Logging inversion evaluation method and device for high-permeability organic thermal transfer ribbon in thick-layer shale

Abstract

The invention discloses a logging inversion evaluation method and device for a high-permeability organic thermal transfer ribbon in thick-layer shale, and relates to the technical field of logging inversion, and the method comprises the steps: collecting conventional logging data of a target well section, and recognizing an argillaceous rock stratum through an intersection chart; electric imaging logging processing is conducted on the argillaceous rock stratum, and the structure type is determined according to electric imaging logging data; taking a lamina structure in the structure type as a target evaluation layer section, obtaining a total organic carbon content curve, an organic matter porosity curve and an effective permeability curve through data inversion, and executing curve fusion to determine an organic carbon effectiveness factor curve; and according to the organic carbon effectiveness factor curve, identifying and delimiting the high-permeability organic thermal transfer ribbon in the thick-layer shale. The technical problem that in the prior art, it is difficult to accurately identify the high-permeability organic thermal transfer ribbon in the thick-layer mud shale is solved, and the technical effect of accurately identifying and delimiting the high-permeability organic thermal transfer ribbon in the thick-layer mud shale is achieved.

Description

Technical Field

[0001] This invention relates to the field of well logging inversion technology, specifically to a well logging inversion evaluation method and apparatus for high-permeability organic carbon zones in thick mudstone and shale. Background Technology

[0002] Thick mudstone and shale sedimentary structures are well-developed and highly heterogeneous within the layers. Different structural types exhibit significant differences in organic matter occurrence and pore flow characteristics. If the evaluation process relies primarily on a single well logging parameter or a simple lithological classification method, it is difficult to simultaneously characterize the degree of organic carbon enrichment and permeability, which can easily lead to biases in judging the effectiveness of the layers. This makes it difficult to accurately identify and delineate high-permeability organic carbon zones with good permeability and organic matter advantages. Summary of the Invention

[0003] This application provides a well logging inversion evaluation method and apparatus for high-permeability organic carbon bands in thick mudstone and shale, which is used to address the technical problem of accurately identifying high-permeability organic carbon bands in thick mudstone and shale in the prior art.

[0004] In view of the above problems, this application provides a well logging inversion evaluation method and device for high-permeability organic carbon zones in thick mudstone and shale.

[0005] The first aspect of this application provides a well logging inversion evaluation method for high-permeability organic carbon zones in thick mudstone and shale, the method comprising:

[0006] Conventional logging data of the target well section is collected, and argillaceous rock layers are identified using cross-plotting charts. Electrical imaging logging is performed on the argillaceous rock layers, and the structural type is determined based on the electrical imaging logging data. The lamellar structure within the structural type is selected as the target evaluation layer. Total organic carbon content curves, organic matter porosity curves, and effective permeability curves are obtained through data inversion. Curve fusion is performed to determine the organic carbon availability factor curve. Based on the organic carbon availability factor curve, high-permeability organic carbon zones in thick mudstone and shale are identified and delineated.

[0007] A second aspect of this application provides a well logging inversion evaluation device for high-permeability organic carbon zones in thick mudstone and shale, the device comprising:

[0008] The data acquisition module is used to acquire conventional logging data of the target well section and identify argillaceous rock layers through cross-plotting. The structure type determination module is used to perform electrical imaging logging processing on the argillaceous rock layers and determine the structure type based on the electrical imaging logging data. The curve fusion module is used to take the lamellar structure in the structure type as the target evaluation segment, obtain the total organic carbon content curve, organic matter porosity curve and effective permeability curve through data inversion, and perform curve fusion to determine the organic carbon availability factor curve. The identification module is used to identify and delineate the high-permeability organic carbon zone in the thick mudstone and shale based on the organic carbon availability factor curve.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] This application collects conventional logging data of the target well section and identifies argillaceous rock layers using cross-plotting. Electrical imaging logging is then performed on the argillaceous rock layers to determine the structural type based on the logging data. The lamellar structure within this structural type is selected as the target evaluation layer. Data inversion is used to obtain total organic carbon content curves, organic matter porosity curves, and effective permeability curves. Curve fusion is then performed to determine the organic carbon availability factor curve. Based on the organic carbon availability factor curve, high-permeability organic carbon zones in thick shale and mudstone are identified and delineated. This invention solves the technical problem of accurately identifying high-permeability organic carbon zones in thick shale and mudstone in existing technologies. By identifying the structural type of the argillaceous rock layers and constructing an organic carbon availability factor based on multi-parameter logging inversion and fusion, the technical effect of accurately identifying and delineating high-permeability organic carbon zones in thick shale and mudstone is achieved. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A schematic diagram of the well logging inversion evaluation method for high-permeability organic carbon zones in thick mudstone and shale, provided in the embodiments of this application;

[0013] Figure 2 A schematic diagram of the well logging inversion evaluation device for high-permeability organic carbon zones in thick mudstone and shale, provided in an embodiment of this application.

