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

Abstract

The embodiment of the invention provides a hydrocarbon source rock organic carbon content logging prediction method and device, equipment and a medium, and relates to the technical field of oil-gas exploration and development. The method comprises the steps of obtaining logging data of hydrocarbon source rocks in a target single well, determining a maximum horizontal principal stress curve based on the logging data, determining a resistivity curve after the influence of ground stress is considered based on the logging data and the maximum horizontal principal stress curve, and further, determining a resistivity curve after the influence of the ground stress is considered based on the resistivity curve after the influence of the ground stress is considered. And predicting the organic carbon content of each depth point through a delta LogR model to obtain a logging prediction result of the organic carbon content. According to the method, the resistivity curve considering the influence of the crustal stress is determined, so that the actual condition of the stratum can be reflected more truly, the resistivity curve considering the influence of the crustal stress is introduced into the delta logR model, and the logging data is combined to predict the organic carbon content, so that the accuracy of logging prediction of the organic carbon content of the hydrocarbon source rock is improved.

Description

Technical Field

[0001] This application relates to the field of oil and gas exploration and development technology, and in particular to a method, apparatus, equipment and medium for predicting the organic carbon content of source rocks by logging. Background Technology

[0002] With the continuous growth of global energy demand, the exploration and development of oil and gas resources is gradually shifting from conventional to unconventional reservoirs, and from shallow to deep and ultra-deep layers. In particular, ultra-deep tight sandstones and their source rocks under strong tectonic compressive stress have become a key and hot area of ​​current sedimentological research; this is not only an important direction for future oil and gas exploration, but also a crucial choice for ensuring energy security. In oil and gas exploration, the evaluation of source rocks is a vital step, and the prediction of organic carbon content is one of the core components of source rock evaluation.

[0003] In related technologies, methods such as ΔLogR or multiple regression are commonly used to predict the organic carbon content of deep and ultra-deep source rocks. However, since the traditional ΔLogR method is based on marine facies and normally compacted shallow and medium-depth strata, the logging response of solid organic matter (kerogen) is affected as the burial depth of the source rock increases. Strong compaction reduces the sonic transit time, especially in areas with high tectonic compression stress. Source rocks are more sensitive to tectonic compression stress and undergo dehydration and compaction under strong tectonic compression, resulting in a significant increase in resistivity. In this case, the magnitude of the amplitude difference ΔlogR after the inverse superposition of the sonic transit time 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 source rocks, there is a problem of low prediction accuracy.

[0004] Therefore, there is an urgent need to provide a well logging prediction scheme for the organic carbon content of source rocks with high accuracy. Summary of the Invention

[0005] This application provides a method, apparatus, equipment, and medium for predicting the organic carbon content of source rocks through well logging, in order to solve the problem of low prediction accuracy when using related technologies to predict the organic carbon content of deep and ultra-deep source rocks.

[0006] In a first aspect, embodiments of this application provide a well logging method for predicting the organic carbon content of source rocks, including:

[0007] Obtain logging data of source rocks in the target well;

[0008] Based on well logging data, determine the maximum horizontal principal stress curve of the source rock in the target well;

[0009] Based on well logging data and the maximum horizontal principal stress curve, the resistivity curve after considering the influence of geostress is determined.

[0010] Based on the resistivity curve considering the influence of geostress, the organic carbon content of the source rock in the target well at various depths is predicted using the ΔLogR model, and the well logging prediction results of the organic carbon content of the source rock in the target well are obtained.

[0011] In one possible implementation, the logging data includes a resistivity curve. Based on the logging data and the maximum horizontal principal stress curve, a resistivity curve considering the influence of geostress is determined, including: for the resistivity contained in the resistivity curve, determining the maximum horizontal principal stress corresponding to the depth point of 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 considering the influence of geostress at the depth point; and obtaining the resistivity curve considering the influence of geostress based on the resistivity corresponding to each depth point contained in the resistivity curve.

