit is formed. All slice units are arranged in order to generate a difference ratio vector group.

[0078] The sequence angle calculation submodule traverses the slice unit sequence based on the difference ratio vector group, extracts the three-dimensional vector data of adjacent units, projects the vectors into the polar coordinate system, 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, reversely deduces the angle value and records the order to generate a sequence of adjacent angles;

[0079] The sequential angle calculation submodule extracts the three-dimensional vectors of the current unit and its adjacent units in sequence from the first slice unit according to the constructed difference ratio vector group. For example, the first and second units are [0.32, 0.48, 0.20] and [0.44, 0.32, 0.24] respectively. The two vectors are projected into the polar coordinate system for angle calculation. Assuming that the angle between vectors a and b is θ, the modulus of vectors a and b is first obtained. The modulus calculation formula is , substituting into Similarly, the modulus of vector b is , and then perform vector dot product: , and finally use the cosine similarity formula ,Depend on , and finally record the angle into the angle sequence, repeatedly calculate all adjacent slice pairs, and generate the angle value sequence in sequence.

[0080] The lithologic attribution determination submodule calls the adjacent angle sequence, compares the angle data with the 45-degree lithologic boundary threshold determined based on the core calibration data fitting, counts the length of the slice interval with continuous angle values ​​less than the threshold, marks the interval endpoints as the lithologic unit division points, and assigns increasing unit numbers in the order of the division points to generate the lithologic sequence number.

[0081] Based on the aforementioned angle sequence, the lithologic attribution determination submodule sequentially determines whether each pair of angles is less than 45 degrees, a lithologic demarcation threshold. This threshold, derived from core calibration experiments, indicates that adjacent units do not experience significant abrupt changes in lithologic attributes. If three or more consecutive angles within a segment are less than 45 degrees, the segment is considered to be the same lithologic unit, and the start and end slices are marked as the endpoints of the unit boundary. For example, if the angle sequence [20.0°, 18.5°, 23.4°, 19.8°] appears consecutively, then the corresponding units 1 to 5 are considered to be a unified lithologic unit. Unit 1 is used as the starting point and unit 5 as the end point, and is labeled lithologic unit 1. The next lithologic unit is then determined at the next unassigned unit until all slices are traversed. Finally, each lithologic unit is sequentially numbered to generate a lithologic sequence. This process, based on the strong correlation between angle and lithologic consistency, ensures reasonable segmentation of homologous lithologies.

[0082] See also Figure 4 , the reservoir property integration module includes:

[0083] The physical property data integration submodule receives the lithologic sequence number and extracts the density curve sampling point value, the acoustic wave time difference curve amplitude value, and the porosity curve discrete value according to the depth segment corresponding to the number. The sliding average algorithm is used with a fixed window step size to calculate the arithmetic mean of three adjacent sampling points for each of the three types of data to generate a physical property mean window set.

[0084] After receiving the lithologic sequence number, the physical property data integration submodule extracts the raw data of density curve, acoustic transit time curve, and porosity curve according to the depth range corresponding to each number. The density curve adopts a conventional sampling step of 0.1 meters, with a total of 30 sampling points between 2500 meters and 2503 meters. The raw density data array is set to [2.42, 2.46, 2.47, …, 2.44] g / cm³, and the sampling value unit of the acoustic transit time curve is μs / m , set to [85.3,86.7,87.0,…,84.7], porosity is a percentage system, set to [17.8,18.1,18.3,…,17.9], apply the sliding window method to the three types of data, set the window to 3 sampling points, the step size is 1, traverse the entire curve, and perform arithmetic average operation on the three points in each window. For example, the density of the first window is [2.42,2.46,2.47], and its average value is g / cm³, the acoustic time difference and porosity are calculated in this way. In window 2, if the acoustic time difference is [86.7, 87.0, 88.6], the average is , generating a complete set of density, acoustic transit time, and porosity sliding mean windows.