[0014] Figure labeling: Data acquisition module 11, structure type determination module 12, curve fusion module 13, identification module 14. Detailed Implementation

[0015] This application provides a well logging inversion evaluation method and device for high-permeability organic carbon zones in thick mudstone and shale. It addresses the technical problem of accurately identifying high-permeability organic carbon zones in thick mudstone and shale in existing technologies. By identifying the structural type of the mudstone strata and constructing an organic carbon availability factor based on multi-parameter well logging inversion and fusion, the technical effect of accurately identifying and delineating high-permeability organic carbon zones in thick mudstone and shale is achieved.

[0016] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0017] It should be noted that any variation of the terms "comprising" and "having" is intended to cover non-exclusive inclusion, for example, a process, method, apparatus, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such process, method, product, or apparatus.

[0018] Example 1, as Figure 1 As shown, this application provides a well logging inversion evaluation method for high-permeability organic carbon zones in thick mudstone and shale, the method comprising:

[0019] Step S100: Collect conventional logging data for the target well section and identify the argillaceous rock strata using cross-plots.

[0020] In this embodiment of the application, when collecting conventional logging data of the target well section, the logging instrument is lowered into the wellbore and continuously operated using a cable logging method to measure the formation point by point and obtain conventional logging data such as natural gamma (GR), sonic transit time (AC), density (DEN), and neutron porosity (CNL) that vary with well depth. Natural gamma is used to characterize the change in clay content in the formation, sonic transit time is used to characterize the formation structure characteristics, and density and neutron porosity are used to characterize the rock matrix and pore response.

[0021] Next, when identifying argillaceous rock formations using the GR-AC cross-plot, natural gamma (GR) logging values ​​and sonic transit time (AC) logging values ​​at the same well depth are paired point by point, and a cross-plot is drawn. Taking all logging data points within the target well section as the whole, a statistical distribution analysis is performed on the GR logging values. Data points with GR logging values ​​higher than the median of this statistical distribution and continuously distributed are identified as high GR data points. Then, the range of sonic transit time values ​​corresponding to the high GR data points is statistically analyzed, and this range is used as the sonic transit time variation interval corresponding to the argillaceous rock formation. When a data point simultaneously satisfies the condition that its GR logging value belongs to the set of high GR data points and its sonic transit time logging value falls within the corresponding sonic transit time variation interval, the lithology corresponding to the data point is determined to be argillaceous rock, thus completing the identification of argillaceous rock formations based on the GR-AC cross-plot.

[0022] Finally, when confirming the identification results of the argillaceous rock layers using the M-N cross plot, the density (DEN), neutron porosity (CNL), and sonic transit time (AC) logging data were read point by point according to well depth, and parameters were calculated. The M parameter was calculated according to a preset scaling factor, a fixed constant used to unify the dimensions of the sonic transit time and density combination parameters. Specifically, the difference between the fluid sonic transit time reference value and the sonic transit time logging value was used as the numerator, and the difference between the volumetric density logging value and the fluid density reference value was used as the denominator, multiplied by the preset scaling factor. The N parameter was calculated using the difference between the fluid neutron porosity reference value and the neutron porosity logging value as the numerator, and the difference between the volumetric density logging value and the fluid density reference value as the denominator. The calculated M and N parameters are plotted point by point in a cross-plot. The distribution range of the data points that have been identified as argillaceous rock layers by the GR-AC cross-plot in the M-N cross-plot is determined as the parameter distribution range of the argillaceous rock layers. When the M and N parameters of any data point fall into the parameter distribution range at the same time, the lithology identification result is confirmed as argillaceous rock layer.

[0023] Step S200: Perform electrical imaging logging on the argillaceous rock layer and determine the structural type based on the electrical imaging logging data.

[0024] In this embodiment of the application, firstly, electrical imaging logging is performed on the mudstone strata. The electrical imaging logging instrument is run along the wellbore to perform high-resolution scanning of the well wall and obtain electrical imaging logging data reflecting the difference in resistivity of the well wall. The electrical imaging logging data continuously records the electrical response characteristics of different parts of the well wall in the form of images, which is used to characterize the sedimentary structure and microstructure inside the strata.

[0025] Next, based on the electro-imaging logging data, the core-scale laminarity index was determined. The core-scale laminarity index is used to quantitatively characterize the degree of laminar development in the electro-imaging images. Subsequently, according to the value of the core-scale laminarity index, the argillaceous rock layers were divided into massive, layered, and laminar structures, and the classification results were used as the structural types of the argillaceous rock layers.

[0026] Furthermore, in the method provided in the application embodiments, determining the structure type based on electrical imaging logging data further includes:

[0027] Based on the electrical imaging logging data, the core scale laminarity index is determined; based on the core scale laminarity index, the argillaceous rock layer is divided into blocky structure, layered structure and laminar structure as the structure type.

