Method and apparatus for calculating total organic carbon content, processor and storage medium

Abstract

The application provides a total organic carbon content calculation method, device, processor and machine readable storage medium, and relates to the technical field of shale oil and gas geological exploration. The modeling method comprises the following steps: obtaining total organic carbon content core detection parameters and a plurality of logging parameters of a target depth; determining a lamination index by using the logging parameters, and selecting an organic carbon content parameter from the plurality of logging parameters; wherein the organic carbon content parameter is a logging parameter that can best reflect the change of the organic matter content of the formation among the plurality of logging parameters; establishing an initial calculation model of the total organic carbon content by using the lamination index and the organic carbon content parameter; performing linear regression on the total organic carbon content core detection parameters of the target depth by using the lamination index and the organic carbon content parameter, and determining the regression constant in the initial calculation model; and determining the total organic carbon content calculation model based on the regression constant and the initial calculation model. Through the application, the high-resolution formation lithology information can be reflected, and the calculation accuracy and the calculation efficiency are improved.

Description

Technical Field

[0001] This invention relates to the field of shale oil and gas geological exploration technology, specifically to a method for calculating total organic carbon content, a device for calculating total organic carbon content, a processor, and a machine-readable storage medium. Background Technology

[0002] Shale oil and gas, as an unconventional oil and gas resource, boasts large reserves and wide distribution, attracting significant attention worldwide. Total organic carbon (TOC) content is a crucial indicator for evaluating the abundance of source rocks and is of great importance in shale oil and gas assessment. TOC content is not only a fundamental factor influencing shale oil and gas enrichment but also determines the amount of shale oil and gas generated, affecting its occurrence and enrichment, and thus influencing the size of shale oil and gas resources. Conducting high-precision calculations of TOC content in shale reservoirs has significant theoretical and practical value for sweet spot assessment and, consequently, for the scientific and accurate evaluation of shale oil and gas resources.

[0003] Existing methods for calculating and evaluating total organic carbon (TOC) content mainly include experimental methods and comprehensive evaluation based on seismic data. Experimental methods rely on a large amount of core data, resulting in high costs for core sampling and experimental analysis. Seismic data evaluation methods offer high planar coverage but low vertical resolution, and the evaluation results depend on the accuracy of single-well TOC calculations. Summary of the Invention

[0004] To address the technical problems of high cost, low vertical resolution, and low calculation accuracy in existing technologies for calculating total organic carbon (TOC), this invention provides a method for calculating TOC, a device for calculating TOC, a processor, and a machine-readable storage medium. This TOC calculation method can reflect high-resolution stratigraphic lithology information, accurately reflect stratigraphic information, and improve the accuracy of calculations.

[0005] The accuracy and efficiency of calculations.

[0006] To achieve the above objectives, the first aspect of the present invention provides a method for calculating total organic carbon (TOC) content. This method includes: acquiring core logging parameters and multiple well logging parameters for TOC content at a target depth; determining a lamination index using the well logging parameters, and selecting an organic carbon content parameter from the multiple well logging parameters; wherein the organic carbon content parameter is the well logging parameter that best reflects the change in formation organic matter content among the multiple well logging parameters; establishing an initial calculation model for TOC content using the lamination index and the organic carbon content parameter; performing linear regression on the core logging parameters for TOC content at the target depth using the lamination index and the organic carbon content parameter to determine a regression constant in the initial calculation model; and determining a TOC calculation model based on the regression constant and the initial calculation model.

[0007] Furthermore, the logging parameters include natural gamma logging parameters; determining the lamination index using the logging parameters includes: plotting a natural gamma curve using the natural gamma logging parameters; determining the number of peaks and troughs within a unit window length of the natural gamma curve; and determining the lamination index based on the number of peaks and troughs within a unit window length of the natural gamma curve.

[0008] Furthermore, the logging parameters include resistivity logging parameters; determining the striation index using the logging parameters includes: plotting a resistivity curve using the resistivity logging parameters; determining the number of peaks and troughs within a unit window length of the resistivity curve; and determining the striation index based on the number of peaks and troughs within a unit window length of the resistivity curve.

