A method and device for establishing a shale organic matter carbon content calculation model

Abstract

The application discloses a shale organic matter carbon content calculation model establishing method and device, and relates to the technical field of oil and gas exploitation.The shale without organic matter is simplified as a mixture of pores, clay and non-clay minerals, the relationship principle between acoustic travel time and porosity is established based on a modified resistivity-porosity-saturation model, the delta log R value of the logging test data group at different depths is calculated, and multiple data groups are divided into a training set and a test set, the target shale organic matter carbon content calculation model is established through the training set, and the test set is verified combined with a first preset condition, if the verification is consistent, the shale organic matter carbon content calculation model of the target shale horizon is determined, and finally, the TOC logging calculation of the target shale horizon is carried out according to the model.The shale organic matter carbon content calculation model established by the application is more suitable for the TOC calculation of the shale gas reservoir with high clay content by considering the conductivity of the clay and the bound water therein and the difference between the acoustic travel time of the clay minerals and other minerals.

Description

Technical Field

[0001] This invention relates to the field of oil and gas extraction technology, specifically to a method and apparatus for establishing a calculation model for the carbon content of organic matter in shale. Background Technology

[0002] Shale reservoirs have extremely low porosity and permeability, making multi-stage, multi-cluster fracturing in horizontal wells a key technology for shale gas development. Optimizing fracturing section length and perforation location by combining geological sweet spots with differentiated segmentation design can lay the foundation for effective shale gas development. Furthermore, the total organic carbon (TOC) content of shale is one of the most important parameters characterizing its hydrocarbon generation potential and gas-bearing capacity; therefore, obtaining TOC information is crucial for selecting shale gas reservoir sweet spots and optimizing perforation locations.

[0003] Related technologies often use the ΔlogR model to determine TOC. This model uses the Archie formula and the Wyllie time-averaging equation to establish the theoretical relationship between acoustic transit time and resistivity in the water saturation Sw=1 layer. When the logarithm of unit resistivity within the solid acoustic transit time range corresponds to an acoustic transit time of -50μs / ft, i.e., the superposition factor is 0.02, ΔlogR is calculated, and a TOC calculation model is established based on ΔlogR and the thermal variation index LOM. The TOC of the target block is then calculated based on this calculation model.

[0004] However, since the derivation of the ΔlogR model does not take into account the conductivity of clay and the bound water therein, nor the difference in acoustic transit time between clay minerals and other minerals, it is not suitable for TOC calculation of shale gas reservoirs with high clay content. Summary of the Invention

[0005] This invention provides a method and apparatus for establishing a calculation model for the organic carbon content of shale, addressing the problem that related technologies, due to the failure to consider the conductivity of clay and bound water in the derivation of the ΔlogR model, as well as the difference in acoustic transit time between clay minerals and other minerals, are unsuitable for TOC calculations in shale gas reservoirs with high clay content.

[0006] Firstly, a method for establishing a calculation model for the carbon content of organic matter in shale is provided, including:

[0007] Obtain well logging test data sets at N different depths in the target shale formation, wherein each well logging test data set includes compensated neutron value, density value, natural gamma value, sonic transit time value, formation true resistivity logging response value, clay content value, clay resistivity value and organic carbon content test value, and N is a positive integer greater than 10;

[0008] Based on the compensated neutron value, density value, and clay content value in each well logging test data group, a clay content well logging calculation model is established. The clay content well logging calculation model is a model used to reflect the mapping relationship between the shale clay content index and the clay content value.

[0009] Based on the sonic transit time, formation true resistivity logging response, clay content, and clay resistivity values ​​in each well logging test data set, the ΔlogR value for each well logging test data set is determined using a modified resistivity-porosity-saturation model and a ΔlogR calculation model. The modified resistivity-porosity-saturation model and the ΔlogR calculation model are models based on the theoretical relationship between sonic transit time and resistivity.

[0010] S sets of well logging test data are selected. Based on the compensated neutron value, density value, clay content value, ΔlogR value, natural gamma value, organic carbon content test value of each of the S sets of well logging test data, and the clay content well logging calculation model, a calculation model for the organic carbon content of the target shale is established, where S is a positive integer greater than 1 and less than N.

[0011] The organic carbon content calculation model of the target shale is tested and verified using each of the NS unselected well logging test data groups. If the test verification results all meet the first preset condition, the organic carbon content calculation model of the target shale is determined as the shale organic carbon content calculation model of the target shale layer.

[0012] Optionally, the step of establishing a clay content logging calculation model based on the compensated neutron value, density value, and clay content value in each of the logging test data sets includes:

[0013] The clay content index of each well logging test data group is determined based on the compensated neutron value and density value in each well logging test data group;

[0014] Based on the clay content index and clay content value of each well logging test data group, a clay content well logging calculation model is established using the least squares method.

[0015] Optionally, determining the clay content index of each well logging test data set based on the compensated neutron value and density value in each well logging test data set includes:

[0016] Based on the compensated neutron value and density value in each well logging test data set, the clay content index of each well logging test data set is determined using the following formula.

[0017]

[0018] In the formula, I cl Clay content index, v / v; CNL, compensated neutron value, %; DEN, density value, g / cm³. 3 ;DEN limestone The density of limestone is given in g / cm³. 3 The value is 2.71 g / cm³. 3 ;DEN f Pore ​​fluid density, g / cm³ 3 The value is 1g / cm 3 .

[0019] Optionally, the step of establishing a clay content logging calculation model based on the clay content index and clay content value of each logging test data group using the least squares method includes:

[0020] Based on the clay content index and clay content value of each well logging test data group, the clay content well logging calculation model is determined by fitting the model using the least squares method according to the following formula, and the mapping relationship between the fitted clay content index and the clay content value is determined as the clay content well logging calculation model.

[0021]

[0022] In the formula, V sh The clay content is expressed as v / v; (V sh ) log The calculated value for clay content is v / v; I cl is the clay content index, v / v; a1 and a2 are fitting parameters.

[0023] Optionally, determining the ΔlogR value for each well logging test data set based on the sonic transit time, formation true resistivity logging response, clay content, and clay resistivity value in each set of well logging test data, using a modified resistivity-porosity-saturation model and a ΔlogR calculation model, includes:

[0024] Based on the sonic transit time, formation true resistivity logging response, clay content, and clay resistivity values ​​in each set of well logging test data, and using the modified resistivity-porosity-saturation model and constraints described below, determine the corresponding sonic transit time and formation true resistivity logging response values ​​in the modified resistivity-porosity-saturation model for each set of well logging test data when the constraint value is minimized.

[0025]

[0026] In the formula, R o The true resistivity logging response value of the formation with no organic matter and a water saturation of 1, in ohm mm; Vsh R represents the clay content, v / v; sh R represents the resistivity of clay, in ohms (mm). w Δt is the formation water resistivity, in ohms / mm², with a value of 0.1 ohms / mm²; Δt is the acoustic transit time, in μs / ft; Δt f The transit time of sound waves in the pore fluid is Δt, in μs / ft, with a value of 189 μs / ft. sh The time difference of sound waves in clay, in μs / ft, is taken as 89.407 μs / ft; Δt m Δt represents the acoustic transit time of the matrix composed of minerals other than clay minerals in the shale, in μs / ft, with a value of 55 μs / ft; a is the lithology coefficient, with a value of 1; Δt i Rt represents any acoustic time difference in the modified resistivity-porosity-saturation model, in μs / ft. i The formation true resistivity logging response value, ohm mm, varies with any acoustic time difference value in the modified resistivity-porosity-saturation model.

[0027] The acoustic transit time value and the formation true resistivity logging response value corresponding to the minimum constraint condition value are determined as the target acoustic transit time value and the target formation true resistivity logging response value.

[0028] Based on the sonic transit time, formation true resistivity logging response, clay content, clay resistivity, target sonic transit time, and target formation true resistivity logging response in each well logging test data set, the ΔlogR value for each well logging test data set is determined according to the following ΔlogR calculation model.

[0029]

[0030]

[0031] In the formula, R t R represents the true resistivity logging response of the formation, in ohms; tb1 Δt is the true resistivity logging response value of the target formation, in ohms / mm; Δt is the sonic transit time value, in μs / ft; Δt b1 The target acoustic time difference is expressed in μs / ft; m is the cementation index, with a value of 2; V sh R represents the clay content, v / v; sh R represents the resistivity of clay, in ohms (mm). w The formation water resistivity is given in ohm / mm², with a value of 0.1 ohm / mm²; Δt f The transit time of sound waves in the pore fluid is Δt, in μs / ft, with a value of 189 μs / ft. sh The time difference of sound waves in clay, in μs / ft, is taken as 89.407 μs / ft; Δt mis the acoustic transit time value of the matrix composed of shale minerals other than clay minerals, in μs / ft, with a value of 55 μs / ft; a is the lithology coefficient, with a value of 1.

[0032] Optionally, the step of establishing a calculation model for the organic carbon content of the target shale based on the compensated neutron value, density value, clay content value, ΔlogR value, natural gamma value, and organic carbon content test value of each of the S sets of well logging test data, and the clay content well logging calculation model, includes:

[0033] Based on the compensated neutron value, density value, clay content value, ΔlogR value, natural gamma value, and organic carbon content test value of each of the S well logging test data sets, and the clay content well logging calculation model, a linear regression analysis is performed to fit the model, and the fitted model is determined as the target shale organic carbon content calculation model.

[0034]

[0035] In the formula, TOC is the measured value of organic carbon content, %; (TOC) log Calculated organic carbon content, %; ΔlogR is the ΔlogR value; GR is the natural gamma value, API; (V sh ) log is the calculated clay content, v / v; b1, b2, b3, b4, b5 are fitting parameters.

[0036] Optionally, the first preset condition means that each of the NS unselected well logging test data groups tests and verifies the calculation model of organic carbon content of the target shale, and the difference between the calculated value of organic carbon content and the tested value of organic carbon content obtained from the test and verification results is less than 0.5.

[0037] Optionally, after determining the target shale organic carbon content calculation model as the shale organic carbon content calculation model for the target shale stratum, the method further includes:

[0038] Obtain a set of well logging test prediction data for the target shale formation, including compensated neutron value, density value, natural gamma value, sonic transit time value, formation true resistivity logging response value, clay content value, and clay resistivity value.