[0085] The steady-state index calculation submodule calls the physical property mean window set, calculates the difference between the maximum and minimum values ​​of the porosity curve in each window, superimposes the range span of the permeability curve in the window, and uses the product of the porosity difference and the permeability span as the steady-state discriminant factor to generate a steady-state index sequence;

[0086] The porosity difference is defined as the porosity range within the window ≤ 5%;

[0087] The steady-state index calculation submodule calls the above-mentioned physical property mean window set and performs the product operation of porosity range and permeability span on each window. The porosity range is defined as the difference between the maximum and minimum values ​​in the window. The porosity in window 1 is set to [17.3, 18.2, 18.5], and the range is , below the 5% limit, meeting the definition of steady-state porosity, the permeability data are extracted synchronously according to the same window, the unit is mD, for example, the value is [120,160,170], the range is mD, then the steady-state discriminant factor is Similarly, the porosity range of window 2 is 1.4, and the permeability span is 60, so the factor is , record the discriminant factor results of all windows to form a steady-state index sequence for the next screening.

[0088] The reservoir depth screening submodule calls the steady-state indicator sequence, sets the steady-state discrimination threshold to 0.8 times the sequence mean, traverses the discriminant factor values ​​in the sequence, marks the coordinates of the starting and ending points of the depth segments that continuously exceed the threshold, merges adjacent depth segments with a preset spacing of less than 2 meters, and generates a steady-state reservoir window depth segment.

[0089] The reservoir depth screening submodule uses the aforementioned steady-state indicator sequence, sets the discrimination threshold to 0.8 times the mean of the sequence, sets the discrimination factor to [60.0, 84.0, 40.5], and the mean is , the threshold is On this basis, all windows are individually judged to see if their discriminant factors are above the threshold. If two or more consecutive windows, such as Window 1 and Window 2, have values ​​above 49.2, their starting and ending window numbers are mapped to depth coordinates. Assuming a window width of 0.2 meters, Window 1 starts at 2500.0 meters and ends at 2500.6 meters. Window 2 starts at 2500.2 meters and ends at 2500.8 meters. The adjacent spacing of these two windows is 0.2 meters, which is less than the preset merging spacing of 2 meters. These two windows are then merged into a single steady-state reservoir segment [2500.0, 2500.8]. All consecutive window segments that meet these criteria are then merged and output as a steady-state reservoir window depth segment set. This result indicates that when the porosity-permeability combined index meets the set steady-state judgment criteria within the window interval, it can be used to identify reservoir depth windows.

[0090] See also Figure 5 , the capping response extraction module includes:

[0091] The gas derivative analysis submodule collects gas measurement data The sampling step of the concentration series, The gradient change of the concentration sequence, The extreme value distribution of the concentration sequence and the local amplitude of the fluorescence curve are analyzed. For each of the four curves, a differential window is constructed using adjacent sampling points. The mean difference between the values ​​of the middle point and the points on both sides of the window is calculated. The depth coordinates of the extreme points where the mean difference exceeds the baseline fluctuation range are marked. The density distribution and amplitude ratio of the extreme points of multiple gas components are statistically analyzed to generate a peak intensity sequence.

[0092] The gas derivative analysis submodule collects gas measurement data concentration, concentration, Concentration and fluorescence curves: First, construct a differential window for the four sets of curve data based on the set sampling step size. The window width is three points. The difference between the middle point and the two side points of each window is extracted and the arithmetic average is taken as the difference mean of the window. For example, The window data near 2510.0 meters is [1.12%, 1.26%, 1.18%], the midpoint is 1.26%, and the mean of both sides is (1.12+1.18) / 2=1.15%, so the mean of the difference is , the same treatment Expressed as gradient, if the gradient is [0.08, 0.10, 0.09], the average difference between the midpoint and the adjacent points is , the same operation is performed on the other curves. Take the extreme window value as [1.20%, 1.42%, 1.36%], the midpoint is the maximum value 1.42%, and the difference with the mean of 1.28% on both sides is 0.14%. The fluorescence amplitude is [0.80, 1.02, 0.95], then the amplitude difference is , compare all difference means with their respective set baseline fluctuation ranges, set The baseline fluctuation was 0.08%, The peak intensity sequence was constructed as the input for subsequent parameter fusion.