[0009] Furthermore, the initial calculation model for total organic carbon content was obtained in the following manner:

[0010]

[0011] Where TOC is the total organic carbon content, and LamIndex is the laminar indices. base Y is the baseline value of the laminar indices, and Y is the organic carbon content parameter. base is the baseline value for the organic carbon content parameter, and a, b, and c are regression constants.

[0012] Furthermore, the step of selecting the organic carbon content parameter from multiple logging parameters includes: performing regression analysis on the multiple logging parameters and the total organic carbon content core detection parameters at the target depth to determine the correlation coefficients between the multiple logging parameters and the total organic carbon content core detection parameters at the target depth; determining the maximum correlation coefficient among the correlation coefficients between the multiple logging parameters and the total organic carbon content core detection parameters at the target depth; and determining the logging parameter corresponding to the maximum correlation coefficient as the organic carbon content parameter.

[0013] Furthermore, the step of selecting the organic carbon content parameter from multiple logging parameters includes: obtaining the maximum and minimum values ​​of each logging parameter at the target depth; determining the sensitivity index of the logging parameter based on the maximum and minimum values ​​of the logging parameter at the target depth; determining the maximum sensitivity index from all sensitivity indices; and determining the logging parameter corresponding to the maximum sensitivity index as the organic carbon content parameter.

[0014] Furthermore, the sensitivity index of the logging parameters is obtained in the following way:

[0015]

[0016] Where S is the sensitivity index, C max C represents the maximum value of the logging parameters at the target depth.min This represents the minimum value of the logging parameters at the target depth.

[0017] A second aspect of the present invention provides a total organic carbon (TOC) content calculation device, comprising: an acquisition module for acquiring core detection parameters of TOC content at a target depth and multiple logging parameters; a parameter determination module for determining a lamination index using the logging parameters and selecting an organic carbon content parameter from the multiple logging parameters; wherein the organic carbon content parameter is the logging parameter that best reflects the change in formation organic matter content among the multiple logging parameters; an initial calculation model determination module for establishing an initial calculation model of TOC content using the lamination index and the organic carbon content parameter; a regression constant determination module for performing linear regression on the core detection parameters of TOC content at the target depth using the lamination index and the organic carbon content parameter to determine the regression constant in the initial calculation model; and a TOC content calculation model determination module for determining a TOC content calculation model based on the regression constant and the initial calculation model.

[0018] Furthermore, the logging parameters include natural gamma logging parameters; determining the lamination index using the logging parameters includes: plotting a natural gamma curve using the natural gamma logging parameters; determining the number of peaks and troughs within a unit window length of the natural gamma curve; and determining the lamination index based on the number of peaks and troughs within a unit window length of the natural gamma curve.

[0019] Furthermore, the logging parameters include resistivity logging parameters; determining the striation index using the logging parameters includes: plotting a resistivity curve using the resistivity logging parameters; determining the number of peaks and troughs within a unit window length of the resistivity curve; and determining the striation index based on the number of peaks and troughs within a unit window length of the resistivity curve.

[0020] Furthermore, the initial calculation model for total organic carbon content was obtained in the following manner:

[0021]

[0022] Where TOC is the total organic carbon content, and LamIndex is the laminar indices. base Y is the baseline value of the laminar indices, and Y is the organic carbon content parameter. base is the baseline value for the organic carbon content parameter, and a, b, and c are regression constants.

[0023] Furthermore, the step of selecting the organic carbon content parameter from multiple logging parameters includes: performing regression analysis on the multiple logging parameters and the total organic carbon content core detection parameters at the target depth to determine the correlation coefficients between the multiple logging parameters and the total organic carbon content core detection parameters at the target depth; determining the maximum correlation coefficient among the correlation coefficients between the multiple logging parameters and the total organic carbon content core detection parameters at the target depth; and determining the logging parameter corresponding to the maximum correlation coefficient as the organic carbon content parameter.