[0039] The organic carbon content of the target shale formation is determined based on the calculation model of the organic carbon content of the target shale formation and the well logging test prediction data set.

[0040] Secondly, a device for establishing a calculation model for the carbon content of shale organic matter is provided, including:

[0041] The first acquisition module is used to acquire well logging test data sets at N different depths of the target shale layer. Each well logging test data set includes compensated neutron value, density value, natural gamma value, sonic transit time value, formation true resistivity logging response value, clay content value, clay resistivity value, and organic carbon content test value. N is a positive integer greater than 10.

[0042] The first modeling module is used to establish a clay content logging calculation model based on the compensated neutron value, density value and clay content value in each logging test data group. The clay content logging calculation model is a model used to reflect the mapping relationship between the shale clay content index and the clay content value.

[0043] The first determining module is used to determine the ΔlogR value of each well logging test data group based on the sonic transit time value, formation true resistivity logging response value, clay content value, and clay resistivity value in each well logging test data group, and based on a modified resistivity-porosity-saturation model and a ΔlogR calculation model. The modified resistivity-porosity-saturation model and the ΔlogR calculation model are models based on the theoretical relationship between sonic transit time and resistivity.

[0044] The second modeling module is used to select S sets of well logging test data, and establish a calculation model for the organic carbon content of the target shale based on the compensated neutron value, density value, clay content value, ΔlogR value, natural gamma value, organic carbon content test value of each of the S sets of well logging test data, as well as the clay content well logging calculation model, where S is a positive integer greater than 1 and less than N.

[0045] The verification module is used to test and verify the target shale organic carbon content calculation model using each of the NS unselected well logging test data groups. If the test and verification results all meet the first preset condition, the target shale organic carbon content calculation model is determined as the shale organic carbon content calculation model of the target shale formation.

[0046] Optionally, the first modeling module includes:

[0047] The first determining unit is used to determine the clay content index of each well logging test data group based on the compensated neutron value and density value in each well logging test data group;

[0048] The first modeling unit is used to establish a clay content logging calculation model based on the clay content index and clay content value of each logging test data group using the least squares method.

[0049] Optionally, the first determining unit specifically includes:

[0050] Based on the compensated neutron value and density value in each well logging test data set, the clay content index of each well logging test data set is determined using the following formula.

[0051]

[0052] In the formula, I cl Clay content index, v / v; CNL, compensated neutron value, %; DEN, density value, g / cm³. 3 ;DEN limestone The density of limestone is given in g / cm³. 3 The value is 2.71 g / cm³. 3 ;DEN f Pore ​​fluid density, g / cm³ 3 The value is 1g / cm 3 .

[0053] Optionally, the first modeling unit specifically includes:

[0054] Based on the clay content index and clay content value of each well logging test data group, the clay content well logging calculation model is determined by fitting the model using the least squares method according to the following formula, and the mapping relationship between the fitted clay content index and the clay content value is determined as the clay content well logging calculation model.

[0055]

[0056] In the formula, V sh The clay content is expressed as v / v; (V sh ) log The calculated value for clay content is v / v; I cl is the clay content index, v / v; a1 and a2 are fitting parameters.

[0057] Optionally, the first determining module includes:

[0058] The second determining unit is used to determine, based on the sonic transit time value, formation true resistivity logging response value, clay content value, and clay resistivity value in each of the well logging test data sets, and according to the modified resistivity-porosity-saturation model and constraint conditions described below, the corresponding sonic transit time value and formation true resistivity logging response value in the modified resistivity-porosity-saturation model for each of the well logging test data sets when the constraint condition value is minimized.

[0059]

[0060]

[0061] In the formula, R o The true resistivity logging response value of the formation with no organic matter and a water saturation of 1, in ohm mm; V sh R represents the clay content, v / v; sh R represents the resistivity of clay, in ohms (mm). w Δt is the formation water resistivity, in ohms / mm², with a value of 0.1 ohms / mm²; Δt is the acoustic transit time, in μs / ft; Δt f The transit time of sound waves in the pore fluid is Δt, in μs / ft, with a value of 189 μs / ft. sh The time difference of sound waves in clay, in μs / ft, is taken as 89.407 μs / ft; Δt m Δt represents the acoustic transit time of the matrix composed of minerals other than clay minerals in the shale, in μs / ft, with a value of 55 μs / ft; a is the lithology coefficient, with a value of 1; Δt i Rt represents any acoustic time difference in the modified resistivity-porosity-saturation model, in μs / ft. i The formation true resistivity logging response value, ohm mm, varies with any acoustic time difference value in the modified resistivity-porosity-saturation model.

[0062] The third determining unit is used to determine the acoustic transit time value and the formation true resistivity logging response value corresponding to the minimum constraint condition value as the target acoustic transit time value and the target formation true resistivity logging response value.

[0063] The fourth determining unit is used to determine the ΔlogR value for each well logging test data group based on the sonic transit time value, formation true resistivity logging response value, clay content value, clay resistivity value, target sonic transit time value, and target formation true resistivity logging response value in each well logging test data group, according to the following ΔlogR calculation model.

[0064]

[0065]

[0066] In the formula, R t R represents the true resistivity logging response of the formation, in ohms; tb1 Δt is the true resistivity logging response value of the target formation, in ohms / mm; Δt is the sonic transit time value, in μs / ft; Δt b1 The target acoustic time difference is expressed in μs / ft; m is the cementation index, with a value of 2; V sh R represents the clay content, v / v; sh R represents the resistivity of clay, in ohms (mm). w The formation water resistivity is given in ohm / mm², with a value of 0.1 ohm / mm²; Δt fThe transit time of sound waves in the pore fluid is Δt, in μs / ft, with a value of 189 μs / ft. sh The time difference of sound waves in clay, in μs / ft, is taken as 89.407 μs / ft; Δt m is the acoustic transit time value of the matrix composed of shale minerals other than clay minerals, in μs / ft, with a value of 55 μs / ft; a is the lithology coefficient, with a value of 1.

[0067] Optionally, the second modeling module specifically includes:

[0068] Based on the compensated neutron value, density value, clay content value, ΔlogR value, natural gamma value, and organic carbon content test value of each of the S well logging test data sets, and the clay content well logging calculation model, a linear regression analysis is performed to fit the model, and the fitted model is determined as the target shale organic carbon content calculation model.

[0069]

[0070] In the formula, TOC is the measured value of organic carbon content, %; (TOC) log Calculated organic carbon content, %; ΔlogR is the ΔlogR value; GR is the natural gamma value, API; (V sh ) log is the calculated clay content, v / v; b1, b2, b3, b4, b5 are fitting parameters.

[0071] Optionally, the first preset condition in the verification module refers to the fact that each of the NS unselected well logging test data groups tests and verifies the target shale organic carbon content calculation model, and the difference between the calculated organic carbon content and the tested organic carbon content is less than 0.5.

[0072] Optionally, the shale organic carbon content calculation model establishment device further includes:

[0073] The second acquisition module is used to acquire the well logging test prediction data set of the target shale formation. The well logging test prediction data set includes compensated neutron value, density value, natural gamma value, sonic transit time value, formation true resistivity well logging response value, clay content value, and clay resistivity value.

[0074] The second determining module is used to determine the organic carbon content of the target shale formation based on the shale organic carbon content calculation model and the well logging test prediction data set.

[0075] Thirdly, a device for establishing a calculation model for the carbon content of organic matter in shale is provided, the device comprising:

[0076] Processor and memory for storing processor-executable instructions;

[0077] The processor is configured to perform any of the methods described in the first aspect above.

[0078] The technical solutions provided in this application can bring at least the following beneficial effects:

[0079] In this embodiment of the invention, well logging test data sets at no less than 10 different depths of the target shale formation are obtained. Based on the compensated neutron value, density value, and clay content value in each well logging test data set, a clay content well logging calculation model is established to reflect the mapping relationship between the shale clay content index and the clay content value. Based on the sonic transit time value, formation true resistivity well logging response value, clay content value, and clay resistivity value in each well logging test data set, the ΔlogR value of each well logging test data set is determined based on the modified resistivity-porosity-saturation model and the ΔlogR calculation model. S well logging tests are selected. The data set uses the compensated neutron value, density value, clay content value, ΔlogR value, natural gamma value, and organic carbon content test value of each well logging test data set in the S well logging test data sets, as well as the clay content well logging calculation model, to establish a target shale organic carbon content calculation model. The target shale organic carbon content calculation model is then tested and verified using each of the unselected NS well logging test data sets. If the test and verification results all meet the first preset condition, then the target shale organic carbon content calculation model is determined as the shale organic carbon content calculation model for the target shale formation. In other words, the basic idea of ​​this invention is to simplify organic-free shale into a mixture of pores, clay, and non-clay minerals. Based on a modified resistivity-porosity-saturation model, a relationship between acoustic transit time and porosity is established. The ΔlogR value of logging test data sets at different depths is calculated, and multiple sets of logging test data are divided into training and testing sets. A model for calculating the organic carbon content of the target shale is established using the training set, and verified using the testing set in conjunction with a first preset condition. If the condition is met, the calculation model for the organic carbon content of the target shale layer is determined. Finally, TOC logging calculations are performed on the target shale layer based on this model. Because this invention considers the conductivity of clay and bound water, as well as the difference in acoustic transit time between clay minerals and other minerals, it improves the traditional theoretical relationship between acoustic transit time and resistivity. Therefore, the established shale organic carbon content calculation model is more suitable for TOC calculations in shale gas reservoirs with high clay content. Attached Figure Description

[0080] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0081] Figure 1 This is a flowchart illustrating a method for establishing a calculation model for the carbon content of organic matter in shale, as provided in an embodiment of the present invention.

[0082] Figure 2 This is a flowchart illustrating another method for establishing a calculation model for the organic carbon content of shale provided in an embodiment of the present invention;

[0083] Figure 3 This is a schematic diagram of a device for establishing a calculation model for the carbon content of shale organic matter provided in an embodiment of the present invention;

[0084] Figure 4 This is a schematic diagram of the structure of a terminal 400 provided in an embodiment of the present invention. Detailed Implementation

[0085] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0086] Before providing a detailed explanation of the embodiments of the present invention, the terms, application scenarios, and system architectures involved in the embodiments of the present invention will be explained.