[0093] The parameter fusion calculation submodule calls the peak intensity sequence, extracts the standard deviation of the acoustic impedance curve of the argillaceous rock section and the inverse coefficient of the resistivity curve, aligns the standard deviation with the inverse coefficient by depth, calculates the product 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 capping response coefficient.

[0094] After calling the above peak intensity sequence, the parameter fusion calculation submodule further extracts the acoustic impedance curve and resistivity curve data in the mudstone section. The sampling value of the acoustic impedance curve in the depth range is set to [12400, 12800, 12650]. The standard deviation is calculated as follows: , unit is kPa·s / m, the resistivity curve is [4.0, 5.0, 3.3]Ω·m, then its reciprocal is [0.25, 0.20, 0.30], then the two are aligned according to the depth and the product value is calculated respectively. For example, if the standard deviation is 12.5, the product is 0.25 when the reciprocal is , and normalize all product values. If the maximum value is 3.57 and the current value is 3.13, it is normalized to Then normalize the peak intensity sequence such as [0.63, 0.75, 0.52], the maximum value is 0.75, and the normalized values ​​are [0.84, 1.00, 0.69], and take the weighted average of the two. If the weights of both are 0.5, the final capping response coefficient is , and generate the complete response coefficient sequence in sequence.

[0095] The depth range determination submodule sets a dynamic threshold based on the coefficient sequence distribution characteristics based on the capping response coefficient, traverses the coefficient values ​​in the depth segment, filters the coordinates of the interval endpoints that continuously exceed the threshold, merges adjacent intervals and eliminates isolated intervals to generate the depth range of the capping effective zone;

[0096] The dynamic threshold is calculated based on the mean and standard deviation of the capping response coefficient series.

[0097] The depth range determination submodule sets a dynamic threshold based on the capping response coefficient sequence generated above. The threshold is obtained by adding a certain multiple of the standard deviation to the mean of the sequence. The response coefficient is set to [0.858, 0.74, 1.00] and the mean is , the standard deviation is , then the dynamic threshold is the mean plus 0.5 times the standard deviation, that is All depth points with values ​​greater than 0.931 were screened, and only those with a value of 1.00 met the criteria. The depth intervals before and after these values ​​were selected as candidate segments for capping responses. Adjacent segments were merged if they were within 2 meters, while isolated values ​​were removed. The final output was the depth range of the complete capping effective zone. This result indicates that this segment exhibits capping response characteristics when combined with the gas responses and geophysical properties.

[0098] See also Figure 6 , the combination priority generation module includes:

[0099] The parameter integration submodule uses the start and end coordinates of the steady-state reservoir window depth segment and the boundary data of the effective capping zone depth range to extract the sampling point sequence of the reservoir physical stability index, the discrete distribution value of the fluorescence peak intensity, and the fluctuation curve of the capping layer closure index. The three types of parameters are aligned according to the overlapping areas of the depth segments using the interpolation method to generate a parameter alignment set.

[0100] The parameter integration submodule calls the depth segment information corresponding to the stable reservoir window and the effective sealing zone identified in the early stage. The reservoir start and end are set to [2510.0, 2511.0] meters, the sealing section is [2510.2, 2511.4] meters, and the overlapping interval is [2510.2, 2511.0] meters. Three types of parameter curve data are extracted within this range, namely the reservoir physical property stability index sequence, the fluorescence peak intensity discrete value, and the sealing closure index fluctuation value. The depth interval is 0.2 meters to obtain three data at each depth point. For example, at 2510.2 meters, the three values ​​are 0.85, 0.72, and 0.60. Then, the linear interpolation method is used to fill the data for any missing depth point. If there is no fluorescence value at 2510.6 meters, but 2510.4 and 2510.8 meters are 0.65 and 0.75 respectively, the interpolation result is After alignment, a complete three-value structure of each depth point is formed, and finally a parameter alignment set sorted by overlapping depth segments is formed as the input structure for subsequent priority calculation.