[0024] Furthermore, the step of selecting the organic carbon content parameter from multiple logging parameters includes: obtaining the maximum and minimum values ​​of each logging parameter at the target depth; determining the sensitivity index of the logging parameter based on the maximum and minimum values ​​of the logging parameter at the target depth; determining the maximum sensitivity index from all sensitivity indices; and determining the logging parameter corresponding to the maximum sensitivity index as the organic carbon content parameter.

[0025] Furthermore, the sensitivity index of the logging parameters is obtained in the following way:

[0026]

[0027] Where S is the sensitivity index, C max C represents the maximum value of the logging parameters at the target depth. min This represents the minimum value of the logging parameters at the target depth.

[0028] A third aspect of the present invention provides a processor configured to execute the total organic carbon content calculation method described above.

[0029] A fourth aspect of the present invention provides a machine-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform the total organic carbon content calculation method described above.

[0030] The present invention has at least the following technical effects through the technical solution provided by the present invention:

[0031] The total organic carbon (TOC) content calculation method of this invention first obtains core detection parameters and multiple well logging parameters for the TOC content at the target depth. The laminarity index is determined using the well logging parameters, and an organic carbon content parameter is selected from these parameters. This organic carbon content parameter is one that reflects changes in formation organic matter content. Then, an initial calculation model for TOC content is established using the laminarity index and the organic carbon content parameter. Linear regression is performed on the TOC content core detection parameters at the target depth using the laminarity index and the organic carbon content parameter to determine the regression constant in the initial calculation model. Based on the regression constant and the initial calculation model, a TOC content calculation model is determined. The TOC content calculation method provided by this invention can reflect high-resolution formation lithology information, accurately reflect formation information, and improve the accuracy and efficiency of the calculation.

[0032] Other features and advantages of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0033] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0034] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0035] Figure 1 A flowchart illustrating the method for calculating total organic carbon content provided in this embodiment of the invention;

[0036] Figure 2 This is a diagram showing the calculation results of the total organic carbon content of well C1 in the total organic carbon content calculation method provided in this embodiment of the invention.

[0037] Figure 3 This is a schematic diagram of a total organic carbon content calculation device provided in an embodiment of the present invention. Detailed Implementation

[0038] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0039] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0040] In this invention, unless otherwise stated, directional terms such as "upper," "lower," "top," and "bottom" are generally used to describe the relative positions of components in relation to the directions shown in the accompanying drawings or in relation to the vertical, perpendicular, or gravitational directions.

[0041] As described in the background section, existing semiconductor structures have poor performance. This will be explained in detail below with reference to the accompanying drawings.

[0042] Please refer to Figure 1 The first aspect of this invention provides a method for calculating total organic carbon content, comprising: S101: obtaining core detection parameters and multiple logging parameters for total organic carbon content at a target depth; S102: determining a lamination index using the logging parameters and selecting an organic carbon content parameter from the multiple logging parameters; wherein the organic carbon content parameter is the logging parameter that best reflects the change in formation organic matter content among the multiple logging parameters; S103: establishing an initial calculation model for total organic carbon content using the lamination index and the organic carbon content parameter; S104: performing linear regression on the core detection parameters for total organic carbon content at the target depth using the lamination index and the organic carbon content parameter to determine the regression constant in the initial calculation model; S105: determining a total organic carbon content calculation model based on the regression constant and the initial calculation model.

[0043] Specifically, in this embodiment of the invention, core detection parameters and multiple logging parameters for the total organic carbon content at the target depth are obtained. The logging parameter package includes natural gamma-ray logging parameters, electrical imaging logging parameters, natural gamma-ray spectroscopy logging parameters, and resistivity logging parameters. Natural gamma-ray logging parameters and electrical imaging logging parameters can reflect high-resolution formation lithology information, while natural gamma-ray spectroscopy logging parameters and resistivity logging parameters can reflect the organic matter content in the formation. Then, the lamination index is determined using the logging parameters. From the multiple logging parameters, the logging parameter that reflects the change in formation organic matter content is selected as the organic carbon content parameter. An initial calculation model for the total organic carbon content is established using the lamination index and the organic carbon content parameter. Then, using the lamination index and the organic carbon content parameter, a linear regression is performed on the core detection parameters for the total organic carbon content at the target depth to determine the regression constant in the initial calculation model. The regression constant is then substituted into the initial calculation model to obtain the final total organic carbon content calculation model.

