Improved delta logR-based hydrocarbon generation potential evaluation method, device and equipment

Abstract

The invention discloses a hydrocarbon generation potential evaluation method, device and equipment based on an improved delta logR, and belongs to the technical field of petroleum geological prospecting, and the method comprises the steps: firstly, carrying out the precise standardization of logging data, and eliminating a system error; secondly, based on a GR-AC and GR-DEN two-parameter intersection chart, establishing a regional unified lithology interpretation standard, and realizing accurate lithology identification; secondly, an optimal logging parameter combination is screened through correlation analysis, the slope is calculated through the reference well extreme value according to different lithology, and a lithology-divided delta logR-TOC quantitative model is constructed; carrying out comprehensive performance evaluation and optimization on the model by adopting a multi-index error system; and finally, predicting the TOC of the whole region by using the optimal model, calculating the hydrocarbon generation intensity by combining with the effective hydrocarbon source rock thickness, drawing a multi-dimensional plane graph, and realizing comprehensive evaluation of the regional hydrocarbon generation potential, thereby solving the problems of incomplete standardization, insufficient lithology constraint and poor model adaptability of a traditional method, and remarkably improving the TOC prediction precision and the reliability of hydrocarbon generation potential evaluation.

Description

Technical Field

[0001] This application belongs to the field of petroleum geological exploration technology, specifically relating to a method, apparatus and equipment for assessing hydrocarbon generation potential based on an improved ΔlogR. Background Technology

[0002] Current technologies for predicting TOC in source rocks are typically based on well logging data. The traditional ΔlogR method constructs the ΔlogR value through the difference in AC and RT responses, establishing a quantitative relationship with TOC. Existing technologies utilize a single AC-RT parameter combination for TOC prediction in the Bohai Bay Basin, suggesting this method can effectively identify high organic matter abundance intervals. Furthermore, existing technologies have improved the consistency of regional TOC predictions by optimizing the ΔlogR model using standardized well logging curves for mudstone and shale in the Songliao Basin. These technologies also incorporate well logging lithology classification, making preliminary attempts to establish TOC prediction equations based on lithology, but these methods have not adequately addressed the issue of lithology misjudgment caused by logging errors. However, the aforementioned studies generally suffer from key technical bottlenecks: First, the data standardization methods rely solely on histogram shifting for correction, failing to consider systematic biases arising from data collected by different instruments at different ages; second, they lack lithologically differentiated modeling strategies, resulting in insufficient adaptability of models to different lithologies such as shale and carbonate rocks; and third, they neglect the constraining effects of multiple parameters, including natural gamma (GR), density (DEN), and neutron porosity (CNL), on lithological responses, failing to establish a precise correspondence between logging curves and lithology. Under complex geological conditions characterized by widespread logging lithological errors and uneven logging data coverage, existing methods struggle to achieve high-precision TOC prediction. There is an urgent need for innovative technologies that integrate data standardization, lithological modeling, and multi-parameter lithological constraints to provide more reliable technical support for evaluating the hydrocarbon generation potential of source rocks. Summary of the Invention

[0003] The purpose of this application is to provide a method, apparatus and equipment for assessing hydrocarbon generation potential based on an improved ΔlogR, in order to solve the problems of incomplete standardization of well logging data, insufficient lithological constraints, low accuracy of multi-parameter fusion and poor model adaptability in the prediction of total organic carbon (TOC) content in source rocks by the existing ΔlogR method.

[0004] The first aspect of this application provides a method for assessing hydrocarbon generation potential based on an improved ΔlogR, comprising: Acquire the logging data to be processed, and standardize the logging data by using the standard data corresponding to the pre-specified standard layer and standard well to obtain the standardized logging data; Based on the standardized well logging data, the lithology corresponding to all target strata in the study area is obtained, and the lithology classification results for the entire area are obtained; the target strata refer to the formations in the study area. Based on the standardized logging data and the lithology classification results of the whole area, the correlation coefficient method was used to obtain the correlation coefficient between each logging data and the pre-collected measured TOC value. Parameters with large correlation coefficients were screened, and the optimal combination of logging parameters was determined after eliminating multicollinearity. Based on the lithological classification results of the whole area, a benchmark well is determined. The slope is obtained based on the standardized logging data corresponding to the benchmark well. Based on the slope and the optimal logging parameter combination, multiple lithological models fitted with ΔlogR values ​​are obtained. The performance of lithological models fitted with multiple ΔlogR values ​​was evaluated, and the lithological model with the best performance was determined as the optimal lithological model. The target TOC values ​​corresponding to the target intervals of all wells in the study area are obtained using the lithological optimal model. The average target TOC value is obtained based on the target TOC values ​​of the wells. The hydrocarbon generation intensity is obtained based on the average target TOC value. Based on the hydrocarbon generation intensity, a hydrocarbon generation intensity planar diagram is prepared, and the average TOC planar diagram, effective source rock thickness planar diagram, and hydrocarbon generation intensity planar diagram are drawn using the double-fox minimum tension method for interpolation, thereby realizing a multi-dimensional evaluation of hydrocarbon generation potential level.

[0005] In one possible implementation, well logging data to be processed is acquired, and the well logging data is standardized using pre-specified standard layers and standard data corresponding to standard wells to obtain standardized well logging data, including: Obtain the AC, RT, GR, DEN, and CNL logging curves corresponding to the well logs to obtain the logging data to be processed; The well logging data to be processed is standardized to obtain the standardized well logging data as follows: in, This represents the logging data to be processed. This indicates the upper limit value corresponding to the logging data to be processed. This represents the lower limit value corresponding to the logging data to be processed. This represents the average value of standard well logging data. This represents the average value of the logging data from the standard well. This represents the upper limit of the data corresponding to the standard well in the standard layer. This represents the lower limit of the data corresponding to the standard well in the standard layer. This represents the standardized well logging data. .

[0006] In one possible implementation, based on the standardized well logging data, the lithology corresponding to all target strata in the study area is obtained to obtain the lithology classification results for the entire area, including: Based on the standardized well logging data, a GR-AC and GR-DEN dual-parameter cross plot is constructed, and lithological boundary values ​​are determined according to the GR-AC and GR-DEN dual-parameter cross plot, thereby forming a regional unified lithological interpretation standard. Based on the lithological boundary values, the lithology of all target strata in the study area is divided to obtain the lithological classification results for the entire area.

[0007] In one possible implementation, based on the standardized logging data and the lithology classification results of the entire area, the correlation coefficient method is used to obtain the correlation coefficient between each logging data and the pre-acquired measured TOC. Parameters with large correlation coefficients are screened, and after eliminating multicollinearity, the optimal combination of logging parameters is determined. This includes: based on the correlation analysis between each logging curve and TOC, firstly, the important parameter sonic transit time curve in the classical ΔlogR method is retained, then logging parameters with a correlation greater than 0.2 and logging parameters that have important determinants of TOC are retained, and logging parameters are combined with sonic transit time curves respectively.