[0028] In this embodiment, the core calibration bedding index is first determined based on electrical imaging logging data. The electrical imaging logging data consists of a wellbore resistivity image extending along the well depth. Different resistivity responses are represented by different brightness and darkness distributions on the image. Sedimentary bedding in argillaceous rock layers appears as alternating bright and dark bands along the bedding direction on the electrical imaging logging image, with the interfaces between the bands corresponding to bedding interfaces. The electrical imaging logging image is segmented using a fixed-length calculation depth window. Within each calculation depth window, the band interfaces are identified and counted. The ratio of the number of identified band interfaces to the thickness of the calculation depth window is calculated to obtain the number of bedding interfaces per unit thickness. This ratio is used as the numerical expression of the bedding index, thus forming a core calibration bedding index that continuously varies with well depth.

[0029] Next, the argillaceous rock strata are classified into structural types based on the lamination index. In this process, the core-scaled lamination index is compared with a preset structural classification threshold. When the core-scaled lamination index is less than 1.5, it indicates a small number of laminar interfaces per unit thickness, and the internal structure of the argillaceous rock strata is homogeneous; it is classified as a blocky structure. When the core-scaled lamination index is between 1.5 and 7.5, it indicates a moderate number of laminar interfaces per unit thickness, and the argillaceous rock strata exhibit layered characteristics; it is classified as a layered structure. When the core-scaled lamination index is greater than 7.5, it indicates a large number of continuous laminar interfaces per unit thickness, and the argillaceous rock strata show obvious lamination development; it is classified as a laminar structure. Through the above steps, the core-scaled lamination index is determined based on electrical imaging logging data, and the argillaceous rock strata are classified into blocky, layered, and laminar structures as structural types.

[0030] Step S300: Using the lamellar structure in the structure type as the target evaluation segment, obtain the total organic carbon content curve, organic matter porosity curve and effective permeability curve through data inversion, and perform curve fusion to determine the organic carbon availability factor curve.

[0031] In this embodiment, the lamellar structure is first selected as the target evaluation segment, which is a mudstone stratum with clear and continuous lamellar development. For the target evaluation segment, total organic carbon content curves, organic matter porosity curves, and effective permeability curves are obtained through data inversion. The total organic carbon content curve is calculated using the U-index method, while the organic matter porosity curve and effective permeability curve are obtained based on conventional well logging data inversion. These curves are used to characterize the degree of organic matter enrichment, organic matter porosity development characteristics, and effective seepage capacity in the target evaluation segment, respectively.

[0032] Subsequently, curve fusion was performed to determine the organic carbon availability factor curve. In this process, based on geological and regional characteristics, the weight distributions of total organic carbon content, organic matter porosity, and effective permeability were calibrated. The normalized parameter curves were then sequentially weighted according to these weight distributions to form the organic carbon availability factor curve.

[0033] Furthermore, the method provided in the application embodiments, which obtains the total organic carbon content curve, organic matter porosity curve, and effective permeability curve through data inversion, also includes:

[0034] For the target evaluation interval, the U-index method is used to calculate the organic carbon content and obtain its total organic carbon content curve; for the target evaluation interval, the organic matter porosity curve and effective permeability parameter curve are obtained based on conventional well logging data inversion.

[0035] In this embodiment, when using the U-index method to calculate the total organic carbon content curve, the well logging input curve data for calculation is first read point by point within the target evaluation section, and the data correspondence at the same well depth is completed. Then, according to the calculation relationship of the U-index method, the U-index is calculated for each well depth point to obtain a U-index sequence that changes continuously with well depth. Subsequently, using the total organic carbon content obtained from core geochemical experiments as a calibration reference, a one-to-one correspondence conversion relationship between the U-index and the total organic carbon content is established. The U-index sequence is converted point by point into the total organic carbon content value, and the converted total organic carbon content is continuously output along the well depth, thereby forming the total organic carbon content curve of the target evaluation section.

[0036] Next, for the target evaluation interval, organic matter porosity curves and effective permeability parameter curves are obtained through inversion based on conventional well logging data. In this process, a well logging inversion model is first constructed using a sample-driven training method. Conventional well logging data samples and electrical imaging porosity spectrum samples are used as input features, and organic matter porosity and pore structure parameters are used as training labels. After model training is completed, the conventional well logging data and electrical imaging porosity spectrum data corresponding to the target evaluation interval are input into the well logging inversion model to perform pore structure inversion and mobile fluid analysis. The inversion results are then integrated to form organic matter porosity curves and effective permeability parameter curves that continuously vary with well depth.

[0037] Furthermore, the method provided in the application embodiments, in obtaining the organic matter porosity curve and the effective permeability parameter curve, further includes:

[0038] A well logging inversion model is constructed, wherein the well logging inversion model adopts sample-driven training, and the sample sequence uses conventional well logging data samples and electrical imaging pore spectrum samples as input features, and organic matter porosity and pore structure parameters as training labels; based on the well logging inversion model, pore structure inversion and mobile fluid analysis based on the target evaluation segment are performed, and organic matter porosity curve and effective permeability curve are integrated and output.