[0087] First, the terms used in the embodiments of this invention will be introduced.

[0088] Compensated neutron value

[0089] Compensated neutron values ​​are logging values ​​obtained using the compensated neutron logging method. Compensated neutron logging is an isotope source neutron logging method that utilizes two detectors to eliminate the influence of the wellbore on the measurement results. Compensated neutron logging uses an isotope neutron source (18Ci americium-beryllium neutron source) to emit fast neutrons into the formation from the wellbore. Two thermal neutron detectors with different source spacings (long and short) then measure the thermal neutrons that have been slowed down by the formation and scattered back into the wellbore, obtaining two count rates. The ratio of the count rate of the detector with the short source spacing (26 cm) to that with the long source spacing (38 cm) mainly reflects the formation's ability to slow down fast neutrons, indicating changes in the formation's hydrogen content.

[0090] density value

[0091] Density is the mass per unit volume of shale at a given shale layer.

[0092] Natural Gamma Value

[0093] The natural gamma value is a logging value obtained using the natural gamma logging method. Natural gamma logging records the count rate or standardized reading caused by all gamma photons with energies greater than 100 keV.

[0094] sound wave time difference

[0095] Acoustic transit time (ACT) values ​​are logging values ​​obtained using the acoustic transit time logging method. Acoustic transit time logging involves placing a controlled acoustic source into the well. The sound waves emitted by the source cause vibrations in surrounding particles, generating volume waves (P-waves and S-waves) in the formation and induced interface waves (pseudo-Rayleigh waves and Stoneley waves) at the wellbore-drilling fluid interface. These waves, acting as carriers of formation information, are received by a downhole receiver and recorded at the surface; this is acoustic logging. Acoustic logging is divided into acoustic velocity logging and acoustic amplitude logging. The acoustic transit time values ​​in this invention are derived from acoustic velocity logging.

[0096] Formation true resistivity logging response value

[0097] The formation true resistivity logging response value is obtained by using dual lateral logging data and correcting for three influencing factors: wellbore, surrounding rock-layer thickness, and invasion.

[0098] clay content value

[0099] The clay content value is the content of clay minerals in the strata. Clay minerals include kaolinite, montmorillonite, chlorite, illite, etc. The clay content value can be obtained by X-ray diffraction whole-rock mineral analysis.

[0100] clay resistivity value

[0101] The resistivity of clay refers to the degree to which clay impedes electric current. The resistivity of clay is related to its composition, water content, temperature, and other factors. Different types of clay have different compositions, and therefore different resistivities.

[0102] Organic carbon content test value

[0103] The organic carbon content test value refers to the mass of organic carbon in a unit mass of rock, usually expressed as a mass fraction (%), and can be measured by combustion oxidation-nondispersive infrared absorption method.

[0104] Secondly, the application scenarios involved in the embodiments of the present invention will be introduced.

[0105] Before fracturing and perforating shale blocks with high clay content, it is necessary to determine the total organic carbon (TOC) content of shale in different strata. Then, based on the TOC data, the fracturing section length and perforation location are optimized, and differentiated segmentation design is carried out. Since the TOC values ​​obtained by traditional core laboratory testing are limited by the core sampling situation during drilling, the obtained TOC values ​​are scattered, and the core laboratory testing is costly and time-consuming. Therefore, the shale organic carbon content calculation model establishment method and device provided in the embodiments of the present invention can quickly and accurately establish a shale organic carbon content calculation model applicable to shale with high clay content. Based on this shale organic carbon content calculation model, TOC calculations can be performed on all strata of the shale block, providing a basis for optimizing the fracturing section length and perforation location.

[0106] Finally, the system architecture involved in the embodiments of the present invention will be described.

[0107] The method for establishing a shale organic carbon content calculation model provided in this embodiment of the invention can be applied to a terminal with data processing capabilities. Specifically, the terminal can be a smartphone, tablet computer, laptop computer, desktop computer, or other terminal capable of data processing.

[0108] Figure 1 This is a flowchart illustrating a method for establishing a calculation model for the organic carbon content of shale provided in an embodiment of the present invention. See also... Figure 1 The method includes the following steps:

[0109] Step 101: Obtain well logging test data sets at N different depths in the target shale formation. Each well logging test data set includes compensated neutron value, density value, natural gamma value, sonic transit time value, formation true resistivity logging response value, clay content value, clay resistivity value, and organic carbon content test value. N is a positive integer greater than 10.

[0110] Step 102: Based on the compensated neutron value, density value, and clay content value in each well logging test data set, establish a clay content well logging calculation model. The clay content well logging calculation model is used to reflect the mapping relationship between the shale clay content index and the clay content value.

[0111] Step 103: Based on the sonic transit time, formation true resistivity logging response, clay content, and clay resistivity values ​​in each well logging test data set, determine the ΔlogR value for each well logging test data set based on the modified resistivity-porosity-saturation model and the ΔlogR calculation model. The modified resistivity-porosity-saturation model and the ΔlogR calculation model are models based on the theoretical relationship between sonic transit time and resistivity.

[0112] Step 104: Select S sets of well logging test data. Based on the compensated neutron value, density value, clay content value, ΔlogR value, natural gamma value, organic carbon content test value of each set of well logging test data in the S sets of well logging test data, and the clay content well logging calculation model, establish a calculation model for the organic carbon content of the target shale. S is a positive integer greater than 1 and less than N.

[0113] Step 105: Use each of the unselected NS well logging test data sets to test and verify the target shale organic carbon content calculation model. If the test and verification results all meet the first preset condition, then the target shale organic carbon content calculation model is determined as the shale organic carbon content calculation model for the target shale layer.

[0114] In this embodiment of the invention, organic-free shale is simplified into a mixture of pores, clay, and non-clay minerals. Based on a modified resistivity-porosity-saturation model, the relationship between acoustic transit time and porosity is established. The ΔlogR value of logging test data sets at different depths is calculated. Multiple sets of logging test data sets are divided into training and testing sets. A model for calculating the organic carbon content of the target shale is established using the training set and verified using the testing set in conjunction with a first preset condition. If the condition is met, the calculation model for the organic carbon content of the target shale layer is determined. Finally, TOC logging calculations are performed on the target shale layer based on this model. Because this invention considers the conductivity of clay and bound water, as well as the difference in acoustic transit time between clay minerals and other minerals, it improves the traditional theoretical relationship between acoustic transit time and resistivity. Therefore, the established shale organic carbon content calculation model is more suitable for TOC calculations in shale gas reservoirs with high clay content.

[0115] Optionally, based on the compensated neutron value, density value, and clay content value in each well logging test data set, a well logging calculation model for clay content is established, including:

[0116] The clay content index for each well logging test data set is determined based on the compensated neutron value and density value in each well logging test data set.

[0117] Based on the clay content index and clay content value of each well logging test data group, a clay content well logging calculation model is established using the least squares method.

[0118] Optionally, based on the compensated neutron value and density value in each well logging test data set, the clay content index of each well logging test data set is determined, including:

[0119] Based on the compensated neutron value and density value in each well logging test data set, the clay content index for each well logging test data set is determined using the following formula.

[0120]

[0121] In the formula, I cl Clay content index, v / v; CNL, compensated neutron value, %; DEN, density value, g / cm³. 3 ;DEN limestone The density of limestone is given in g / cm³. 3 The value is 2.71 g / cm³. 3 ;DEN f Pore ​​fluid density, g / cm³ 3 The value is 1g / cm 3 .

[0122] Optionally, based on the clay content index and clay content value of each well logging test data set, a clay content well logging calculation model is established using the least squares method, including:

[0123] Based on the clay content index and clay content value of each well logging test data group, the clay content well logging calculation model is determined by fitting the model using the least squares method based on the following formula, and the mapping relationship between the fitted clay content index and clay content value is determined.

[0124]

[0125] In the formula, V sh The clay content is expressed as v / v; (V sh ) log The calculated value for clay content is v / v; I cl is the clay content index, v / v; a1 and a2 are fitting parameters.

[0126] Optionally, based on the sonic transit time, formation true resistivity logging response, clay content, and clay resistivity values ​​in each well logging test data set, and using a modified resistivity-porosity-saturation model and a ΔlogR calculation model, the ΔlogR value for each well logging test data set is determined, including:

[0127] Based on the sonic transit time, formation true resistivity logging response, clay content, and clay resistivity values ​​in each well logging test data set, and using the following modified resistivity-porosity-saturation model and constraints, determine the corresponding sonic transit time and formation true resistivity logging response values ​​in the modified resistivity-porosity-saturation model for each well logging test data set when the constraint value is minimized.

[0128]

[0129]

[0130] In the formula, R oThe true resistivity logging response value of the formation with no organic matter and a water saturation of 1, in ohm mm; V sh R represents the clay content, v / v; sh R represents the resistivity of clay, in ohms (mm). w Δt is the formation water resistivity, in ohms / mm², with a value of 0.1 ohms / mm²; Δt is the acoustic transit time, in μs / ft; Δt f The transit time of sound waves in the pore fluid is Δt, in μs / ft, with a value of 189 μs / ft. sh The time difference of sound waves in clay, in μs / ft, is taken as 89.407 μs / ft; Δt m Δt represents the acoustic transit time of the matrix composed of minerals other than clay minerals in the shale, in μs / ft, with a value of 55 μs / ft; a is the lithology coefficient, with a value of 1; Δt i Rt represents the acoustic time difference (μs / ft) in the corrected resistivity-porosity-saturation model. i The formation true resistivity logging response value, ohmm, varies with any acoustic time difference in the corrected resistivity-porosity-saturation model.

[0131] The acoustic transit time value and the formation true resistivity logging response value corresponding to the minimum constraint condition value are determined as the target acoustic transit time value and the target formation true resistivity logging response value.

[0132] Based on the sonic transit time, formation true resistivity logging response, clay content, clay resistivity, target sonic transit time, and target formation true resistivity logging response in each well logging test data set, the ΔlogR value for each well logging test data set is determined using the following ΔlogR calculation model.

