Method and equipment for predicting total organic carbon in source rock stratum containing carbonate rock

Abstract

The invention discloses a carbonate rock-containing hydrocarbon source rock stratum total organic carbon prediction method and equipment, and the method comprises the steps: firstly obtaining actually measured total organic carbon data, interval transit time well logging data, apparent resistivity well logging data and natural gamma well logging data of a test region, and then utilizing the characteristic that the gamma value of carbonate rock is relatively low to predict the total organic carbon of the hydrocarbon source rock stratum. When a ternary logging parameter prediction model is constructed, the natural gamma logging data is used for correcting a delta LogR method, and parameters are adjusted based on the corrected delta LogR method; and constructing a ternary logging parameter prediction model based on the interval transit time logging data, the apparent resistivity logging data, the natural gamma logging data and the actually measured total organic carbon data, and then predicting the total organic carbon of a to-be-predicted area by using the ternary logging parameter prediction model. The prediction model can accurately predict the total organic carbon content of the hydrocarbon source rock stratum section containing the carbonate rock, is beneficial to overall hydrocarbon source rock evaluation, and has great significance in promoting exploration and development of regional oil and gas resources.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of oil and gas geological exploration and development, and particularly relates to a method and device for predicting total organic carbon of a carbonate rock-containing source rock layer. BACKGROUND

[0002] The source rock is the material basis for oil and gas generation, and the total organic carbon content (TOC) is an important index in the evaluation of oil and gas resources of the source rock.

[0003] In the related art, methods for evaluating the total organic carbon content (TOC) include a multivariate statistical model of geochemical data of core testing and unconventional logging methods. However, these methods cannot accurately predict the total organic carbon content in a region with a carbonate rock development background, especially cannot accurately predict the total organic carbon content in a carbonate rock-containing source rock layer, which is not conducive to the overall evaluation of the source rock. Therefore, the related art has the problem of poor accuracy in predicting the total organic carbon of the carbonate rock-containing source rock layer.

[0004] Therefore, how to accurately predict the total organic carbon of the source rock layer is a problem to be solved in the field. SUMMARY

[0005] Embodiments of the application provide a method and device for predicting the total organic carbon of a carbonate rock-containing source rock layer, thereby achieving more accurate prediction of the total organic carbon of the carbonate rock-containing source rock layer.

[0006] Other characteristics and advantages of the application will become apparent from the following detailed description, or will be learned by practice of the application.

[0007] According to a first aspect of the embodiments of the application, a method for predicting the total organic carbon of a carbonate rock-containing source rock layer is provided, comprising:

[0008] Obtaining measured total organic carbon data, acoustic travel time logging data, apparent resistivity logging data and natural gamma logging data of a test region;

[0009] Correcting the ΔLogR method using the natural gamma logging data, and constructing a ternary logging parameter prediction model based on the acoustic travel time logging data, the apparent resistivity logging data and the natural gamma logging data and the measured total organic carbon data based on the corrected ΔLogR method;

[0010] Predicting the total organic carbon of a region to be predicted based on the ternary logging parameter prediction model.

[0011] In some embodiments of the present application, based on the foregoing scheme, the method for constructing a ternary logging parameter prediction model based on the acoustic travel time logging data, the apparent resistivity logging data and the natural gamma logging data based on the modified ΔLogR method comprises:

[0012] A ternary logging model is constructed by using the following ternary logging parameter formula:

[0013] TOC ΔLogR+GR = A x lg(RT / RT baseline ) + B x (Δt-Δt baseline ) + C x GR;

[0014] The measured total organic carbon data is taken as TOC ΔLogR+GR , the resistivity value of the test area is taken as RT, the acoustic travel time value of the test area is taken as Δt, and the natural gamma value of the test area is taken as GR, which are substituted into the above ternary logging parameter formula, and multiple linear regression analysis is performed to calculate the values of A, B and C as A1, B1 and C1 respectively.

[0015] Wherein, RT baseline is a resistivity reference line value, and Δt baseline is an acoustic travel time reference line value.

[0016] A ternary logging parameter prediction model is constructed by using A1, B1 and C1, and the formula is as follows:

[0017] TOC1 ΔLogR+GR = A1 x lg(RT1 / RT baseline ) + B1 x (Δt1-Δt baseline ) + C1 x GR1;

[0018] Wherein, TOC1 ΔLogR+GR is the total organic carbon data of the area to be predicted, RT1 is the resistivity value of the area to be predicted, Δt1 is the acoustic travel time value of the area to be predicted, and GR1 is the natural gamma value of the area to be predicted.

[0019] In some embodiments of the present application, based on the foregoing scheme, the total organic carbon of the area to be predicted is predicted based on the ternary logging parameter prediction model, which comprises:

[0020] Acoustic travel time logging data, apparent resistivity logging data and natural gamma logging data of the area to be predicted are obtained.

[0021] The acoustic travel time logging data, the current apparent resistivity logging data and the natural gamma logging data of the area to be predicted are substituted into the ternary logging parameter prediction model to obtain the total organic carbon prediction value of the area to be predicted.

[0022] In some embodiments of this application, based on the foregoing scheme, the step of performing multiple linear regression analysis to calculate the values ​​of A, B, and C as A1, B1, and C1, respectively, includes:

[0023] After performing multiple linear regression analysis, obtain the regression coefficient table;

[0024] According to the regression coefficient table, and in the ternary logging parameter formula, lg(RT / RT) baseline The unstandardized coefficients related to (Δt1-Δt) are denoted as A1; and (Δt1-Δt) baseline The unstandardized coefficients related to ) are designated as B1; and the unstandardized coefficients related to GR are designated as C1.

[0025] In some embodiments of this application, based on the foregoing scheme, the method further includes:

[0026] Collinearity analysis was performed on the ternary logging parameter prediction model.

[0027] The tolerance and variance inflation factor are obtained based on the collinearity analysis.

[0028] The ternary logging parameter prediction model is evaluated based on the tolerance and the variance expansion factor.

[0029] In some embodiments of this application, based on the foregoing scheme, the method further includes:

[0030] Substitute the acoustic transit time logging data, apparent resistivity logging data, and natural gamma logging data of the test area into the ternary logging parameter prediction model to calculate the predicted value of total organic carbon.

