Methods, apparatus, media, and equipment for predicting TOC in lacustrine source rocks based on seismic attribute constraints.

Abstract

This invention relates to a method, apparatus, medium, and equipment for predicting the total oxygen consumption (TOC) of lacustrine source rocks based on seismic attribute constraints. The method includes: selecting curves with high correlation to the TOC of lacustrine source rocks and calculating the TOC curve of the lacustrine source rocks; calculating various elastic parameter curves, performing cross-interaction analysis with the lacustrine source rock TOC curve to select elastic parameters sensitive to the TOC of lacustrine source rocks, and establishing a regression formula between the TOC of lacustrine source rocks and the sensitive parameters; calculating the correlation between wellbore seismic attributes and the TOC curve of lacustrine source rocks, and selecting several highly correlated seismic attributes; using the several highly correlated seismic attributes, determining the seismic attribute threshold value for characterizing lacustrine source rocks, and obtaining an attribute fusion body; using actual drilling data and regional interpretation stratigraphic data to obtain an initial low-frequency model; constructing an attribute-constrained low-frequency model; using the attribute-constrained low-frequency model to invert the TOC of lacustrine source rocks and calculate the TOC data volume.

Description

Technical Field

[0001] This invention relates to a method, apparatus, medium, and equipment for predicting TOC in lacustrine source rocks based on seismic attribute constraints, belonging to the field of geological exploration technology. Background Technology

[0002] As exploration of hydrocarbon-rich depressions in China's coastal waters intensifies, the evaluation of new depressions becomes particularly crucial. Currently, while the total resources of the remaining depressions are substantial (exceeding 10 billion tons of oil equivalent), the resources of individual depressions are generally small, with 90% of depressions having less than 300 million tons. These remaining depressions are mostly located at basin edges with complex geological conditions. If only planar distribution identification of semi-deep to deep lakes is conducted, it is difficult to accurately estimate potential geological resources, thus affecting the evaluation of the exploration potential of new depressions. Total organic carbon (TOC) content is a major indicator of the hydrocarbon generation capacity of source rocks and an important parameter for assessing depression resources.

[0003] Currently, quantitative prediction of TOC (Total Organic Carbon) in source rocks mainly relies on seismic inversion technology. First, rock physical analysis is used to determine the elastic parameters sensitive to TOC in source rocks. Second, a quantitative relationship is established between these elastic parameters and seismically inverted wave impedance. Then, a low-frequency seismic inversion model is constructed using well data interpolation. Finally, combined with the wave impedance obtained from seismic inversion, the quantitative prediction of TOC is completed based on the aforementioned quantitative relationship. However, the number of wells drilled in new offshore depressions is small, and the well locations are mostly distributed in structurally high areas. Furthermore, the lateral thickness of source rock strata varies greatly, limiting the accuracy of low-frequency models built solely based on well data, thus reducing the reliability of TOC prediction for source rocks. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a method, apparatus, medium, and equipment for predicting TOC (Total Organic Carbon) in lacustrine source rocks based on seismic attribute constraints. This method utilizes seismic attributes to establish constraints, thereby improving the accuracy of establishing low-frequency models for TOC inversion in marine areas with few wells. The low-frequency models established by this method align with regional sedimentary development characteristics and research findings, thus enhancing the accuracy of TOC prediction for source rocks.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A method for predicting the total oxygen consumption (TOC) of lacustrine source rocks based on seismic attribute constraints includes: S1: Select curves with high correlation to TOC of lacustrine source rocks and calculate the TOC curves of lacustrine source rocks using multiple regression formulas. S2: Calculate various elastic parameter curves using actual drilling data, then perform cross-analysis with lacustrine source rock TOC curves, select elastic parameters sensitive to lacustrine source rock TOC, and establish regression formulas between lacustrine source rock TOC and sensitive elastic parameters. S3: Extract various seismic attributes from the wellbore, calculate the correlation between the seismic attributes from the wellbore and the TOC curve of the lacustrine source rocks, and select several seismic attributes with high correlation. S4: Using several highly correlated seismic attributes and combining them with actual drilling data, determine the seismic attribute thresholds that characterize the development features of lacustrine source rocks, and fuse them to obtain an attribute fusion body; S5: Based on actual drilling data and regional interpretation stratigraphic data, obtain initial low-frequency model data; S6: Weighted fusion of the attribute fusion body obtained in step S4 and the initial low-frequency model data obtained in step S5 is performed to construct an attribute-constrained low-frequency model. S7: Based on the attribute-constrained low-frequency model constructed in step S6, wave impedance inversion is carried out to obtain the elastic parameter data volume. Then, according to the regression formula between the TOC of lacustrine source rocks and the sensitive elastic parameters established in step S2, the TOC data volume is calculated.