[0133]

[0134]

[0135] In the formula, R t R represents the true resistivity logging response of the formation, in ohms; tb1 Δt is the true resistivity logging response value of the target formation, in ohms / mm; Δt is the sonic transit time value, in μs / ft; Δt b1 The target acoustic time difference is expressed in μs / ft; m is the cementation index, with a value of 2; V sh R represents the clay content, v / v; sh R represents the resistivity of clay, in ohms (mm). w The formation water resistivity is given in ohm / mm², with a value of 0.1 ohm / mm²; Δt f The transit time of sound waves in the pore fluid is Δt, in μs / ft, with a value of 189 μs / ft. shThe time difference of sound waves in clay, in μs / ft, is taken as 89.407 μs / ft; Δt m is the acoustic transit time value of the matrix composed of shale minerals other than clay minerals, in μs / ft, with a value of 55 μs / ft; a is the lithology coefficient, with a value of 1.

[0136] Optionally, based on the compensated neutron value, density value, clay content value, ΔlogR value, natural gamma value, and organic carbon content test value of each of the S well logging test data sets, and the clay content well logging calculation model, a calculation model for the organic carbon content of the target shale is established, including:

[0137] Based on the compensated neutron value, density value, clay content value, ΔlogR value, natural gamma value, and organic carbon content test values ​​of each of the S well logging test data sets, and the clay content well logging calculation model, linear regression analysis was performed to fit the following calculation model. The fitted model was then determined as the target shale organic carbon content calculation model.

[0138]

[0139] In the formula, TOC is the measured value of organic carbon content, %; (TOC) log Calculated organic carbon content, %; ΔlogR is the ΔlogR value; GR is the natural gamma value, API; (V sh ) log is the calculated clay content, v / v; b1, b2, b3, b4, b5 are fitting parameters.

[0140] Optionally, the first preset condition refers to the fact that each of the NS unselected well logging test data groups tests and verifies the calculation model of organic carbon content in the target shale, and the difference between the calculated value of organic carbon content and the tested value of organic carbon content obtained from the test and verification results is less than 0.5.

[0141] Optionally, after determining the target shale organic carbon content calculation model as the shale organic carbon content calculation model for the target shale strata, it also includes:

[0142] Obtain the well logging test prediction data set of the target shale formation. The well logging test prediction data set includes compensated neutron value, density value, natural gamma value, sonic transit time value, formation true resistivity well logging response value, clay content value, and clay resistivity value.

[0143] Based on the calculation model of organic carbon content in the target shale formation and the prediction data set of well logging tests, the organic carbon content of the target shale formation is determined.

[0144] All the above-mentioned optional technical solutions can be combined in any way to form optional embodiments of the present invention, and the embodiments of the present invention will not be described in detail one by one.

[0145] Figure 2 This is a schematic flowchart illustrating another method for establishing a calculation model for the organic carbon content of shale provided in an embodiment of the present invention. See also... Figure 2 The method includes the following steps:

[0146] Step 201: Obtain well logging test data sets at N different depths in the target shale formation. Each well logging test data set includes compensated neutron value, density value, natural gamma value, sonic transit time value, formation true resistivity logging response value, clay content value, clay resistivity value, and organic carbon content test value. N is a positive integer greater than 10.

[0147] Among them, the compensated neutron value, density value, natural gamma value, sonic transit time value, and formation true resistivity logging response value are all test values ​​obtained after logging the target shale formation. For example, the compensated neutron value is the compensated neutron value of the target shale formation obtained after logging using the compensated neutron logging method; the density value and natural gamma value are the density value and natural gamma value of the target shale formation obtained after logging using the natural gamma logging method; the sonic transit time value is the sonic transit time value of the target shale formation obtained after logging using the sonic transit time logging method; and the formation true resistivity logging response value and clay resistivity value are the formation true resistivity logging response values ​​of the target shale formation obtained after logging using the resistivity logging method. In practical applications, the above values ​​can be obtained by the logging instrument directly sending the test results to the device, or they can be obtained by user input. This embodiment of the invention does not specifically limit this.

[0148] The clay content and organic carbon content values ​​are obtained through laboratory testing. For example, the clay content can be determined by whole-rock X-ray diffraction experiments, and the organic carbon content (TOC) can be determined by combustion oxidation-non-dispersive infrared absorption method. In practical applications, these values ​​can be obtained by the experimental equipment sending the results to the device or by user input; this embodiment of the invention does not impose specific limitations on this.

[0149] It should also be noted that each well logging test data set contains depth information. This depth information can be the actual depth of the shale formation or continuous depth information calibrated by serial numbers. The depth information in well logging test data sets at N different depths will be different. For example, when continuous depth information is calibrated by serial numbers, the different depth information in well logging test data sets at 10 different depths can be represented as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10. Furthermore, to improve the accuracy of the shale organic carbon content calculation model, the number of well logging test data sets should be no less than 10, and the more sets, the higher the model accuracy. This embodiment of the invention does not limit the upper limit of the number of well logging test data sets.

[0150] Step 202: Based on the compensated neutron value, density value, and clay content value in each well logging test data set, establish a clay content well logging calculation model. The clay content well logging calculation model is used to reflect the mapping relationship between the shale clay content index and the clay content value.

[0151] It should be noted that the embodiments of this invention are for establishing a TOC calculation model applicable to shale reservoirs with high clay content. Therefore, when establishing the TOC technical model, the first step is to establish a mapping relationship between the measured clay content and the calculated clay content of shale strata at each depth. In establishing this mapping relationship, the model can be directly fitted using the clay content logging calculation formula, or the clay content index of each logging test data set can be calculated first, followed by the clay content index and clay content value for each logging test data set, and then fitted using the clay content logging calculation formula to establish the clay content logging calculation model.

[0152] When first calculating the clay content index for each well logging test data set, and then fitting the clay content index and clay content value of each well logging test data set to establish a clay content well logging calculation model based on the clay content well logging calculation formula, the specific steps are as follows:

[0153] Step 2021: Determine the clay content index for each well logging test data set based on the compensated neutron value and density value in each well logging test data set.

[0154] It should be noted that neutron logging instruments typically use limestone porosity as the standard calibration. The porosity in this embodiment of the invention refers to the porosity assuming calcite as the rock matrix. The apparent neutron porosity of clay minerals is usually greater than the apparent density porosity. Therefore, the clay content index can be calculated using the density value and compensated neutron value from each logging test data set, as shown in the following formula:

[0155]

[0156] In the formula, I clClay content index, v / v; CNL, compensated neutron value, %; DEN, density value, g / cm³. 3 ;DEN limestone The density of limestone is given in g / cm³. 3 The value is 2.71 g / cm³. 3 ;DEN f Pore ​​fluid density, g / cm³ 3 The value is 1g / cm 3 .

[0157] Step 2022: Based on the clay content index and clay content value of each well logging test data group, establish a clay content well logging calculation model using the least squares method.

[0158] Based on the clay content index and clay content value of each well logging test data group, the clay content well logging calculation model is determined by fitting the model using the least squares method based on the following formula, and the mapping relationship between the fitted clay content index and clay content value is determined.

[0159]

[0160] In the formula, V sh The clay content is expressed as v / v; (V sh ) log The calculated value for clay content is v / v; I cl is the clay content index, v / v; a1 and a2 are fitting parameters.

[0161] In other words, after determining the clay content index in each well logging test data set, the clay content value and the calculated clay content index in each well logging test data set are substituted into the above formula. The least squares method is then used for fitting to obtain the optimal a1 and a2 fitting parameter values. Substituting the a1 and a2 fitting parameter values ​​into the above formula yields the clay content well logging calculation model. In the actual fitting process, the lsqcurvefit function in MATLAB can be used for fitting.

[0162] Step 203: Based on the sonic transit time, formation true resistivity logging response, clay content, and clay resistivity values ​​in each well logging test data set, determine the ΔlogR value for each well logging test data set based on the modified resistivity-porosity-saturation model and the ΔlogR calculation model. The modified resistivity-porosity-saturation model and the ΔlogR calculation model are models based on the theoretical relationship between sonic transit time and resistivity.

[0163] Specifically, the ΔlogR value for each well logging test data set can be determined through the following steps 2031-2033.

[0164] Step 2031: Based on the sonic transit time, formation true resistivity logging response, clay content, and clay resistivity values ​​in each well logging test data set, and using the following modified resistivity-porosity-saturation model and constraints, determine the corresponding sonic transit time and formation true resistivity logging response values ​​in the modified resistivity-porosity-saturation model for each well logging test data set when the constraint value is minimized.

[0165]

[0166]

[0167] In the formula, R o The true resistivity logging response value of the formation with no organic matter and a water saturation of 1, in ohm mm; V sh R represents the clay content, v / v; sh R represents the resistivity of clay, in ohms (mm). w Δt is the formation water resistivity, in ohms / mm², with a value of 0.1 ohms / mm²; Δt is the acoustic transit time, in μs / ft; Δt f The transit time of sound waves in the pore fluid is Δt, in μs / ft, with a value of 189 μs / ft. sh The time difference of sound waves in clay, in μs / ft, is taken as 89.407 μs / ft; Δt m Δt represents the acoustic transit time of the matrix composed of minerals other than clay minerals in the shale, in μs / ft, with a value of 55 μs / ft; a is the lithology coefficient, with a value of 1; Δt i Rt represents the acoustic time difference (μs / ft) in the corrected resistivity-porosity-saturation model. i The formation true resistivity logging response value, ohmm, varies with any acoustic time difference in the corrected resistivity-porosity-saturation model.

[0168] It should be noted that the principle behind the above steps is as follows:

[0169] The relationship between formation true resistivity and porosity is defined using a modified resistivity-porosity-saturation model.

[0170]

[0171] In the formula, R t The logging response value is the true resistivity of the formation, in ohms (mm); V sh Clay content, v / v; S w R represents water saturation, v / v; sh φ is the clay resistivity (ohm / mm²); φ is the porosity (v / v); m is the cementation index (value 2); n is the saturation index; a is the lithology coefficient (value 1); R wThe resistivity of the formation water is 0.1 ohm / mm.

[0172] For water saturation S w For the segment with a value of 1, simplifying and transforming the expression, we get:

[0173]

[0174] In the formula, Ro is the resistivity of a shale formation that contains no organic matter and has a water saturation of 1, in ohm mm.