[0044] The total organic carbon content calculation method provided by this invention can reflect high-resolution stratigraphic lithology information, accurately reflect stratigraphic information, and improve the accuracy and efficiency of calculation.

[0045] Furthermore, the logging parameters include natural gamma logging parameters; determining the lamination index using the logging parameters includes: plotting a natural gamma curve using the natural gamma logging parameters; determining the number of peaks and troughs within a unit window length of the natural gamma curve; and determining the lamination index based on the number of peaks and troughs within a unit window length of the natural gamma curve.

[0046] Specifically, in this embodiment of the invention, the lamination index can be determined using natural gamma logging parameters, a natural gamma curve can be plotted using natural gamma logging parameters, a unit window length can be set, a section of the curve with a unit window length can be intercepted on the natural gamma curve, and the number of peaks and troughs within the unit window length of the natural gamma logging parameters can be counted. The number of peaks and troughs is used as the lamination index.

[0047] Furthermore, the logging parameters include resistivity logging parameters; determining the striation index using the logging parameters includes: plotting a resistivity curve using the resistivity logging parameters; determining the number of peaks and troughs within a unit window length of the resistivity curve; and determining the striation index based on the number of peaks and troughs within a unit window length of the resistivity curve.

[0048] Specifically, in this embodiment of the invention, the lamination index can be determined using resistivity logging parameters. A resistivity curve is plotted using these parameters, a unit window length is set, and a segment of the curve within this unit window length is selected. The number of peaks and troughs within the unit window length is then counted, and this number is used as the lamination index. The lamination index can be used to characterize the degree of lamination development in shale.

[0049] Furthermore, the step of selecting the organic carbon content parameter from multiple logging parameters includes: performing regression analysis on the multiple logging parameters and the total organic carbon content core detection parameters at the target depth to determine the correlation coefficients between the multiple logging parameters and the total organic carbon content core detection parameters at the target depth; determining the maximum correlation coefficient among the correlation coefficients between the multiple logging parameters and the total organic carbon content core detection parameters at the target depth; and determining the logging parameter corresponding to the maximum correlation coefficient as the organic carbon content parameter.

[0050] Specifically, in this embodiment of the invention, the organic carbon content parameter can be determined using correlation coefficients. Regression analysis is performed on multiple logging parameters and the core detection parameters of total organic carbon content at the target depth to obtain the correlation coefficients between the multiple logging parameters and the core detection parameters of total organic carbon content at the target depth. The maximum correlation coefficient is determined from the multiple correlation coefficients, and the logging parameter corresponding to the maximum correlation coefficient is taken as the organic carbon content parameter.

[0051] The total organic carbon content calculation method provided by this invention can select the logging parameters with the highest correlation to actual core parameters as organic carbon content parameters, accurately reflect formation information, and improve the accuracy and efficiency of calculation.

[0052] Further, the step of selecting the organic carbon content parameter from multiple logging parameters includes: obtaining the maximum and minimum values ​​of each logging parameter at the target depth; determining the sensitivity index of the logging parameter based on the maximum and minimum values ​​at the target depth; determining the maximum sensitivity index from all sensitivity indices; and determining the logging parameter corresponding to the maximum sensitivity index as the selected parameter.

[0053] Organic carbon content parameter.

[0054] Furthermore, the sensitivity index of the logging parameters is obtained in the following way:

[0055]

[0056] Where S is the sensitivity index, C max C represents the maximum value of the logging parameters at the target depth. min This represents the minimum value of the logging parameters at the target depth.