[0101] Based on the parameter alignment set, the priority calculation submodule traverses the absolute value of the reservoir physical stability index, the normalized value of the fluorescence peak intensity, and the weight coefficient of the cap layer sealing index at each depth point, multiplies the three points point by point in depth order, and accumulates the product of three adjacent depth points with weighted coefficients of [0.5, 1, 0.5] as the local priority value to generate a priority value sequence;

[0102] The priority calculation submodule calls the above parameter alignment set, traverses each depth point in order, and extracts three types of index data, namely the absolute value of the reservoir physical stability index, the normalized value of the fluorescence intensity, and the weight coefficient of the closure index. 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 , assuming the weight coefficient vector is [0.5, 1.0, 0.5], and the calculation is performed based on three adjacent depth points as windows. 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 the local priority value is ,

[0103] Repeat the window moving calculation operation to form a priority value sequence record for all intermediate depth points.

[0104] Table 2 Example of calculation of merit value

[0105]

[0106] Table 2 lists the values ​​of the three types of parameters at the actual depth points and their point product results, and calculates the local priority values ​​of the window centered at 2510.2 meters.

[0107] The map generation submodule calls the priority value sequence, compares the priority value of each depth point in the sequence with the values ​​of adjacent depth points, identifies the coordinates of the endpoints of intervals with continuous priority values ​​higher than the sequence mean, and reorganizes the intervals in descending order according to the size of the priority values ​​to generate a priority map of the source-reservoir-caprock assemblage.

[0108] The graph generation submodule uses the above-mentioned priority value sequence to calculate the average value of the entire sequence. If the sequence is [0.25, 0.31, 0.35, 0.30, 0.45], the average value is , screening all continuous intervals with a preference value greater than the mean. If the preference value of the depth segment from 2510.2 to 2510.6 meters is [0.35, 0.37, 0.45], then mark its starting endpoint 2510.2 meters and its ending endpoint 2510.6 meters as the preference segment. If multiple segments exist, sort them by the maximum preference value within the segment, prioritizing the higher preference value. Finally, a source-reservoir-caprock preference map is generated. This map clearly shows the distribution structure of the preference combinations within the depth interval. This result shows that the interval of the preference map can be constructed by multiplying the parameters and weighted averaging.

[0109] A QT basin mud logging data processing method is provided. The QT basin mud logging data processing method is performed based on the QT basin mud logging data processing system, and includes the following steps:

[0110] S1: Gamma, density, resistivity, and acoustic travel time parameters are acquired through logging instruments. Vertical depth slices are generated at preset intervals for the wellbore trajectory. The standard deviation formula is used to calculate the fluctuation of parameters in adjacent slices. Effective mutation interfaces are screened based on the coefficient of variation threshold, and the boundary coordinates of the parameter mutation zone are output.

[0111] The coefficient of variation threshold is determined based on the gradient descent optimization method;

[0112] 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 angle, determine the lithology category based on the angle threshold, and generate the lithology sequence number;

[0113] S3: Obtain density logging values, acoustic transit time values, and porosity curves based on the lithologic sequence number. Use the sliding average algorithm to establish a processing window with a width of 3 meters. Calculate the product of the porosity range and permeability span within the window, and output the steady-state reservoir window depth segment where the product is lower than the preset value.

[0114] S4: Obtain the gas hydrocarbon curve and fluorescence intensity curve through the spectrum detection device, and calculate 、 、 The second derivative peak of the concentration curve is used to extract the joint standard deviation and inverse coefficient of the gamma, density, and resistivity parameters of the argillaceous rock section, and the depth range of the effective sealing zone where the inverse coefficient exceeds the threshold is output;

[0115] S5: Call the steady-state reservoir window depth segment and the effective capping zone depth range, input the reservoir physical property stability index, fluorescence peak intensity, and capping layer sealing index into the multiplication and accumulation operation function, generate a priority value sequence, and construct a source-reservoir-capping combination priority map in descending order.