[0031] The predicted total organic carbon and the measured total organic carbon data are analyzed using a regression-standardized residual plot to obtain the residual between the predicted total organic carbon and the measured total organic carbon data, thus obtaining the first residual.

[0032] The ternary logging parameter prediction model is evaluated based on the first residual.

[0033] In some embodiments of this application, based on the foregoing scheme, the method further includes:

[0034] The total organic carbon in the test area is predicted based on the ΔLogR method, and the residual between the predicted value and the measured total organic carbon data is calculated to obtain the second residual.

[0035] When the first residual is less than the second residual, the ternary logging parameter prediction model is deemed valid.

[0036] According to a second aspect of the embodiments of this application, a device for predicting the total organic carbon of carbonate source rock strata is provided, comprising:

[0037] The acquisition module is used to acquire measured total organic carbon data, sonic transit time logging data, apparent resistivity logging data, and natural gamma logging data of the test area.

[0038] The model building module is used to correct the ΔLogR method using the natural gamma logging data, and to build a ternary logging parameter prediction model based on the corrected ΔLogR method, which is based on the sonic transit time logging data, the apparent resistivity logging data, the natural gamma logging data, and the measured total organic carbon data.

[0039] The prediction module is used to predict the total organic carbon in the area to be predicted based on the ternary logging parameter prediction model.

[0040] According to a third aspect of the embodiments of this application, a device for predicting total organic carbon in carbonate source rock formations is provided, comprising a processor and a memory, wherein the memory stores computer program instructions executable by the processor, and when the processor executes the computer program instructions, it implements the steps of the method described in any of the first aspects above.

[0041] According to a fourth aspect of the embodiments of this application, a computer-readable storage medium is provided, wherein computer program instructions are stored therein, and when executed by a processor, the computer program instructions cause the processor to perform the steps of the method as described in any of the first aspects above.

[0042] In this application, to predict the total organic carbon (TOC) of carbonate-bearing source rocks, the following methods are employed: First, measured TOC data, sonic transit time logging data, apparent resistivity logging data, and natural gamma ray logging data are acquired from the test area. Then, taking advantage of the low gamma ray values ​​of carbonate rocks, the ΔLogR method is modified using the natural gamma ray logging data when constructing the ternary logging parameter prediction model. Based on the modified ΔLogR method, a ternary logging parameter prediction model is constructed using the sonic transit time logging data, apparent resistivity logging data, natural gamma ray logging data, and measured TOC data. This ternary logging parameter prediction model is then used to predict the TOC of the area to be predicted. Compared to the ΔLogR method, which has only two parameters and no adjustment coefficients, the ternary logging parameter prediction model of this application introduces three parameters, with adjustment coefficients A, B, and C added before each parameter. Using this method to predict the TOC of carbonate-bearing source rocks yields more accurate results. Since the prediction model of this application utilizes natural gamma logging data, by incorporating natural gamma logging data as part of the prediction model, and taking into account the low gamma value of carbonate rocks, the prediction model of this application can accurately predict the total organic carbon content of carbonate source rock strata, which is beneficial to the overall evaluation of source rocks and is of great significance to promoting the exploration and development of regional oil and gas resources.

[0043] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0044] 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. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0045] Figure 1 A flowchart illustrating a method for predicting total organic carbon in carbonate source rock formations is shown in one embodiment.

[0046] Figure 2 A detailed flowchart of a method for predicting total organic carbon in carbonate source rock formations is shown in one embodiment.

[0047] Figure 3 A detailed flowchart of a method for predicting total organic carbon in carbonate source rock formations is shown in one embodiment.

[0048] Figure 4A detailed flowchart of a method for predicting total organic carbon in carbonate source rock formations is shown in one embodiment.

[0049] Figure 5 A table of regression coefficients for ternary independent variables is shown in one embodiment;

[0050] Figure 6 A regression-standardized residual plot is shown in one embodiment;

[0051] Figure 7 An interpretation diagram of the geochemical logging curve for well A in one embodiment is shown;

[0052] Figure 8 A graph showing the linear relationship between measured TOC and ΔLogR in one embodiment is shown.

[0053] Figure 9 An interpretation diagram of the geochemical logging curve of well A in an embodiment of the relevant technology is shown;

[0054] Figure 10 A block diagram of a total organic carbon prediction device for carbonate source rock formations is shown in one embodiment;

[0055] Figure 11 A schematic diagram of a total organic carbon prediction device for carbonate source rock formations is shown in one embodiment. Detailed Implementation

[0056] 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 some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0057] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0058] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0059] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0060] It should also be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such uses of these terms can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described.

[0061] Source rocks are the material basis for hydrocarbon generation, and total organic carbon (TOC) content is a crucial indicator in the assessment of hydrocarbon resources in source rocks. Related technologies for evaluating TOC include multivariate statistical models based on geochemical data from core testing and unconventional logging methods. However, these methods cannot accurately predict TOC content for areas with carbonate rock development, which is detrimental to overall source rock evaluation. Therefore, the methods for predicting TOC in carbonate-bearing source rock formations suffer from poor accuracy.

[0062] TOC (Total Organic Carbon) detection is a widely used method in environmental monitoring, water quality analysis, pharmaceutical industry, and food safety. TOC detection is primarily used to analyze and detect the total organic carbon content in water and liquid samples. Total organic carbon is an important environmental parameter that reflects the concentration of organic matter in water and liquid samples.

[0063] Specifically, existing methods for evaluating total organic carbon (TOC) are relatively mature, including those utilizing geochemical data from core testing, the ΔLogR method, multivariate statistical models, and unconventional logging methods. However, while geochemical data from core testing is highly accurate, it is limited by the small number and dispersion of core samples, as well as the long testing cycle. The ΔLogR method, based on well logging data, is affected by the high resistivity of the carbonate background, easily misidentifying carbonate rocks as source rocks. Multivariate statistical models construct TOC calculation models for a specific region using GR, CVL, DEN, and AC as multivariate parameters, thus exhibiting regional limitations. Unconventional logging methods calculate TOC by using nuclear magnetic resonance logging and geochemical element logging to calculate framework porosity, density porosity, etc., but these methods are costly and difficult to widely apply.