[0008] In one possible implementation, a benchmark well is determined based on the lithological classification results of the entire region, and the inclination is obtained based on the standardized logging data corresponding to the benchmark well, including: Based on the lithological classification results of the entire region, the well with the largest continuous thickness of mudstone and / or the largest measured TOC sample size was determined as the benchmark well; Based on the standardized logging data, the extreme values ​​of logging parameters corresponding to the benchmark well are extracted; Based on the extreme values ​​of the logging parameters, the slope is obtained as follows: K=(N-Nmin) / (Δt-Δtmin) Where K represents the slope, N represents the standardized logging data, Nmin represents the minimum value of the standardized logging data, Δt represents the sonic transit time logging curve value, and Δtmin represents the minimum value of the sonic transit time logging curve.

[0009] In one possible implementation, based on the slope and the optimal combination of logging parameters, multiple lithological models fitted with ΔlogR values ​​are obtained, including: Based on the slope and the optimal combination of logging parameters, the ΔlogR value is obtained as follows: ΔlogR=(N-Nmin)+K×(Δt-Δtmax) Wherein, Δtmax represents the maximum value of the sonic time-of-flight logging curve.

[0010] Based on the ΔlogR value and the pre-collected measured TOC value, the fitted lithological model is as follows: TOC = A × ΔlogR + B Wherein, TOC represents organic carbon content, A represents the first coefficient, and B represents the second coefficient.

[0011] In one possible implementation, the performance of lithological models fitted with multiple ΔlogR values ​​is evaluated, and the best-performing lithological model is determined as the optimal lithological model, including: The performance of lithological models fitted with multiple ΔlogR values ​​was evaluated using the average value, average absolute error, sum of absolute errors, sum of weighted percentage errors, average absolute percentage error, and / or weighted average percentage error. The lithological model with the best performance was determined as the optimal lithological model.

[0012] In one possible implementation, the lithology-optimal model is used to obtain the target TOC values ​​corresponding to the target intervals of all well logging in the study area; the average target TOC value is obtained based on the target TOC values ​​corresponding to the well logging; and the hydrocarbon generation intensity is obtained based on the average target TOC value, including: The target TOC values ​​corresponding to the target intervals of all well logging in the study area were obtained using the aforementioned lithological optimal model. The average target TOC value is obtained based on the target TOC value corresponding to the well logging, and the effective source rock thickness and average mudstone density are calculated. The effective source rock thickness represents the formation thickness with TOC ≥ 0.5%. Based on the average target TOC value, effective source rock thickness, and average mudstone density, the hydrocarbon generation intensity is obtained as follows: Q 气 =ρ 泥 ×H×TOC avg ×K×D Among them, Q 气 ρ represents hydrocarbon generation intensity. 泥 The value represents the density of mudstone and shale, H represents the effective source rock thickness, and TOC... avg K represents the average TOC, K represents the organic carbon recovery coefficient, and D represents the hydrocarbon production rate.

[0013] A second aspect of this application provides a hydrocarbon generation potential assessment device, comprising: The data preprocessing module is used to acquire the logging data to be processed, and to standardize the logging data by using the standard data corresponding to the pre-specified standard layer and standard well, so as to obtain the standardized logging data. The lithology classification module is used to obtain the lithology corresponding to all target strata in the study area based on the standardized well logging data, and to obtain the lithology classification results for the entire area; the target strata refer to the formations in the study area. The parameter filtering module is used to obtain the correlation coefficient between each logging data and the pre-collected measured TOC value based on the standardized logging data and the lithology classification results of the whole area, and to filter parameters with large correlation coefficients and determine the optimal logging parameter combination after eliminating multicollinearity. The parameter fitting module is used to determine the benchmark well based on the lithology classification results of the whole area, obtain the slope based on the standardized logging data corresponding to the benchmark well, and obtain a lithology model fitted with multiple ΔlogR values ​​based on the slope and the optimal logging parameter combination. The model evaluation module is used to evaluate the performance of lithological models fitted with multiple ΔlogR values ​​and determine the lithological model with the best performance as the optimal lithological model. The hydrocarbon generation evaluation module is used to obtain the target TOC value corresponding to the target interval of all wells in the study area using the lithological optimal model, obtain the average target TOC value based on the target TOC value corresponding to the wells, and obtain the hydrocarbon generation intensity based on the average target TOC value. The multi-dimensional evaluation module is used to generate a hydrocarbon generation intensity planar map based on the hydrocarbon generation intensity, and to draw an average TOC planar map, an effective source rock thickness planar map, and a hydrocarbon generation intensity planar map by interpolation using the double-fox minimum tension method, thereby realizing a multi-dimensional evaluation of the hydrocarbon generation potential level.

[0014] A third aspect of this application provides an electronic device, including a processor and a memory; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the hydrocarbon generation potential assessment method based on improved ΔlogR as described in any of the first aspects.

[0015] The beneficial effects of this application are as follows: 1. To address the problem that traditional well logging data standardization relies solely on histogram shifting correction and cannot eliminate systematic biases across time periods and instruments, this application innovatively adopts a formula method that integrates extreme values ​​and means with automated programming processing to accurately quantify and correct the well logging responses of unstandardized wells. This not only solves the data biases between wells caused by non-geological factors but also significantly improves the comparability of well logging data between wells. Compared to traditional methods, this upgrades from coarse correction processing to a precise formulaic and automated closed-loop processing model, providing a unified and reliable standardized data foundation for subsequent lithology identification and TOC model construction.

[0016] 2. Overcoming the bottleneck of traditional lithology classification relying on logging data and being susceptible to errors such as cuttings return time deviation and wellbore enlargement leading to misjudgment, this application constructs a multi-parameter cross-plot based on standardized GR, AC, DEN logging curves and sets clear boundary values, establishing a unified regional lithology interpretation standard. This significantly improves the accuracy of lithology identification, accurately classifying different lithologies such as mudstone, shale, coal, and carbonaceous shale. Compared with the traditional reliance on single logging data, this achieves a lithology classification model with multi-parameter synergistic constraints, providing reliable lithological constraints for the subsequent construction of lithology-specific TOC prediction models.

[0017] 3. This application overcomes the limitations of the traditional ΔlogR method, which only uses AC-RT dual parameters and lacks effective geological constraints, resulting in low accuracy in the correlation between logging parameters and TOC. By evaluating curve coverage and analyzing feature importance, this application selects core parameters such as CNL and DEN, constructs a multi-parameter correlation model between logging parameters and TOC, and adds geological constraint information related to lithology and organic matter occurrence. This significantly improves the correlation accuracy between logging parameters and TOC. Compared with the traditional single parameter combination, it forms a data-driven multi-parameter optimization mode, providing more comprehensive multi-dimensional data support for the ΔlogR model.

[0018] 4. To address the problem that the traditional ΔlogR method uses a single model to adapt to all lithologies and has poor adaptability to different lithologies such as mudstone, shale, and carbonate rocks, this application optimizes the selection criteria for benchmark wells and uses the extreme values ​​of benchmark wells to calculate the slope K. For different lithologies such as mudstone, shale, carbonaceous shale, and coal seams, specific coefficients A and B are fitted to construct quantitative models, achieving accurate prediction for different lithologies. Compared with the traditional single model, this forms a lithology-specific model adaptation mode, which significantly reduces the prediction error for different lithologies.