[0039] In this embodiment, a well logging inversion model is first constructed. This model adopts a sample-driven training method and its architecture is a multi-layer feedforward structure, consisting of an input layer, a hidden layer, and an output layer. The input layer receives conventional well logging data samples and electrical imaging porosity spectrum samples. The conventional well logging data samples include well logging parameters such as natural gamma, sonic transit time, density, and neutron porosity. The electrical imaging porosity spectrum samples reflect the pore scale distribution and pore morphology characteristics. The hidden layer performs weighted combination and iterative calculation on the input features to establish a nonlinear mapping relationship between the well logging response and pore characteristics. The output layer provides the physical property parameter results corresponding to the input features.

[0040] During the training phase of the well logging inversion model, a sample sequence is constructed. Conventional well logging data samples and electrical imaging porosity spectrum samples are used as input features, while organic matter porosity and pore structure parameters are used as training labels. The organic matter porosity reflects the proportion of organic matter pores on a two-dimensional scale, and the pore structure parameters reflect the connectivity, complexity, and spatial arrangement of the pores. By inputting the sample sequence batch by batch into the well logging inversion model, the model parameters are iteratively updated, allowing the model output to gradually approximate the training labels. This establishes the correspondence between the well logging response and the organic matter porosity features and pore structure parameters, thus completing the training of the well logging inversion model.

[0041] After training the well logging inversion model, pore structure inversion and mobile fluid analysis were performed on the target evaluation interval based on the model. Conventional well logging data and electrical imaging pore spectrum data corresponding to the target evaluation interval were input point-by-point into the well logging inversion model to calculate pore structure parameters and organic matter pore development results at the corresponding well depth. Based on this, pore connectivity and effective pore space were determined by combining the pore structure parameters, screening out pores capable of participating in fluid transport, and converting the effective pore space into seepage capacity parameters. By integrating the pore structure inversion results and mobile fluid analysis results, the calculation results were continuously output along the well depth, ultimately forming the organic matter porosity curve and effective permeability curve corresponding to the target evaluation interval.

[0042] Furthermore, in the method provided in the application embodiments, the process of determining the organic carbon availability factor curve through curve fusion also includes:

[0043] Based on geological and production area characteristics, the weight distribution of total organic carbon content, organic matter porosity, and effective permeability is determined. Based on the weight distribution, the normalized total organic carbon content, organic matter porosity, and effective permeability are sequentially weighted based on a curve sequence to obtain the organic carbon availability factor curve.

[0044] In this embodiment, the weight distribution of each parameter in the comprehensive evaluation is first determined based on the geological and production area characteristics of the study area. Geological characteristics include sedimentary environment type, lithological assemblage characteristics, and laminar flow development. Production area characteristics include the focus on organic matter enrichment and seepage capacity during actual exploration and development. During the weighting process, based on the aforementioned geological and production area characteristics, technical experts assign weight coefficients to total organic carbon content, organic matter porosity, and effective permeability, ensuring that the contribution of different parameters in the comprehensive evaluation matches their actual control effect, thereby completing the determination of the weight distribution of total organic carbon content, organic matter porosity, and effective permeability.

[0045] After determining the weight distribution, the total organic carbon content curve, organic matter porosity curve, and effective permeability curve were normalized. The normalization process employed a numerical mapping method to convert the values ​​of each parameter curve within the target evaluation layer into dimensionless values, thus eliminating dimensional differences between different parameters. During the normalization process, the total organic carbon content, organic matter porosity, and effective permeability were numerically converted point-by-point along the well depth to obtain normalized results corresponding one-to-one with the original curves.

[0046] After normalization, a curve-based weighted calculation is performed. Normalized total organic carbon content, organic matter porosity, and effective permeability values ​​are read at the same well depth. Each value is then multiplied by its corresponding weighting coefficient to obtain a weighted result for each parameter at that well depth. These weighted results are then superimposed to obtain a comprehensive calculated value for that well depth. By repeatedly performing the multiplication and superposition calculations along the well depth direction for all well depth points, an organic carbon availability factor curve that continuously varies with well depth is formed.

[0047] Furthermore, in the method provided in the application embodiments, the curve fusion process further includes:

[0048] Acquire energy spectrum logging data of the target well section and read the mineral composition of the target evaluation section; analyze the brittleness index of the target section based on the mineral composition; use the brittleness index as a coupling parameter in curve fusion.

[0049] In this embodiment, energy spectrum logging data for the target well section is first acquired. During this process, energy spectrum logging is performed within the well. The logging instrument moves along the wellbore and records energy spectrum counts point-by-point in the target evaluation layer. The energy spectrum logging data outputs elemental response curves based on the energy window counts for potassium, uranium, and thorium. Subsequently, the elemental content of the energy spectrum logging data is determined. Following the standard interpretation procedure for energy spectrum logging, the counts of each energy window are converted into potassium, uranium, and thorium contents, and corresponding elemental content curves are continuously generated along the well depth in the target evaluation layer. After obtaining the elemental content curves, the elemental content is converted into mineral volume fractions based on the conversion relationship between elemental content and mineral composition, yielding the mineral composition of the target evaluation layer. This mineral composition includes at least quartz, feldspar, calcite, dolomite, and clay minerals, and is output as a curve with well depth, thereby enabling the reading of the mineral composition of the target evaluation layer.

