Well logging prediction method, apparatus, and device for organic carbon content in source rock, and medium

Abstract

A well logging prediction method for the organic carbon content in a source rock, comprising: acquiring well logging data of a source rock in a target single well (S201); on the basis of the well logging data, determining a maximum horizontal principal stress curve of the source rock in the target single well (S202); on the basis of the well logging data and the maximum horizontal principal stress curve, determining a resistivity curve after considering the influence of crustal stress (S203); and on the basis of the resistivity curve after considering the influence of crustal stress, predicting the organic carbon content in the source rock in the target single well at each depth point by means of a ΔLog R model, so as to obtain a well logging prediction result of the organic carbon content in the source rock in the target single well (S204). Also provided are a well logging prediction apparatus and device for the organic carbon content in a source rock. By determining the resistivity curve after considering the influence of crustal stress, the actual situation of the stratum can be more truly reflected, improving the accuracy of well logging prediction of the organic carbon content in the source rock.

Description

Hydrocarbon source rock organic carbon content logging prediction method, device, equipment and medium

[0001] The application claims priority to the Chinese patent application No. 202411233298.0, filed on September 3, 2024, and entitled "Hydrocarbon source rock organic carbon content logging prediction method, device, equipment and medium", the content of which is incorporated herein by reference in its entirety. TECHNICAL FIELD

[0002] The present application relates to, but is not limited to, the technical field of oil and gas exploration and development, and more specifically, relates to a hydrocarbon source rock organic carbon content logging prediction method, device, equipment and medium. BACKGROUND

[0003] With the increasing global energy demand, the exploration and development of oil and gas resources gradually shifts from conventional oil and gas reservoirs to unconventional oil and gas reservoirs, and from shallow layers to deep and ultra-deep layers. In particular, ultra-deep tight sandstone and its hydrocarbon source rock under strong tectonic extrusion stress have become a key and hot field of current sedimentology research; this is not only an important direction for future oil and gas exploration, but also an important choice to ensure energy security. In oil and gas exploration, the evaluation of hydrocarbon source rock is a crucial link, and the prediction of organic carbon content is one of the core contents of hydrocarbon source rock evaluation.

[0004] In related technologies, methods such as ΔLogR or multiple regression are usually used to predict the organic carbon content of deep and ultra-deep hydrocarbon source rocks. However, since the traditional ΔLogR method is based on marine and normally compacted shallow layers, when the burial depth of the hydrocarbon source rock increases, the logging response of the solid organic matter (kerogen) will be affected. Strong compaction causes the acoustic time difference to decrease, especially in areas with high tectonic extrusion stress, where the hydrocarbon source rock is sensitive to tectonic extrusion stress and becomes densified due to dehydration, and its resistivity increases significantly. At this time, the size of the amplitude difference ΔlogR after reversing and superimposing the acoustic time difference curve and the resistivity curve is not always linearly related to the organic carbon content. Therefore, when directly using methods such as ΔLogR or multiple regression to predict the organic carbon content of deep and ultra-deep hydrocarbon source rocks, there is a problem of low prediction accuracy.

[0005] Therefore, there is an urgent need to provide a hydrocarbon source rock organic carbon content logging prediction scheme with high accuracy. SUMMARY

[0006] The following is a summary of the subject matter described in detail herein. This summary is not intended to limit the scope of the claims.

[0007] The purpose of the present application is to provide a hydrocarbon source rock organic carbon content logging prediction method, device, equipment and medium, so as to improve the problem of low prediction accuracy when the related technology is used to predict the organic carbon content of deep and ultra-deep hydrocarbon source rocks.

[0008] In a first aspect, the embodiments of the present application provide a hydrocarbon source rock organic carbon content logging prediction method, comprising:

[0009] Obtaining logging data of hydrocarbon source rocks in a target single well;

[0010] Based on the logging data, determining the maximum horizontal principal stress curve of the hydrocarbon source rocks in the target single well;

[0011] Based on the logging data and the maximum horizontal principal stress curve, determining the resistivity curve considering the influence of geostress;

[0012] Based on the resistivity curve considering the influence of geostress, predicting the organic carbon content of the hydrocarbon source rocks in the target single well at each depth point by the ΔLogR model to obtain the logging prediction result of the organic carbon content of the hydrocarbon source rocks in the target single well.

[0013] In a possible implementation, the logging data includes a resistivity curve, and based on the logging data and the maximum horizontal principal stress curve, the resistivity curve considering the influence of geostress is determined, including: for the resistivity included in the resistivity curve, determining the maximum horizontal principal stress corresponding to the depth point corresponding to the resistivity in the maximum horizontal principal stress curve; calculating the quotient of the resistivity and the maximum horizontal principal stress to obtain the resistivity of the depth point considering the influence of geostress; and obtaining the resistivity curve considering the influence of geostress according to the resistivity of each depth point considering the influence of geostress respectively corresponding to each depth point included in the resistivity curve.

[0014] In a possible implementation, the logging data further includes a compressional wave time difference curve and a bulk density curve, and based on the resistivity curve considering the influence of geostress, the organic carbon content of the hydrocarbon source rocks in the target single well at each depth point is predicted by the ΔLogR model to obtain the logging prediction result of the organic carbon content of the hydrocarbon source rocks in the target single well, including: superimposing the resistivity curve considering the influence of geostress and the compressional wave time difference curve in reverse, calculating the difference between the resistivity curve considering the influence of geostress and the compressional wave time difference curve to obtain the amplitude difference of the hydrocarbon source rocks in the target single well at each depth point; and substituting the amplitude difference of the hydrocarbon source rocks in the target single well at each depth point into the ΔLogR model to obtain the logging prediction result of the organic carbon content of the hydrocarbon source rocks in the target single well.

[0015] In a possible implementation, the well logging data further includes a shear wave slowness curve, and the maximum horizontal principal stress curve of the source rock in the target single well is determined based on the well logging data, including: determining the overburden pressure and the pore pressure of the source rock in the target single well based on the bulk density curve and the compressional wave slowness curve according to a one-dimensional rock mechanics model; determining the maximum horizontal principal stress curve of the source rock in the target single well based on a combined spring model based on the Poisson's ratio, the Young's modulus, the overburden pressure and the pore pressure, the Poisson's ratio and the Young's modulus being determined based on the compressional wave slowness curve, the shear wave slowness curve and the bulk density curve; and the overburden pressure, the pore pressure and the maximum horizontal principal stress of the source rock in the target single well satisfying the following formula:

[0016] wherein P0 is the overburden pressure, P p is the pore pressure, SH max is the maximum horizontal principal stress corresponding to a depth point in the maximum horizontal principal stress curve; Z represents the depth, TVD represents the vertical depth, ρ b is the density logging, g is the gravitational constant; P pn is the normal compaction pore pressure, α is the Biot coefficient, Δt0 is the mudstone logging acoustic slowness at the calculation point, Δt n is the acoustic slowness of the mudstone normal compaction trend line corresponding to the calculation point, n is the Eaton coefficient; υ is the Poisson's ratio, E is the Young's modulus, ε H is the correlation coefficient of the maximum horizontal principal stress, and ε h is the correlation coefficient of the minimum horizontal principal stress.