[0175] Taking the logarithm of both sides of the expression, we get the expression

[0176] log 10 R o =-log 10 [aV sh R w (1-V sh )+φ m R sh ]+log 10 [aR sh R w (1-V sh )]

[0177] Shale reservoirs have high clay content, complex mineral composition, and strong heterogeneity. In addition, the acoustic transit time of different minerals varies greatly, as shown in Table 1.

[0178] Table 1 Theoretical acoustic transit time response values ​​of common minerals in shale reservoirs.

[0179]

[0180] The acoustic transit time values ​​of clay and quartz differ significantly, approximately 1.6 times that of quartz; while the acoustic transit time values ​​of calcite, dolomite, and pyrite differ less from those of quartz. Therefore, the volumetric model of shale without organic matter can be simplified to three parts: pores, clay, and non-clay.

[0181] The relationship between acoustic transit time and porosity of shale without organic matter can be expressed as:

[0182] Δt=φΔt f +V sh Δt sh +(1-φ-V sh )Δt m

[0183] In the formula, Δt is the acoustic time difference, μs / ft; Δt f Δt represents the transit time of sound waves in the pore fluid, in μs / ft, with a value of 189 μs / ft; sh The transit time of the clay acoustic wave is given in μs / ft, with a value of 89.407 μs / ft; Δtm The acoustic transit time is denoted as μs / ft for the matrix of shale composed of minerals other than clay minerals, with a value of 55 μs / ft.

[0184] Therefore, the porosity of shale without organic matter can be expressed as:

[0185]

[0186] Substituting the above equation into the logarithm 10 R o =-log 10 [aV sh R w (1-V sh )+φ m R sh ]+log 10 [aR sh R w (1-V sh From this, the theoretical relationship between acoustic transit time and resistivity for shale formations lacking organic matter and with a water saturation of 1 can be obtained. This theoretical relationship is the modified resistivity-porosity-saturation model.

[0187]

[0188] It should also be noted that the specific principle for determining the sonic transit time and formation true resistivity logging response value in the modified resistivity-porosity-saturation model when the constraint value is minimized in each logging test data set, based on the modified resistivity-porosity-saturation model and constraints, is as follows:

[0189] A coordinate system is constructed with sonic transit time as the horizontal axis and the logarithmic resistivity value as the vertical axis. For each well logging test data set, a point A(Δt, log_t) is plotted in the constructed coordinate system using the sonic transit time value and the formation true resistivity logging response value from each well logging test data set. 10 R t );

[0190] Based on the modified resistivity-porosity-saturation model, and combined with the clay content calculation value calculated by the clay content logging calculation model constructed in step 2022, the sonic transit time-resistivity theoretical relationship curve of the logging test data set is further plotted in the coordinate system.

[0191] Using constraints, equation Determine the point (Δt) on the sonic transit time-resistivity theoretical relationship curve from point A to the point on the curve corresponding to the well logging test data set. bl log 10 R tbl This minimizes the constraint value.

[0192] Step 2032: Determine the sonic transit time value and the formation true resistivity logging response value corresponding to the minimum constraint value as the target sonic transit time value and the target formation true resistivity logging response value.

[0193] Step 2033: Based on the sonic transit time, formation true resistivity logging response, clay content, clay resistivity, target sonic transit time, and target formation true resistivity logging response in each well logging test data set, determine the ΔlogR value for each well logging test data set using the following ΔlogR calculation model.

[0194]

[0195]

[0196] In the formula, R t R represents the true resistivity logging response of the formation, in ohms; tb1 Δt is the true resistivity logging response value of the target formation, in ohms / mm; Δt is the sonic transit time value, in μs / ft; Δt b1 The target acoustic time difference is expressed in μs / ft; m is the cementation index, with a value of 2; V sh R represents the clay content, v / v; sh R represents the resistivity of clay, in ohms (mm). w The formation water resistivity is given in ohm / mm², with a value of 0.1 ohm / mm²; Δt f The transit time of sound waves in the pore fluid is Δt, in μs / ft, with a value of 189 μs / ft. sh The time difference of sound waves in clay, in μs / ft, is taken as 89.407 μs / ft; Δt m is the acoustic transit time value of the matrix composed of shale minerals other than clay minerals, in μs / ft, with a value of 55 μs / ft; a is the lithology coefficient, with a value of 1.

[0197] It should be noted that step 203 must be performed once for each well logging test data set to determine the ΔlogR value for each well logging test data set.

[0198] Step 204: Select S sets of well logging test data. Based on the compensated neutron value, density value, clay content value, ΔlogR value, natural gamma value, organic carbon content test value of each set of well logging test data in the S sets of well logging test data, and the clay content well logging calculation model, establish a calculation model for the organic carbon content of the target shale. S is a positive integer greater than 1 and less than N.

[0199] It should be noted that once the clay content logging calculation model and the ΔlogR value for each logging test data set are determined, a mapping relationship can be established between the calculated organic carbon content, the calculated clay content, the natural gamma value, and the ΔlogR value. To fit the relationship between these parameters, a shale organic carbon content calculation model is established, and S logging test data sets are selected from N sets as the training set for parameter fitting. Specifically, during parameter fitting, linear regression analysis can be performed based on the following calculation model. The five fitted parameters b1, b2, b3, b4, and b5 are substituted into the model to determine the target shale organic carbon content calculation model. In actual fitting, linear regression analysis methods such as Linear Regression and Logistic Regression can be selected; this embodiment of the invention does not specifically limit the methods used.

[0200]

[0201] In the formula, TOC is the measured value of organic carbon content, %; (TOC) log Calculated organic carbon content, %; ΔlogR is the ΔlogR value; GR is the natural gamma value, API; (V sh ) log is the calculated clay content, v / v; b1, b2, b3, b4, b5 are fitting parameters.

[0202] Step 205: Use each of the unselected NS well logging test data sets to test and verify the target shale organic carbon content calculation model. If the test and verification results all meet the first preset condition, then the target shale organic carbon content calculation model is determined as the shale organic carbon content calculation model for the target shale formation.

[0203] It should be noted that after establishing the calculation model for the organic carbon content of the target shale, it is necessary to test and verify the model using data from unselected well logging test data sets. If the test verification results all meet the first preset condition, it indicates that the calculation model for the organic carbon content of the target shale has reliable accuracy and can be used to calculate the organic carbon content of the same stratum in the target shale. If the test verification results do not meet the first preset condition, step 204 needs to be executed again, selecting more than S well logging test data sets to re-perform linear regression analysis and fitting, until the test verification results of the established calculation model for the organic carbon content of the target shale all meet the first preset condition.

[0204] It should also be noted that the first preset condition can be either the ratio between the calculated organic carbon content and the tested organic carbon content is less than a predetermined threshold, or the difference between the calculated and tested organic carbon content is less than a predetermined threshold. This embodiment of the invention does not specify a particular threshold for this. The smaller the predetermined threshold, the higher the accuracy that the target shale organic carbon content calculation model needs to meet, and the higher the accuracy of the established shale organic carbon content calculation model in calculating organic carbon content.

[0205] In one possible implementation, the first preset condition is that each of the NS unselected logging test data groups tests and verifies the target shale organic carbon content calculation model, and the difference between the calculated organic carbon content and the tested organic carbon content is less than 0.5.

[0206] Specifically, steps 201-205 will be described using an embodiment.

[0207] This study focuses on the Lower Silurian Longmaxi Formation shale in the AAA area of ​​the Sichuan Basin. Data is sourced from the 2019 paper by Zhu et al., "An improved method for evaluating the TOC content of a shale formation using the dual-differenceΔlogR method." Table 2 shows 91 well logging data sets. The first column represents the depth index, the second the clay content, the third the organic carbon content, the fourth the sonic transit time, the fifth the compensated neutron value, the sixth the density value, the seventh the natural gamma ray value, and the eighth the formation true resistivity logging response. Furthermore, based on the actual logging response, the resistivity of the shale interval adjacent to the organic-rich shale layer (with a TOC close to 0, pure lithology, and large thickness) was selected as the clay resistivity value R. sh The clay resistivity values ​​of all 91 well logging test data sets were 11 ohm mm.

[0208] Table 2. Well logging and laboratory analysis data

[0209]

[0210]

[0211]

[0212]

[0213] Based on steps 201-205, with well logging test data sets 1-71 as the training set and well logging test data sets 72-91 as the test set, the fitted calculation model for the target shale organic carbon content is expressed as follows:

[0214]

[0215] Based on the target shale organic carbon content calculation model, the model was tested and verified using a test set. The average absolute error between the calculated and tested organic carbon content values ​​was 0.3253, and all values ​​were less than 0.5, meeting the first preset condition. Therefore, this target shale organic carbon content calculation model was selected as the shale organic carbon content calculation model.

[0216] For the Wang and Zhao models in related technologies, TOC logging calculations were performed using the same training and test sets as the TOC calculation model in this invention. The accuracy of the TOC calculation model in this invention was compared with that of previous models.

[0217] For the Wang model, the mean absolute error of the test set calculation results is 0.5683; for the Zhao model, the mean absolute error of the test set calculation results is 0.587; and the mean absolute error of the test set calculation results of the shale organic carbon content calculation model provided in this embodiment of the invention is 0.3253. The comparison shows that the TOC calculation model in this embodiment of the invention has the highest accuracy.

[0218] Step 206: Obtain the well logging test prediction data set for the target shale formation. The well logging test prediction data set includes compensated neutron value, density value, natural gamma value, sonic transit time value, formation true resistivity logging response value, clay content value, and clay resistivity value.

[0219] It should be noted that after establishing the shale organic carbon content calculation model, the organic carbon content of target shale in the same block can be calculated. This calculation process eliminates the need to obtain TOC (total organic carbon) test values ​​determined through laboratory experiments, thus saving calculation time.

[0220] Step 207: Determine the organic carbon content of the target shale formation based on the calculation model of shale organic carbon content and the well logging test prediction data set.