[0019] 5. To overcome the limitations of traditional model performance evaluation which only uses a single error index and cannot fully reflect model reliability, this application introduces six major indices: Mean, MAE, MAPE, WAPE, SAE, and SWPE. It compares the model accuracy of different curve combinations according to lithology, forming a data-driven differentiated model selection strategy, which significantly improves the reliability of each lithology model. Compared with the traditional single index evaluation, it achieves "multi-dimensional and comprehensive quantitative verification" and avoids the evaluation bias caused by a single index.

[0020] 6. Breaking through the limitations of traditional methods that focus solely on single-point TOC prediction and cannot comprehensively characterize regional hydrocarbon generation potential, this application constructs a multi-dimensional evaluation system of "average TOC-effective thickness-hydrogenation intensity" to achieve quantitative and visual characterization of regional hydrocarbon generation potential. Compared with traditional single TOC prediction, it forms a full-chain evaluation model from single-point data to regional potential, providing a comprehensive basis for exploration decisions and promoting the improvement of oil and gas reserve discovery efficiency. Attached Figure Description

[0021] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0022] Figure 1 A flowchart of a method for assessing hydrocarbon generation potential based on an improved ΔlogR provided for this application; Figure 2 This is a flowchart illustrating the implementation of this application; Figure 3 A comparison chart of the GR curve before and after standardization; in, Figure 3 a is the frequency distribution of the GR curve before standardization; Figure 3 b is the frequency distribution diagram of the GR curve after standardization.

[0023] Figure 4 This is a cross-plot of lithological logging data. Figure 5 A scatter plot showing the correlation between well logging and TOC; Figure 6 These are fitting diagrams of each lithological model after error analysis; in, Figure 6 a is the fitting diagram of the TOC-ΔlogR model for the AC-GR combination of mudstone and shale; Figure 6 b is the fitting diagram of the TOC-ΔlogR model for the AC-CNL combination of carbonaceous shale; Figure 6 c is the fitting graph of the AC-RT combined TOC-ΔlogR model for the coal seam.

[0024] Figure 7 This requires a planar diagram showing the average TOC and effective thickness of five segments; Figure 8 This is a contour map of the effective source rock thickness in the five sections. Figure 9 The diagram shows the current intensity of vital energy in the five segments.

[0025] Figure 10 This is a schematic diagram of a hydrocarbon generation potential assessment device.

[0026] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0027] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0028] The embodiments of this application are described in detail below with reference to the accompanying drawings.

[0029] Example 1; like Figure 1 As shown in the embodiments of this application, a method for evaluating hydrocarbon generation potential based on an improved ΔlogR is provided, including: S101. Obtain the logging data to be processed, and standardize the logging data by using the standard data corresponding to the pre-specified standard layer and standard well to obtain the standardized logging data. S102. Based on the standardized well logging data, obtain the lithology corresponding to all target strata in the study area to obtain the lithology classification results for the entire area; the target strata refer to the formations in the study area. S103. Based on the standardized logging data and the lithology classification results of the whole area, the correlation coefficient method is used to obtain the correlation coefficient between each logging data and the pre-collected measured TOC value. Parameters with large correlation coefficients are screened, and the optimal combination of logging parameters is determined after eliminating multicollinearity. S104. Determine the benchmark well based on the lithological classification results of the whole area, obtain the slope based on the standardized logging data corresponding to the benchmark well, and obtain a lithological model fitted with multiple ΔlogR values ​​based on the slope and the optimal logging parameter combination. S105. Evaluate the performance of the lithological models fitted with multiple ΔlogR values, and determine the lithological model with the best performance as the optimal lithological model. S106. Using the lithological optimal model, obtain the target TOC values ​​corresponding to the target intervals of all wells in the study area, obtain the average target TOC value based on the target TOC values ​​corresponding to the wells, and obtain the hydrocarbon generation intensity based on the average target TOC value. S107. Based on the hydrocarbon generation intensity, prepare a hydrocarbon generation intensity planar diagram, and use the double-fox minimum tension method to interpolate and draw an average TOC planar diagram, an effective source rock thickness planar diagram, and a hydrocarbon generation intensity planar diagram to achieve a multi-dimensional evaluation of the hydrocarbon generation potential level.

[0030] In one possible implementation, well logging data to be processed is acquired, and the well logging data is standardized using pre-specified standard layers and standard data corresponding to standard wells to obtain standardized well logging data, including: Acquire the corresponding acoustic transit time (AC) and resistivity (RT), natural gamma (GR), density (DEN), and neutron porosity (CNL) logging curves to obtain the logging data to be processed; The well logging data to be processed is standardized to obtain the standardized well logging data as follows: in, This represents the logging data to be processed. This indicates the upper limit value corresponding to the logging data to be processed. This represents the lower limit value corresponding to the logging data to be processed. This represents the average value of standard well logging data. This represents the average value of the logging data from the standard well. This represents the upper limit of the data corresponding to the standard well in the standard layer. This represents the lower limit of the data corresponding to the standard well in the standard layer. This represents the standardized well logging data. .

[0031] In one possible implementation, based on the standardized well logging data, the lithology corresponding to all target strata in the study area is obtained to obtain the lithology classification results for the entire area, including: Based on the standardized well logging data, a GR-AC and GR-DEN dual-parameter cross plot is constructed, and lithological boundary values ​​are determined according to the GR-AC and GR-DEN dual-parameter cross plot, thereby forming a regional unified lithological interpretation standard. Based on the lithological boundary values, the lithology of all target strata in the study area is divided to obtain the lithological classification results for the entire area.

[0032] In one possible implementation, based on the standardized logging data and the lithology classification results of the entire area, the correlation coefficient method is used to obtain the correlation coefficient between each logging data and the pre-acquired measured TOC. Parameters with large correlation coefficients are screened, and after eliminating multicollinearity, the optimal combination of logging parameters is determined. This includes: based on the correlation analysis between each logging curve and TOC, firstly, the important parameter sonic transit time curve in the classical ΔlogR method is retained, then logging parameters with a correlation greater than 0.2 and logging parameters that have important determinants of TOC are retained, and logging parameters are combined with sonic transit time curves respectively.

[0033] In one possible implementation, a benchmark well is determined based on the lithological classification results of the entire region, and the inclination is obtained based on the standardized logging data corresponding to the benchmark well, including: Based on the lithological classification results of the entire region, the well with the largest continuous thickness of mudstone and / or the largest measured TOC sample size was determined as the benchmark well; For example, well logs can be arranged in descending order of continuous shale thickness to obtain the first sequence number of the well logs. Well logs can also be arranged in descending order of measured TOC sample size to obtain the second sequence number of the well logs. The first and second sequence numbers can then be weighted and summed (e.g., each with a weight of 0.5, or the first sequence number assigned 0.6 and the second sequence number assigned 0.4). The well log with the smallest sum value is taken as the benchmark well, thus achieving dual-index determination.