[0116] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A QT Basin logging data processing system, characterized in that: The system comprises: The well section structure analysis module 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 interface effectiveness, output the boundary coordinates of the parameter mutation zone, and transmit them to the lithologic sequence reconstruction module; The lithologic sequence reconstruction module 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 angle between adjacent slices based on the cosine similarity algorithm, determine the attribution, generate the lithologic sequence number, and pass it to the reservoir physical property integration module; The reservoir physical property integration module is used to receive the lithologic sequence number, obtain density, acoustic wave, and porosity data, establish a 3-meter-wide processing window using a sliding average algorithm, calculate the product of the porosity difference and the permeability span, output the steady-state reservoir window depth segment, and pass it to the combined priority generation module; The capping response extraction module is used to obtain gas logging data and fluorescence curves, calculate the second-order derivative peaks of CH4, C2H6, and CO2, 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 capping zone, and pass it to the combination priority generation module; The system further comprises: A combination priority generation module is configured to receive the steady-state reservoir window depth segment and the capping effective zone depth range, call the reservoir physical property stability index, fluorescence peak intensity, and capping layer closure index, perform multiplication and accumulation operations to generate priority values, construct a source-reservoir-capping combination priority map based on descending order, and transmit the map to an external decision-making terminal; The source-reservoir-cap combination priority map includes a reservoir physical property cumulative index sequence, a capping strength priority value sequence, and a hydrocarbon generation fluorescence response sequence; The combination priority generation module includes: The parameter integration submodule calls the starting and ending coordinates of the steady-state reservoir window depth segment and the boundary data of the capping effective zone depth range, extracts the sampling point sequence of the reservoir physical stability index, the discrete distribution value of the fluorescence peak intensity, and the fluctuation curve of the capping layer closure index, and uses the interpolation method to match and align the three types of parameters according to the overlapping areas of the depth segments to generate a parameter alignment set; Based on the parameter alignment set, the priority calculation submodule traverses the absolute value of the reservoir physical stability index, the normalized value of the fluorescence peak intensity, and the weight coefficient of the capping layer sealing index at each depth point, multiplies the three points point by point in depth order, and accumulates the product results of three adjacent depth points with weighted coefficients of [0.5, 1, 0.5] as the local priority value to generate a priority value sequence; The map generation submodule calls the priority value sequence, compares the priority value of each depth point in the sequence with the values ​​of adjacent depth points, identifies the coordinates of the endpoints of intervals whose continuous priority values ​​are higher than the sequence mean, reorganizes the intervals in descending order according to the size of the priority values, and generates a priority map of the source-reservoir-caprock combination.

2. The QT basin logging data processing system according to claim 1, characterized in that: The parameter mutation zone boundary coordinates include a gamma mutation point sequence, a density gradient inflection point set, and a resistivity transition interval; the lithologic sequence number specifically refers to a sandstone sequence identifier, a mudstone attribution code, and a limestone layer label; the steady-state reservoir window depth segment includes a porosity equilibrium segment, a permeability stable span, and a sonic time difference slow-changing zone; the effective sealing zone depth range covers the top boundary of the mud isolation, the gas derivative peak segment, and the inverse anomaly sealing domain; The porosity equalization section is a continuous section with a porosity extreme difference of ≤5%.

3. The QT basin logging data processing system according to claim 2, characterized in that: The well section structure analysis module includes: The data slicing submodule obtains the gamma value, density value, resistivity value, and acoustic wave delay value from the logging data, divides the slice unit into 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 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, 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 units that exceed the standard, calculates the ratio of the multi-parameter standard deviation to the mean, and generates adjacent parameter fluctuation values ​​and parameter variation coefficients; 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 thresholds, extracts the top and bottom boundary coordinates of the vertical depth interval where the variation coefficient continuously exceeds the limit, and generates the parameter mutation zone boundary coordinates.