[0006] The method for predicting TOC of lacustrine source rocks based on seismic attribute constraints, preferably, includes curves in step S1 that are highly correlated with TOC of lacustrine source rocks, such as gamma curves, sonic transit time curves, resistivity curves, or density curves.

[0007] The TOC prediction method for lacustrine source rocks based on seismic attribute constraints, preferably, includes the following TOC curves for lacustrine source rocks in step S1: ; In the formula, TOC is in wt%, GR is the gamma curve, AC is the acoustic transit time curve, Rt is the resistivity curve, and ρ is the density curve.

[0008] The TOC prediction method for lacustrine source rocks based on seismic attribute constraints, preferably, includes the following elastic parameter curves in step S2: Lamé coefficient elastic parameter curve, shear modulus elastic parameter curve, bulk modulus elastic parameter curve, Young's modulus elastic parameter curve, and Poisson's ratio elastic parameter curve.

[0009] The TOC prediction method for lacustrine source rocks based on seismic attribute constraints, preferably, uses the following regression formula between the TOC of lacustrine source rocks and sensitive elastic parameters in step S2: ; In the formula, λ is the Lamé coefficient.

[0010] The aforementioned method for predicting TOC in lacustrine source rocks based on seismic attribute constraints, preferably, includes the following specific steps in step S3: First, various seismic attribute volumes, including sweet spot attribute, chaotic attribute, and variance attribute, are calculated. Then, the seismic attributes at the wellbore location are extracted to form attribute curves corresponding to the well logging curves. Next, the correlation between the TOC curve of the lacustrine source rock and each seismic attribute curve is calculated. Finally, based on the correlation results, the sweet spot attribute and chaotic attribute are selected as TOC-sensitive seismic attributes.

[0011] The method for predicting TOC of lacustrine source rocks based on seismic attribute constraints is preferably described in step S7, where the attribute-constrained low-frequency model calculated in S6 is used to obtain the sensitive parameters (Lame coefficient λ and density ρ) of lacustrine source rocks using elastic impedance inversion technology. Then, the TOC data volume is calculated according to the regression formula between the TOC of lacustrine source rocks and the sensitive elastic parameters in S2.

[0012] A second aspect of the present invention provides a TOC prediction device for lacustrine source rocks based on seismic attribute constraints, comprising: The first processing unit is used to select curves that are highly correlated with the TOC of lacustrine source rocks and calculate the TOC curves of lacustrine source rocks using a multiple regression formula. The second processing unit is used to calculate various elastic parameter curves using actual drilling data, and then perform cross-analysis with the TOC curves of lacustrine source rocks to select elastic parameters that are sensitive to the TOC of lacustrine source rocks, and establish regression formulas between the TOC of lacustrine source rocks and sensitive parameters. The third processing unit is used to extract various seismic attributes from the wellbore, calculate the correlation between the seismic attributes from the wellbore and the TOC curve of the lacustrine source rocks, and select several seismic attributes with high correlation. The fourth processing unit is used to determine the seismic attribute threshold value that characterizes the development characteristics of lacustrine source rocks by using several highly correlated seismic attributes and combining them with actual drilling data, and then fuse them to obtain an attribute fusion body. The fifth processing unit is used to obtain initial low-frequency model data based on actual drilling data and regional interpretation stratigraphic data; The sixth processing unit is used to perform weighted fusion of the attribute fusion body in the fourth processing unit and the initial low-frequency model data in the fifth processing unit to construct an attribute-constrained low-frequency model. The seventh processing unit is used to perform wave impedance inversion based on the attribute-constrained low-frequency model constructed by the sixth processing unit to obtain the elastic parameter data volume. Then, according to the regression formula of lacustrine source rock TOC and sensitive parameters established by the second processing unit, the TOC data volume is calculated.

[0013] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method for predicting TOC of lacustrine source rocks based on seismic attribute constraints.

[0014] A fourth aspect of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for predicting TOC of lacustrine source rocks based on seismic attribute constraints.

[0015] The present invention has the following advantages due to the adoption of the above technical solutions: 1. This invention utilizes seismic attributes to establish constraints, thereby improving the accuracy of establishing low-frequency models for source rock TOC inversion in marine areas with few wells. The method for establishing low-frequency models is consistent with the understanding of regional sedimentary facies studies, ensuring the accuracy of source rock TOC prediction.