[0221] In this embodiment of the invention, well logging test data sets at no less than 10 different depths of the target shale formation are obtained. Based on the compensated neutron value, density value, and clay content value in each well logging test data set, a clay content well logging calculation model is established to reflect the mapping relationship between the shale clay content index and the clay content value. Based on the sonic transit time value, formation true resistivity well logging response value, clay content value, and clay resistivity value in each well logging test data set, the ΔlogR value of each well logging test data set is determined based on the modified resistivity-porosity-saturation model and the ΔlogR calculation model. S well logging tests are selected. The data set uses the compensated neutron value, density value, clay content value, ΔlogR value, natural gamma value, and organic carbon content test value of each well logging test data set in the S well logging test data sets, as well as the clay content well logging calculation model, to establish a target shale organic carbon content calculation model. The target shale organic carbon content calculation model is then tested and verified using each of the unselected NS well logging test data sets. If the test and verification results all meet the first preset condition, then the target shale organic carbon content calculation model is determined as the shale organic carbon content calculation model for the target shale formation. In other words, the basic idea of ​​this invention is to simplify organic-free shale into a mixture of pores, clay, and non-clay minerals. Based on a modified resistivity-porosity-saturation model, a relationship between acoustic transit time and porosity is established. The ΔlogR value of logging test data sets at different depths is calculated, and multiple sets of logging test data are divided into training and testing sets. A model for calculating the organic carbon content of the target shale is established using the training set, and verified using the testing set in conjunction with a first preset condition. If the condition is met, the calculation model for the organic carbon content of the target shale layer is determined. Finally, TOC logging calculations are performed on the target shale layer based on this model. Because this invention considers the conductivity of clay and bound water, as well as the difference in acoustic transit time between clay minerals and other minerals, it improves the traditional theoretical relationship between acoustic transit time and resistivity. Therefore, the established shale organic carbon content calculation model is more suitable for TOC calculations in shale gas reservoirs with high clay content.

[0222] All the above-mentioned optional technical solutions can be combined in any way to form optional embodiments of the present invention, and the embodiments of the present invention will not be described in detail one by one.

[0223] Figure 3 This is a schematic diagram of a device for establishing a calculation model for the carbon content of shale organic matter, provided in an embodiment of the present invention. See also... Figure 3 The device may include:

[0224] The first acquisition module 301 is used to acquire well logging test data sets at N different depths of the target shale layer. Each well logging test data set includes compensated neutron value, density value, natural gamma value, sonic transit time value, formation true resistivity logging response value, clay content value, clay resistivity value and organic carbon content test value, where N is a positive integer greater than 10.

[0225] The first modeling module 302 is used to establish a clay content logging calculation model based on the compensated neutron value, density value and clay content value in each logging test data group. The clay content logging calculation model is a model used to reflect the mapping relationship between the shale clay content index and the clay content value.

[0226] The first determining module 303 is used to determine the ΔlogR value of each well logging test data group based on the sonic transit time value, formation true resistivity logging response value, clay content value, and clay resistivity value in each well logging test data group, and based on the modified resistivity-porosity-saturation model and ΔlogR calculation model. The modified resistivity-porosity-saturation model and ΔlogR calculation model are models based on the theoretical relationship between sonic transit time and resistivity.

[0227] The second modeling module 304 is used to select S sets of well logging test data, and establish a calculation model for the organic carbon content of the target shale based on the compensated neutron value, density value, clay content value, ΔlogR value, natural gamma value, organic carbon content test value of each well logging test data set in the S sets of well logging test data, as well as the clay content well logging calculation model. S is a positive integer greater than 1 and less than N.

[0228] The verification module 305 is used to test and verify the target shale organic carbon content calculation model using each of the NS unselected well logging test data groups. If the test and verification results all meet the first preset condition, the target shale organic carbon content calculation model is determined as the shale organic carbon content calculation model of the target shale layer.

[0229] Optionally, the first modeling module includes:

[0230] The first determining unit is used to determine the clay content index of each well logging test data group based on the compensated neutron value and density value in each well logging test data group;

[0231] The first modeling unit is used to establish a clay content logging calculation model based on the clay content index and clay content value of each logging test data group using the least squares method.

[0232] Optionally, the first determining unit specifically includes:

[0233] Based on the compensated neutron value and density value in each well logging test data set, the clay content index for each well logging test data set is determined using the following formula.

[0234]

[0235] In the formula, I cl Clay content index, v / v; CNL, compensated neutron value, %; DEN, density value, g / cm³. 3 ;DEN limestone The density of limestone is given in g / cm³. 3 The value is 2.71 g / cm³. 3 ;DEN f Pore ​​fluid density, g / cm³ 3 The value is 1g / cm 3 .

[0236] Optionally, the first modeling unit specifically includes:

[0237] Based on the clay content index and clay content value of each well logging test data group, the clay content well logging calculation model is determined by fitting the model using the least squares method based on the following formula, and the mapping relationship between the fitted clay content index and clay content value is determined.

[0238]

[0239] In the formula, V sh The clay content is expressed as v / v; (V sh ) log The calculated value for clay content is v / v; I cl is the clay content index, v / v; a1 and a2 are fitting parameters.

[0240] Optionally, the first determining module includes:

[0241] The second determining unit is used to determine, based on the sonic transit time, formation true resistivity logging response, clay content, and clay resistivity values ​​in each well logging test data set, and according to the following modified resistivity-porosity-saturation model and constraints, the corresponding sonic transit time and formation true resistivity logging response values ​​in the modified resistivity-porosity-saturation model for each well logging test data set when the constraint value is minimized.

[0242]

[0243]

[0244] In the formula, R o The true resistivity logging response value of the formation with no organic matter and a water saturation of 1, in ohm mm; V shR represents the clay content, v / v; sh R represents the resistivity of clay, in ohms (mm). w Δt is the formation water resistivity, in ohms / mm², with a value of 0.1 ohms / mm²; Δt is the acoustic transit time, in μs / ft; Δt f The transit time of sound waves in the pore fluid is Δt, in μs / ft, with a value of 189 μs / ft. sh The time difference of sound waves in clay, in μs / ft, is taken as 89.407 μs / ft; Δt m Δt represents the acoustic transit time of the matrix composed of minerals other than clay minerals in the shale, in μs / ft, with a value of 55 μs / ft; a is the lithology coefficient, with a value of 1; Δt i Rt represents the acoustic time difference (μs / ft) in the corrected resistivity-porosity-saturation model. i The formation true resistivity logging response value, ohmm, varies with any acoustic time difference in the corrected resistivity-porosity-saturation model.

[0245] The third determining unit is used to determine the acoustic transit time value and the formation true resistivity logging response value corresponding to the minimum constraint value as the target acoustic transit time value and the target formation true resistivity logging response value.

[0246] The fourth determining unit is used to determine the ΔlogR value for each well logging test data set based on the sonic transit time, formation true resistivity logging response, clay content, clay resistivity, target sonic transit time, and target formation true resistivity logging response, according to the following ΔlogR calculation model.

[0247]

[0248]

[0249] In the formula, R t R represents the true resistivity logging response of the formation, in ohms; tb1 Δt is the true resistivity logging response value of the target formation, in ohms / mm; Δt is the sonic transit time value, in μs / ft; Δt b1 The target acoustic time difference is expressed in μs / ft; m is the cementation index, with a value of 2; V sh R represents the clay content, v / v; sh R represents the resistivity of clay, in ohms (mm). w The formation water resistivity is given in ohm / mm², with a value of 0.1 ohm / mm²; Δt f The transit time of sound waves in the pore fluid is Δt, in μs / ft, with a value of 189 μs / ft. sh The time difference of sound waves in clay, in μs / ft, is taken as 89.407 μs / ft; Δt mis the acoustic transit time value of the matrix composed of shale minerals other than clay minerals, in μs / ft, with a value of 55 μs / ft; a is the lithology coefficient, with a value of 1.

[0250] Optionally, the second modeling module specifically includes:

[0251] Based on the compensated neutron value, density value, clay content value, ΔlogR value, natural gamma value, and organic carbon content test values ​​of each of the S well logging test data sets, and the clay content well logging calculation model, linear regression analysis was performed to fit the following calculation model. The fitted model was then determined as the target shale organic carbon content calculation model.

[0252]

[0253] In the formula, TOC is the measured value of organic carbon content, %; (TOC) log Calculated organic carbon content, %; ΔlogR is the ΔlogR value; GR is the natural gamma value, API; (V sh ) log is the calculated clay content, v / v; b1, b2, b3, b4, b5 are fitting parameters.

[0254] Optionally, the first preset condition in the verification module refers to the fact that each of the NS unselected well logging test data groups tests and verifies the target shale organic carbon content calculation model, and the difference between the calculated organic carbon content and the tested organic carbon content is less than 0.5.

[0255] Optionally, the apparatus for establishing a calculation model for the carbon content of shale organic matter also includes:

[0256] The second acquisition module is used to acquire the well logging test prediction data set of the target shale formation. The well logging test prediction data set includes compensated neutron value, density value, natural gamma value, sonic transit time value, formation true resistivity well logging response value, clay content value, and clay resistivity value.

[0257] The second determination module is used to determine the organic carbon content of the target shale layer based on the shale organic carbon content calculation model and well logging test prediction data set.

[0258] In this embodiment of the invention, organic-free shale is simplified into a mixture of pores, clay, and non-clay minerals. Based on a modified resistivity-porosity-saturation model, the relationship between acoustic transit time and porosity is established. The ΔlogR value of logging test data sets at different depths is calculated. Multiple sets of logging test data sets are divided into training and testing sets. A model for calculating the organic carbon content of the target shale is established using the training set and verified using the testing set in conjunction with a first preset condition. If the condition is met, the calculation model for the organic carbon content of the target shale layer is determined. Finally, TOC logging calculations are performed on the target shale layer based on this model. Because this invention considers the conductivity of clay and bound water, as well as the difference in acoustic transit time between clay minerals and other minerals, it improves the traditional theoretical relationship between acoustic transit time and resistivity. Therefore, the established shale organic carbon content calculation model is more suitable for TOC calculations in shale gas reservoirs with high clay content.

[0259] It should be noted that the shale organic carbon content calculation model establishment device provided in the above embodiments is only illustrated by the division of the above functional modules when establishing the shale organic carbon content calculation model. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the shale organic carbon content calculation model establishment device and the shale organic carbon content calculation model establishment method provided in the above embodiments belong to the same concept, and the specific implementation process is detailed in the method embodiments, which will not be repeated here.