[0017] In a possible implementation, the well logging data of the source rock in the target single well is obtained, including: obtaining well logging data of the source rock in the target single well, the well logging data including the dipole acoustic array logging data and the conventional well logging data; extracting the compressional wave slowness curve and the shear wave slowness curve of the source rock in the target single well based on the dipole acoustic array logging data; and obtaining the resistivity curve and the bulk density curve of the source rock in the target single well based on the conventional well logging data.

[0018] In a possible implementation, the well logging prediction method of the organic carbon content of the source rock further includes: obtaining core measured data of the source rock in the target single well, the core measured data including measured values of the organic carbon content of the source rock in the target single well at multiple depth points; generating a crossplot of the measured organic carbon content and the predicted organic carbon content of the source rock in the target single well based on the core measured data and the well logging prediction result of the organic carbon content of the source rock in the target single well, the crossplot being used to represent the accuracy of the well logging prediction result of the organic carbon content of the source rock in the target single well.

[0019] In a second aspect, the present application provides a device for well logging prediction of the organic carbon content of a source rock, including:

[0020] an acquisition module configured to acquire logging data of a source rock in a target single well;

[0021] a first determination module configured to determine a maximum horizontal principal stress curve of the source rock in the target single well based on the logging data;

[0022] a second determination module configured to determine a resistivity curve considering the influence of the ground stress based on the logging data and the maximum horizontal principal stress curve;

[0023] a prediction module configured to predict the organic carbon content of the source rock in the target single well at each depth point by using a Delta Log R model based on the resistivity curve considering the influence of the ground stress, to obtain a logging prediction result of the organic carbon content of the source rock in the target single well.

[0024] In a third aspect, the present application provides an electronic device, comprising: a processor, and a memory connected with the processor in communication;

[0025] the memory is configured to store computer-executable instructions;

[0026] the processor is configured to execute the computer-executable instructions stored in the memory, to implement the method of any one of the first aspect.

[0027] In a fourth aspect, the present application provides a computer-readable storage medium, the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are configured to implement the method of any one of the first aspect when executed.

[0028] In a fifth aspect, the present application provides a computer program product, comprising a computer program, and the computer program is configured to implement the method of any one of the first aspect when executed.

[0029] In a sixth aspect, the present application provides a computer program, and the computer program is configured to implement the method of any one of the first aspect when executed on a computer.

[0030] The hydrocarbon source rock organic carbon content logging prediction method, device, equipment and medium provided by the embodiment of the application, obtain logging data of a hydrocarbon source rock in a target single well, determine a maximum horizontal principal stress curve of the hydrocarbon source rock in the target single well based on the logging data, and determine a resistivity curve considering the influence of ground stress based on the logging data and the maximum horizontal principal stress curve. Further, the organic carbon content of the hydrocarbon source rock at each depth point in the target single well is predicted by a ΔLogR model based on the resistivity curve considering the influence of ground stress, and a logging prediction result of the organic carbon content of the hydrocarbon source rock in the target single well is obtained. In this process, the resistivity curve considering the influence of ground stress is determined by considering the influence of ground stress, so that it can more truly reflect the actual situation of the formation. Further, the resistivity curve considering the influence of ground stress is introduced into the ΔlogR model, and the organic carbon content is predicted in combination with the logging data, so that it performs better in the logging prediction of the organic carbon content of the hydrocarbon source rock, and the accuracy of the logging prediction of the organic carbon content of the hydrocarbon source rock is further improved, especially under the condition of strong tectonic extrusion stress. BRIEF DESCRIPTION OF DRAWINGS

[0031] The accompanying drawings, which are incorporated herein and form a part of the specification, illustrate embodiments consistent with the present application and, together with the description, further serve to explain the principles of the application.

[0032] FIG. 1 is a schematic diagram of an application scenario of a hydrocarbon source rock organic carbon content logging prediction method provided by an exemplary embodiment of the application;

[0033] FIG. 2 is a schematic diagram of a flow of a hydrocarbon source rock organic carbon content logging prediction method provided by an exemplary embodiment of the application;

[0034] FIG. 3 is a crossplot of measured organic carbon content and predicted organic carbon content provided by an exemplary embodiment of the application;

[0035] FIG. 4 is another schematic diagram of a flow of a hydrocarbon source rock organic carbon content logging prediction method provided by an exemplary embodiment of the application;

[0036] FIG. 5 is a logging prediction result map of the organic carbon content of a hydrocarbon source rock in a target single well provided by an exemplary embodiment of the application;

[0037] FIG. 6 is a schematic diagram of a structure of a hydrocarbon source rock organic carbon content logging prediction device provided by an exemplary embodiment of the application;

[0038] FIG. 7 is a schematic diagram of a structure of an electronic device provided by an exemplary embodiment of the application.

[0039] Label explanation: 11-client; 12-server; 60-hydrocarbon source rock organic carbon content logging prediction device; 61-acquisition module; 62-first determination module; 63-second determination module; 64-prediction module; 70-electronic device; 71-processor; 72-memory; 73-communication interface.

[0040] The specific embodiments of the application have been shown and described in the foregoing drawings and specification, it being understood that the application is not limited to the embodiments shown and described, but rather, includes all embodiments consistent with the claimed concept. DETAILED DESCRIPTION

[0041] The exemplary embodiments will be described in detail herein with reference to the attached drawings. The description of the exemplary embodiments is intended to apply to all alternative embodiments, as would be understood by one skilled in the art. To the extent that they do not particularize to the application and are not specifically covered or claimed herein, it is therefore understood that such changes in the nature of the exemplary embodiments will occur to those skilled in the art upon a reading and understanding of this detailed application and are herein described by way of example only.

[0042] The terms "first", "second", and the like, herein and in the claims, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the use of such terms is merely for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order, unless explicitly stated otherwise. It is to be understood that the terms "including", "comprising", "consisting" and "having" and variations thereof, are meant to cover the case where non-exclusive inclusion is intended, e.g., processes, systems, products, or apparatuses that comprise, contain, or consist of a series of steps or units, without necessarily being limited to the clear, explicitly listed steps or units, but can include other steps or units not expressly listed, or inherent to such processes, systems, products, or apparatuses.

[0043] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards, and provide corresponding operation portal for user to choose authorization or refusal.