[0050] Next, the brittleness index of the target formation is analyzed. In this process, the volume fractions of quartz, feldspar, calcite, and dolomite are read at each well depth, and these brittle mineral volume fractions are summed. Then, at the same well depth, the volume fractions of all minerals are read and summed to obtain the total mineral volume fraction. The sum of the brittle mineral volume fractions is then divided by the total mineral volume fraction to obtain the brittleness index value at that well depth. This summation and ratio calculation process is repeated for all well depths to form a brittleness index curve that continuously varies with well depth.

[0051] Finally, the brittleness index is used as a coupling parameter in curve fusion. In this process, before performing curve fusion calculations for total organic carbon content, organic matter porosity, and effective permeability, the brittleness index curve is normalized to be consistent with other parameters. Normalized values ​​of total organic carbon content, organic matter porosity, and effective permeability are read at the same well depth, multiplied according to predetermined weighting coefficients, and summed to obtain the basic fusion value. The normalized brittleness index value is then read at the same well depth, and coupled with the basic fusion value to obtain a fusion result considering the brittleness effect. Finally, the basic fusion and coupling calculation steps are repeated for all well depths along the well depth to form an organic carbon availability factor curve that includes brittleness index constraints.

[0052] Step S400: Identify and delineate the high-permeability organic carbon zone in thick mudstone and shale based on the organic carbon availability factor curve.

[0053] Furthermore, in the method provided in the application embodiments, identifying and delineating high-permeability organic carbon zones in thick mudstone and shale based on the organic carbon availability factor curve further includes:

[0054] An empirical threshold is set, wherein the empirical threshold is determined based on local geological statistics; for the organic carbon availability factor curve, if the organic carbon availability factor value is greater than the empirical threshold and the continuous thickness of the high value segment is greater than the preset minimum sweet spot thickness, it is identified as a high permeability organic carbon zone.

[0055] In this embodiment, when identifying and delineating high-permeability organic carbon zones in thick mudstone and shale based on the organic carbon availability factor curve, an empirical threshold is first set. This empirical threshold is determined based on geological statistical analysis of existing drilling, logging, and production data in the study area and is used to distinguish between high and low levels of organic carbon availability. Subsequently, the organic carbon availability factor curve values ​​are read point by point along the well depth. When the organic carbon availability factor value at a certain well depth is greater than the empirical threshold, that well depth is determined as a high-value point of organic carbon availability.

[0056] Next, the high-value points of organic carbon availability distributed continuously along the well depth are merged into intervals to form high-value segments of organic carbon availability factor, and the continuous thickness of each high-value segment is calculated. In the thickness discrimination process, a minimum sweet spot thickness is introduced as a constraint condition. The sweet spot thickness refers to the effective thickness of a section in geological exploration that represents a relatively superior section in terms of reservoir physical properties, oil and gas content, or developability. This type of section has high porosity, permeability, and oil saturation, and is a key target for oil and gas exploration and development. When the continuous thickness of a high-value segment of organic carbon availability factor is greater than the preset minimum sweet spot thickness, this high-value segment is identified as a high-permeability organic carbon zone, thus completing the identification and delineation of high-permeability organic carbon zones in thick mudstone and shale based on the organic carbon availability factor curve.

[0057] Furthermore, in the method provided in the application embodiments, after identifying and delineating the high-permeability organic carbon zone in thick mudstone and shale, it further includes:

[0058] For the high-permeability organic carbon zone, quantitative matching of vertical location and corresponding seismic response characteristics is performed to determine the quantitative relationship of attributes; based on the quantitative relationship of attributes, information inversion extrapolation is performed on the high-permeability organic carbon zone to determine the three-dimensional prediction result.

[0059] In the embodiments of this application, when performing quantitative matching between the vertical position of a high-permeability organic carbon band and its corresponding seismic response characteristics, the top and bottom well depth boundaries of the high-permeability organic carbon band in the well are first converted into a top and bottom time boundary, respectively, based on the time-depth relationship between well logging and seismic data. The time window corresponding to the high-permeability organic carbon band is then determined using the top and bottom time boundaries. Subsequently, low-frequency attributes, wave impedance attributes, and frequency attenuation attributes within the time window were extracted at the well location. The calculation process for the low-frequency attribute involved performing a spectral transformation on the seismic traces within the time window to obtain the amplitude spectrum. The amplitude spectrum values ​​were then summed point-by-point within a preset low-frequency band and divided by the number of sampling points in the band to obtain the low-frequency attribute value. The calculation process for the wave impedance attribute involved performing wave impedance inversion on the seismic traces within the time window to obtain a numerical sequence of wave impedance changes over time. The wave impedance values ​​within the time window were then summed point-by-point and divided by the number of sampling points to obtain the wave impedance attribute value. The calculation process for the frequency attenuation attribute involved performing a spectral transformation on the seismic traces within the time window to obtain the amplitude spectrum. The amplitude spectrum values ​​were then summed point-by-point within a preset high-frequency band to obtain the high-frequency energy. The amplitude spectrum values ​​were then summed point-by-point within a preset low-frequency band to obtain the low-frequency energy. The high-frequency energy was then divided by the low-frequency energy to obtain the frequency attenuation attribute value. The three types of attribute values ​​mentioned above are mapped to the high-permeability organic carbon zone of the well to form a well point sample set. The average values ​​of low-frequency attribute values, wave impedance attribute values ​​and frequency attenuation attribute values ​​of each well in the sample set are calculated to obtain the average values ​​of low-frequency attribute, wave impedance attribute and frequency attenuation attribute of the high-permeability organic carbon zone. These three sets of average values ​​are used as the quantitative relationship of the attributes of the high-permeability organic carbon zone.