[0260] Figure 4 This is a schematic diagram of the structure of a terminal 400 provided in an embodiment of the present invention. The terminal 400 can be: a smartphone, a tablet computer, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 player (Moving Picture Experts Group Audio Layer IV), a laptop computer, or a desktop computer. The terminal 400 may also be referred to as a user device, a portable terminal, a laptop terminal, a desktop terminal, or other names.

[0261] Typically, terminal 400 includes a processor 401 and a memory 402.

[0262] Processor 401 may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. Processor 401 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). Processor 401 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 401 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, processor 401 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0263] The memory 402 may include one or more computer-readable storage media, which may be non-transitory. The memory 402 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory 402 is used to store at least one instruction, which is executed by the processor 401 to implement the shale organic carbon content calculation model establishment method provided in the method embodiments of this application.

[0264] In some embodiments, the terminal 400 may also optionally include a peripheral device interface 403 and at least one peripheral device. The processor 401, memory 402, and peripheral device interface 403 can be connected via a bus or signal line. Each peripheral device can be connected to the peripheral device interface 403 via a bus, signal line, or circuit board. Specifically, the peripheral device includes at least one of the following: a radio frequency circuit 404, a touch display screen 404, a camera 406, an audio circuit 407, a positioning component 408, and a power supply 409.

[0265] Peripheral device interface 403 can be used to connect at least one I / O (Input / Output) related peripheral device to processor 401 and memory 402. In some embodiments, processor 401, memory 402 and peripheral device interface 403 are integrated on the same chip or circuit board; in some other embodiments, any one or two of processor 401, memory 402 and peripheral device interface 403 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0266] The radio frequency (RF) circuit 404 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The RF circuit 404 communicates with communication networks and other communication devices via electromagnetic signals. The RF circuit 404 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals back into electrical signals. Optionally, the RF circuit 404 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, etc. The RF circuit 404 can communicate with other terminals through at least one wireless communication protocol. This wireless communication protocol includes, but is not limited to: metropolitan area networks (MANs), various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks (WLANs), and / or WiFi (Wireless Fidelity) networks. In some embodiments, the RF circuit 404 may also include circuitry related to NFC (Near Field Communication), which is not limited in this application.

[0267] Display screen 404 is used to display a UI (User Interface). This UI may include graphics, text, icons, videos, and any combination thereof. When display screen 405 is a touch display screen, it also has the ability to collect touch signals on or above its surface. These touch signals can be input as control signals to processor 401 for processing. In this case, display screen 405 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, there may be one display screen 405, which serves as the front panel of terminal 400; in other embodiments, there may be at least two display screens 405, respectively disposed on different surfaces of terminal 400 or in a folded design; in still other embodiments, display screen 405 may be a flexible display screen, disposed on a curved or folded surface of terminal 400. Furthermore, display screen 405 may be configured as a non-rectangular, irregular shape, i.e., a non-rectangular screen. Display screen 405 may be made of materials such as LCD (Liquid Crystal Display) or OLED (Organic Light-Emitting Diode).

[0268] The camera assembly 406 is used to acquire images or videos. Optionally, the camera assembly 406 includes a front-facing camera and a rear-facing camera. Typically, the front-facing camera is located on the front panel of the terminal, and the rear-facing camera is located on the back of the terminal. In some embodiments, there are at least two rear-facing cameras, which are any one of a main camera, a depth-sensing camera, a wide-angle camera, and a telephoto camera, to achieve background blurring by fusion of the main camera and the depth-sensing camera, panoramic shooting by fusion of the main camera and the wide-angle camera, VR (Virtual Reality) shooting, or other fusion shooting functions. In some embodiments, the camera assembly 406 may also include a flash. The flash can be a single-color temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm-light flash and a cool-light flash, which can be used for light compensation at different color temperatures.

[0269] The audio circuit 407 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, converting them into electrical signals that are input to the processor 401 for processing, or to the radio frequency circuit 404 for voice communication. For stereo sound acquisition or noise reduction purposes, multiple microphones may be used, each positioned at a different location on the terminal 400. The microphone may also be an array microphone or an omnidirectional microphone. The speaker is used to convert electrical signals from the processor 401 or the radio frequency circuit 404 into sound waves. The speaker may be a conventional diaphragm speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can convert electrical signals not only into audible sound waves but also into inaudible sound waves for purposes such as distance measurement. In some embodiments, the audio circuit 407 may also include a headphone jack.

[0270] The positioning component 408 is used to locate the current geographical location of the terminal 400 in order to enable navigation or LBS (Location Based Service). The positioning component 408 can be a positioning component based on the US GPS (Global Positioning System), China's BeiDou system, Russia's Granas system, or the EU's Galileo system.

[0271] Power supply 409 is used to power the various components in terminal 400. Power supply 409 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When power supply 409 includes a rechargeable battery, the rechargeable battery can support wired charging or wireless charging. The rechargeable battery can also be used to support fast charging technology.

[0272] In some embodiments, the terminal 400 further includes one or more sensors 410. The one or more sensors 410 include, but are not limited to: an accelerometer 411, a gyroscope 412, a pressure sensor 413, a fingerprint sensor 414, an optical sensor 415, and a proximity sensor 416.

[0273] Accelerometer 411 can detect the magnitude of acceleration along the three coordinate axes of a coordinate system established by terminal 400. For example, accelerometer 411 can be used to detect the components of gravitational acceleration along the three coordinate axes. Processor 401 can control touchscreen 405 to display the user interface in landscape or portrait view based on the gravitational acceleration signal acquired by accelerometer 411. Accelerometer 411 can also be used for games or for acquiring user motion data.

[0274] The gyroscope sensor 412 can detect the orientation and rotation angle of the terminal 400. The gyroscope sensor 412, in conjunction with the accelerometer sensor 411, can collect 3D motion data from the user on the terminal 400. Based on the data collected by the gyroscope sensor 412, the processor 401 can perform the following functions: motion sensing (e.g., changing the UI based on the user's tilt), image stabilization during shooting, game control, and inertial navigation.

[0275] The pressure sensor 413 can be disposed on the side bezel of the terminal 400 and / or on the lower layer of the touch display screen 405. When the pressure sensor 413 is disposed on the side bezel of the terminal 400, it can detect the user's grip signal on the terminal 400, and the processor 401 can perform left / right hand recognition or quick operation based on the grip signal collected by the pressure sensor 413. When the pressure sensor 413 is disposed on the lower layer of the touch display screen 405, the processor 401 can control the operable controls on the UI interface based on the user's pressure operation on the touch display screen 405. The operable controls include at least one of button controls, scroll bar controls, icon controls, and menu controls.

[0276] The fingerprint sensor 414 is used to collect the user's fingerprint. The processor 401 identifies the user's identity based on the fingerprint collected by the fingerprint sensor 414, or the fingerprint sensor 414 identifies the user's identity based on the collected fingerprint. When the user's identity is identified as trusted, the processor 401 authorizes the user to perform relevant sensitive operations, including unlocking the screen, viewing encrypted information, downloading software, making payments, and changing settings. The fingerprint sensor 414 can be located on the front, back, or side of the terminal 400. When the terminal 400 has physical buttons or a manufacturer's logo, the fingerprint sensor 414 can be integrated with the physical buttons or manufacturer's logo.

[0277] An optical sensor 415 is used to collect ambient light intensity. In one embodiment, the processor 401 can control the display brightness of the touch screen 405 based on the ambient light intensity collected by the optical sensor 415. Specifically, when the ambient light intensity is high, the display brightness of the touch screen 405 is increased; when the ambient light intensity is low, the display brightness of the touch screen 405 is decreased. In another embodiment, the processor 401 can also dynamically adjust the shooting parameters of the camera assembly 406 based on the ambient light intensity collected by the optical sensor 415.

[0278] The proximity sensor 416, also known as a distance sensor, is typically located on the front panel of the terminal 400. The proximity sensor 416 is used to detect the distance between the user and the front of the terminal 400. In one embodiment, when the proximity sensor 416 detects that the distance between the user and the front of the terminal 400 is gradually decreasing, the processor 401 controls the touchscreen display 405 to switch from a screen-on state to a screen-off state; when the proximity sensor 416 detects that the distance between the user and the front of the terminal 400 is gradually increasing, the processor 401 controls the touchscreen display 405 to switch from a screen-off state to a screen-on state.

[0279] That is, embodiments of the present invention not only provide a terminal, including a processor and a memory for storing processor-executable instructions, wherein the processor is configured to execute... Figure 1 or Figure 2 Furthermore, embodiments of the present invention also provide a computer-readable storage medium storing a computer program that, when executed by a processor, can implement... Figure 1 or Figure 2 The method for establishing a calculation model for the organic carbon content of shale in the illustrated embodiments.