[0044] First, some terms involved in the present application are explained:

[0045] Delta Log R method: a method for predicting organic carbon content based on logging data, which uses the difference between different logging curves (such as acoustic travel time, bulk density and resistivity, etc.) to estimate the organic carbon content in source rocks.

[0046] Multiple regression analysis method: a statistical method for predicting the organic carbon content in source rocks by establishing a multiple regression model between logging data and organic carbon content.

[0047] In the related art, the traditional Delta Log R method is based on marine and normally compacted shallow formations. When the burial depth of source rocks increases, the logging response of solid organic matter (kerogen) will be affected. For example, as the burial depth increases, the compaction effect is strong, which reduces the acoustic travel time, especially in areas with high tectonic extrusion stress. Mudstone (source rock) is sensitive to tectonic extrusion stress, and its resistivity will increase significantly due to dehydration and densification. At this time, the size of the amplitude difference ΔlogR after reversing the acoustic travel time curve and the resistivity curve is not linearly related to the organic carbon content. Therefore, when directly using the Delta Log R or multiple regression method to predict the organic carbon content of deep and ultra-deep source rocks, there is a problem of low prediction accuracy.

[0048] Based on the above, the embodiments of the present application provide a source rock organic carbon content logging prediction scheme. By considering the influence of ground stress, the ground stress and resistivity curve of different burial depths are associated to obtain the resistivity curve considering the influence of ground stress. Further, the resistivity curve considering the influence of ground stress is introduced into the ΔlogR model, and the logging data is combined to predict the organic carbon content, thereby improving the accuracy of source rock organic carbon content logging prediction.

[0049] FIG. 1 is a schematic diagram of an application scenario of the source rock organic carbon content logging prediction method provided by the exemplary embodiments of the present application. As shown in FIG. 1, the application scenario includes a client 11 and a server 12, wherein the number of clients 11 can be at least one. In actual application, when the server 12 detects the logging data of the source rock in the target single well submitted by the user through the client 11, it executes the source rock organic carbon content logging prediction method provided by the present application to obtain the logging prediction result of the source rock organic carbon content in the target single well. Correspondingly, the client 11 obtains the logging prediction result of the source rock organic carbon content in the target single well sent by the server 12.

[0050] It should be noted that the server 12 can also be replaced by a server cluster or other computing device with certain computing power. The client 11 can be a computer, a mobile phone, a notebook computer or a personal digital assistant (PDA) and the like.

[0051] It should be further noted that the target source rock in the single well in the present application is a deep source rock or an ultra-deep source rock, wherein the deep source rock refers to a source rock with a burial depth of 4500-6000 m, and the ultra-deep source rock refers to a source rock with a burial depth of more than 6000 m.

[0052] The hydrocarbon source rock organic carbon content logging prediction method according to the exemplary embodiments of the present application will be described below with reference to FIG. 2 in combination with the application scenario of FIG. 1. It should be noted that the above-mentioned application scenario is only shown for the purpose of facilitating the understanding of the spirit and principles of the present application, and the embodiments of the present application are not limited by the application scenario shown in FIG. 1.

[0053] FIG. 2 is a flowchart of the hydrocarbon source rock organic carbon content logging prediction method provided by the exemplary embodiments of the present application. As shown in FIG. 2, the hydrocarbon source rock organic carbon content logging prediction method in the embodiments of the present application includes the following steps:

[0054] S201, obtaining logging data of a target source rock in a single well.

[0055] For example, as shown in FIG. 1, a user obtains logging data of a target source rock in a single well provided by a relevant geological exploration department or a relevant geological information platform through a client 11, processes the logging data according to a preset rule to obtain logging data of the target source rock in the single well, and sends the logging data to a server 12; correspondingly, the server 12 receives the logging data of the target source rock in the single well sent by the client 11. The preset rule can include but is not limited to a data processing algorithm and a data fine-tuning strategy in the prior art, etc.

[0056] Optionally, before or after processing the logging data according to the preset rule, a processing for filtering interference such as de-noising processing, etc. can be further included, and / or a data enhancement processing such as interpolation, sampling, data fine-tuning, etc. can be further included, etc.

[0057] S202, determining a maximum horizontal principal stress curve of the target source rock in the single well based on the logging data.

[0058] For example, in this step, based on the logging data, the maximum horizontal principal stress values of the target source rock in the single well at various depth points are determined according to a pre-set calculation formula, an empirical formula and a geomechanics model, etc., and the maximum horizontal principal stress values at the various depth points are plotted into a curve, thereby obtaining the maximum horizontal principal stress curve of the target source rock in the single well.

[0059] S203, determining a resistivity curve considering the influence of geostress based on the logging data and the maximum horizontal principal stress curve.

[0060] For example, the logging data and the maximum horizontal principal stress curve are associated, the effect of the ground stress is taken into account in the calculation of the resistivity curve, the logging data of each depth point and the maximum horizontal principal stress are combined, the resistivity curve considering the effect of the ground stress is determined, and therefore the more accurate formation resistivity distribution is obtained.

[0061] In some embodiments, the logging data includes a resistivity curve, and the resistivity curve considering the effect of the ground stress is determined based on the logging data and the maximum horizontal principal stress curve, including: for the resistivity included in the resistivity curve, determining the maximum horizontal principal stress corresponding to the depth point corresponding to the resistivity in the maximum horizontal principal stress curve; calculating the quotient of the resistivity and the maximum horizontal principal stress to obtain the resistivity of the depth point considering the effect of the ground stress; and obtaining the resistivity curve considering the effect of the ground stress according to the resistivity of each depth point corresponding to the resistivity curve considering the effect of the ground stress.

[0062] The resistivity considering the effect of the ground stress satisfies the following formula:

[0063] The resistivity considering the effect of the ground stress satisfies the following formula: max The resistivity considering the effect of the ground stress satisfies the following formula:

[0064] S204, based on the resistivity curve considering the effect of the ground stress, the organic carbon content of the hydrocarbon source rock at each depth point in the target single well is predicted through the ΔLogR model to obtain the logging prediction result of the organic carbon content of the hydrocarbon source rock in the target single well.

[0065] For example, the resistivity curve considering the effect of the ground stress is used instead of the original resistivity curve, the organic carbon content of the hydrocarbon source rock at each depth point in the target single well is predicted through the ΔLogR model based on the traditional ΔLogR method, and therefore the logging prediction result of the organic carbon content of the hydrocarbon source rock in the target single well is obtained.

[0066] The hydrocarbon source rock organic carbon content logging prediction method provided by the embodiments of the present application determines the resistivity curve considering the effect of the ground stress, so that it can more truly reflect the actual situation of the formation; further, the resistivity curve considering the effect of the ground stress is introduced into the ΔlogR model, and the organic carbon content is predicted in combination with the logging data, so that it performs better in the hydrocarbon source rock organic carbon content logging prediction, and in particular under the condition of strong tectonic extrusion stress, the accuracy of the hydrocarbon source rock organic carbon content logging prediction is further improved.