[0060] Subsequently, based on the quantitative attribute relationship, information inversion and extrapolation were performed on the high-permeability organic carbon belt to determine the three-dimensional prediction results. For each seismic sampling point in the three-dimensional seismic data volume, low-frequency attribute calculation, wave impedance attribute calculation, and frequency attenuation attribute calculation were repeatedly performed to obtain the low-frequency attribute value, wave impedance attribute value, and frequency attenuation attribute value for that sampling point. The differences between these three attribute values ​​at the sampling point and the mean values ​​of the low-frequency attribute, wave impedance attribute, and frequency attenuation attribute in the quantitative attribute relationship were calculated, and the absolute values ​​were taken. These three absolute differences were compared with their respective preset tolerance thresholds. When all three absolute differences were less than the corresponding tolerance thresholds, the sampling point was determined to meet the seismic response conditions of the high-permeability organic carbon belt. By repeatedly performing attribute calculation and difference discrimination processes on all sampling points in the three-dimensional seismic data volume, a set of sampling points meeting the conditions was obtained. The spatially connected region of this set was used as the spatial distribution range of the high-permeability organic carbon belt, thus obtaining the three-dimensional prediction results of the high-permeability organic carbon belt.

[0061] Furthermore, in the method provided in the application embodiments, after identifying and delineating the high-permeability organic carbon zone in thick mudstone and shale, it further includes:

[0062] Using the high-permeability organic carbon band identified by a single well logging point as verification data, the three-dimensional prediction results are calibrated and corrected to determine the corrected prediction results. Based on the corrected prediction results and the corrected prediction results, a comprehensive evaluation map is generated, wherein the map elements include at least the location, parameter sequence, and lateral distribution trend of the high-permeability organic carbon band.

[0063] In this embodiment, when using the high-permeability organic carbon band identified by a single well logging point as verification data to calibrate and correct the 3D prediction results, firstly, based on the well's time-depth relationship, the top and bottom well depth boundaries of the high-permeability organic carbon band identified by the single well logging point are converted into corresponding seismic time positions. Then, the prediction results at the same seismic time position are read from the 3D prediction results, and the position of the high-permeability organic carbon band corresponding to this prediction result is compared with the position of the high-permeability organic carbon band identified by the single well logging point, calculating the time deviation between the predicted and measured positions. Subsequently, a correction process is performed on the 3D prediction results. Based on the time deviation value, the spatial position of the 3D prediction results is adjusted, shifting the high-permeability organic carbon band at the corresponding well point position in the 3D prediction results upwards or downwards, aligning the corrected prediction position with the high-permeability organic carbon band position identified by the single well logging point in the time domain, thereby removing the positional deviation between the prediction results and the well logging identification results. By repeating the deviation calculation and position adjustment process for all well points, the calibration and correction of the 3D prediction results are completed, yielding the corrected prediction results.

[0064] Next, a comprehensive evaluation map is generated based on the corrected prediction results. This process begins by extracting the spatial distribution range of high-permeability organic carbon bands from the corrected prediction results within the study area, and marking their locations on a plan or profile map. Subsequently, the parameter sequence corresponding to the high-permeability organic carbon bands is loaded into the same map. This parameter sequence includes the continuous spatial distribution of parameters such as organic carbon availability factor, total organic carbon content, organic matter porosity, and effective permeability. Finally, based on the spatial continuity of the high-permeability organic carbon bands in the corrected prediction results, adjacent distribution units are connected to form the lateral distribution trend of the high-permeability organic carbon bands, thus generating a comprehensive evaluation map that simultaneously includes the location, parameter sequence, and lateral distribution trend of the high-permeability organic carbon bands.