[0057] Specifically, in this embodiment of the invention, the organic carbon content parameter can be determined using a sensitivity index. The maximum and minimum values ​​of each logging parameter at the target depth are determined, and the sensitivity index S of that logging parameter is determined based on these maximum and minimum values. The formula for calculating the sensitivity index S is as follows: S is the sensitivity index, C max C represents the maximum value of the logging parameters at the target depth. min This represents the minimum value of the logging parameter at the target depth. After determining the sensitivity indices of multiple logging parameters, the maximum sensitivity index is determined, and the logging parameter corresponding to the maximum sensitivity index S is identified as the organic carbon content parameter.

[0058] The total organic carbon content calculation method provided by this invention can select the logging parameters that are closest to the actual core parameters as the organic carbon content parameters, so as to truly reflect the formation information and improve the accuracy and efficiency of the calculation.

[0059] Furthermore, the initial calculation model for total organic carbon content was obtained in the following manner:

[0060]

[0061] Where TOC is the total organic carbon content, and LamIndex is the laminar indices. baseY is the baseline value of the laminar indices, and Y is the organic carbon content parameter. base is the baseline value for the organic carbon content parameter, and a, b, and c are regression constants.

[0062] Specifically, in this embodiment of the invention, after determining the laminar indices and organic carbon content parameters, an initial calculation model for the total organic carbon content is established using the laminar indices and organic carbon content parameters. In the initial calculation model, TOC represents the total organic carbon content, LamIndex represents the laminar indices, and Y represents the organic carbon content.

[0063] The parameters for organic carbon content, a, b, and c, are regression constants. LamIndex base Y represents the baseline value of the lariage index. base The baseline value for the organic carbon content parameter is a constant specified based on regional experience.

[0064] Next, using the laminarity index and organic carbon content parameters, linear regression was performed on the total organic carbon content core detection parameters at the target depth to determine the regression constants in the initial calculation model. Definition (YY base If ) = U, then the formula This can be equivalent to TOC = a*L + b*U + c. Substituting the laminarity index, organic carbon content parameters, and total organic carbon content core detection parameters at the same detection depth into each set yields multiple sets of data points (Li, Ui, TOCi), where i = 1, 2, ..., n. These multiple sets of data points are approximated to the linear model TOC = a*L + b*U + c by minimizing the error. The least squares method is used to determine the values ​​of the parameters a, b, and c that minimize the error. Substituting the values ​​of the regression constants a, b, and c into the initial calculation model determines the total organic carbon content calculation model.

[0065] In the computational model, the lamination index (LamIndex) and the organic carbon content parameter (Y) are calculated using logarithmic and simple linear methods, respectively. The lamination index (LamIndex) characterizes the degree of lamination development in shale. Under actual stratigraphic conditions, the number of laminae can rapidly vary from a few layers per meter to thousands per meter, depending on lithology and total organic carbon content. The degree of lamination development exhibits a near-exponential relationship with increasing total organic carbon content. Therefore, by taking the logarithm of the LamIndex, a large number can be transformed into a smaller value for calculating the total organic carbon content, improving the accuracy and efficiency of the calculation. The organic carbon content parameter (Y) has a relatively small range of variation; for example, its density value is generally between 2.5 and 2.9 g / cm³. 3 Since the neutron value is generally between -10 and 30%, and the uranium content is generally between 0 and 120 ppm, a simple linear form can be used in the calculation to ensure accuracy and computational efficiency.

[0066] Example 1

[0067] Taking the Paleogene strata C1 well in area G of the Qaidam Basin as an example, the total organic carbon content of well logging was calculated, and the invention was described in detail. The Paleogene strata in area G are a set of lacustrine shale strata, and the geological conditions are suitable for this invention.

[0068] Application of the invention.

[0069] First, electrical imaging logging parameters, natural gamma ray spectroscopy logging parameters, natural gamma ray logging parameters, and resistivity logging parameters were obtained using the MAXIS-500 logging series instruments. Then, the core samples from well C1 were tested and analyzed using the LECO CS-230 experimental instrument to obtain... Figure 2 The total organic carbon content shown is the core test parameter, i.e., the core TOC.