[0067] In some embodiments, the well logging data further comprises a compressional wave slowness curve and a bulk density curve, and the organic carbon content of the source rock in the target single well at each depth point is predicted based on the resistivity curve considering the effect of the ground stress through a delta log R model to obtain a well logging prediction result of the organic carbon content of the source rock in the target single well, including: inversely superimposing the resistivity curve considering the effect of the ground stress and the compressional wave slowness curve, calculating the difference between the resistivity curve considering the effect of the ground stress and the compressional wave slowness curve to obtain the amplitude difference of the source rock in the target single well at each depth point; and substituting the amplitude difference of the source rock in the target single well at each depth point into the delta log R model to obtain the well logging prediction result of the organic carbon content of the source rock in the target single well.

[0068] The compressional wave is one of seismic waves, and has a fast propagation speed and mainly propagates through compression and expansion of matter. The compressional wave slowness curve records the time of propagation of the compressional wave (P wave) in the formation.

[0069] For example, the amplitude difference ΔlogR of the source rock in the target single well at each depth point satisfies the following formula: ΔlogR=log(R / R Baseline )+K(Δt-Δt Baseline )

[0070] R Baseline is the baseline resistivity, and the unit is Ω·m; Δt is the compressional wave slowness, and the unit is μs / ft; Δt Baseline is the baseline compressional wave slowness, and the unit is μs / ft; and K is the superposition coefficient, which can be 0.02. The baseline refers to the overlapping part of the resistivity and the compressional wave slowness of the non-source rock.

[0071] Further, the amplitude difference of the source rock in the target single well at each depth point is substituted into the delta log R model to obtain the well logging prediction result of the organic carbon content of the source rock in the target single well, wherein the well logging prediction result of the organic carbon content TOC at each depth point satisfies the following formula: TOC=ΔlogR×10(2.297-0.1688LOM)

[0072] LOM is a parameter related to maturity.

[0073] In the embodiments of the present application, the resistivity curve considering the effect of the ground stress is introduced into the delta log R model, and the organic carbon content is predicted in combination with the well logging data, so that the accuracy of the well logging prediction of the organic carbon content of the source rock is improved.

[0074] In some embodiments, the well logging data further comprises a shear wave slowness curve, and the maximum horizontal principal stress curve of the source rock in the target single well is determined based on the well logging data, comprising: determining the overburden pressure and the pore pressure of the source rock in the target single well based on the bulk density curve and the compressional wave slowness curve according to a one-dimensional rock mechanics model; determining the maximum horizontal principal stress curve of the source rock in the target single well based on the Poisson's ratio, the Young's modulus, the overburden pressure and the pore pressure according to a combined spring model, the Poisson's ratio and the Young's modulus being determined based on the compressional wave slowness curve, the shear wave slowness curve and the bulk density curve; and the overburden pressure, the pore pressure and the maximum horizontal principal stress of the source rock in the target single well satisfying the following formula:

[0075] wherein P0 is the overburden pressure, P p is the pore pressure, SH max is the maximum horizontal principal stress corresponding to a depth point in the maximum horizontal principal stress curve, both in units of MPa; Z represents the depth, TVD represents the vertical depth, both in units of m; p b is the density logging, in units of g / cm 3 ; g is the gravitational constant, being 9.8 N / kg; P pn is the normal compaction pore pressure, in units of MPa; a is the Biot coefficient, usually taking a value of 1; At0 is the logging acoustic slowness of the mudstone at the calculation point, in units of ps / ft; At n is the acoustic slowness of the normal compaction trend line of the mudstone corresponding to the calculation point, in units of ps / ft; n is the Eaton coefficient, taking a value range of 3-9; v is the Poisson's ratio, being dimensionless; E is the Young's modulus, in units of GPa; e H is the correlation coefficient of the maximum horizontal principal stress, e h is the correlation coefficient of the minimum horizontal principal stress.

[0076] For example, the overburden pressure and the pore pressure of the source rock in the target single well are determined based on the bulk density curve and the compressional wave slowness curve according to a one-dimensional rock mechanics model and a density extrapolation method; further, the maximum horizontal principal stress curve of the source rock in the target single well is determined based on the Poisson's ratio, the Young's modulus, the overburden pressure and the pore pressure according to a combined spring model. It can be understood that the process of determining the Poisson's ratio and the Young's modulus based on the compressional wave slowness curve, the shear wave slowness curve and the bulk density curve is the same as in related technologies, which will not be described here. It should be noted that the shear wave is another kind of seismic wave, which propagates slowly and mainly propagates through shear material, and the shear wave slowness curve records the time of the shear wave (S wave) propagating in the formation.

[0077] Correspondingly, the minimum horizontal principal stress Sh min satisfies the following formula:

[0078] In some embodiments, the method for obtaining the logging data of the source rock in the target single well comprises: obtaining the logging data of the source rock in the target single well, wherein the logging data comprises the data based on the dipole acoustic array logging and the conventional logging; extracting the P-wave slowness curve and the S-wave slowness curve of the source rock in the target single well based on the data based on the dipole acoustic array logging; and obtaining the resistivity curve and the bulk density curve of the source rock in the target single well based on the conventional logging data.

[0079] In some embodiments, the dipole acoustic array logging is an advanced acoustic logging technology, which generates acoustic waves in the downhole tool and records the propagation time and attenuation characteristics of the acoustic waves in the formation through the array receiver. Correspondingly, for the dipole acoustic array logging data of the source rock in the target single well, the high-resolution P-wave slowness curve and the S-wave slowness curve of the source rock in the target single well are extracted based on the best practices and experience summary for logging data processing and geological analysis in the prior art. The conventional logging includes resistivity logging, density logging, natural gamma logging, etc. The resistivity curve and the bulk density curve of the source rock in the target single well are directly obtained through the conventional logging data of the source rock in the target single well.

[0080] In the related art, the evaluation of deep or ultra-deep source rock still relies on laboratory rock pyrolysis experiments, and traditional methods for predicting organic carbon content, such as ΔLogR and multiple regression, have poor effects in predicting the organic carbon content of deep and ultra-deep source rock. To solve this problem, the embodiments of the present application verify the prediction results of the organic carbon content considering the influence of the ground stress by combining with the rock pyrolysis experiment, thereby ensuring the accuracy of the prediction results of the organic carbon content considering the influence of the ground stress.