[0034] Based on the standardized logging data, the extreme values ​​of logging parameters corresponding to the benchmark well are extracted; Based on the extreme values ​​of the logging parameters, the slope is obtained as follows: K=(N-Nmin) / (Δt-Δtmin) Where K represents the slope, N represents the standardized logging data, Nmin represents the minimum value of the standardized logging data, Δt represents the sonic transit time logging curve value, and Δtmin represents the minimum value of the sonic transit time logging curve.

[0035] In one possible implementation, based on the slope and the optimal combination of logging parameters, multiple lithological models fitted with ΔlogR values ​​are obtained, including: Based on the slope and the optimal combination of logging parameters, the ΔlogR value is obtained as follows: ΔlogR=(N-Nmin)+K×(Δt-Δtmax) Wherein, Δtmax represents the maximum value of the sonic time-of-flight logging curve.

[0036] Based on the ΔlogR value and the pre-collected measured TOC value, the fitted lithological model is as follows: TOC = A × ΔlogR + B Wherein, TOC represents organic carbon content, A represents the first coefficient, and B represents the second coefficient.

[0037] In one possible implementation, the performance of lithological models fitted with multiple ΔlogR values ​​is evaluated, and the best-performing lithological model is determined as the optimal lithological model, including: The performance of lithological models fitted with multiple ΔlogR values ​​was evaluated using the average value, average absolute error, sum of absolute errors, sum of weighted percentage errors, average absolute percentage error, and / or weighted average percentage error. The lithological model with the best performance was determined as the optimal lithological model.

[0038] In one possible implementation, the lithology-optimal model is used to obtain the target TOC values ​​corresponding to the target intervals of all well logging in the study area; the average target TOC value is obtained based on the target TOC values ​​corresponding to the well logging; and the hydrocarbon generation intensity is obtained based on the average target TOC value, including: The target TOC values ​​corresponding to the target intervals of all well logging in the study area were obtained using the aforementioned lithological optimal model. The average target TOC value is obtained based on the target TOC value corresponding to the well logging, and the effective source rock thickness and average mudstone density are calculated. The effective source rock thickness represents the formation thickness with TOC ≥ 0.5%. Based on the average target TOC value, effective source rock thickness, and average mudstone density, the hydrocarbon generation intensity is obtained as follows: Q 气 =ρ 泥 ×H×TOC avg ×K×D Among them, Q 气 ρ represents hydrocarbon generation intensity. 泥 H represents the density of mudstone and shale, H represents the effective source rock thickness, TOC represents the average TOC, K represents the organic carbon recovery coefficient, and D represents the hydrocarbon production rate.

[0039] Example 2; This embodiment is based on Embodiment 1, and the specific principle is as follows.

[0040] Step S1: Perform data standardization. Stable formations in the study area are selected as standard layers, and wells with complete logging data are selected as standard wells. Logging parameters are screened through curve coverage evaluation. Extreme values ​​and mean values ​​of logging data from standard wells and standard layers are statistically analyzed. For five types of logging curves (AC, RT, GR, DEN, and CNL), the following formula is used: Standardization transformation is performed, histogram comparison is used for verification, and an algorithm program is written to automatically eliminate systematic errors, outputting standardized well logging data that is consistent across the entire region; Step S2: Conduct precise lithology identification. Objective errors such as cuttings return time deviation and wellbore enlargement during logging can easily lead to misjudgment of lithology. Based on the standardized logging curves (GR, AC, DEN) output in Step S1, the parameter ranges of known lithology samples are statistically analyzed. A GR-AC and GR-DEN dual-parameter cross plot is constructed to determine lithology boundary values, forming a unified regional lithology interpretation standard. The target strata across the entire area are classified into lithologies, and this is verified and corrected through core sampling. The lithology classification results for the entire area are then output. Step S3: Perform well logging-TOC correlation analysis. Collect measured TOC data, match the standardized well logging data from Step S1 with the lithology classification results from Step S2 to form a dataset, calculate the correlation coefficient between each well logging parameter and the measured TOC using the correlation coefficient method, screen parameters with high correlation coefficients, and determine the combination of well logging parameters after eliminating multicollinearity; Step S4: Construct the TOC prediction model. Wells with large continuous shale thickness and a large number of measured TOC samples from Step S2 are selected as benchmark wells. Based on the improved ΔlogR method with multi-parameter fusion under lithological constraints proposed in this application, the extreme values ​​of logging parameters of the benchmark wells are extracted, and the slope K is calculated according to the formula K=(N-Nmin) / (Δt-Δtmin). Based on the optimal parameter combination from Step S3, the ΔlogR value is calculated according to the formula ΔlogR=(N-Nmin)+K×(Δt-Δtmax). The ΔlogR values ​​of each lithology are linearly fitted with the measured TOC data to determine the specific coefficients A and B, establishing a lithological model (TOC=A×ΔlogR+B), which is then embedded into the lithological classification from Step S2 to form a prediction closed loop. Step S5: Perform model performance evaluation. Construct a multi-index error quantification analysis system that includes the mean, mean absolute error (MAE), mean absolute percentage error (MAPE), weighted average percentage error (WAPE), sum of absolute errors (SAE), and sum of weighted percentage errors (SWPE). Apply the model from step S4 to the verification well to calculate error indices, compare and select the optimal model by lithology, analyze geological adaptability, and output the optimal TOC prediction model for each lithology. Step S6: Conduct hydrocarbon generation potential assessment. Calculate the TOC value of the target formation in all wells using the optimal model from Step S5. Statistically calculate the average TOC value, effective source rock thickness (formation thickness with TOC ≥ 0.5%), and average mudstone density. Obtain the organic carbon recovery coefficient K and hydrocarbon production rate D using previous research charts, and apply formula Q. 气 =ρ 泥 ×H×TOC avg The hydrocarbon generation intensity was calculated using the ×K×D method, and a hydrocarbon generation intensity planar map was drawn. The average TOC planar map, effective source rock thickness planar map, and hydrocarbon generation intensity planar map were drawn using the double-fox minimum tension method interpolation. A multi-dimensional evaluation system was constructed to classify hydrocarbon generation potential levels.

[0041] In this embodiment, in the well logging data standardization step S1, the standard layer must meet the conditions of stable distribution and clear well logging response in the study area, and the standard well must be consistent with the main hydrocarbon source rock strata in the study area; in the formula, Nx is the standard layer data of a well to be standardized, and its value range is Nmin and Nmax; N'x refers to the well logging data of the standard layer after standardization of a well; Mx is the standard layer data of the standard well, and its value range is Mmin and Mmax.