[0064] Therefore, each of the above methods has its own advantages and disadvantages, but they are all difficult to accurately predict the total organic carbon content in areas with carbonate rock development, which is not conducive to the overall evaluation of source rocks. Therefore, it is crucial to construct a total organic carbon (TOC) prediction model for carbonate-bearing source rock intervals.

[0065] To address the aforementioned problems, this application discloses the following technical solution.

[0066] According to a first aspect of the embodiments of this application, a method for predicting the total organic carbon in carbonate source rock strata is provided. Figure 1 A flowchart illustrating a method for predicting total organic carbon in carbonate source rock formations is shown in one embodiment. Figure 1 As shown, the method may include steps 101 to 103.

[0067] Step 101: Obtain measured total organic carbon data, sonic transit time logging data, apparent resistivity logging data, and natural gamma logging data for the test area.

[0068] The test area is the region within the oil wells with relatively complete data, especially including measured total organic carbon (TOC) data. Measuring TOC in every single oil well area would be extremely resource-intensive. Therefore, in practice, a specific area is often selected as the test area. For example, if an area contains 100 oil wells, one well is chosen as the test area. TOC data from this well is measured using various instruments. Then, for areas within the same rock strata, the data from this test area can be used as a sample to build a model for prediction.

[0069] In this application, step 101 first obtains the measured total organic carbon (TOC) data, sonic transit time logging (STL) data, apparent resistivity logging (ALR) data, and natural gamma ray logging (NGR) data for the test area. The measured TOC data is the actual measured TOC in the wellbore for the test area, precisely measured using instruments.

[0070] Natural gamma ray logging (GR data, hereinafter referred to as GR) is a logging method that studies geological problems by measuring the intensity of rays emitted during the decay of naturally occurring radioactive nuclides in the rock formation. Applications of GR include: ① determining lithology. ② stratigraphic correlation. ③ estimating clay content. Low natural gamma ray values ​​are observed in large wellbore locations.

[0071] Acoustic transit time logging data (AC data, referred to below) refers to the time difference between the received acoustic waves. This difference can be used to perform related calculations and solve for various quantities. The acoustic transit time method plays an important role in determining the location of objects, measuring drilling, and detecting formation vibrations.

[0072] Apparent resistivity logging data (RT data, referred to below) is a logging method for studying the electric field distribution in various media. During measurement, an artificial electric field is created by passing an electric current through the medium. The distribution characteristics of this field are determined by the resistivity of the surrounding medium. Therefore, by measuring the electric field distribution characteristics in various media, the resistivity of the medium can be determined.

[0073] Step 101 of this application obtains the measured total organic carbon data, sonic transit time logging data, apparent resistivity logging data and natural gamma logging data of the test area to obtain the sample used to build the model, and then proceeds to step 102.

[0074] Step 102: Correct the ΔLogR method using natural gamma logging data, and construct a ternary logging parameter prediction model based on the corrected ΔLogR method, which is based on sonic transit time logging data, apparent resistivity logging data, natural gamma logging data, and measured total organic carbon data.

[0075] The formula corresponding to the ΔLogR method in the relevant technology is as follows:

[0076] ΔLogR=lg(RT / RT baseline )+0.006×(Δt-Δt baseline )

[0077] In this embodiment, the low gamma value of carbonate rocks is utilized to correct the above ΔLogR formula, where lg(RT / RT) is used. baseline ), Δt-Δt baseline Based on this, add a GR parameter, and combine it with the relationship with the measured TOC to establish lg(RT / RT) baseline ), tt baselineA linear regression model of ternary logging parameters based on GR.

[0078] Specifically, in some embodiments of this application, based on the aforementioned scheme, the method for constructing a ternary logging parameter prediction model based on sonic transit time logging data, apparent resistivity logging data, and natural gamma logging data in step 102 of this application includes:

[0079] A ternary logging model is constructed using the following ternary logging parameter formulas;

[0080] TOC ΔLogR+GR =A×lg(RT / RT) baseline )+B×(Δt-Δt baseline )+C×GR;

[0081] The measured total organic carbon data is used as the TOC. ΔLogR+GR The resistivity value of the test area is taken as RT, the sonic transit time value of the test area is taken as Δt, and the natural gamma value of the test area is taken as GR. These values ​​are substituted into the above ternary logging parameter formula, and multiple linear regression analysis is performed to calculate the values ​​of A, B and C as A1, B1 and C1, respectively.

[0082] Among them, RT baseline The resistivity baseline value is Δt. baseline This is the baseline value for acoustic time difference;

[0083] A ternary logging parameter prediction model is constructed using A1, B1, and C1, and the formula is as follows:

[0084] TOC1 ΔLogR+GR =A1×lg(RT1 / RT) baseline )+B1×(Δt1-Δt baseline )+C1×GR1;

[0085] Among them, TOC1 ΔLogR+GR RT1 is the total organic carbon data of the region to be predicted, Δt1 is the resistivity value of the region to be predicted, GR1 is the acoustic transit time value of the region to be predicted, and GR1 is the natural gamma value of the region to be predicted.

[0086] This application inputs the measured total organic carbon data, sonic transit time logging data, apparent resistivity logging data, and natural gamma logging data obtained in step 101 as sample data into a ternary logging model. The values ​​of A, B, and C are calculated as A1, B1, and C1, respectively, through multiple linear regression analysis. Then, a ternary logging parameter prediction model is constructed based on the values ​​of A1, B1, and C1. This model can be used to predict the total organic carbon in other areas outside the test area.

[0087] For example, the ternary logging parameter prediction model obtained by this application through calculation is as follows:

[0088] TOC ΔLogR+GR =1.234+0.012GR-1.377lg(RT / RT baseline )-0.012(Δt-Δt baseline )

[0089] Therefore, by using the method of this application, since the natural gamma value is introduced into the model as GR, the total organic carbon of other regions to be predicted can be predicted more accurately, and the prediction results have high precision.