[0070] Resistivity curves are plotted using resistivity logging parameters. A unit window length is set, and a section of the resistivity curve with a unit window length is extracted. The number of peaks and troughs within the unit window length of the resistivity logging parameters is counted, and the number of peaks and troughs is used as the striation index.

[0071] Regression analysis was performed on multiple logging parameters and core samples from the target depth of well C1 to obtain correlation coefficients between these parameters. The maximum correlation coefficient was then determined, and the logging parameter corresponding to this coefficient was used as the organic carbon content parameter. Further analysis showed that the uranium content (Uran) obtained from natural gamma-ray spectroscopy logging effectively reflects changes in formation organic matter content; therefore, uranium content was chosen as the organic carbon content parameter Y for calculating the total organic carbon content.

[0072] An initial calculation model for total organic carbon content was established using the lamination index and organic carbon content parameters:

[0073]

[0074] The parameter LamIndex was specified based on the Paleogene stratigraphic geological conditions and regional experience in region G. base and Uran base The value of LamIndex is determined in this embodiment. base =20, Uran base =5.

[0075] Based on the obtained laminarity index and uranium content, linear regression was performed on the core detection parameters of total organic carbon content at the target depth to determine the values ​​of a, b, and c in the formula. In this embodiment, a = 0.3153, b = 0.0976, and c = -0.1715. Substituting the regression constants into the initial calculation model, the total organic carbon content calculation model was obtained.

[0076] By substituting the laminarity index and uranium content into the above total organic carbon content calculation model, a total organic carbon content curve calculated by well logging method can be obtained, thus realizing the calculation of total organic carbon content of a single well.

[0077] A second aspect of the present invention provides a total organic carbon (TOC) content calculation device, comprising: an acquisition module for acquiring core detection parameters of TOC content at a target depth and multiple logging parameters; a parameter determination module for determining a lamination index using the logging parameters and selecting an organic carbon content parameter from the multiple logging parameters; wherein the organic carbon content parameter is the logging parameter that best reflects the change in formation organic matter content among the multiple logging parameters; an initial calculation model determination module for establishing an initial calculation model of TOC content using the lamination index and the organic carbon content parameter; a regression constant determination module for performing linear regression on the core detection parameters of TOC content at the target depth using the lamination index and the organic carbon content parameter to determine the regression constant in the initial calculation model; and a TOC content calculation model determination module for determining a TOC content calculation model based on the regression constant and the initial calculation model.

[0078] Furthermore, the logging parameters include natural gamma logging parameters; determining the lamination index using the logging parameters includes: plotting a natural gamma curve using the natural gamma logging parameters; determining the number of peaks and troughs within a unit window length of the natural gamma curve; and determining the lamination index based on the number of peaks and troughs within a unit window length of the natural gamma curve.

[0079] Furthermore, the logging parameters include resistivity logging parameters; determining the striation index using the logging parameters includes: plotting a resistivity curve using the resistivity logging parameters; determining the number of peaks and troughs within a unit window length of the resistivity curve; and determining the striation index based on the number of peaks and troughs within a unit window length of the resistivity curve.

[0080] Furthermore, the initial calculation model for total organic carbon content was obtained in the following manner:

[0081]

[0082] Where TOC is the total organic carbon content, and LamIndex is the laminar indices. base Y is the baseline value of the laminar indices, and Y is the organic carbon content parameter. base is the baseline value for the organic carbon content parameter, and a, b, and c are regression constants.

[0083] Furthermore, the step of selecting the organic carbon content parameter from multiple logging parameters includes: performing regression analysis on the multiple logging parameters and the total organic carbon content core detection parameters at the target depth to determine the correlation coefficients between the multiple logging parameters and the total organic carbon content core detection parameters at the target depth; determining the maximum correlation coefficient among the correlation coefficients between the multiple logging parameters and the total organic carbon content core detection parameters at the target depth; and determining the logging parameter corresponding to the maximum correlation coefficient as the organic carbon content parameter.