[0042] In this embodiment, in the precise lithology prediction step S2, the lithological boundary values ​​need to be determined by the clustering characteristics of known lithological samples in the cross-plot, specifically including mudstone and shale: GR>83API, DEN>2.35g / cm³. 3 Furthermore, AC < 71 μs / m; carbonaceous shale: GR > 83 API, DEN > 2.35 g / cm³ 3 And AC > 71 μs / m; Coal and rock: GR > 83 API and DEN < 2.35 g / cm³ 3 Sandstone: GR < 83 API, DEN < 2.35 g / cm³ 3 Furthermore, AC < 71 μs / m; after lithological classification, it is necessary to verify the lithological description results of core samples and correct the deviation of the boundary values ​​to ensure the accuracy of the classification.

[0043] In this embodiment, during the well logging-TOC correlation modeling process in step S3, well logging data and lithology analysis are integrated, well logging parameters are screened through curve coverage evaluation, a direct correlation between well logging parameters and TOC is established, and characteristic combinations of differential responses between curve combinations are identified, providing a data-driven optimization basis for the construction of the ΔlogR model.

[0044] In this embodiment, in the model construction of the improved ΔlogR method under lithological constraints proposed in this application in step S4, the ΔlogR value is constructed using the formula ΔlogR=(N-Nmin)+K×(Δt-Δtmax), where N is a logging curve value of a certain well section in the predicted well section, Δt is the AC value of the predicted well section, Nmin is the minimum value of a logging curve value of a certain lithological section in the benchmark well, and Δtmax is the maximum value of AC in the lithological section of the benchmark well. For the ΔlogR-TOC data of different lithologies, a lithological quantitative model (TOC=A×ΔlogR+B) is established according to the lithological independent fitting coefficients A and B.

[0045] In this embodiment, in the model performance evaluation of step S5, a multi-index error quantification analysis system is proposed, and the calculation method of each error index is as follows: , , , , ,in, To measure the TOC value, To predict the TOC value, n For the sample size, The values ​​represent the sample weights. MAE is the mean absolute error, MAPE is the mean absolute percentage error, WAPE is the weighted mean absolute percentage error, SAE is the sum of absolute errors, and SWPE is the weighted sum of absolute percentage errors. The smaller the values ​​of each indicator, the better the model performance.

[0046] In this embodiment, in step S6: hydrocarbon generation potential evaluation, the TOC of a single well is predicted based on the multi-parameter lithology-constrained ΔlogR model, and a multi-dimensional evaluation system of "average TOC-effective thickness-hydrogenation intensity" is constructed by integrating the effective source rock thickness. The effective source rock thickness is statistically calculated based on the formation thickness with TOC ≥ 0.5%, and the average mudstone density is obtained by calculating the standardized DEN data in step S1. The double-fox minimum tension interpolation method needs to cover the data points of all wells in the study area to ensure the continuity and accuracy of the planar distribution map.

[0047] Example 3; This embodiment is a further example based on Embodiment 1 and Embodiment 2, and the specific principle is as follows.

[0048] The Xichong gas-bearing area is located in the tectonic transition zone of the north-central Sichuan Basin, spanning the northwest of Nanchong City. Situated in the transition zone between the western Sichuan depression and the central Sichuan uplift, its hydrocarbon generation capacity is unclear, thus necessitating research on source rock conditions. Traditional organic geochemical analysis methods rely on laboratory rock analysis, which suffers from low efficiency and high cost. With the advancement of well logging technology, the method of predicting TOC based on well logging data has been widely applied in the evaluation system of organic matter abundance in source rocks (Zhou Chunrun et al., 2024; Liu Chao et al., 2014; Xie Haochen et al., 2013). The stratigraphic position studied in this study is the fifth member of the Xujiahe Formation, a Late Triassic shallow-laminar to semi-deep-laminar facies deposit characterized by "high shale ratio, thick single layer, and excellent hydrocarbon source." The lithology is dominated by black and grayish-black mudstone and shale, black carbonaceous shale, and coal seams, with a small amount of siltstone interbedded. In terms of tectonic evolution, the area is located on the slope of the Central Sichuan Paleo-uplift. During the depositional period of the Xuwu Formation, it was controlled by the western slope break of the Luzhou-Kaijiang Paleo-uplift, forming a NE-trending "paragenetic reservoir" type lithological trap, which has the advantage of intrasource hydrocarbon accumulation.

[0049] To address the unclear hydrocarbon generation capacity of the Xuwu section in the Xichong gas-bearing area, this study proposes a multi-parameter fusion-improved ΔlogR method under lithological constraints. Based on automated data standardization and precise lithological classification, combined with well logging-TOC correlation analysis, a TOC prediction model is constructed using the lithologically constrained multi-parameter improved ΔlogR method. The accuracy is ensured through multi-index evaluation, ultimately forming a multi-dimensional hydrocarbon generation potential evaluation system, improving single-well efficiency and supporting resource development and energy security.

[0050] according to Figure 2 The flowchart shown is analyzed in detail below: Step S1: Data standardization. To address the differences in logging data coverage caused by variations in acquisition years and instruments in the study area, a formula-based method is used to correct the logging data of the study layers. Standardized formulas are used to mathematically transform the logging responses of unstandardized wells. Extreme values ​​and means from selected standard wells are statistically analyzed, and automated processing is implemented to eliminate systematic errors, unifying the data distribution across the entire area and providing a reliable foundation for subsequent analysis. To eliminate systematic errors caused by differences in acquisition years and instruments, the logging data (AC, RT, GR, etc.) of the five sections in the Xichong gas-bearing area are standardized using a formula-based method: using well PY2 as the standard well, the extreme values ​​(maximum and minimum values) and means of each curve are statistically analyzed. Standardized formulas are used to mathematically transform the logging responses of unstandardized wells, and automated correction is implemented through programming to ensure the consistency and comparability of logging curves (sonic transit time, resistivity, etc.) across the entire area. Figure 3 This provides standardized data for subsequent lithological identification and model building, reducing interference from non-geological factors.

[0051] Step S2: Conduct precise lithology identification. Errors such as cuttings return time deviation and wellbore enlargement during logging can easily lead to lithology misjudgment, and the TOC-logging relationship varies significantly among different lithologies. Therefore, it is necessary to establish a unified regional lithology interpretation standard based on the logging curve response characteristics of different lithologies, accurately classify the lithologies of the entire area, and support the construction of a dedicated TOC prediction model. For precise lithology identification and classification, addressing the lithology misjudgment problem caused by logging errors, extract the GR, AC, DEN, and other logging curve characteristics of each well in the study area, and establish a unified regional lithology interpretation standard (e.g., high GR values ​​for shale and medium AC values ​​for sandstone). Construct a lithology logging cross-plot ( Figure 4 The parameters GR, DEN, and AC are used to accurately classify mudstone, shale, coal, and carbonaceous shale. Sandstone is distinguished by a GR value less than 83, while mudstone, coal, and carbonaceous shale are distinguished by a GR value greater than 83. Coal is distinguished by a density value less than 2.35, and carbonaceous shale is distinguished by a sonic transit time greater than 71. The rest are mudstone and shale.