[0090] In some embodiments of this application, based on the foregoing scheme, the method for performing multiple linear regression analysis in the above steps of this application to calculate the values ​​of A, B, and C as A1, B1, and C1 respectively includes:

[0091] After performing multiple linear regression analysis, obtain the regression coefficient table.

[0092] This application can calculate TOC using SPSS multiple linear regression analysis. ΔLogR+GR With lg(RT / RTbaseline), (Δt1-Δt baseline The ternary linear relationship between GR and A1 can be obtained, resulting in values ​​A1, B1, and C1 for A, B, and C, respectively. SPSS multiple linear regression analysis is a statistical method used to study the relationship between one or more independent variables and a dependent variable. SPSS multiple linear regression analysis primarily focuses on the linear relationship between two variables, that is, how one variable (dependent variable) changes with changes in one or more other variables (independent variables). This analytical method helps researchers understand the degree and direction of the influence of independent variables on the dependent variable, thereby building predictive models. The results of multiple linear regression analysis can be presented in various forms, including model path diagrams, model result predictions, standardized regression coefficients, etc. These results help to understand the relationship between independent and dependent variables and assess the goodness of fit of the model.

[0093] In SPSS, after performing multiple linear regression analysis, standardized and unstandardized coefficients can be obtained by viewing the regression coefficient table. Standardized coefficients help us understand the relative influence between variables, while unstandardized coefficients help us understand the specific numerical relationships between variables, as they directly reflect the impact of unit changes in independent variables on the dependent variable. Generally, the larger the absolute value of the unstandardized coefficient, the greater the influence of the corresponding independent variable on the dependent variable.

[0094] According to the regression coefficient table, it will be compared with the ternary logging parameter formula, and lg(RT / RT) baselineThe unstandardized coefficients related to (Δt1-Δt) are denoted as A1; and (Δt1-Δt) baseline The unstandardized coefficients related to ) are designated as B1; and the unstandardized coefficients related to GR are designated as C1.

[0095] The TOC can be found using the regression coefficient table. ΔLogR+GR With lg(RT / RT) baseline ), (Δt1-Δt) baseline The ternary linear relationship of GR is used to calculate the values ​​of A, B, and C, which are A1, B1, and C1, respectively.

[0096] After obtaining the ternary logging parameter prediction model through the above steps, proceed to the following steps.

[0097] Step 103: Predict the total organic carbon in the area to be predicted based on the ternary logging parameter prediction model.

[0098] After obtaining the ternary logging parameter prediction model through the above steps, the total organic carbon in the area to be predicted can be predicted.

[0099] Specifically, such as Figure 2 As shown, Figure 2 A detailed flowchart of a method for predicting total organic carbon in carbonate source rock formations is shown in one embodiment. In some embodiments of this application, based on the aforementioned scheme, step 103 of this application, the method for predicting the total organic carbon in the area to be predicted based on a ternary logging parameter prediction model, includes steps 201-202:

[0100] Step 201: Obtain sonic transit time logging data, apparent resistivity logging data, and natural gamma logging data for the area to be predicted.

[0101] For oil wells, it is necessary to acquire sonic transit time logging data, apparent resistivity logging data, and natural gamma logging data for each well, because these data are needed for various aspects of the analysis of the oil well. Therefore, this application can directly acquire sonic transit time logging data, apparent resistivity logging data, and natural gamma logging data for the area to be predicted.

[0102] Step 202: Substitute the sonic transit time logging data, current apparent resistivity logging data, and natural gamma logging data of the area to be predicted into the ternary logging parameter prediction model to obtain the predicted total organic carbon value of the area to be predicted.

[0103] This application allows users to substitute sonic transit time logging data, current apparent resistivity logging data, and natural gamma logging data of the area to be predicted into a ternary logging parameter prediction model to obtain the predicted total organic carbon value of the area to be predicted.

[0104] The total organic carbon prediction obtained using this application is more accurate and more practical.

[0105] The above methods can be used to obtain the predicted total organic carbon value of the area to be predicted. This application utilizes the Dedao ternary logging parameter prediction model to evaluate the accuracy of the ternary logging parameter prediction model, thereby discarding models with poor accuracy and further improving the precision.

[0106] Specifically, in some embodiments of this application, based on the foregoing scheme, such as Figure 3 As shown, Figure 3 A detailed flowchart of a method for predicting total organic carbon in carbonate source rock formations is shown in one embodiment; the method of this application also includes steps 301-302.

[0107] Step 301: Perform collinearity analysis on the ternary logging parameter prediction model.

[0108] Collinearity analysis is a method used to detect and evaluate collinearity among variables in multiple regression analysis. Collinearity, or linear correlation between variables, is a significant problem in multiple regression analysis because it can severely affect the stability and interpretability of the model. In SPSS, collinearity analysis is assessed by calculating two key indicators: "Tolerance" and "Variance Inflation Factor (VIF)".

[0109] This application assesses the accuracy of the ternary logging parameter prediction model by performing collinearity analysis.

[0110] Step 302: Obtain tolerance and variance inflation factor based on collinearity analysis.

[0111] Collinearity analysis of the ternary logging parameter prediction model yields the tolerance and variance inflation factor. Tolerance, a measure of the degree of collinearity among variables, should typically be greater than 0.1. A low tolerance indicates strong collinearity. The VIF variance inflation factor should generally be less than 10.

[0112] Step 303: Evaluate the ternary logging parameter prediction model based on tolerance and variance expansion factor.

[0113] Once the tolerance and variance inflation factor are obtained, the ternary logging parameter prediction model can be evaluated based on these factors. Specifically, this application considers ternary logging parameter prediction models that meet the tolerance greater than 0.1 and variance inflation factor less than 10 to be reliable and effective. This is because when the tolerance is greater than 0.1 and the variance inflation factor is less than 10, it means that there is no significant linear relationship between the three independent variables, and the ternary linear regression model is considered to have a certain degree of stability and interpretability.

[0114] In one embodiment of this application, the application can also perform analysis using SPSS saliency features.

[0115] The smaller the significance value, the more significant the effect of this parameter on the result. Therefore, in this ternary parameter, lg(RT / RT) baseline The effect on the result is greatest, Δt-Δt baseline It has the least impact on the outcome.