[0012] In one possible implementation, the logging data also includes P-wave transit time curves and bulk density curves. Based on the resistivity curve considering the influence of geostress, the organic carbon content of the source rock in the target well at each depth point is predicted using a ΔLogR model to obtain the logging prediction results of the organic carbon content of the source rock in the target well. This includes: superimposing the resistivity curve considering the influence of geostress and the P-wave transit time curve in reverse, calculating the difference between the resistivity curve considering the influence of geostress and the P-wave transit time curve to obtain the amplitude difference of the source rock at each depth point in the target well; and substituting the amplitude difference of the source rock at each depth point in the target well into the ΔLogR model to obtain the logging prediction results of the organic carbon content of the source rock in the target well.

[0013] In one possible implementation, the logging data further includes shear wave transit time curves. Based on the logging data, the maximum horizontal principal stress curve of the source rock in the target well is determined, including: determining the overlying formation pressure and pore pressure of the source rock in the target well based on the bulk density curve and P-wave transit time curve, according to a one-dimensional rock mechanics model; determining the maximum horizontal principal stress curve of the source rock in the target well based on Poisson's ratio, Young's modulus, overlying formation pressure, and pore pressure, according to a combined spring model, where Poisson's ratio and Young's modulus are determined based on the P-wave transit time curve, shear wave transit time curve, and bulk density curve; the overlying formation pressure, pore pressure, and maximum horizontal principal stress of the source rock in the target well satisfy the following formula:

[0014]

[0015] Where P0 is the overlying formation pressure, P p For pore pressure, SH maxZ represents the maximum horizontal principal stress at the depth point in the maximum horizontal principal stress curve; Z represents depth, TVD represents vertical depth, and ρ represents the maximum horizontal principal stress. b For density logging, g is the gravitational constant; P pn The normal compaction pore pressure is given by α, the Biot coefficient is given by Δt0, and the sonic transit time of the mudstone logging at the calculation point is given by Δt. n The sonic transit time is given by the calculation point corresponding to the normal compaction trend line of mudstone, where n is the Eaton coefficient, υ is Poisson's ratio, E is Young's modulus, and ε is... H ε is the correlation coefficient for the maximum horizontal principal stress. h The correlation coefficient is the minimum horizontal principal stress.

[0016] In one possible implementation, acquiring logging data of source rocks in a target well includes: acquiring logging data of source rocks in a target well, the logging data including dipole sonic array logging data and conventional logging data; extracting P-wave transit time curves and S-wave transit time curves of source rocks in the target well based on dipole sonic array logging data; and acquiring resistivity curves and bulk density curves of source rocks in the target well based on conventional logging data.

[0017] In one possible implementation, the well logging prediction method for source rock organic carbon content further includes: acquiring core measured data of source rock in a target well, the core measured data including measured organic carbon content values ​​of source rock at multiple depth points in the target well; and generating a cross-plot of measured organic carbon content and predicted organic carbon content of source rock in the target well based on the core measured data and the well logging prediction results of source rock organic carbon content in the target well, the cross-plot being used to characterize the accuracy of the well logging prediction results of source rock organic carbon content in the target well.

[0018] Secondly, this application provides a well logging prediction device for the organic carbon content of source rocks, comprising:

[0019] The acquisition module is used to acquire logging data of source rocks in a target single well;

[0020] The first determining module is used to determine the maximum horizontal principal stress curve of the source rock in the target well based on well logging data;

[0021] The second determination module is used to determine the resistivity curve after considering the influence of geostress based on well logging data and the maximum horizontal principal stress curve.

[0022] The prediction module is used to predict the organic carbon content of the source rock in the target well at various depths based on the resistivity curve after considering the influence of geostress, using the ΔLogR model, and obtain the well logging prediction results of the organic carbon content of the source rock in the target well.

[0023] Thirdly, this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0024] Memory is used to store instructions executed by the computer;

[0025] A processor for executing computer-executable instructions stored in memory to implement the method described in any of the first aspects.

[0026] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed, are used to implement the method described in any of the first aspects.