[0052] Step S3: Conduct well logging-TOC correlation analysis. Based on the standardized results of previous well logging data and lithological analysis, establish a direct correlation model between well logging data and TOC content. Quantitatively evaluate the correlation between well logging curves and measured TOC to provide a basis for subsequent model construction. Integrate well logging parameters such as AC and CNL to establish a correlation model, evaluate the correlation between each curve and measured TOC, and clarify the degree of correlation between each well logging parameter and TOC. Figure 5This provides a basis for constructing the ΔlogR model. Due to the complex and diverse lithology of the study area, the correlation between organic matter occurrence characteristics and well logging responses varies significantly among different lithologies (such as sandstone, shale, coal seams, and carbonaceous shale). Therefore, the overall linear correlation between single well logging curves (AC, CNL, RT, DEN, GR) and TOC is limited. Among them, the coefficient of determination (R²) between CNL and TOC is relatively small. 2 The highest relative value is 0.2374, which is the R-value of RT. 2 The lowest, only 0.0085, for AC, DEN, and GR, R 2 The values ​​were 0.1699, 0.2261, and 0.0852, respectively, all at relatively low levels.

[0053] Step S4: Constructing the TOC prediction model. Based on the improved ΔlogR method under lithological constraints proposed in this application, the slope K is calculated using the extreme values ​​of the benchmark well. The ΔlogR value is constructed by combining the linear differences between AC and RT, GR, DEN, CNL, and other curves. A quantitative TOC model is established for each lithology to achieve accurate prediction of TOC under different lithologies. Well L18, with sufficient test sample size and a continuous shale thickness ≥10m, is selected as the benchmark well (Table 1). The K value is calculated using its extreme values ​​(K=(N-Nmin) / (Δt-Δtmin)). The ΔlogR value is constructed using the formula ΔlogR=(N-Nmin)+K×(Δt-Δtmax) (N is the GR / DEN logging curve value, and Nmin is the minimum value of the corresponding curve of the benchmark well). For the ΔlogR-TOC data of different lithologies (shale, carbonaceous shale, coal seam), specific coefficients A and B are fitted to establish a lithological quantitative model (TOC=A×ΔlogR+B) (Table 2).

[0054] Table 1 Data from benchmark well L18 Table 2 Fitting models for various curve combinations under lithological constraints Step S5: Conduct model performance evaluation. Using error indices such as MAE, MAPE, and WAPE, the performance differences of the improved ΔlogR method model are quantitatively evaluated to ensure the model's reliability in TOC prediction. A multi-index quantitative analysis system is introduced in the model performance evaluation, including Mean, Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Weighted Average Percentage Error (WAPE), Sum of Absolute Errors (SAE), and Sum of Weighted Percentage Errors (SWPE), to compare the accuracy of different curve combination models for different lithologies. By quantifying error indices, the geological adaptability of the model is revealed, and the optimal model for each lithology is selected.

[0055] The shale and mudstone sections fall under the low-to-medium TOC prediction scenario (Table 3). The measured mean TOC is 1.789. The performance of different curve combination models varies significantly: the AC-GR combination performs best, with its predicted mean perfectly matching the measured value (both 1.789). It exhibits a significant advantage in key error indicators: MAE reaches 0.635, a 6.2% reduction compared to the AC-CNL combination and an 8.7% reduction compared to the AC-DEN combination; MAPE is 42.7%, a 6.6% reduction compared to the AC-DEN combination and a 10.5% reduction compared to the AC-RT combination. The AC-RT combination performs the worst, with a MAPE as high as 47.7% and an SAE of 137.606, both the highest among all combinations. The AC-CNL and AC-DEN combinations are at a mid-level, showing no significant competitive advantage. Therefore, the optimal prediction model for the shale and mudstone sections is the AC-GR combination: TOC = 0.0137 × ΔLogR + 1.9091.

[0056] Table 3. Error statistics of the multi-parameter fusion improved ΔlogR method model for shale under lithological constraints. The carbonaceous shale section falls under the high TOC prediction scenario (Table 4), with a measured average TOC of 6.156. The performance of different curve combination models varies significantly: the AC-CNL combination performs best, with a predicted average of 6.155 (a deviation of only 0.016% from the measured value). It exhibits clear advantages in key error indicators, with a MAE of 2.266 (3.2% lower than the AC-DEN combination and 8.5% lower than the AC-GR combination) and a MAPE of 46.5% (9.5% lower than the AC-DEN combination and 10.7% lower than the AC-RT combination). The AC-GR combination is severely ineffective, with a predicted average of 7.146 (15.8% higher than the measured value) and a MAPE of 61.8%, requiring strict avoidance. The AC-DEN and AC-RT combinations perform moderately, with both exceeding 51% MAPE, and all combinations in this lithological section exceeding 46% MAPE. Therefore, the optimal prediction model for the carbonaceous shale section is the AC-CNL combination: TOC = -0.0609 × △LogR + 7.2921.

[0057] Table 4. Error Statistics of the Multi-Parameter Fusion Improved ΔlogR Method Model for Carbonaceous Shale under Lithological Constraints The coal seam segment belongs to the highest TOC prediction scenario (Table 5), with a measured mean TOC of 10.018. The performance of different curve combination models varies significantly: the AC-RT combination performs best, with a prediction mean of 10.017 (smallest deviation), showing significant advantages in key error indicators. MAE is 2.112 (9.4% lower than the AC-DEN combination and 16.5% lower than the AC-GR combination), and MAPE is 23.3% (10.7% lower than the AC-DEN combination and 16.8% lower than the AC-GR combination). The AC-DEN combination is partially superior (SWPE = 0.233). The AC-CNL and AC-GR combinations perform weakly. Therefore, the optimal prediction model for the coal seam segment is the AC-RT combination: TOC = 7.2005 × ΔLogR + 5.2091 ( Figure 6 ).

[0058] Table 5. Error Statistics of the Multi-Parameter Fusion Improved Coal-Rock ΔlogR Method Model under Lithological Constraints Step S6: Complete the hydrocarbon generation potential assessment. Based on the TOC prediction results, calculate the average TOC and cumulative thickness of the study intervals in each well, and draw the average TOC planar map and the effective source rock thickness planar map; then, calculate the average density of the study intervals in each well using the DEN curves of each well in the study area, and then introduce the organic carbon recovery coefficient K and hydrocarbon production rate D (both obtained from previous research charts), according to the formula Q... 气 =ρ 泥 The hydrocarbon generation intensity was calculated using the formula ×H×TOC×K×D, and a hydrocarbon generation intensity planar map was drawn to ultimately complete a comprehensive evaluation of the hydrocarbon generation potential of the study area. The target stratigraphic level required five segments of average TOC data to be predicted using the ΔLogR method based on lithology. Specifically, the logging curve data for mudstone, shale, carbonaceous shale, and coalstone in each well were statistically analyzed. Then, the constructed ΔLogR model was used to calculate the predicted TOC values ​​for different lithologies in each well, thereby calculating the average TOC value for each well. This average TOC value served as the basis for the average TOC value of each well on the planar map. Simultaneously, the mudstone and shale density (ρ) of the study stratigraphic segment in each well was statistically analyzed. 泥 =25.24×10 8 t / km 3 The data on the effective source rock thickness of the five sections were obtained from various wells in the study area. When analyzing each well, a TOC greater than 0.5% was clearly defined as the lower limit of the effective source rock. Specifically, the TOC value of different lithologies in each well was predicted using a pre-constructed ΔLogR model. Mudstone, shale, carbonaceous shale, and coal rock sections that met this lower limit were selected. The thicknesses of these sections were accumulated to obtain the effective source rock thickness (H) data for each well. Through the statistics of numerous wells in the study area, a basic data set of effective source rock thickness was constructed, providing a foundation for subsequent characterization of hydrocarbon generation potential.