[0084] Furthermore, the step of selecting the organic carbon content parameter from multiple logging parameters includes: obtaining the maximum and minimum values ​​of each logging parameter at the target depth; determining the sensitivity index of the logging parameter based on the maximum and minimum values ​​of the logging parameter at the target depth; determining the maximum sensitivity index from all sensitivity indices; and determining the logging parameter corresponding to the maximum sensitivity index as the organic carbon content parameter.

[0085] Furthermore, the sensitivity index of the logging parameters is obtained in the following way:

[0086]

[0087] Where S is the sensitivity index, C max C represents the maximum value of the logging parameters at the target depth. min This represents the minimum value of the logging parameters at the target depth.

[0088] A third aspect of the present invention provides a processor configured to execute the total organic carbon content calculation method described above.

[0089] A fourth aspect of the present invention provides a machine-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform the total organic carbon content calculation method described above.

[0090] The preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the specific details of the above embodiments. Within the scope of the technical concept of the present invention, various simple modifications can be made to the technical solution of the present invention, and these simple modifications all fall within the protection scope of the present invention.

[0091] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not describe the various possible combinations separately.

[0092] Furthermore, various different embodiments of the present invention can be combined in any way, as long as they do not violate the spirit of the present invention, they should also be regarded as the content disclosed by the present invention.

Claims

1. A method for calculating total organic carbon content, characterized in that, The calculation of the total organic carbon content includes: Obtain core testing parameters and multiple logging parameters for the total organic carbon content at the target depth; The laminar index is determined using the logging parameters, and an organic carbon content parameter is selected from multiple logging parameters; wherein, the organic carbon content parameter is the logging parameter that best reflects the change in formation organic matter content among the multiple logging parameters; An initial calculation model for total organic carbon content was established using the lamination index and organic carbon content parameters; Using the laminar index and the organic carbon content parameter, linear regression is performed on the total organic carbon content core detection parameters at the target depth to determine the regression constant in the initial calculation model; The total organic carbon content calculation model is determined based on the regression constant and the initial calculation model.

2. The method for calculating total organic carbon content according to claim 1, characterized in that, The logging parameters include natural gamma logging parameters; The determination of the laminarity index using the logging parameters includes: Natural gamma logging parameters were used to plot natural gamma curves; Determine the number of peaks and troughs within a unit window of the natural gamma curve; The laminar index is determined based on the number of peaks and troughs within a unit window length of the natural gamma curve.

3. The method for calculating total organic carbon content according to claim 1, characterized in that, The logging parameters include resistivity logging parameters; The determination of the laminarity index using the logging parameters includes: Resistivity curves were plotted using the aforementioned resistivity logging parameters; Determine the number of peaks and troughs within a unit window length of the resistivity curve; The laminar index is determined based on the number of peaks and troughs within a unit window length of the resistivity curve.

4. The method for calculating total organic carbon content according to claim 1, characterized in that, The initial calculation model for total organic carbon content was obtained in the following way: Where TOC is the total organic carbon content, and LamIndex is the laminar indices. base Y is the baseline value of the laminar indices, and Y is the organic carbon content parameter. base is the baseline value for the organic carbon content parameter, and a, b, and c are regression constants.

5. The method for calculating total organic carbon content according to claim 1, characterized in that, The selection of organic carbon content parameters from multiple logging parameters includes: Regression analysis was performed on multiple logging parameters and core detection parameters of total organic carbon content at the target depth to determine the correlation coefficients between the multiple logging parameters and the core detection parameters of total organic carbon content at the target depth. Determine the maximum correlation coefficient among multiple logging parameters and core detection parameters for total organic carbon content at the target depth; The logging parameter corresponding to the maximum correlation coefficient is determined as the organic carbon content parameter.

6. The method for calculating total organic carbon content according to claim 1, characterized in that, The selection of organic carbon content parameters from multiple logging parameters includes: Obtain the maximum and minimum values ​​of each logging parameter at the target depth; The sensitivity index of the logging parameter is determined based on the maximum and minimum values ​​of the logging parameter at the target depth. Determine the maximum sensitivity index from all sensitivity indices; The logging parameters corresponding to the maximum sensitivity index are determined as the organic carbon content parameters.