[0116] The above discloses an evaluation method for a ternary logging parameter prediction model. This application can also use the following methods to evaluate the ternary logging parameter prediction model.

[0117] In some embodiments of this application, such as Figure 4 As shown, Figure 4 A detailed flowchart of a method for predicting total organic carbon in carbonate source rock formations is shown in one embodiment; based on the aforementioned scheme, the method for evaluating the ternary logging parameter prediction model in this application further includes steps 401-403.

[0118] Step 401: Substitute the acoustic transit time logging data, apparent resistivity logging data, and natural gamma logging data of the test area into the ternary logging parameter prediction model to calculate the predicted value of total organic carbon.

[0119] After obtaining the ternary logging parameter prediction model, this application can substitute well data and natural gamma logging data into the ternary logging parameter prediction model to calculate the predicted value of total organic carbon in the test area.

[0120] Step 402: Analyze the predicted total organic carbon and the measured total organic carbon data using the regression-standardized residual plot to obtain the residual between the predicted and measured total organic carbon data, thus obtaining the first residual.

[0121] After obtaining the predicted total organic carbon (TOC) value for the test area, the regression-standardized residual plot can be used to analyze the predicted TOC value and the measured TOC data to obtain the residual between the predicted TOC value and the measured TOC data, thus obtaining the first residual.

[0122] This application can continue to be processed using SPSS software. Specifically, a regression standardized residual plot is a graphical representation used to analyze the residuals in a regression model. The residual is the difference between the actual observed value and the value predicted by the regression equation. The standardized residual is the residual divided by the standard deviation of the residuals, making the residuals of different observations comparable. The purpose of this graph is to check whether the assumptions of the regression model are reasonable, especially whether the residuals satisfy the assumptions of normality and homogeneity of variance.

[0123] Step 403: Evaluate the ternary logging parameter prediction model based on the first residual.

[0124] After obtaining the first residual, it can be determined that the standardized residuals of the ternary logging parameter linear regression model meet the normal distribution test and are relatively uniformly distributed, satisfying the assumption of homogeneity of variance. Therefore, the ternary logging parameter linear regression model can be considered reliable.

[0125] The above discloses an evaluation method for a ternary logging parameter prediction model. This application can also use the following methods to evaluate the ternary logging parameter prediction model.

[0126] In some embodiments of this application, based on the foregoing scheme, the method of this application further includes:

[0127] The total organic carbon in the test area is predicted based on the ΔLogR method, and the residual between the predicted value and the measured total organic carbon data is calculated to obtain the second residual.

[0128] In this application, the calculation of the second residual can also be performed using the regression-standardized residual plot in SPSS software.

[0129] When the first residual is less than the second residual, the ternary logging parameter prediction model is deemed valid.

[0130] Calculations show that the ternary logging parameter prediction model is effective when the first residual is less than the second residual.

[0131] The above sections have described the embodiments of this application. The following sections will continue to describe the application embodiments of this application.

[0132] This application provides a method for predicting the total organic carbon in carbonate source rock strata, including:

[0133] Core samples from different depths were obtained for lithological identification, measured TOC data, and corresponding depth logging curves (including GR, AC, and RT values).

[0134] Taking advantage of the low GR values ​​of carbonate rocks, GR parameter correction is introduced to establish lg(RT / RT) baseline ), Δt-Δt baselineA linear regression model of ternary logging parameters based on GR.

[0135] TOC ΔLOGR+GR =A×lg(RT / RT) baseline )+B×(Δt-Δt baseline )+C×GR

[0136] By using the consistency rate between standardized residual diagnostic prediction data and measured data, the goal is to achieve the effect of predicting organic carbon based on well logging curves throughout the region.

[0137] The reliability of the model in this application can be verified by comparing the data obtained by the ΔLogR method with the data obtained in this application. Specifically, the predictions corresponding to the ΔLogR method are as follows:

[0138] Based on the theoretical basis of ΔLogR, the corresponding data of core samples at different depths were calculated.

[0139] ΔLogR=Lg(RT / RT baseline )+0.006×(Δt-Δt baseline )

[0140] Calculations show that:

[0141] TOC ΔLogR =A×ΔLogR+B

[0142] Analyzing the relationship between ΔLogR and measured TOC data reveals that resistivity has a significant impact on ΔLogR. However, due to the characteristics of source rocks, such as high resistivity and large time lag, the presence of high-resistivity carbonate rock samples can lead to ΔLogR values ​​that are similar to those of source rocks, resulting in misclassification as source rocks using this method.

[0143] Therefore, the method in this application is more reliable than the method using ΔLogR.

[0144] The following describes further application examples of this application.

[0145] To achieve the evaluation of high-quality source rocks and thus the accurate evaluation of TOC using well logging data, this application provides an embodiment of a method for predicting total organic carbon in carbonate rock development sections. This method specifically includes the following:

[0146] Step 1: Collect and organize the measured TOC and logging curves (AC, RT, GR) data from the well site.

[0147] Based on the collection and collation of analytical and testing data within the region, a well (Well A) in the eastern uplift area of ​​the Liaohe Depression, with relatively complete analytical and testing data, well logging curves, and well logging data, was selected as an example. This well exhibits the characteristics of alternating carbonate and mudstone sedimentation in the target hydrocarbon source rock section. Detailed geological stratification and well-to-well stratigraphic correlation were conducted to provide a comprehensive analysis of the regional geological conditions.

[0148] In this embodiment of the application, the study area is structurally located on the eastern uplift zone of the eastern depression of the Liaohe Depression. This uplift zone is bordered to the north by the Sujiatun Fault, connecting to the Shenyang Uplift; to the south by the Teng'ao Fault, connecting to the Yingkou Uplift; to the west by the Tong'erbao Fault, connecting to the eastern depression; and to the east by the Liu'erbao Fault, connecting to the Liaodong Subsidence Zone. The area is 1600 km². 2 The strata developed in the study area from bottom to top include the Archean, Proterozoic, Cambrian and Ordovician strata of the Lower Paleozoic, Carboniferous and Permian strata of the Upper Paleozoic, Jurassic and Cretaceous strata of the Mesozoic, and Paleogene, Neogene and Quaternary strata of the Cenozoic. Coal-bearing strata are mainly concentrated in the Lower Permian Shanxi Formation and the Upper Carboniferous Taiyuan Formation. Carbonate rocks are developed at the base of the Benxi Formation, manifested as thick-bedded limestone, argillaceous limestone, and biogenic limestone. Regionally, the sedimentary evolution has undergone a process from marine to transitional marine-continental facies to terrestrial facies, with barrier coastal facies deposits being dominant.