[0059] After obtaining basic data such as average TOC, cumulative effective source rock thickness (H), and shale density (ρmud), two key parameters, the original organic carbon recovery coefficient (K) and hydrocarbon production rate (D), were introduced, and the hydrocarbon generation formula Q was used. 气 =ρ 泥 ×H×TOC×K×D Quantification of Hydrocarbon Generation Capacity: Among them, the organic carbon obtained from the current sampling and testing is the residual organic carbon after hydrocarbon generation and expulsion from the source rocks. For the source rocks of the Xuwu Member in the Xichong gas-bearing area of ​​the study area, which are in the mature to highly mature stage and whose lithology is mainly mudstone and shale, a large amount of hydrocarbons have been expelled after sedimentary evolution and hydrocarbon generation and expulsion processes. It is necessary to restore the currently measured residual organic carbon content to the original organic carbon content in geological history. Based on previous maps, the organic carbon restoration coefficient K of the study area is determined to be 1.103 (Pang Xiongqi et al., 2014). The hydrocarbon generation rate D is a key parameter for calculating gas production in resource evaluation. Its value is mainly controlled by the type of kerogen and the degree of thermal evolution. The source rocks of the Xuwu Member in the study area are mainly Type III kerogen in the mature to highly mature stage. Based on this lithological and thermal evolution characteristics, combined with the existing research maps, D is calculated to be 103 m³ / (t・TOC) (Zhou Daorong et al., 2013). After quantifying the hydrocarbon generation capacity, the interpolation algorithm was optimized using the double-fox minimum tension method, and the average TOC planar plot was drawn. Figure 7 ), effective source rock thickness plan ( Figure 8 ) and hydrocarbon generation intensity plan ( Figure 9 ); Example 4 like Figure 10 As shown in the figure, this application provides a hydrocarbon generation potential assessment device, including: The data preprocessing module 1001 is used to acquire the logging data to be processed, and to standardize the logging data by using the standard data corresponding to the pre-specified standard layer and standard well, so as to obtain the standardized logging data. The lithology classification module 1002 is used to obtain the lithology corresponding to all target strata in the study area based on the standardized well logging data, and to obtain the lithology classification results for the entire area; the target strata refer to the strata in the study area. The parameter screening module 1003 is used to obtain the correlation coefficient between each logging data and the pre-collected measured TOC value based on the standardized logging data and the lithology classification results of the whole area, and to screen parameters with large correlation coefficients and determine the optimal logging parameter combination after eliminating multicollinearity. The parameter fitting module 1004 is used to determine the benchmark well based on the lithology classification results of the whole area, obtain the slope based on the standardized logging data corresponding to the benchmark well, and obtain a lithology model fitted with multiple ΔlogR values ​​based on the slope and the optimal logging parameter combination. The model evaluation module 1005 is used to evaluate the performance of lithological models fitted with multiple ΔlogR values ​​and determine the lithological model with the best performance as the lithological optimal model. The hydrocarbon generation evaluation module 1006 is used to obtain the target TOC value corresponding to the target interval of all wells in the study area using the lithological optimal model, obtain the average target TOC value based on the target TOC value corresponding to the wells, and obtain the hydrocarbon generation intensity based on the average target TOC value. The multi-dimensional evaluation module 1007 is used to generate a hydrocarbon generation intensity planar map based on the hydrocarbon generation intensity, and to draw an average TOC planar map, an effective source rock thickness planar map, and a hydrocarbon generation intensity planar map by interpolation using the double-fox minimum tension method, thereby realizing a multi-dimensional evaluation of the hydrocarbon generation potential level.

[0060] This hydrocarbon generation potential assessment device can perform the technical solutions described in Examples 1, 2, and 3. Its principle and beneficial effects are similar and will not be repeated here.

[0061] Example 5 This application also provides an electronic device, including a processor and a memory; the memory and the processor are interconnected via a bus.

[0062] The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform a hydrocarbon generation potential assessment method based on improved ΔlogR as described in any embodiment of this application.

[0063] For specific examples, memory may include, but is not limited to, random access memory (RAM), read-only memory (ROM), flash memory, first-in-first-out (FIFO) memory, and / or first-in-last-out (FILO) memory, etc.; specifically, processor may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor may be implemented using at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), PLA (Programmable Logic Array). Furthermore, the processor may include a main processor and coprocessors. The main processor, also known as the CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state.

[0064] All or part of the steps in the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a readable memory. When the program is executed, it performs the steps of the above method embodiments; and the aforementioned memory (storage medium) includes: read-only memory (ROM), RAM, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disk, and any combination thereof.

[0065] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processing unit of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0066] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0067] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0068] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the claims.

[0069] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for assessing hydrocarbon generation potential based on an improved ΔlogR, characterized in that, include: Acquire the logging data to be processed, and standardize the logging data by using the standard data corresponding to the pre-specified standard layer and standard well to obtain the standardized logging data; Based on the standardized well logging data, the lithology corresponding to all target strata in the study area is obtained, and the lithology classification results for the entire area are obtained; the target strata refer to the formations in the study area. Based on the standardized logging data and the lithology classification results of the whole area, the correlation coefficient method was used to obtain the correlation coefficient between each logging data and the pre-collected measured TOC value. Parameters with large correlation coefficients were screened, and the optimal combination of logging parameters was determined after eliminating multicollinearity. Based on the lithological classification results of the whole area, a benchmark well is determined. The slope is obtained based on the standardized logging data corresponding to the benchmark well. Based on the slope and the optimal logging parameter combination, multiple lithological models fitted with ΔlogR values ​​are obtained. The performance of lithological models fitted with multiple ΔlogR values ​​was evaluated, and the lithological model with the best performance was determined as the optimal lithological model. The target TOC values ​​corresponding to the target intervals of all wells in the study area are obtained using the lithological optimal model. The average target TOC value is obtained based on the target TOC values ​​of the wells. The hydrocarbon generation intensity is obtained based on the average target TOC value. Based on the hydrocarbon generation intensity, a hydrocarbon generation intensity planar diagram is prepared, and the average TOC planar diagram, effective source rock thickness planar diagram, and hydrocarbon generation intensity planar diagram are drawn using the double-fox minimum tension method for interpolation, thereby realizing a multi-dimensional evaluation of hydrocarbon generation potential level.