[0081] Therefore, based on the above embodiments, in some embodiments, the method for predicting the organic carbon content of the source rock by logging further comprises: obtaining the core measured data of the source rock in the target single well, wherein the core measured data comprises the measured values of the organic carbon content of the source rock in the target single well at multiple depth points; and generating a crossplot of the measured organic carbon content and the predicted organic carbon content of the source rock in the target single well based on the core measured data and the logging prediction results of the organic carbon content of the source rock in the target single well, wherein the crossplot is used to characterize the accuracy of the logging prediction results of the organic carbon content of the source rock in the target single well.

[0082] In some embodiments, the core measured data is obtained by rock pyrolysis experiment, and the rock pyrolysis experiment is an experimental technique for studying the decomposition behavior and products of rocks under high temperature conditions. The rock samples of the source rock are pyrolyzed, and after the pyrolysis is completed, the remaining solid products are taken out, and the organic carbon content of the remaining solid products is determined. Through the rock pyrolysis experiment, the measured values of the organic carbon content of the source rock in the target single well at multiple depth points can be obtained.

[0083] Further, based on the core measured data and the logging prediction result of the organic carbon content of the source rock in the target single well, a crossplot of the measured organic carbon content and the predicted organic carbon content of the source rock in the target single well is generated. For example, FIG. 3 is a crossplot of the measured organic carbon content and the predicted organic carbon content provided by an example embodiment of the present application. As shown in FIG. 3, the x-axis represents the measured value of the organic carbon content obtained by rock pyrolysis experiment, in percentage (%); the y-axis represents the logging prediction result of the organic carbon content of the source rock in the target single well, in percentage (%); the diagonal line (dotted line) represents the case where the measured organic carbon content is equal to the predicted organic carbon content in an ideal situation, that is, if all the data points fall on this line, it means that the prediction value of the prediction model is completely accurate, and by observing the position of the data points relative to this line, the accuracy of the prediction model can be evaluated, if the data points are mostly concentrated near this line, it means that the prediction of the model is relatively accurate, if the data points deviate far from this line, it means that there is a large error in the prediction of the model; R 2 is the correlation coefficient, which is used to quantify the strength and direction of the linear relationship between the measured organic carbon content and the predicted organic carbon content; for example, if the absolute value of the correlation coefficient is close to 1 (for example, 0.8 to 1 or -0.8 to -1), it means strong correlation; if the absolute value of the correlation coefficient is between 0.5 and 0.8, it means moderate correlation; if the absolute value of the correlation coefficient is between 0.3 and 0.5, it means weak correlation; if the absolute value of the correlation coefficient is less than 0.3, it means almost no correlation; by the correlation coefficient R 2 = 0.8313 shown in FIG. 3, it means that there is a strong correlation between the measured organic carbon content and the predicted organic carbon content, and further indicates that the accuracy of the logging prediction result of the organic carbon content of the source rock in the target single well is high.

[0084] FIG. 4 is another flowchart of the method for logging prediction of the organic carbon content of the source rock provided by an example embodiment of the present application. As shown in FIG. 4, the method for logging prediction of the organic carbon content of the source rock in the example embodiment of the present application includes the following steps:

[0085] S401, obtaining the logging data and the core measured data of the source rock in the target single well, the logging data including the data based on the dipole acoustic array logging and the conventional logging data; the core measured data including the measured values of the organic carbon content of the source rock in the target single well at multiple depth points.

[0086] For example, a well in a certain block of the Tarim Oilfield at an ultra-deep burial depth is selected as the target single well. Correspondingly, the logging data and the core measured data of the source rock in the target single well are obtained.

[0087] S402, based on the dipole acoustic array logging data, extracting the P-wave time difference curve and the S-wave time difference curve of the source rock in the target single well.

[0088] S403, obtaining a resistivity curve and a bulk density curve of the source rock in the target single well based on the conventional logging data.

[0089] S404, determining a maximum horizontal principal stress curve of the source rock in the target single well based on the one-dimensional rock mechanics model and the combined spring model according to the compressional wave time difference curve, the shear wave time difference curve and the bulk density curve.

[0090] For example, the overburden pressure P0, the pore pressure P p , the maximum horizontal principal stress SH max and the minimum horizontal principal stress Sh min respectively satisfy the following formulas:

[0091] Wherein, Z represents the depth, TVD represents the vertical depth, and the unit is m; p b is the density logging, and the unit is g / cm 3 ; g is the gravitational constant, which is 9.8 N / kg; P pn is the normal compaction pore pressure, and the unit is MPa; a is the Biot coefficient, and the value is usually 1; At0 is the logging acoustic time difference of the calculation point mudstone, and the unit is μs / ft; At n is the acoustic time difference of the normal compaction trend line of the calculation point mudstone, and the unit is μs / ft; n is the Eaton coefficient, and the value range is 3-9; υ is the Poisson's ratio, which is dimensionless; E is the Young's modulus, and the unit is GPa; ε H is the correlation coefficient of the maximum horizontal principal stress corresponding to the depth point in the maximum horizontal principal stress curve; ε h is the correlation coefficient of the minimum horizontal principal stress corresponding to the depth point in the minimum horizontal principal stress curve; the units of P0, P p , SH max and Sh min are MPa.

[0092] S405, determining the resistivity curve considering the influence of the ground stress based on the resistivity curve and the maximum horizontal principal stress curve.

[0093] That is, for the resistivity contained in the resistivity curve, the maximum horizontal principal stress corresponding to the depth point in the maximum horizontal principal stress curve is determined, the quotient of the resistivity and the maximum horizontal principal stress is calculated, the resistivity considering the influence of the ground stress corresponding to the depth point is obtained, and the resistivity curve considering the influence of the ground stress is obtained according to the resistivity considering the influence of the ground stress corresponding to each depth point contained in the resistivity curve.

[0094] Wherein, the resistivity considering the influence of the ground stress satisfies the following formula:

[0095] wherein Rt is the original resistivity of a depth point in the resistivity curve, SH max is the maximum horizontal principal stress corresponding to the depth point in the maximum horizontal principal stress curve, and R is the resistivity of the depth point after considering the stress influence.

[0096] S406, based on the resistivity curve after considering the influence of the ground stress and the longitudinal wave time difference curve, the organic carbon content of the target single well hydrocarbon source rock at each depth point is predicted by the ΔLogR model, and the logging prediction result of the organic carbon content of the target single well hydrocarbon source rock is obtained.

[0097] That is, the resistivity curve after considering the influence of the ground stress and the longitudinal wave time difference curve are reversely superimposed, the difference between the resistivity curve after considering the influence of the ground stress and the longitudinal wave time difference curve is calculated, the amplitude difference of the target single well hydrocarbon source rock at each depth point is obtained, and the amplitude difference of the target single well hydrocarbon source rock at each depth point is substituted into the ΔLogR model, and the logging prediction result of the organic carbon content of the target single well hydrocarbon source rock is obtained.