4. The QT basin logging data processing system according to claim 3, characterized in that: The lithologic sequence reconstruction module includes: The parameter mutation zone extraction submodule 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, divides the transverse slice units according to the mutation zone coordinate range, calculates the weight ratio of the three types of difference data in multiple units to the total fluctuation, constructs a three-dimensional proportion vector, and generates a difference proportion vector group; The sequence angle calculation submodule traverses the slice unit sequence based on the difference ratio vector group, extracts the three-dimensional vector data of adjacent units, projects the vectors into the polar coordinate system, 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, reversely deduces the angle value and records the order to generate a sequence of adjacent angles; The lithologic attribution determination submodule calls the adjacent angle sequence, compares the angle data with the 45-degree lithologic boundary threshold determined based on the core calibration data fitting, counts the length of the slice interval with continuous angle values ​​less than the threshold, marks the interval endpoints as the lithologic unit division points, assigns increasing unit numbers in the order of the division points, and generates a lithologic sequence number.

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

6. The QT basin logging data processing system according to claim 5, characterized in that: The capping response extraction module includes: The gas derivative analysis submodule collects the sampling step length of the CH4 concentration sequence, the gradient change of the C2H6 concentration sequence, the extreme value distribution of the CO2 concentration sequence, and the local amplitude of the fluorescence curve in the gas measurement data. For each of the four curves, a differential window is constructed using adjacent sampling points. The mean difference between the numerical value of the middle point and the points on both sides of the window is calculated. The depth coordinates of the extreme points where the mean difference exceeds the baseline fluctuation range are marked. The density distribution and amplitude ratio of the extreme points of multiple gas components are calculated to generate a peak intensity sequence. The parameter fusion calculation submodule calls the peak intensity sequence, extracts the standard deviation of the acoustic impedance curve of the argillaceous rock section and the inverse coefficient of the resistivity curve, aligns the standard deviation with the inverse coefficient according to depth, calculates the product 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 capping response coefficient; The depth range determination submodule sets a dynamic threshold based on the coefficient sequence distribution characteristics based on the capping response coefficient, traverses the coefficient values ​​in the depth segment, screens the interval endpoint coordinates that continuously exceed the threshold, merges adjacent intervals and eliminates isolated interval segments to generate the capping effective zone depth range; The dynamic threshold is calculated based on the mean and standard deviation of the capping response coefficient sequence.

7. A QT Basin logging data processing method, characterized in that: The method is used to implement the QT basin logging data processing system according to any one of claims 1 to 6, comprising the following steps: S1: Gamma, density, resistivity, and acoustic travel time parameters are acquired through logging instruments. Vertical depth slices are generated at preset intervals for the wellbore trajectory. The standard deviation formula is used to calculate the fluctuation of parameters in adjacent slices. Effective mutation interfaces are screened based on the coefficient of variation threshold, and the boundary coordinates of the parameter mutation zone are output. The coefficient of variation threshold is determined based on gradient descent optimization; S2: calling the boundary coordinates of the parameter mutation zone, extracting the resistivity difference ratio vector, the natural gamma difference ratio vector, and the density difference ratio vector, inputting the adjacent slice vectors into the cosine similarity algorithm to calculate the angle, determining the lithology category based on the angle threshold, and generating a lithology sequence number; S3: Obtain density logging values, acoustic transit time values, and porosity curves based on the lithologic sequence numbers, establish a processing window with a width of 3 meters using a sliding average algorithm, 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 a preset value; S4: Obtain the total hydrocarbon curve and fluorescence intensity curve of the gas logging through the spectrum detection device, calculate the second derivative peak of the CH4, C2H6, and CO2 concentration curves, extract the joint standard deviation and inverse coefficient of the gamma, density, and resistivity parameters of the argillaceous rock section, and output the depth range of the effective sealing zone where the inverse coefficient exceeds the threshold; S5: Call the steady-state reservoir window depth segment and the capping effective zone depth range, input the reservoir physical property stability index, fluorescence peak intensity, and capping layer sealing index into the multiplication and accumulation operation function, generate a priority value sequence, and construct a source-reservoir-capping combination priority map in descending order.

Images