[0065] In summary, the embodiments of this application have at least the following technical effects:

[0066] This application collects conventional logging data of the target well section and identifies argillaceous rock layers using cross-plotting. Electrical imaging logging is then performed on the argillaceous rock layers to determine the structural type based on the logging data. The lamellar structure within this structural type is selected as the target evaluation layer. Data inversion is used to obtain total organic carbon content curves, organic matter porosity curves, and effective permeability curves. Curve fusion is then performed to determine the organic carbon availability factor curve. Based on the organic carbon availability factor curve, high-permeability organic carbon zones in thick shale and mudstone are identified and delineated. This invention solves the technical problem of accurately identifying high-permeability organic carbon zones in thick shale and mudstone in existing technologies. By identifying the structural type of the argillaceous rock layers and constructing an organic carbon availability factor based on multi-parameter logging inversion and fusion, the technical effect of accurately identifying and delineating high-permeability organic carbon zones in thick shale and mudstone is achieved.

[0067] Example 2, based on the same inventive concept as the well logging inversion evaluation method for high-permeability organic carbon zones in thick mudstone and shale in the foregoing examples, such as... Figure 2 As shown, this application provides a well logging inversion evaluation device for high-permeability organic carbon zones in thick mudstone and shale. The device and method embodiments in this application are based on the same inventive concept. The device includes:

[0068] The data acquisition module 11 is used to acquire conventional logging data of the target well section and identify argillaceous rock layers through cross-plotting. The structure type determination module 12 is used to perform electrical imaging logging processing on the argillaceous rock layers and determine the structure type based on the electrical imaging logging data. The curve fusion module 13 is used to take the lamellar structure in the structure type as the target evaluation layer, obtain the total organic carbon content curve, organic matter porosity curve and effective permeability curve through data inversion, and perform curve fusion to determine the organic carbon availability factor curve. The identification module 14 is used to identify and delineate the high-permeability organic carbon zone in the thick mudstone and shale based on the organic carbon availability factor curve.

[0069] Furthermore, the device is also used to perform the following functions:

[0070] Based on the electrical imaging logging data, the core scale laminarity index is determined; based on the core scale laminarity index, the argillaceous rock layer is divided into blocky structure, layered structure and laminar structure as the structure type.

[0071] Furthermore, the device is also used to perform the following functions:

[0072] For the target evaluation interval, the U-index method is used to calculate the organic carbon content and obtain its total organic carbon content curve; for the target evaluation interval, the organic matter porosity curve and effective permeability parameter curve are obtained based on conventional well logging data inversion.

[0073] Furthermore, the device is also used to perform the following functions:

[0074] A well logging inversion model is constructed, wherein the well logging inversion model adopts sample-driven training, and the sample sequence uses conventional well logging data samples and electrical imaging pore spectrum samples as input features, and organic matter porosity and pore structure parameters as training labels; based on the well logging inversion model, pore structure inversion and mobile fluid analysis based on the target evaluation segment are performed, and organic matter porosity curve and effective permeability curve are integrated and output.

[0075] Furthermore, the device is also used to perform the following functions:

[0076] Based on geological and production area characteristics, the weight distribution of total organic carbon content, organic matter porosity, and effective permeability is determined. Based on the weight distribution, the normalized total organic carbon content, organic matter porosity, and effective permeability are sequentially weighted based on a curve sequence to obtain the organic carbon availability factor curve.

[0077] Furthermore, the device is also used to perform the following functions:

[0078] Acquire energy spectrum logging data of the target well section and read the mineral composition of the target evaluation section; analyze the brittleness index of the target section based on the mineral composition; use the brittleness index as a coupling parameter in curve fusion.

[0079] Furthermore, the device is also used to perform the following functions:

[0080] An empirical threshold is set, wherein the empirical threshold is determined based on local geological statistics; for the organic carbon availability factor curve, if the organic carbon availability factor value is greater than the empirical threshold and the continuous thickness of the high value segment is greater than the preset minimum sweet spot thickness, it is identified as a high permeability organic carbon zone.

[0081] Furthermore, the device is also used to perform the following functions:

[0082] For the high-permeability organic carbon zone, quantitative matching of vertical location and corresponding seismic response characteristics is performed to determine the quantitative relationship of attributes; based on the quantitative relationship of attributes, information inversion extrapolation is performed on the high-permeability organic carbon zone to determine the three-dimensional prediction result.

[0083] Furthermore, the device is also used to perform the following functions:

[0084] Using the high-permeability organic carbon band identified by a single well logging point as verification data, the three-dimensional prediction results are calibrated and corrected to determine the corrected prediction results. Based on the corrected prediction results and the corrected prediction results, a comprehensive evaluation map is generated, wherein the map elements include at least the location, parameter sequence, and lateral distribution trend of the high-permeability organic carbon band.

[0085] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0086] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A well logging inversion evaluation method for high-permeability organic carbon zones in thick mudstone and shale, characterized in that, The method includes: Collect conventional logging data for the target well section and identify argillaceous rock layers using cross-plot charts; Electrical imaging logging was performed on the argillaceous rock strata, and the structural type was determined based on the electrical imaging logging data. Using the lamellar structure in the aforementioned structural type as the target evaluation segment, the total organic carbon content curve, organic matter porosity curve, and effective permeability curve are obtained through data inversion, and curve fusion is performed to determine the organic carbon availability factor curve. Based on the organic carbon availability factor curve, high-permeability organic carbon zones in thick mudstone and shale were identified and delineated.