[0027] Fifthly, this application provides a computer program product, including a computer program that, when executed, implements the method described in any of the first aspects.

[0028] The well logging prediction method, apparatus, equipment, and medium for source rock organic carbon content provided in this application acquire well logging data of source rock in a 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. Furthermore, based on the resistivity curve considering the influence of geostress, the organic carbon content of the source rock in the target well at various depths is predicted using a ΔLogR model, resulting in a well logging prediction result for the organic carbon content of the source rock in the target well. In this process, by considering the influence of geostress, a resistivity curve considering the influence of geostress is determined, allowing it to more realistically reflect the actual situation of the formation. Furthermore, the resistivity curve considering the influence of geostress is introduced into the ΔlogR model, combined with well logging data, to predict the organic carbon content, resulting in better performance in well logging prediction of source rock organic carbon content, especially under strong tectonic compression stress conditions, further improving the accuracy of well logging prediction of source rock organic carbon content. Attached Figure Description

[0029] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0030] Figure 1 A schematic diagram illustrating an application scenario of the well logging prediction method for source rock organic carbon content provided as an exemplary embodiment of this application;

[0031] Figure 2 A schematic flowchart of a well logging method for predicting the organic carbon content of source rocks, provided as an exemplary embodiment of this application;

[0032] Figure 3A cross-plot of measured organic carbon content and predicted organic carbon content provided for an exemplary embodiment of this application;

[0033] Figure 4 Another flowchart illustrating the method for predicting the organic carbon content of source rocks by well logging, provided as an exemplary embodiment of this application;

[0034] Figure 5 A well logging prediction result diagram of the organic carbon content of the source rock in the target single well, provided as an exemplary embodiment of this application;

[0035] Figure 6 A schematic diagram of a well logging prediction device for source rock organic carbon content provided as an exemplary embodiment of this application;

[0036] Figure 7 A schematic diagram of the structure of an electronic device provided as an exemplary embodiment of this application.

[0037] Explanation of reference numerals in the attached figures:

[0038] 11-Client;

[0039] 12-Server;

[0040] 60-Source rock organic carbon content logging prediction device;

[0041] 61-Acquisition Module;

[0042] 62-First Determined Module;

[0043] 63-Second Determining Module;

[0044] 64 - Prediction Module;

[0045] 70 - Electronic devices;

[0046] 71-processor;

[0047] 72-Memory;

[0048] 73-Communication interface.

[0049] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0050] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0051] The terms “first,” “second,” etc., used in the specification and claims of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, products, or apparatus.

[0052] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with relevant laws, regulations and standards, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0053] First, some of the terms used in this application will be explained:

[0054] The ΔLogR method is an organic carbon content prediction method based on well logging data. It uses the differences between different well logging curves (such as sonic transit time, bulk density, and resistivity) to estimate the organic carbon content in source rocks.

[0055] Multiple regression analysis is a statistical method that uses a multiple regression model between well logging data and organic carbon content to predict the organic carbon content in source rocks.

[0056] In related technologies, the traditional ΔLogR method is based on marine and normally compacted shallow to medium-depth strata. As the burial depth of source rocks increases, the logging response of their solid organic matter (kerogen) will be affected. For example, with the increase of burial depth, the strong compaction effect reduces the sonic transit time. Especially in areas with large tectonic compression stress, mudstone (source rocks) is more sensitive to tectonic compression stress. Under strong tectonic compression, dehydration and densification will significantly increase its resistivity. At this time, the magnitude of the amplitude difference ΔlogR after the inverse superposition of the sonic transit time curve and the resistivity curve is not always linearly related to the organic carbon content. Therefore, when directly using ΔLogR or multiple regression methods to predict the organic carbon content of deep and ultra-deep source rocks, there is a problem of low prediction accuracy.

[0057] Based on the above, this application provides a well logging prediction scheme for the organic carbon content of source rocks. By considering the influence of geostress, the geostress and resistivity curves at different burial depths are correlated to obtain a resistivity curve considering the influence of geostress. Furthermore, the resistivity curve considering the influence of geostress is introduced into the ΔlogR model, and the organic carbon content is predicted in combination with well logging data, thereby improving the accuracy of well logging prediction of organic carbon content in source rocks.