[0149] In this embodiment, the main source rock development zones in the study area, from bottom to top, are the Benxi Formation, Taiyuan Formation, and Shanxi Formation. The source rocks are characterized by high gamma rays, high resistivity, large time lag, and low density. The carbonate rocks at the bottom of the target layer are characterized by low gamma rays, high resistivity, small time lag, and high density, which are very similar to sandstone, but the density is even greater, ranging from 2.75 to 3 g / cm³. 3 The resistivity is also much greater than that of sandstone, reaching a maximum of 10,000 Ω·m. Locally, it contains argillaceous limestone, with elevated gamma values ​​around 100-140 API. This results in a more similar appearance to source rocks on well logging curves, leading to a decrease in the accuracy of total organic carbon prediction methods in this layer, making it difficult to make a relatively accurate assessment of the oil and gas exploration potential of the area.

[0150] This step corresponds to step 101 above.

[0151] Step 2: Establish lg(RT / RT) baseline ), Δt-Δt baseline A linear regression model of ternary logging parameters based on GR.

[0152] In this embodiment, the low gamma value of carbonate rocks is utilized to correct the above ΔLogR formula, where lg(RT / RT) is used. baseline ), Δt-Δt baseline Based on this, add a GR parameter, and combine it with the relationship with the measured TOC to establish lg(RT / RT) baseline), tt baseline A linear regression model of ternary logging parameters based on GR.

[0153] TOC ΔLogR+GR =A×lg(RT / RT) baseline )+B×(Δt-Δt baseline )+C×GR

[0154] The TOC can be calculated directly using the data analysis software SPASS. ΔLogR+GR The ternary linear relationship with the above three well logging curves:

[0155] TOC LogR+GR =1.234+0.012GR-1.377lg(RT / RT baseline )-0.012(Δt-Δt baseline )

[0156] The above steps correspond to step 102 of this application.

[0157] After obtaining the ternary logging parameter prediction model, it can be evaluated. Specific methods include the following: [e.g., ...] Figure 5 As shown, Figure 5 A table of regression coefficients for ternary independent variables is shown in one embodiment. After performing multiple linear regression analysis in SPSS, standardized and unstandardized coefficients can be obtained by viewing the regression coefficient table. Standardized coefficients help us understand the relative influence between variables, while unstandardized coefficients help us understand the specific numerical relationship between variables, as they directly reflect the impact of unit changes in independent variables on the dependent variable. Generally, the larger the absolute value of the unstandardized coefficient, the greater the influence of the corresponding independent variable on the dependent variable.

[0158] Continue to refer to Figure 5 The significance analysis also shows that the smaller the significance value, the more significant the effect of this parameter on the result. Therefore, among the ternary parameters, lg(RT / RT) baseline The effect on the result is greatest, Δt-Δt baseline It has the least impact on the outcome.

[0159] Continue to refer to Figure 5 Collinearity analysis is a method used to detect and evaluate collinearity among variables in multiple regression analysis. Collinearity, or linear correlation between variables, is a significant problem in multiple regression analysis because it can severely affect the stability and interpretability of the model. In SPSS, collinearity analysis is assessed by calculating two key indicators: "tolerance" and "variance inflation factor (VIF)".

[0160] Tolerance is an indicator of the degree of collinearity among variables, and it should generally be greater than 0.1. A low tolerance indicates a strong collinearity problem among the variables. The VIF variance inflation factor should generally be less than 10. Only when both of these indicators are met can it be considered that there is no serious linear relationship between the three independent variables, and the ternary linear regression model is considered to have a certain degree of stability and explanatory power.

[0161] The stability and interpretability of the ternary linear regression model can be confirmed using the methods described above.

[0162] Further verification can be performed using the following methods.

[0163] Step 3: Analyze and calculate TOC ΔLogR+GR Similarity to the measured TOC.

[0164] In this embodiment of the application, it is necessary to verify the establishment of lg(RT / RT) baseline ), tt baseline TOC, a three-dimensional linear regression model of logging parameters with GR as the main component. ΔLogR+GR Based on the similarity to the measured TOC, SPASS software provides a regression-standardized residual plot.

[0165] like Figure 6 As shown, Figure 6 A regression standardized residual plot is shown in one embodiment. A regression standardized residual plot is a graphical representation used to analyze residuals in a regression model. Residuals are the differences between actual observed values ​​and values ​​predicted by the regression equation. Standardized residuals are the residuals divided by the standard deviation of the residuals, making the residuals of different observations comparable. The purpose of this plot is to check whether the assumptions of the regression model are reasonable, specifically whether the residuals satisfy the assumptions of normality and homogeneity of variance.

[0166] As shown in the figure, the standardized residuals of the ternary logging parameter linear regression model meet the normal distribution test and are relatively uniformly distributed, satisfying the assumption of homogeneity of variance. Therefore, the ternary logging parameter linear regression model can be considered reliable.

[0167] Applying the above ternary logging parameter linear regression model to the target well section allows for a more intuitive comparison of its relationship with measured TOC, such as... Figure 7 As shown, Figure 7 An interpretation diagram of the geochemical logging curve for well A in one embodiment is shown; TOC ΔLogR+GR Similarity to the measured TOC compared to the actual TOC ΔLogR The similarity to the measured TOC is significantly improved, especially in carbonate-bearing rock strata where the prediction accuracy is significantly improved.

[0168] In addition, the following methods can be used to evaluate the ternary logging parameter prediction model.

[0169] Based on the theoretical basis of ΔLogR, the corresponding data of core samples at different depths were calculated, and the linear relationship with the measured TOC was obtained.