2. The well logging inversion evaluation method for high-permeability organic carbon zones in thick mudstone and shale as described in claim 1, characterized in that, The structure type is determined based on electrical imaging logging data, including: Determine the core scale laminarity index based on electrical imaging logging data; Based on the core scale laminar index, the argillaceous rock layers are divided into massive structure, layered structure and laminar structure, which are referred to as the structure types.

3. The well logging inversion evaluation method for high-permeability organic carbon zones in thick mudstone and shale as described in claim 1, characterized in that, The total organic carbon content curve, organic matter porosity curve, and effective permeability curve were obtained through data inversion, including: For the target evaluation segment, the U-index method is used to calculate the organic carbon content and obtain its total organic carbon content curve. For the target evaluation interval, organic matter porosity curves and effective permeability parameter curves are obtained by inversion based on conventional well logging data.

4. The well logging inversion evaluation method for high-permeability organic carbon zones in thick mudstone and shale as described in claim 3, characterized in that, Obtain organic matter porosity curves and effective permeability parameter curves, including: A well logging inversion model is constructed, wherein the well logging inversion model adopts sample-driven training, and the sample sequence uses conventional well logging data samples and electrical imaging porosity spectrum samples as input features, and organic matter porosity and pore structure parameters as training labels. Based on the well logging inversion model, pore structure inversion and mobile fluid analysis are performed based on the target evaluation layer, and organic matter porosity curve and effective permeability curve are integrated and output.

5. The well logging inversion evaluation method for high-permeability organic carbon zones in thick mudstone and shale as described in claim 4, characterized in that, The execution curve fusion determines the organic carbon availability factor curve, including: Based on geological and production area characteristics, the weight distribution of total organic carbon content, organic matter porosity, and effective permeability was determined. Based on the weight distribution, the total organic carbon content, organic matter porosity, and effective permeability after normalization are sequentially weighted based on the curve sequence to obtain the organic carbon availability factor curve.

6. The well logging inversion evaluation method for high-permeability organic carbon zones in thick mudstone and shale as described in claim 5, characterized in that, During curve fusion, the method further includes: Acquire energy spectrum logging data for the target well section and read the mineral composition of the target evaluation layer; Based on the mineral composition, the brittleness index of the target layer was analyzed; The fragility index is used as a coupling parameter in curve fusion.

7. The well logging inversion evaluation method for high-permeability organic carbon zones in thick mudstone and shale as described in claim 1, characterized in that, Based on the organic carbon availability factor curve, high-permeability organic carbon zones in thick mudstone and shale are identified and delineated, including: An empirical threshold is set, wherein the empirical threshold is determined based on local geological statistics; For the organic carbon availability factor curve, if the organic carbon availability factor value is greater than the empirical threshold and the continuous thickness of the high-value segment is greater than the preset minimum sweet spot thickness, it is identified as a high-permeability organic carbon band.

8. The well logging inversion evaluation method for high-permeability organic carbon zones in thick mudstone and shale according to claim 1, characterized in that, After identifying and delineating the highly permeable organic carbon zone in thick mudstone and shale, the following steps are taken: For the high-permeability organic carbon zone, a quantitative matching of vertical location and corresponding seismic response characteristics is performed to determine the quantitative relationship of attributes; Based on the quantitative relationship of the aforementioned attributes, information inversion extrapolation is performed on the highly permeable organic carbon bands to determine the three-dimensional prediction results.

9. The well logging inversion evaluation method for high-permeability organic carbon zones in thick mudstone and shale as described in claim 8, characterized in that, After identifying and delineating the highly permeable organic carbon zone in thick mudstone and shale, the following steps are taken: The high-permeability organic carbon band identified by a single well logging point is used as verification data to calibrate and correct the three-dimensional prediction results, and the corrected prediction results are determined. Based on the corrected prediction results, a comprehensive evaluation map is generated, wherein the map elements include at least the location, parameter sequence, and lateral distribution trend of the highly permeable organic carbon band.

10. A well logging inversion evaluation device for high-permeability organic carbon zones in thick mudstone and shale, characterized in that, The apparatus is used to perform the well logging inversion evaluation method for high-permeability organic carbon zones in thick mudstone and shale as described in any one of claims 1-9, and the apparatus comprises: The data acquisition module is used to collect conventional logging data of the target well section and identify argillaceous rock layers through cross-plot charts; The structure type determination module is used to perform electrical imaging logging processing on the argillaceous rock layer and determine the structure type based on the electrical imaging logging data. The curve fusion module is used to take the lamellar structure in the structure type as the target evaluation segment, obtain the total organic carbon content curve, organic matter porosity curve and effective permeability curve through data inversion, and perform curve fusion to determine the organic carbon availability factor curve. The identification module is used to identify and delineate high-permeability organic carbon zones in thick mudstone and shale based on the organic carbon availability factor curve.

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