7. The method for calculating total organic carbon content according to claim 6, characterized in that, The sensitivity index of well logging parameters is obtained in the following way: Where S is the sensitivity index, C max C represents the maximum value of the logging parameters at the target depth. min This represents the minimum value of the logging parameters at the target depth.

8. A device for calculating total organic carbon content, characterized in that, The total organic carbon content calculation device includes: The acquisition module is used to acquire core testing parameters for total organic carbon content at the target depth and multiple logging parameters; The parameter determination module is used to determine the laminarity index using the logging parameters and select the organic carbon content parameter from multiple logging parameters; wherein, the organic carbon content parameter is the logging parameter that best reflects the change in formation organic matter content among multiple logging parameters; The initial calculation model determination module is used to establish an initial calculation model for total organic carbon content using laminarity index and organic carbon content parameters; The regression constant determination module is used to perform linear regression on the total organic carbon content core detection parameters at the target depth using the laminarity index and the organic carbon content parameters, so as to determine the regression constant in the initial calculation model. The total organic carbon content calculation model determination module is used to determine the total organic carbon content calculation model based on the regression constant and the initial calculation model.

9. The total organic carbon content calculation device according to claim 8, characterized in that, The logging parameters include natural gamma logging parameters; The determination of the laminarity index using the logging parameters includes: Natural gamma logging parameters were used to plot natural gamma curves; Determine the number of peaks and troughs within a unit window of the natural gamma curve; The laminar index is determined based on the number of peaks and troughs within a unit window length of the natural gamma curve.

10. The total organic carbon content calculation device according to claim 8, characterized in that, The logging parameters include resistivity logging parameters; The determination of the laminarity index using the logging parameters includes: Resistivity curves were plotted using the aforementioned resistivity logging parameters; Determine the number of peaks and troughs within a unit window length of the resistivity curve; The laminar index is determined based on the number of peaks and troughs within a unit window length of the resistivity curve.

11. The total organic carbon content calculation device according to claim 8, characterized in that, The initial calculation model for total organic carbon content was obtained in the following way: Where TOC is the total organic carbon content, and LamIndex is the laminar indices. base Y is the baseline value of the laminar indices, and Y is the organic carbon content parameter. base is the baseline value for the organic carbon content parameter, and a, b, and c are regression constants.

12. The total organic carbon content calculation device according to claim 8, characterized in that, The selection of organic carbon content parameters from multiple logging parameters includes: Regression analysis was performed on multiple logging parameters and core detection parameters of total organic carbon content at the target depth to determine the correlation coefficients between the multiple logging parameters and the core detection parameters of total organic carbon content at the target depth. Determine the maximum correlation coefficient among multiple logging parameters and core detection parameters for total organic carbon content at the target depth; The logging parameter corresponding to the maximum correlation coefficient is determined as the organic carbon content parameter.

13. The total organic carbon content calculation device according to claim 8, characterized in that, The selection of organic carbon content parameters from multiple logging parameters includes: Obtain the maximum and minimum values ​​of each logging parameter at the target depth; The sensitivity index of the logging parameter is determined based on the maximum and minimum values ​​of the logging parameter at the target depth. Determine the maximum sensitivity index from all sensitivity indices; The logging parameters corresponding to the maximum sensitivity index are determined as the organic carbon content parameters.

14. The total organic carbon content calculation device according to claim 13, characterized in that, The sensitivity index of well logging parameters is obtained in the following way: Where S is the sensitivity index, C max C represents the maximum value of the logging parameters at the target depth. min This represents the minimum value of the logging parameters at the target depth.

15. A processor, characterized in that, It is configured to perform the total organic carbon content calculation method according to any one of claims 1 to 7.

16. A machine-readable storage medium storing instructions thereon, characterized in that, When executed by a processor, this instruction causes the processor to be configured to perform the total organic carbon content calculation method according to any one of claims 1 to 7.

Images