[0170] The commonly used method for predicting total organic carbon (TOC) based on well logging curves is the ΔLogR method proposed by PASSEY, which constructs a ΔLogR by superimposing sonic transit time (AC) and resistivity curves (RT). The formula is as follows:

[0171] ΔLOGR=Lg(RT / RT baseline )+0.006×(Δt-Δt baseline )

[0172] However, this method has low accuracy in carbonate rock development sections. Due to the high resistivity of carbonate rocks, a high ΔLogR prediction can be mistakenly identified as a source rock. To correct this problem, various methods have been developed. For example, Zhou Chunrun constructed a parameter T value by superimposing the organic carbon content prediction results of ΔLogR and GR, and further classified the T value to establish a method for predicting organic carbon content based on the combination of ΔLogR and GR. However, this method is suitable for X-ray diffraction analysis and requires quantitative analysis of carbonate rock mineral composition. Wang Jingyu's invention patent proposes a multivariate logging parameter linear regression method, which introduces multiple logging parameters for linear regression. However, resistivity curves (RT) are usually displayed logarithmically, which has too much influence on the conclusions of the linear equation under carbonate rock conditions.

[0173] In this embodiment of the application, according to the ΔLogR formula, it is necessary to extract the resistivity RT and sonic transit time Δt values ​​of the logging curves of core samples at different depths, and determine the RT values ​​within the target layer of the study area. baseline and Δt baseline The former can be obtained from the wellbore curve, while the latter requires analysis of the entire well section of the target formation. RT baseline The resistivity of the formation is low when it contains no organic matter, and increases when it contains organic matter. The baseline value is 22 Ω·m, Δt baseline The baseline value is 74 s / m, indicating that the acoustic transit time is small when the formation does not contain organic matter, and increases when it contains organic matter.

[0174] The above formulas can be used to obtain the calculated ΔLogR data points for core samples at different depths. Using the measured TOC values ​​of these core samples, a univariate linear relationship can be derived, such as... Figure 8 As shown.

[0175] Obtaining ΔLogR and calculating TOC ΔLogR The univariate linear relationship is: TOC ΔLogR = -1.8575ΔLogR + 3.0379, with a relatively low correlation and R² of 0.30.

[0176] In the formula, ΔLogR is calculated from the resistivity RT and sonic transit time Δt of the logging curve, and can be applied to the target well section to obtain the calculated curve TOC. ΔLogR like Figure 9 As shown. By Figure 9 It can be seen that calculating TOC ΔLogR The agreement rate with the measured TOC is low.

[0177] By judgment Figure 7 and Figure 9 To make a comparison, Figure 7 The compliance rate is greater than Figure 9 If the result is positive, then the model is reliable.

[0178] In this application, to predict the total organic carbon (TOC) of carbonate-bearing source rocks, the following methods were first used: Measured TOC data, sonic transit time logging data, apparent resistivity logging data, and natural gamma ray logging data were acquired from the test area. Then, taking advantage of the low gamma ray values ​​of carbonate rocks, the ΔLogR method was modified using natural gamma ray logging data when constructing the ternary logging parameter prediction model. Based on the modified ΔLogR method, a ternary logging parameter prediction model was constructed using sonic transit time logging data, apparent resistivity logging data, natural gamma ray logging data, and measured TOC data. This ternary logging parameter prediction model was then used to predict the TOC of the area to be predicted. Compared to the ΔLogR method, which has only two parameters and no adjustment coefficients, the ternary logging parameter prediction model of this application introduces three parameters, with adjustment coefficients A, B, and C added before each parameter. Using this method to predict the TOC of carbonate-bearing source rocks yields more accurate results. Since the prediction model of this application utilizes natural gamma logging data, by incorporating natural gamma logging data as part of the prediction model, and taking into account the low gamma value of carbonate rocks, the prediction model of this application can accurately predict the total organic carbon content of carbonate source rock strata, which is beneficial to the overall evaluation of source rocks and is of great significance to promoting the exploration and development of regional oil and gas resources.

[0179] This application proposes a regional organic carbon content prediction model for carbonate-bearing source rock strata, which facilitates the precise evaluation of source rocks and is of great significance for promoting the exploration and development of regional oil and gas resources. It provides significant guidance for the exploration and development of oil and gas reservoirs in the eastern convex area of ​​the Liaohe Depression and also serves as a reference for exploration in areas with similar carbonate rock backgrounds.

[0180] This invention is simple to operate, has rigorous procedures, and yields reliable results. The development of an organic carbon content prediction model for regions with carbonate rock backgrounds facilitates the precise evaluation of source rocks and is of great significance for promoting the exploration and development of regional oil and gas resources. It has significant guiding significance for the exploration and development of oil and gas reservoirs in the eastern convex area of ​​the Liaohe Depression and also provides valuable reference for exploration in similar carbonate rock background areas.

[0181] The method of this application has been disclosed above; the apparatus of this application will be disclosed in detail below.

[0182] According to a second aspect of the embodiments of this application, a device for predicting the total organic carbon in carbonate source rock formations is provided. Figure 10 A block diagram of a total organic carbon prediction device for carbonate-bearing source rock formations, as described in an embodiment of this application, is shown. Figure 10 As shown in the embodiment of this application, the total organic carbon prediction device for carbonate source rock formations includes:

[0183] The acquisition module 1001 is used to acquire measured total organic carbon data, sonic transit time logging data, apparent resistivity logging data and natural gamma logging data of the test area.

[0184] The model building module 1002 is used to correct the ΔLogR method using natural gamma logging data. Based on the corrected ΔLogR method, a ternary logging parameter prediction model is constructed based on sonic transit time logging data, apparent resistivity logging data, natural gamma logging data, and measured total organic carbon data.

[0185] The prediction module 1003 is used to predict the total organic carbon in the area to be predicted based on the ternary logging parameter prediction model.

[0186] Based on the same inventive concept, this application also provides a device for predicting the total organic carbon of carbonate source rock formations, referencing... Figure 11 The diagram shows a schematic of the total organic carbon prediction device for carbonate source rock formations in an embodiment of this application. The total organic carbon prediction device for carbonate source rock formations includes one or more memories 1104, one or more processors 1102, and at least one computer program (computer program instruction) stored in the memory 1104 and executable on the processor 1102. When the processor 1102 executes the computer program, it implements the method described above.