[0016] 2. The TOC prediction method for lacustrine source rocks proposed in this invention establishes constraints using seismic attributes, achieving accurate prediction of TOC inversion for source rocks, with results showing high agreement with actual drilling. Attached Figure Description

[0017] Figure 1 This is a graph showing the predicted TOC curve of well L-1 according to an embodiment of the present invention. Figure 2 This is a cross-plot analysis diagram of the TOC curve and elastic parameter curve of well L-1 provided in this embodiment of the invention; Figure 3 This is a graph of the TOC-λρ regression formula for well L-1 provided in this embodiment of the invention; Figure 4 This is a correlation analysis diagram of the TOC curve and seismic attributes of well L-1 provided in this embodiment of the invention; Figure 5 This embodiment of the invention provides a threshold value map for determining seismic attributes through well seismic analysis. Figure 6 This is a calculation diagram of the initial low-frequency model for seismic inversion provided in this embodiment of the invention; Figure 7 A diagram showing the establishment of the seismic attribute-constrained low-frequency model provided in this embodiment of the invention; Figure 8 This is a TOC profile of the source rock obtained through inversion prediction provided in this embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention are described clearly and completely below. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0019] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," "third," "fourth," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect.

[0020] For ease of description, spatial relative terms may be used in the text to describe the relationship of one element or feature relative to another element or feature as shown in the figure. These relative terms include, for example, "inside," "outside," "middle," "outer," "below," "above," etc. Such spatial relative terms are intended to include different orientations of the device in use or operation, other than those depicted in the figure.

[0021] Currently, quantitative prediction of TOC (Total Organic Carbon) in source rocks mainly relies on seismic inversion technology. First, rock physical analysis is used to determine the elastic parameters sensitive to TOC in source rocks. Second, a quantitative relationship is established between these elastic parameters and seismically inverted wave impedance. Then, a low-frequency seismic inversion model is constructed using well data interpolation. Finally, combined with the wave impedance obtained from seismic inversion, the quantitative prediction of TOC is completed based on the aforementioned quantitative relationship. However, the number of wells drilled in new offshore depressions is small, and the well locations are mostly distributed in structurally high areas. Furthermore, the lateral thickness of source rock strata varies greatly, limiting the accuracy of low-frequency models built solely based on well data, thus reducing the reliability of TOC prediction for source rocks.

[0022] To address the aforementioned technical problems, this invention provides a method for predicting the total oxygen consumption (TOC) of lacustrine source rocks based on seismic attribute constraints. This method utilizes seismic attributes to establish constraints, thereby improving the accuracy of establishing low-frequency models for TOC inversion in marine areas with few wells. The low-frequency models established by this method align with regional sedimentary facies studies, thus enhancing the accuracy of TOC prediction for source rocks.

[0023] like Figure 1-8 As shown, the TOC prediction method for lacustrine source rocks based on seismic attribute constraints provided by this invention includes the following specific steps: Step S1: Source Rock TOC Logging Curve Prediction: Select the logging curves from well L-1 that have a high correlation with the TOC of lacustrine source rocks, such as gamma (GR), sonic transit time (AC), resistivity (Rt), and density (ρ) curves. Calculate the lacustrine source rock TOC curve using a multiple regression formula. The unit of TOC is wt%. Figure 1As shown.

[0024] The TOC curves of lacustrine source rocks are as follows: .

[0025] Step S2: Optimization of TOC-sensitive elastic parameters of lacustrine source rocks: Using the P-wave, S-wave, and density curves from well L-1, calculate the Lamé coefficient λ, shear modulus μ, bulk modulus K, Young's modulus E, and Poisson's ratio σ, and then perform cross-plot analysis with the TOC curves of lacustrine source rocks. Figure 2 , 3 The λρ shown is the TOC sensitive parameter curve of lacustrine source rocks. The regression formula between TOC and sensitive elastic parameters of lacustrine source rocks is: .

[0026] Step S3: Optimization of Seismic Attributes Sensitive to Source Rocks: First, calculate various seismic attribute volumes, including sweet spot attributes, chaotic attributes, and variance attributes; then extract the seismic attributes at the wellbore access location to form attribute curves corresponding to the well logging curves; next, calculate the correlation between the TOC curve of the lacustrine source rock and each seismic attribute curve; finally, based on the correlation results, select the sweet spot attribute and chaotic attribute as TOC-sensitive seismic attributes, such as... Figure 4 As shown.