[0058] Figure 1 A schematic diagram illustrating an application scenario of the well logging prediction method for source rock organic carbon content provided as an exemplary embodiment of this application. (e.g.) Figure 1 As shown, this application scenario includes a client 11 and a server 12, wherein there can be at least one client 11. In practical application, when the server 12 detects the logging data of the source rock in the target well submitted by the user through the client 11, it executes the logging prediction method for the organic carbon content of the source rock provided in this application to obtain the logging prediction result of the organic carbon content of the source rock in the target well. Correspondingly, the client 11 receives the logging prediction result of the organic carbon content of the source rock in the target well sent by the server 12.

[0059] It should be noted that server 12 can also be replaced by a server cluster or other computing devices with a certain computing power. Client 11 can be a computer, mobile phone, laptop, or personal digital assistant (PDA), etc.

[0060] It should also be noted that the source rocks in the target single well in this application are deep source rocks or ultra-deep source rocks. Deep source rocks refer to source rocks with a burial depth between 4,500 meters and 6,000 meters, while ultra-deep source rocks refer to source rocks with a burial depth exceeding 6,000 meters.

[0061] The following is combined Figure 1 Application scenarios, refer to Figure 2This application describes a well logging method for predicting the organic carbon content of source rocks according to exemplary embodiments thereof. It should be noted that the above application scenarios are shown only to facilitate understanding of the spirit and principles of this application, and the embodiments of this application are not limited to those described herein. Figure 1 The limitations of the application scenarios shown.

[0062] Figure 2 A schematic flowchart of a well logging prediction method for source rock organic carbon content provided as an exemplary embodiment of this application. Figure 2 As shown, the well logging prediction method for source rock organic carbon content in this application embodiment includes the following steps:

[0063] S201. Obtain logging data of source rocks in the target well.

[0064] For example, such as Figure 1 As shown, the user obtains logging data of source rocks in a target well from relevant geological exploration departments or geological information platforms through client 11, processes the logging data according to preset rules to obtain logging data of source rocks in the target well, and sends the logging data to server 12; correspondingly, server 12 receives the logging data of source rocks in the target well sent by client 11. The preset rules may include, but are not limited to, data processing algorithms and data fine-tuning strategies in existing technologies.

[0065] Optionally, before or after processing the logging data according to preset rules, the data may also include interference removal processing, noise reduction processing, and / or data enhancement processing such as interpolation, sampling, and data fine-tuning, etc.

[0066] S202. Based on well logging data, determine the maximum horizontal principal stress curve of the source rock in the target well.

[0067] For example, in this step, based on well logging data, the maximum horizontal principal stress value of the source rock in the target well at each depth point is determined according to pre-set calculation formulas, empirical formulas, and geomechanical models, and the maximum horizontal principal stress value at each depth point is plotted as a curve, thereby obtaining the maximum horizontal principal stress curve of the source rock in the target well.

[0068] S203. Based on well logging data and the maximum horizontal principal stress curve, determine the resistivity curve after considering the influence of geostress.

[0069] For example, by correlating well logging data with the maximum horizontal principal stress curve, the influence of geostress is incorporated into the calculation of the resistivity curve. By combining well logging data at each depth point with the magnitude of the maximum horizontal principal stress, a resistivity curve that takes into account the influence of geostress is obtained, thereby obtaining a more accurate formation resistivity distribution.

[0070] In some embodiments, the logging data includes a resistivity curve. Based on the logging data and the maximum horizontal principal stress curve, determining the resistivity curve after considering the influence of geostress includes: for the resistivity contained in the resistivity curve, determining the maximum horizontal principal stress corresponding to the depth point of 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 corresponding to the depth point after considering the influence of geostress; and obtaining the resistivity curve after considering the influence of geostress based on the resistivity corresponding to each depth point contained in the resistivity curve.