[0187] Among them, Figure 5In this document, a bus architecture (represented by bus 1100) is used. Bus 1100 may include any number of interconnected buses and bridges, linking various circuits including one or more processors represented by processor 1102 and memory represented by memory 1104. Bus 1100 may also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 1105 provides an interface between bus 1100 and receiver 1101 and transmitter 1103. Receiver 1101 and transmitter 1103 may be the same element, i.e., a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 1102 is responsible for managing bus 1100 and general processing, while memory 1104 may be used to store data used by processor 1102 during operation.

[0188] Based on the same inventive concept, embodiments of this application provide a computer-readable storage medium storing computer program instructions, which, when executed by a processor, cause the processor to perform the steps of the method described above.

[0189] The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions may be stored as one or more instructions or codes on or transmitted via a computer-readable medium. Other examples and embodiments are within the scope and spirit of this application and the appended claims. For example, due to the nature of software, the functions described above may be implemented using software executed by a processor, hardware, firmware, hardwired, or any combination thereof. Furthermore, the functional units may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit.

[0190] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0191] The units described as separate components may or may not be physically separate. Similarly, the components of the control device may or may not be physical units; they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0192] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing computer program instructions, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0193] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method of predicting total organic carbon of a carbonate-containing source rock layer, characterized by, The method comprises the following steps: obtaining measured total organic carbon data, acoustic travel time logging data, apparent resistivity logging data and natural gamma logging data of a test area; correcting a ΔLogR method by using the natural gamma logging data, and constructing a ternary logging parameter prediction model based on the acoustic travel time logging data, the apparent resistivity logging data and the natural gamma logging data and the measured total organic carbon data based on the corrected ΔLogR method; predicting total organic carbon of a to-be-predicted area based on the ternary logging parameter prediction model.

2. The method of claim 1, wherein, The method of constructing the ternary logging parameter prediction model based on the acoustic travel time logging data, the apparent resistivity logging data and the natural gamma logging data based on the corrected ΔLogR method comprises the following steps: constructing a ternary logging model by using a ternary logging parameter formula; TOC ΔLogR+GR = A x lg(RT / RT baseline ) + B x (At - At baseline ) + C x GR; The measured total organic carbon data is taken as TOC ΔLogR+GR The resistivity value of the test area is taken as RT, the acoustic travel time value of the test area is taken as Δt, and the natural gamma value of the test area is taken as GR, which are substituted into the above three-parameter logging formula, and multiple linear regression analysis is performed to obtain the values of A, B and C, which are A1, B1 and C1, respectively. wherein RT baseline is the resistivity baseline value, Δt baseline is the acoustic traveltime baseline value; constructing a ternary logging parameter prediction model by using A1, B1 and C1, and the formula is as follows: TOC1 ΔLogR+GR = A1 x lg(RT1 / RT baseline )+ B1 x (At1 - At baseline )+ C1 x GR1; TOC1 ΔLogR+GR TOC1 RT1, Δt1, and GR1 are the total organic carbon data, the resistivity value, the acoustic traveltime value, and the natural gamma value of the region to be predicted, respectively.

3. The method of claim 2, wherein, The method of predicting total organic carbon of a to-be-predicted area based on the ternary logging parameter prediction model comprises the following steps: obtaining acoustic travel time logging data, apparent resistivity logging data and natural gamma logging data of the to-be-predicted area; obtaining a total organic carbon prediction value of the to-be-predicted area by substituting the acoustic travel time logging data, the current apparent resistivity logging data and the natural gamma logging data of the to-be-predicted area into the ternary logging parameter prediction model.

4. The method of claim 2, wherein, The method of performing multiple linear regression analysis and obtaining values of A, B and C as A1, B1 and C1 respectively comprises the following steps: obtaining a regression coefficient table after performing multiple linear regression analysis; According to the regression coefficient table, the non-standardized coefficient related to lg(RT / RT baseline ) in the ternary logging parameter formula is taken as A1; the non-standardized coefficient related to (Δt1-Δt baseline ) is taken as B1; and the non-standardized coefficient related to GR is taken as C1.

5. The method of any one of claim 2, wherein, The method further comprises the following steps: performing collinearity analysis on the ternary logging parameter prediction model; obtaining a tolerance and a variance inflation factor based on the collinearity analysis; evaluating the ternary logging parameter prediction model according to the tolerance and the variance inflation factor.

6. The method of claim 3, wherein, The method further comprises the following steps: obtaining a total organic carbon prediction value by substituting the acoustic travel time logging data, the apparent resistivity logging data and the natural gamma logging data of the test area into the ternary logging parameter prediction model; analyzing the total organic carbon prediction value and the measured total organic carbon data by using a regression standardized residual plot, obtaining a residual of the total organic carbon prediction value and the measured total organic carbon data, and obtaining a first residual; evaluating the ternary logging parameter prediction model according to the first residual.

7. The method of claim 6, wherein, The method further comprises the following steps: performing basic prediction on total organic carbon of the test area based on a ΔLogR method, and calculating a residual value of a basic prediction value and the measured total organic carbon data, to obtain a second residual; when the first residual is smaller than the second residual, determining that the ternary logging parameter prediction model is valid.

8. A device for predicting total organic carbon of a carbonate-containing source rock layer, characterized by, The method comprises the following steps: an obtaining module, configured to obtain measured total organic carbon data, acoustic travel time logging data, apparent resistivity logging data and natural gamma logging data of a test area; a model establishing module, configured to correct a ΔLogR method by using the natural gamma logging data, and construct a ternary logging parameter prediction model based on the acoustic travel time logging data, the apparent resistivity logging data and the natural gamma logging data and the measured total organic carbon data based on a corrected ΔLogR method; A prediction module is configured to predict total organic carbon of a region to be predicted based on the ternary well logging parameter prediction model.

9. A carbonates-containing hydrocarbon source rock formation total organic carbon prediction device comprising a processor and a memory, characterized in that, The memory stores computer program instructions capable of being executed by the processor, and the processor executes the computer program instructions to implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer program instructions, and the computer program instructions are executed by the processor to cause the processor to implement the steps of the method according to any one of claims 1 to 7.

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