[0027] Step S4: Well-seismic analysis to determine seismic attribute threshold values: Using the seismic attributes selected in the previous step, a combined well-seismic approach is adopted to determine the seismic attribute threshold values, sweet spot attributes, and chaotic attributes that characterize the source rock based on the encountered source rock section. The two attributes are then fused together, such as... Figure 5 As shown.

[0028] Step S5: Calculation of the initial low-frequency model for seismic inversion: Using actual well L-1 data and regional interpreted stratigraphic levels T80 and Tg, the initial low-frequency model data is obtained using an inverse distance weighting method, such as... Figure 6 As shown.

[0029] Step S6: Establishment of seismic attribute-constrained low-frequency model: The attribute fusion body in step S4 and the initial low-frequency model data in step S5 are weighted and fused. The weighting coefficient of the attribute fusion body is 0.5 and the weighting coefficient of the initial low-frequency model is 0.5 to construct attribute-constrained low-frequency model data.

[0030] Step S7: Seismic inversion of source rock TOC data: Using the attribute-constrained low-frequency model obtained in the previous step, the source rock sensitive parameter λρ data volume is obtained by using elastic impedance inversion technology. Then, according to the regression formula of lacustrine source rock TOC and sensitive elastic parameter in step S2, the TOC data volume is calculated.

[0031] This invention utilizes seismic attributes to establish constraints, thereby improving the accuracy of low-frequency models for source rock TOC inversion in marine areas with few wells. The low-frequency models established by this method are consistent with the understanding of regional sedimentary facies studies, ensuring the accuracy of source rock TOC prediction.

[0032] A second aspect of the present invention provides a TOC prediction device for lacustrine source rocks based on seismic attribute constraints, comprising: The first processing unit is used to select curves that are highly correlated with the TOC of lacustrine source rocks and calculate the TOC curves of lacustrine source rocks using a multiple regression formula. The second processing unit is used to calculate various elastic parameter curves using actual drilling data, and then perform cross-analysis with the TOC curves of lacustrine source rocks to select elastic parameters that are sensitive to the TOC of lacustrine source rocks, and establish regression formulas between the TOC of lacustrine source rocks and sensitive parameters. The third processing unit is used to extract various seismic attributes from the wellbore, calculate the correlation between the seismic attributes from the wellbore and the TOC curve of the lacustrine source rocks, and select several seismic attributes with high correlation. The fourth processing unit is used to determine the seismic attribute threshold value for depicting the development characteristics of source rocks in lacustrine facies by using several highly correlated seismic attributes in combination with actual drilling data, and then fuse them to obtain an attribute fusion body. The fifth processing unit is used to obtain initial low-frequency model data based on actual drilling data and regional interpretation stratigraphic data; The sixth processing unit is used to perform weighted fusion of the attribute fusion body in the fourth processing unit and the initial low-frequency model data in the fifth processing unit to construct an attribute-constrained low-frequency model. The seventh processing unit is used to perform wave impedance inversion based on the attribute-constrained low-frequency model constructed by the sixth processing unit to obtain the elastic parameter data volume. Then, according to the regression formula of lacustrine source rock TOC and sensitive parameters established by the second processing unit, the TOC data volume is calculated.

[0033] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method for predicting TOC of lacustrine source rocks based on seismic attribute constraints.

[0034] A fourth aspect of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for predicting TOC of lacustrine source rocks based on seismic attribute constraints.

[0035] This invention is described based on flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to specific embodiments. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing device, generate instructions for implementing the flowcharts and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0036] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0037] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0038] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting the total oxygen consumption (TOC) of lacustrine source rocks based on seismic attribute constraints, characterized in that, include: S1: Select curves with high correlation to TOC of lacustrine source rocks and calculate the TOC curves of lacustrine source rocks using multiple regression formulas. S2: Calculate various elastic parameter curves using actual drilling data, then perform cross-analysis with lacustrine source rock TOC curves, select elastic parameters sensitive to lacustrine source rock TOC, and establish regression formulas between lacustrine source rock TOC and sensitive elastic parameters. S3: Extract various seismic attributes from the wellbore, calculate the correlation between the seismic attributes from the wellbore and the TOC curve of the lacustrine source rocks, and select several seismic attributes with high correlation. S4: Using several highly correlated seismic attributes and combining them with actual drilling data, determine the seismic attribute threshold values ​​that characterize the development features of lacustrine source rocks, and fuse them to obtain an attribute fusion body. S5: Based on actual drilling data and regional interpretation stratigraphic data, obtain initial low-frequency model data; S6: Weighted fusion of the attribute fusion body obtained in step S4 and the initial low-frequency model data obtained in step S5 is performed to construct an attribute-constrained low-frequency model. S7: Based on the attribute-constrained low-frequency model constructed in step S6, wave impedance inversion is carried out to obtain the elastic parameter data volume. Then, according to the regression formula between the TOC of lacustrine source rocks and the sensitive elastic parameters established in step S2, the TOC data volume is calculated.