2. The method for assessing hydrocarbon generation potential based on improved ΔlogR according to claim 1, characterized in that, Acquire the logging data to be processed, and standardize the logging data using pre-specified standard layers and standard data corresponding to standard wells to obtain standardized logging data, including: Obtain the AC, RT, GR, DEN, and CNL logging curves corresponding to the well logs to obtain the logging data to be processed; The well logging data to be processed is standardized to obtain the standardized well logging data as follows: in, This represents the logging data to be processed. This indicates the upper limit value corresponding to the logging data to be processed. This represents the lower limit value corresponding to the logging data to be processed. This represents the average value of standard well logging data. This represents the average value of the logging data from the standard well. This represents the upper limit of the data corresponding to the standard well in the standard layer. This represents the lower limit of the data corresponding to the standard well in the standard layer. This represents the standardized well logging data. .

3. The method for assessing hydrocarbon generation potential based on improved ΔlogR according to claim 1, characterized in that, Based on the standardized well logging data, the lithology corresponding to all target strata in the study area is obtained, resulting in a lithological classification of the entire area, including: Based on the standardized well logging data, a GR-AC and GR-DEN dual-parameter cross plot is constructed, and lithological boundary values ​​are determined according to the GR-AC and GR-DEN dual-parameter cross plot, thereby forming a regional unified lithological interpretation standard. Based on the lithological boundary values, the lithology of all target strata in the study area is divided to obtain the lithological classification results for the entire area.

4. The method for assessing hydrocarbon generation potential based on improved ΔlogR according to claim 1, characterized in that, Based on the standardized logging data and the lithological classification results of the whole area, the correlation coefficient method was used to obtain the correlation coefficient between each logging data and the pre-acquired measured TOC. Parameters with large correlation coefficients were screened, and after eliminating multicollinearity, the optimal logging parameter combination was determined. This included: based on the correlation analysis between each logging curve and TOC, firstly, the important parameter sonic transit time curve in the classical ΔlogR method was retained, then logging parameters with a correlation greater than 0.2 and logging parameters that have important determinants of TOC were retained, and logging parameters were combined with sonic transit time curves respectively.

5. The method for assessing hydrocarbon generation potential based on improved ΔlogR according to claim 1, characterized in that, Based on the lithological classification results of the entire region, benchmark wells are determined, and the inclination is obtained from the standardized logging data corresponding to the benchmark wells, including: Based on the lithological classification results of the entire region, the well with the largest continuous thickness of mudstone and / or the largest measured TOC sample size was determined as the benchmark well; Based on the standardized logging data, the extreme values ​​of logging parameters corresponding to the benchmark well are extracted; Based on the extreme values ​​of the logging parameters, the slope is obtained as follows: K=(N-Nmin) / (Δt-Δtmin) Where K represents the slope, N represents the standardized logging data, Nmin represents the minimum value of the standardized logging data, Δt represents the sonic transit time logging curve value, and Δtmin represents the minimum value of the sonic transit time logging curve.

6. The method for assessing hydrocarbon generation potential based on improved ΔlogR according to claim 5, characterized in that, Based on the slope and the optimal combination of logging parameters, multiple lithological models fitted with ΔlogR values ​​are obtained, including: Based on the slope and the optimal combination of logging parameters, the ΔlogR value is obtained as follows: ΔlogR=(N-Nmin)+K×(Δt-Δtmax) Wherein, Δtmax represents the maximum value of the sonic time-of-flight logging curve. Based on the ΔlogR value and the pre-collected measured TOC value, the fitted lithological model is as follows: TOC = A × ΔlogR + B Where TOC represents organic carbon content, A represents the first coefficient, and B represents the second coefficient.

7. The method for assessing hydrocarbon generation potential based on improved ΔlogR according to claim 1, characterized in that, The performance of lithological models fitted with multiple ΔlogR values ​​was evaluated, and the best-performing lithological model was determined as the optimal lithological model, including: The performance of lithological models fitted with multiple ΔlogR values ​​was evaluated using the average value, average absolute error, sum of absolute errors, sum of weighted percentage errors, average absolute percentage error, and / or weighted average percentage error. The lithological model with the best performance was determined as the optimal lithological model.

8. The method for assessing hydrocarbon generation potential based on improved ΔlogR according to claim 1, characterized in that, The target TOC values ​​corresponding to the target intervals of all well logging in the study area are obtained using the aforementioned lithological optimal model. The average target TOC value is then obtained based on the target TOC values ​​from the well logging, and the hydrocarbon generation intensity is obtained based on the average target TOC value, including: The target TOC values ​​corresponding to the target intervals of all well logging in the study area were obtained using the aforementioned lithological optimal model. The average target TOC value is obtained based on the target TOC value corresponding to the well logging, and the effective source rock thickness and average mudstone density are calculated. The effective source rock thickness represents the formation thickness with TOC ≥ 0.5%. Based on the average target TOC value, effective source rock thickness, and average mudstone density, the hydrocarbon generation intensity is obtained as follows: Q 气 =p 泥 ×H×TOC avg ×K×D Among them, Q 气 ρ represents hydrocarbon generation intensity. 泥 The value represents the density of mudstone and shale, H represents the effective source rock thickness, and TOC... avg K represents the average TOC, K represents the organic carbon recovery coefficient, and D represents the hydrocarbon production rate.

9. A device for assessing hydrocarbon generation potential, characterized in that, include: The data preprocessing module is used to acquire the logging data to be processed, and to standardize the logging data by using the standard data corresponding to the pre-specified standard layer and standard well, so as to obtain the standardized logging data. The lithology classification module is used to obtain the lithology corresponding to all target strata in the study area based on the standardized well logging data, and to obtain the lithology classification results for the entire area; the target strata refer to the formations in the study area. The parameter filtering module is used to obtain the correlation coefficient between each logging data and the pre-collected measured TOC value based on the standardized logging data and the lithology classification results of the whole area, and to filter parameters with large correlation coefficients and determine the optimal logging parameter combination after eliminating multicollinearity. The parameter fitting module is used to determine the benchmark well based on the lithology classification results of the whole area, obtain the slope based on the standardized logging data corresponding to the benchmark well, and obtain a lithology model fitted with multiple ΔlogR values ​​based on the slope and the optimal logging parameter combination. The model evaluation module is used to evaluate the performance of lithological models fitted with multiple ΔlogR values ​​and determine the lithological model with the best performance as the optimal lithological model. The hydrocarbon generation evaluation module is used to obtain the target TOC value corresponding to the target interval of all wells in the study area using the lithological optimal model, obtain the average target TOC value based on the target TOC value corresponding to the wells, and obtain the hydrocarbon generation intensity based on the average target TOC value. The multi-dimensional evaluation module is used to generate a hydrocarbon generation intensity planar map based on the hydrocarbon generation intensity, and to draw an average TOC planar map, an effective source rock thickness planar map, and a hydrocarbon generation intensity planar map by interpolation using the double-fox minimum tension method, thereby realizing a multi-dimensional evaluation of the hydrocarbon generation potential level.

10. An electronic device, characterized in that, Including processor and memory; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the hydrocarbon generation potential assessment method based on improved ΔlogR as described in any one of claims 1 to 8.

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