[0098] For example, the amplitude difference ΔlogR of the target single well hydrocarbon source rock at each depth point satisfies the following formula: ΔlogR=log(R / R Baseline )+K(Δt-Δt Baseline )

[0099] wherein R Baseline is the baseline resistivity, the unit is Ω·m; Δt is the longitudinal wave time difference, the unit is μs / ft; Δt Baseline is the baseline longitudinal wave time difference, the unit is μs / ft; K is the superposition coefficient, which can be 0.02; the baseline refers to the part of the non-hydrocarbon source rock resistivity and longitudinal wave time difference.

[0100] Further, the amplitude difference of the target single well hydrocarbon source rock at each depth point is substituted into the ΔLogR model, so as to obtain the logging prediction result of the organic carbon content of the target single well hydrocarbon source rock, wherein the logging prediction result of the organic carbon content TOC at each depth point satisfies the following formula: TOC=ΔlogR×10(2.297-0.1688LOM)

[0101] Wherein, LOM is a parameter related to maturity.

[0102] S407, based on the core measured data and the logging prediction result of the organic carbon content of the target single well hydrocarbon source rock, a crossplot of the measured organic carbon content and the predicted organic carbon content of the target single well hydrocarbon source rock is generated.

[0103] Based on core measurement data and well logging prediction results of source rock organic carbon content in the target well, a cross-plot of measured and predicted organic carbon content in the source rock of the target well is generated (e.g., Figure 3). Furthermore, based on well logging data, core measurement data, and well logging prediction results of the source rock in the target well, a well logging prediction result map of organic carbon content in the source rock of the target well is generated.

[0104] For example, Figure 5 is a well logging prediction result of the organic carbon content of the source rock in the target single well provided by an exemplary embodiment of this application. As shown in Figure 5, the first channel is the depth channel, where CAL represents the caliber curve in inches (in) and GR represents the natural gamma curve in API (American Petroleum Institute); the second channel is the resistivity curve, where RT represents the deep resistivity in ohms.m and RM represents the medium resistivity in ohms.m; the third channel is the three-porosity curve, where DT represents the P-wave transit time in μm / ft, CNC represents the neutron porosity in %, and DEN represents the bulk density in g / cm³. 3 The fourth curve shows the maximum and minimum horizontal principal stress curves, SH. max Sh represents the maximum horizontal principal stress. min SH represents the minimum horizontal principal stress. max and Sh min The units are all MPa; the fifth channel represents the amplitude difference ΔLogR, which is the re-overlay of sonic transit time and resistivity after considering stress effects; the sixth channel represents the measured organic carbon content in the core (i.e., the discrete points shown in the figure) and the predicted organic carbon content (continuous curve), where DeltaLogR predicts TOC, representing the predicted organic carbon content. Correspondingly, from the plot of the measured organic carbon content in the core and the predicted organic carbon content in the sixth channel shown in Figure 5, the two are in good agreement, further illustrating the high accuracy of the well logging prediction results for the source rock organic carbon content in the target single well.

[0105] In summary, this application has at least the following advantages:

[0106] I. By considering the influence of geostress, a resistivity curve reflecting this influence is determined, allowing it to more accurately reflect the actual formation conditions. Furthermore, this stress-influenced resistivity curve is incorporated into the ΔlogR model, combined with well logging data, to predict organic carbon content. This improves its performance in predicting organic carbon content in source rocks, particularly under strong tectonic compression stress conditions, further enhancing the accuracy of source rock organic carbon content prediction. Simultaneously, the applicability of the traditional ΔLogR method is expanded to include deep or ultra-deep source rocks, which is of significant importance for oil and gas exploration and development.

[0107] II. The organic carbon content prediction result considering the influence of the ground stress is verified by combining the rock pyrolysis experiment, so as to ensure the accuracy of the organic carbon content prediction result considering the influence of the ground stress.

[0108] The following is an apparatus embodiment of the present application, which can be used to execute the method embodiments of the present application. For details not disclosed in the apparatus embodiments of the present application, please refer to the method embodiments of the present application.

[0109] FIG. 6 is a structural schematic diagram of a hydrocarbon source rock organic carbon content logging prediction apparatus provided by an exemplary embodiment of the present application. As shown in FIG. 6, the hydrocarbon source rock organic carbon content logging prediction apparatus 60 comprises an acquisition module 61, a first determination module 62, a second determination module 63, and a prediction module 64, wherein:

[0110] The acquisition module 61 is configured to acquire logging data of a hydrocarbon source rock in a target single well.

[0111] The first determination module 62 is configured to determine a maximum horizontal principal stress curve of the hydrocarbon source rock in the target single well based on the logging data.

[0112] The second determination module 63 is configured to determine a resistivity curve considering the influence of the ground stress based on the logging data and the maximum horizontal principal stress curve.

[0113] The prediction module 64 is configured to predict the organic carbon content of the hydrocarbon source rock at each depth point in the target single well based on the resistivity curve considering the influence of the ground stress through a ΔLogR model, so as to obtain a logging prediction result of the organic carbon content of the hydrocarbon source rock in the target single well.

[0114] In a possible implementation, the logging data comprises a resistivity curve, and the second determination module 63 can be specifically configured to: determine, for a resistivity included in the resistivity curve, a maximum horizontal principal stress corresponding to a depth point corresponding to the resistivity in the maximum horizontal principal stress curve; calculate a quotient of the resistivity and the maximum horizontal principal stress, so as to obtain a resistivity considering the influence of the ground stress corresponding to the depth point; and obtain the resistivity curve considering the influence of the ground stress according to the resistivities considering the influence of the ground stress respectively corresponding to each depth point included in the resistivity curve.

[0115] In a possible implementation, the logging data further comprises a compressional wave time difference curve and a bulk density curve, and the prediction module 64 can be specifically configured to: inversely superimpose the resistivity curve considering the influence of the ground stress and the compressional wave time difference curve, calculate a difference between the resistivity curve considering the influence of the ground stress and the compressional wave time difference curve, so as to obtain an amplitude difference of the hydrocarbon source rock in the target single well at each depth point; and substitute the amplitude difference of the hydrocarbon source rock in the target single well at each depth point into the ΔLogR model, so as to obtain the logging prediction result of the organic carbon content of the hydrocarbon source rock in the target single well.