[0280] Those skilled in the art will understand that Figure 4 The structure shown does not constitute a limitation on terminal 400 and may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0281] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0282] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for establishing a calculation model for the carbon content of organic matter in shale, characterized in that, The method for establishing the calculation model for the carbon content of shale organic matter includes: Obtain well logging test data sets at N different depths in the target shale formation, wherein each well logging test data set includes compensated neutron value, density value, natural gamma value, sonic transit time value, formation true resistivity logging response value, clay content value, clay resistivity value and organic carbon content test value, and N is a positive integer greater than 10; Based on the compensated neutron value, density value, and clay content value in each well logging test data group, a clay content well logging calculation model is established. The clay content well logging calculation model is a model used to reflect the mapping relationship between the shale clay content index and the clay content value. Based on the sonic transit time, formation true resistivity logging response, clay content, and clay resistivity values ​​in each set of well logging test data, and using a modified resistivity-porosity-saturation model and constraints, the corresponding sonic transit time and formation true resistivity logging response values ​​in the modified resistivity-porosity-saturation model for each set of well logging test data are determined when the constraint value is minimized. ； ； In the formula, R o The true resistivity logging response value of the formation with no organic matter and a water saturation of 1, in ohm mm; V sh The clay content is expressed as v / v. R sh The resistivity value of clay is ohm / mm; R w The resistivity of the formation water is 0.1 ohm / mm². ∆t The time difference of sound waves is expressed in μs / ft. ∆t f The transit time of acoustic waves in pore fluid is expressed in μs / ft, with a value of 189 μs / ft. ∆t sh The time difference of sound waves in clay is 89.407 μs / ft. ∆t m The acoustic transit time value, in μs / ft, is the matrix composed of other minerals besides clay minerals in shale, with a value of 55 μs / ft. a This is the lithology coefficient, with a value of 1; ∆t i Let be any acoustic time difference value in the modified resistivity-porosity-saturation model, in μs / ft; Rt i The formation true resistivity logging response value, ohm mm, varies with any acoustic time difference value in the modified resistivity-porosity-saturation model. The acoustic transit time value and the formation true resistivity logging response value corresponding to the minimum constraint condition value are determined as the target acoustic transit time value and the target formation true resistivity logging response value. Based on the sonic transit time, formation true resistivity logging response, clay content, clay resistivity, target sonic transit time, and target formation true resistivity logging response in each set of logging test data, based on the following ∆log R The calculation model determines the ∆log for each set of well logging test data. R value, ； ； In the formula, R t The logging response value is the true resistivity of the formation, in ohm mm; R tb1 The logging response value of the true resistivity of the target formation is in ohm mm; ∆t The time difference of sound waves is expressed in μs / ft. ∆t b1 The target acoustic time difference is expressed in μs / ft. m The bonding index is set to 2. V sh The clay content is expressed as v / v. R sh The resistivity value of clay is ohm / mm; R w The resistivity of the formation water is 0.1 ohm / mm². ∆t f The transit time of acoustic waves in pore fluid is expressed in μs / ft, with a value of 189 μs / ft. ∆t sh The time difference of sound waves in clay is 89.407 μs / ft. ∆t m The acoustic transit time value, in μs / ft, is the matrix composed of other minerals besides clay minerals in shale, with a value of 55 μs / ft. a This is the lithology coefficient, with a value of 1; Select S sets of well logging test data, and based on the compensated neutron value, density value, clay content value, and Δlog of each of the S sets of well logging test data... R The value, natural gamma value, organic carbon content test value, and clay content well logging calculation model are used to establish a calculation model for the organic carbon content of the target shale, where S is a positive integer greater than 1 and less than N; The organic carbon content calculation model of the target shale is tested and verified using each of the NS unselected well logging test data groups. If the test verification results all meet the first preset condition, the organic carbon content calculation model of the target shale is determined as the shale organic carbon content calculation model of the target shale layer.

2. The method according to claim 1, characterized in that, The step of establishing a clay content logging calculation model based on the compensated neutron value, density value, and clay content value in each logging test data set includes: The clay content index of each well logging test data group is determined based on the compensated neutron value and density value in each well logging test data group; Based on the clay content index and clay content value of each well logging test data group, a clay content well logging calculation model is established using the least squares method.

3. The method according to claim 2, characterized in that, The step of determining the clay content index of each well logging test data set based on the compensated neutron value and density value in each well logging test data set includes: Based on the compensated neutron value and density value in each well logging test data set, the clay content index of each well logging test data set is determined using the following formula. ； In the formula, I cl The clay content index is v / v; CNL To compensate for the neutron value, % DEN This is the density value, in g / cm³. 3 ; DEN limestone The density of limestone is given in g / cm³. 3 The value is 2.71 g / cm³. 3 ; DEN f Pore ​​fluid density, g / cm³ 3 The value is 1g / cm 3 .

4. The method according to claim 2, characterized in that, The step involves establishing a clay content logging calculation model based on the clay content index and clay content value of each well logging test data group, using the least squares method, including: Based on the clay content index and clay content value of each well logging test data group, the clay content well logging calculation model is determined by fitting the model using the least squares method according to the following formula, and the mapping relationship between the fitted clay content index and the clay content value is determined as the clay content well logging calculation model. ； In the formula, V sh The value represents the clay content, v / v; V sh ) log This is the calculated value for clay content, v / v; I cl The clay content index is v / v; a 1. a 2 represents the fitting parameters.

5. The method according to claim 1, characterized in that, The compensated neutron value, density value, clay content value, and Δlog are calculated based on each of the S sets of well logging test data. R Using the measured values ​​of natural gamma ray, organic carbon content, and clay content from well logging, a calculation model for the organic carbon content of the target shale is established, including: Based on the compensated neutron value, density value, clay content value, and ∆log of each of the S well logging test data sets. R The values ​​of natural gamma ray, organic carbon content, and the clay content logging calculation model were used. Linear regression analysis was performed to fit the model, and the fitted model was determined as the target shale organic carbon content calculation model. ； In the formula, TOC represents the measured organic carbon content, %; (TOC) log The calculated value for organic carbon content is %; ∆log R For ∆log R value; GR is Natural gamma value, API; V sh ) log This is the calculated value for clay content, v / v; b 1. b 2. b 3. b 4. b 5 represents the fitting parameters.

6. The method according to claim 1, characterized in that, The first preset condition refers to the fact that each of the NS unselected well logging test data groups tests and verifies the calculation model of organic carbon content of the target shale, and the difference between the calculated value of organic carbon content and the tested value of organic carbon content obtained from the test verification results is less than 0.

5.

7. The method according to any one of claims 1-6, characterized in that, After determining the target shale organic carbon content calculation model as the shale organic carbon content calculation model for the target shale strata, the method further includes: Obtain a set of well logging test prediction data for the target shale formation, including compensated neutron value, density value, natural gamma value, sonic transit time value, formation true resistivity logging response value, clay content value, and clay resistivity value. The organic carbon content of the target shale formation is determined based on the calculation model of the organic carbon content of the target shale formation and the well logging test prediction data set.

8. A device for establishing a calculation model for the carbon content of organic matter in shale, characterized in that, The device for establishing the calculation model of carbon content in shale organic matter includes: The first acquisition module is used to acquire well logging test data sets at N different depths of the target shale layer. Each well logging test data set includes compensated neutron value, density value, natural gamma value, sonic transit time value, formation true resistivity logging response value, clay content value, clay resistivity value, and organic carbon content test value. N is a positive integer greater than 10. The first modeling module is used to establish a clay content logging calculation model based on the compensated neutron value, density value and clay content value in each logging test data group. The clay content logging calculation model is a model used to reflect the mapping relationship between the shale clay content index and the clay content value. The first determining module is used to determine the acoustic transit time, formation true resistivity logging response, clay content, and clay resistivity values ​​in each set of logging test data, based on a modified resistivity-porosity-saturation model and ∆log. R The calculation model determines the ∆log for each set of well logging test data. R The value, the modified resistivity-porosity-saturation model and the ∆log R The calculation model is based on the theoretical relationship between acoustic time difference and resistivity. The first determining module specifically includes: The second determining unit is used to determine, based on the sonic transit time value, formation true resistivity logging response value, clay content value, and clay resistivity value in each of the well logging test data sets, and using a modified resistivity-porosity-saturation model and constraint conditions, the corresponding sonic transit time value and formation true resistivity logging response value in the modified resistivity-porosity-saturation model for each of the well logging test data sets when the constraint condition value is minimized. ； ； In the formula, R o The true resistivity logging response value of the formation with no organic matter and a water saturation of 1, in ohm mm; V sh The clay content is expressed as v / v. R sh The resistivity value of clay is ohm / mm; R w The resistivity of the formation water is 0.1 ohm / mm². ∆t The time difference of sound waves is expressed in μs / ft. ∆t f The transit time of acoustic waves in pore fluid is expressed in μs / ft, with a value of 189 μs / ft. ∆t sh The time difference of sound waves in clay is 89.407 μs / ft. ∆t m The acoustic transit time value, in μs / ft, is the matrix composed of other minerals besides clay minerals in shale, with a value of 55 μs / ft. a This is the lithology coefficient, with a value of 1; ∆t i Let be any acoustic time difference value in the modified resistivity-porosity-saturation model, in μs / ft; Rt i The formation true resistivity logging response value, ohm mm, varies with any acoustic time difference value in the modified resistivity-porosity-saturation model. The third determining unit is used to determine the acoustic transit time value and the formation true resistivity logging response value corresponding to the minimum constraint condition value as the target acoustic transit time value and the target formation true resistivity logging response value. The fourth determining unit is used to determine the following based on the acoustic transit time value, formation true resistivity logging response value, clay content value, clay resistivity value, target acoustic transit time value, and target formation true resistivity logging response value in each of the well logging test data sets, according to the following ∆log R The calculation model determines the ∆log for each set of well logging test data. R value, ； ； In the formula, R t The logging response value is the true resistivity of the formation, in ohm mm; R tb1 The logging response value of the true resistivity of the target formation is in ohm mm; ∆t The time difference of sound waves is expressed in μs / ft. ∆t b1 The target acoustic time difference is expressed in μs / ft. m The bonding index is set to 2. V sh The clay content is expressed as v / v. R sh The resistivity value of clay is ohm / mm; R w The resistivity of the formation water is 0.1 ohm / mm². ∆t f The transit time of acoustic waves in pore fluid is expressed in μs / ft, with a value of 189 μs / ft. ∆t sh The time difference of sound waves in clay is 89.407 μs / ft. ∆t m The acoustic transit time value, in μs / ft, is the matrix composed of other minerals besides clay minerals in shale, with a value of 55 μs / ft. a This is the lithology coefficient, with a value of 1; The second modeling module is used to select S sets of well logging test data, and based on the compensated neutron value, density value, clay content value, and ∆log of each of the S sets of well logging test data,... R The value, natural gamma value, organic carbon content test value, and clay content well logging calculation model are used to establish a calculation model for the organic carbon content of the target shale, where S is a positive integer greater than 1 and less than N; The verification module is used to test and verify the target shale organic carbon content calculation model using each of the NS unselected well logging test data groups. If the test and verification results all meet the first preset condition, the target shale organic carbon content calculation model is determined as the shale organic carbon content calculation model of the target shale formation.

9. A device for establishing a calculation model for the carbon content of organic matter in shale, characterized in that, The device includes: processor; Memory used to store processor-executable instructions; The processor is configured to perform the method as described in any one of claims 1-7.

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