2. The method for predicting TOC of lacustrine source rocks based on seismic attribute constraints according to claim 1, characterized in that, The curves that are highly correlated with TOC of lacustrine source rocks in step S1 include: gamma curve, sonic transit time curve, resistivity curve, or density curve.

3. The method for predicting TOC of lacustrine source rocks based on seismic attribute constraints according to claim 1, characterized in that, The TOC curves of the lacustrine source rocks in step S1 are as follows: ； In the formula, TOC is in wt%, GR is the gamma curve, AC is the acoustic transit time curve, Rt is the resistivity curve, and ρ is the density curve.

4. The method for predicting TOC of lacustrine source rocks based on seismic attribute constraints according to claim 3, characterized in that, The various elastic parameter curves in step S2 include: Lamé coefficient elastic parameter curve, shear modulus elastic parameter curve, bulk modulus elastic parameter curve, Young's modulus elastic parameter curve, and Poisson's ratio elastic parameter curve.

5. The method for predicting TOC of lacustrine source rocks based on seismic attribute constraints according to claim 4, characterized in that, The regression formulas for TOC and sensitive elastic parameters of lacustrine source rocks in step S2 are as follows: ； In the formula, λ is the Lamé coefficient.

6. The method for predicting TOC of lacustrine source rocks based on seismic attribute constraints according to claim 5, characterized in that, Step S3 includes the following specific steps: First, various seismic attribute volumes, including sweet spot attribute, chaotic attribute, and variance attribute, are calculated. Then, the seismic attributes at the wellbore location are extracted to form attribute curves corresponding to the well logging curves. Next, the correlation between the TOC curve of the lacustrine source rock and each seismic attribute curve is calculated. Finally, based on the correlation results, the sweet spot attribute and chaotic attribute are selected as TOC-sensitive seismic attributes.

7. The method for predicting TOC of lacustrine source rocks based on seismic attribute constraints according to claim 6, characterized in that, In step S7, the attribute-constrained low-frequency model calculated in S6 is used to obtain the sensitive parameters of lacustrine source rocks using elastic impedance inversion technology. Then, the TOC data volume is calculated based on the regression formula between the TOC of lacustrine source rocks and the sensitive elastic parameters in S2.

8. A TOC prediction device for lacustrine source rocks based on seismic attribute constraints, characterized in that, include: The first processing unit is used to select curves that are highly correlated with the TOC of lacustrine source rocks and calculate the TOC curves of lacustrine source rocks using a multiple regression formula. The second processing unit is used to calculate various elastic parameter curves using actual drilling data, and then perform cross-analysis with the TOC curves of lacustrine source rocks to select elastic parameters that are sensitive to the TOC of lacustrine source rocks, and establish regression formulas between the TOC of lacustrine source rocks and sensitive parameters. The third processing unit is used to extract various seismic attributes from the wellbore, calculate the correlation between the seismic attributes from the wellbore and the TOC curve of the lacustrine source rocks, and select several seismic attributes with high correlation. The fourth processing unit is used to determine the seismic attribute threshold value that characterizes the development characteristics of lacustrine source rocks by using several highly correlated seismic attributes and combining them with actual drilling data, and then fuse them to obtain an attribute fusion body. The fifth processing unit is used to obtain initial low-frequency model data based on actual drilling data and regional interpretation stratigraphic data; The sixth processing unit is used to perform weighted fusion of the attribute fusion body in the fourth processing unit and the initial low-frequency model data in the fifth processing unit to construct an attribute-constrained low-frequency model. The seventh processing unit is used to perform wave impedance inversion based on the attribute-constrained low-frequency model constructed by the sixth processing unit to obtain the elastic parameter data volume. Then, according to the regression formula of lacustrine source rock TOC and sensitive parameters established by the second processing unit, the TOC data volume is calculated.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for predicting TOC of lacustrine source rocks based on seismic attribute constraints as described in any one of claims 1-7.

10. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method for predicting TOC of lacustrine source rocks based on seismic attribute constraints as described in any one of claims 1-7.

Images