[0116] In a possible implementation, the logging data further includes a shear wave slowness curve, and the first determining module 62 can be specifically configured to: determine, according to a one-dimensional rock mechanics model, the overburden pressure and the pore pressure of the source rock in the target single well based on the bulk density curve and the compressional wave slowness curve; determine, according to a combined spring model, the maximum horizontal principal stress curve of the source rock in the target single well based on the Poisson's ratio, the Young's modulus, the overburden pressure and the pore pressure, the Poisson's ratio and the Young's modulus being determined based on the compressional wave slowness curve, the shear wave slowness curve and the bulk density curve; and the overburden pressure, the pore pressure and the maximum horizontal principal stress of the source rock in the target single well satisfy the following formula:

[0117] wherein P0 is the overburden pressure, P p is the pore pressure, SH max is the maximum horizontal principal stress corresponding to a depth point in the maximum horizontal principal stress curve; Z represents the depth, TVD represents the vertical depth, ρ b is the density logging, g is the gravitational constant; P pn is the normal compaction pore pressure, a is the Biot coefficient, Δt0 is the logging acoustic slowness of the mudstone at the calculation point, Δt n is the acoustic slowness of the normal compaction trend line of the mudstone corresponding to the calculation point, n is the Eaton coefficient; υ is the Poisson's ratio, E is the Young's modulus, ε H is the correlation coefficient of the maximum horizontal principal stress, and εh is the correlation coefficient of the minimum horizontal principal stress.

[0118] In a possible implementation, the obtaining module 61 can be specifically configured to: obtain logging data of the source rock in the target single well, the logging data including the dipole acoustic array logging data and the conventional logging data; extract the compressional wave slowness curve and the shear wave slowness curve of the source rock in the target single well based on the dipole acoustic array logging data; and obtain the resistivity curve and the bulk density curve of the source rock in the target single well based on the conventional logging data.

[0119] In a possible implementation, the obtaining module 61 can be further configured to: obtain core measurement data of the source rock in the target single well, the core measurement data including measured values of the organic carbon content of the source rock in the target single well at multiple depth points; and generate a crossplot of the measured organic carbon content and the predicted organic carbon content of the source rock in the target single well based on the core measurement data and the logging prediction result of the organic carbon content of the source rock in the target single well, the crossplot being used to represent the accuracy of the logging prediction result of the organic carbon content of the source rock in the target single well.

[0120] The source rock organic carbon content logging prediction device provided by the embodiments of the present application can execute the technical solutions shown in the source rock organic carbon content logging prediction method embodiments, and the implementation principles and beneficial effects are similar, which will not be repeated here.

[0121] It should be noted that the division of the various modules of the above apparatus is only a logical division of functions, and in actual implementation, all or part of the modules can be integrated into one physical entity, or can be physically separated. These modules can all be implemented in the form of software called by a processing element; all can be implemented in the form of hardware; or some modules can be implemented in the form of software called by a processing element, and some modules can be implemented in the form of hardware. For example, the prediction module can be a separately established processing element, or can be integrated into a certain chip of the above apparatus, in addition, it can also be stored in the form of program code in the memory of the above apparatus, and the function of the above prediction module can be called and executed by a certain processing element of the above apparatus. The implementation of other modules is similar. In addition, all or part of these modules can be integrated together, or can be independently implemented. The processing element here can be an integrated circuit with signal processing capability. In the implementation process, each step of the above method or each module can be completed by the integrated logic circuit of the hardware in the processor element or the instructions in the form of software.

[0122] For example, the above modules can be one or more integrated circuits configured to implement the above method, such as one or more application specific integrated circuits (ASICs), or one or more digital signal processors (DSPs), or one or more field programmable gate arrays (FPGAs), etc. For another example, when a certain module above is implemented in the form of program code called by a processing element, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call program code. For another example, these modules can be integrated together to implement in the form of system on a chip (SOC).

[0123] In the above embodiments, the implementation can be wholly or partially realized by software, hardware, firmware or any combination thereof. When realized by software, the implementation can be wholly or partially realized in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed by a computer, the computer instructions wholly or partially generate the processes or functions according to the embodiments of the present application. The computer can be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions can be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center through wired (for example, coaxial cable, optical fiber, Digital Subscriber Line (Digital Subscriber Line, DSL)) or wireless (for example, infrared, wireless, microwave, etc.) manner. The computer readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. containing one or more available media sets. The available media can be magnetic media (for example, floppy disk, hard disk, magnetic tape), optical media (for example, Digital Video Disc (Digital Video Disc, DVD)) or semiconductor media (for example, solid state disk (solid state disk, SSD)) and the like.

[0124] FIG. 7 is a structural schematic diagram of an electronic device provided by an exemplary embodiment of the present application. As shown in FIG. 7, the electronic device 70 of the present embodiment includes:

[0125] at least one processor 71; and a memory 72 connected to the at least one processor in communication;

[0126] The memory 72 stores instructions executable by the at least one processor 71, and the instructions are executed by the at least one processor 71 to cause the electronic device to perform the method according to any one of the above embodiments.

[0127] Optionally, the memory 72 can be independent or integrated with the processor 71.

[0128] The memory 72 can include a high-speed Random Access Memory (Random Access Memory, RAM), and can also include a non-volatile memory such as at least one disk memory.

[0129] The processor 71 can be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to perform the operations of the embodiments of the present application. Specifically, when implementing the method for predicting the organic carbon content of a hydrocarbon source rock described in the foregoing method embodiments, the electronic device can be a server or other electronic device having a processing function.

[0130] Optionally, the electronic device can further include a communication interface 73. In a specific implementation, if the communication interface 73, the memory 72, and the processor 71 are implemented independently, the communication interface 73, the memory 72, and the processor 71 can be connected to each other through a bus and complete communication therebetween. The bus can be an industry standard architecture (ISA) bus, a peripheral component (PCI) bus, an extended industry standard architecture (EISA) bus, or the like. The bus can be divided into an address bus, a data bus, a control bus, and the like, but does not mean that there is only one bus or only one type of bus.

[0131] Optionally, in a specific implementation, if the communication interface 73, the memory 72, and the processor 71 are integrated on a chip, the communication interface 73, the memory 72, and the processor 71 can complete communication through an internal interface.

[0132] The implementation principle and technical effects of the electronic device provided in the embodiments of the present application can be referred to the foregoing embodiments, and will not be described here.

[0133] The embodiments of the present application further provide a computer readable storage medium, and the computer readable storage medium stores computer execution instructions. When the computer execution instructions are executed, the computer execution instructions are used to implement the method steps in the method embodiments described above. The specific implementation manners and technical effects are similar, and will not be described here.

[0134] The computer readable storage medium described above can be realized by any type of volatile or nonvolatile storage devices or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read only memory (EEPROM), erasable programmable read only memory (EPROM), programmable read only memory (PROM), read only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special purpose computer.

[0135] An exemplary computer readable storage medium is coupled to the processor such that the processor can read information from the computer readable storage medium and the computer readable storage medium can write information to the processor. Of course, the computer readable storage medium can be a part of the processor. The processor and the computer readable storage medium can be located in an application specific integrated circuit. The processor and the computer readable storage medium can exist as discrete components, or be a part of a special purpose computer.

[0136] The embodiment of the present application further provides a computer program product, comprising a computer program which, when executed, implements the method steps in the above method embodiments, and the specific implementation manners and technical effects are similar, which will not be described here.

[0137] The embodiment of the present application further provides a computer program which, when executed on a computer, causes the computer to perform the method steps in the above method embodiments, and the specific implementation manners and technical effects are similar, which will not be described here.

[0138] Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. The specification and examples given are exemplary only and the true scope and spirit of the application is indicated by the following claims. It will be appreciated by persons skilled in the art that numerous variations and / or modifications can be made to the application as described above without departing from the scope or spirit of the application. It is intended that all such variations and / or modifications be included within the scope of the application. The specification and examples given are exemplary only and the true scope and spirit of the application is indicated by the following claims.

[0139] It should be understood that the application is not limited to the precise construction that has been described above and illustrated in the accompanying drawings, and that various modifications and changes can be made by those skilled in the art without departing from the scope of the application. The scope of the application is limited only by the appended claims.

[0140] Other aspects can become apparent from a review of the drawings and detailed description.

Claims

1. A method for predicting organic carbon content of a hydrocarbon source rock from well logs, the method comprising: include: Obtain logging data of source rocks in the target well; Based on the well logging data, the maximum horizontal principal stress curve of the source rock in the target well is determined; Based on the well logging data and the maximum horizontal principal stress curve, the resistivity curve considering the influence of geostress is determined. Based on the resistivity curve after considering the influence of geostress, the organic carbon content of the source rock in the target well at each depth is predicted by the ΔLogR model, and the well logging prediction results of the organic carbon content of the source rock in the target well are obtained.

2. The method for predicting the organic carbon content of a hydrocarbon source rock from well logs according to claim 1, characterized in that, The well logging data includes resistivity curves. The determination of the resistivity curve after considering the influence of geostress, based on the well logging data and the maximum horizontal principal stress curve, includes: For the resistivity contained in the resistivity curve, determine the maximum horizontal principal stress corresponding to the depth point of the resistivity in the maximum horizontal principal stress curve. Calculate the quotient of the resistivity and the maximum horizontal principal stress to obtain the resistivity at the depth point after considering the influence of ground stress. The resistivity curve considering the influence of ground stress is obtained by considering the resistivity at each depth point contained in the resistivity curve.

3. The method for predicting the organic carbon content of a hydrocarbon source rock from well logs according to claim 2, characterized in that, The logging data also includes P-wave transit time curves and bulk density curves. Based on the resistivity curves considering the influence of geostress, the organic carbon content of the source rock in the target well is predicted at various depths using a ΔLogR model, resulting in the logging prediction results of the organic carbon content of the source rock in the target well, including: The resistivity curve after considering the influence of geostress and the P-wave time difference curve are superimposed in reverse, and the difference between the resistivity curve after considering the influence of geostress and the P-wave time difference curve is calculated to obtain the amplitude difference of the source rock at each depth point in the target single well. Substituting the amplitude difference of the source rock at each depth point in the target well into the ΔLogR model, the well logging prediction results of the organic carbon content of the source rock in the target well are obtained.

4. The method for predicting the organic carbon content of a hydrocarbon source rock from well logs according to claim 3, characterized in that, The logging data also includes shear wave transit time curves. The determination of the maximum horizontal principal stress curve of the source rock in the target well based on the logging data includes: Based on the bulk density curve and the P-wave transit time curve, the overlying formation pressure and pore pressure of the source rock in the target well are determined according to the one-dimensional rock mechanics model. determining a maximum horizontal principal stress curve of the source rock in the target single well according to a combined spring model based on a Poisson's ratio, a Young's modulus, the overburden pressure and the pore pressure of the source rock, the Poisson's ratio and the Young's modulus being determined based on the interval transit time curve, the shear wave interval transit time curve and the bulk density curve; the overburden pressure, the pore pressure and the maximum horizontal principal stress of the source rock in the target single well satisfying the following formula: wherein P0 is the overburden pressure, P p is the pore pressure, SH max is the maximum horizontal principal stress corresponding to the depth point in the maximum horizontal principal stress curve; Z represents the depth, TVD represents the vertical depth, p b is the density log, g is the gravitational constant; P pn is the normal compaction pore pressure, a is the Biot coefficient, At0 is the sonic time difference of the mudstone at the calculation point, At n is the sonic time difference of the normal compaction trend line of the mudstone corresponding to the calculation point, n is the Eaton coefficient; v is the Poisson's ratio, E is the Young's modulus, s H is the correlation coefficient of the maximum horizontal principal stress, s h is the correlation coefficient of the minimum horizontal principal stress.

5. The method for predicting the organic carbon content of a hydrocarbon source rock from well logs according to claim 4, characterized in that, The acquisition of logging data of source rocks in the target well includes: Obtain logging data of the source rock in the target well, including logging data based on dipole sonic array and conventional logging data; Based on the dipole acoustic array logging data, the P-wave transit time curve and S-wave transit time curve of the source rock in the target well are extracted. Based on the conventional logging data, the resistivity curve and bulk density curve of the source rock in the target well are obtained.

6. The method for predicting organic carbon content of a hydrocarbon source rock from well logging according to any one of claims 1 to 5, characterized in that, Also includes: Obtain core measurement data of the source rock in the target single well, including the measured values ​​of organic carbon content of the source rock at multiple depth points in the target single well; Based on the core measured data and the logging prediction result of the organic carbon content of the source rock in the target single well, a crossplot of the measured organic carbon content and the predicted organic carbon content of the source rock in the target single well is generated, and the crossplot is used to characterize the accuracy of the logging prediction result of the organic carbon content of the source rock in the target single well.

7. A hydrocarbon source rock organic carbon content well log prediction device characterized by, Comprise: An acquisition module is configured to acquire logging data of source rock in a target single well; A first determination module is configured to determine a maximum horizontal principal stress curve of the source rock in the target single well based on the logging data; A second determination module is configured to determine a resistivity curve considering the influence of geostress based on the logging data and the maximum horizontal principal stress curve; A prediction module is configured to predict the organic carbon content of the source rock in the target single well at each depth point by a ΔLogR model based on the resistivity curve considering the influence of geostress, and obtain a logging prediction result of the organic carbon content of the source rock in the target single well.

8. An electronic device, comprising: Comprise: A processor, and a memory connected with the processor in communication; The memory is configured to store computer execution instructions; The processor is configured to execute the computer execution instructions to implement the source rock organic carbon content logging prediction method in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed to implement the source rock organic carbon content logging prediction method in any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed to implement the source rock organic carbon content logging prediction method in any one of claims 1 to 6.

11. A computer program, characterized in that, The computer program is run on the computer, and the computer executes the source rock organic carbon content logging prediction method in any one of claims 1 to 6.

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