[0071] The resistivity considering the influence of geostress satisfies the following formula:

[0072]

[0073] Where Rt is the original resistivity at a certain depth point in the resistivity curve, and SH max R represents the maximum horizontal principal stress at that depth point in the maximum horizontal principal stress curve, and R represents the resistivity at that depth point after considering the stress effect.

[0074] S204. Based on the resistivity curve 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.

[0075] For example, the resistivity curve considering the influence of geostress is used instead of the original resistivity curve. Based on the traditional ΔLogR method, the organic carbon content of the source rock in the target well at each depth is predicted by the ΔLogR model, thereby obtaining the well logging prediction results of the organic carbon content of the source rock in the target well.

[0076] The well logging prediction method for source rock organic carbon content provided in this application considers the influence of geostress and determines the resistivity curve after considering the influence of geostress, so that it can more realistically reflect the actual situation of the formation. Furthermore, the resistivity curve after considering the influence of geostress is introduced into the ΔlogR model and combined with well logging data to predict organic carbon content, so that it performs better in well logging prediction of source rock organic carbon content, especially under strong tectonic compression stress conditions, further improving the accuracy of well logging prediction of source rock organic carbon content.

[0077] In some embodiments, the logging data also includes P-wave transit time curves and bulk density curves. Based on the resistivity curve considering the influence of geostress, the organic carbon content of the source rock in the target well at each depth is predicted using a ΔLogR model to obtain the logging prediction results of the organic carbon content of the source rock in the target well. This includes: superimposing the resistivity curve considering the influence of geostress and the P-wave transit time curve in reverse, calculating the difference between the resistivity curve considering the influence of geostress and the P-wave transit time curve to obtain the amplitude difference of the source rock at each depth in the target well; and substituting the amplitude difference of the source rock at each depth in the target well into the ΔLogR model to obtain the logging prediction results of the organic carbon content of the source rock in the target well.

[0078] Among them, P-waves are a type of seismic wave that propagates at a relatively high speed and mainly propagates through the compression and expansion of matter. The P-wave time difference curve records the propagation time of P-waves (P waves) in the strata; the volume density curve records the volume density of the strata, reflecting the combined density of solid matter and pore fluids in the strata.

[0079] For example, the amplitude difference ΔlogR of the source rock at various depth points in the target well satisfies the following formula:

[0080] ΔlogR=log(R / R Baseline )+K(Δt-Δt Baseline )

[0081] Among them, R Baseline Baseline resistivity, in Ω·m; Δt is the P-wave transit time, in μs / ft; Δt Baseline The baseline P-wave transit time is expressed in μs / ft; K is the superposition coefficient, which can be 0.02; the baseline refers to the overlapping portion of the non-source rock resistivity and P-wave transit time.

[0082] Furthermore, the amplitude difference of the source rock in the target well at each depth point is substituted into the ΔLogR model to obtain the well logging prediction results of the organic carbon content of the source rock in the target well. The well logging prediction results of the organic carbon content (TOC) at each depth point satisfy the following formula:

[0083] TOC = Δlog R × 10 (2.297-0.1688LOM)

[0084] Among them, LOM is a parameter related to maturity.

[0085] In this embodiment, the accuracy of well logging prediction of organic carbon content in source rocks is improved by incorporating the resistivity curve after considering the influence of geostress into the ΔlogR model and combining it with well logging data.

[0086] In some embodiments, the logging data also includes shear wave transit time curves. Based on the logging data, the maximum horizontal principal stress curve of the source rock in the target well is determined, including: determining the overlying formation pressure and pore pressure of the source rock in the target well based on the bulk density curve and P-wave transit time curve, according to a one-dimensional rock mechanics model; determining the maximum horizontal principal stress curve of the source rock in the target well based on Poisson's ratio, Young's modulus, overlying formation pressure, and pore pressure, according to a combined spring model, where Poisson's ratio and Young's modulus are determined based on the P-wave transit time curve, shear wave transit time curve, and bulk density curve; the overlying formation pressure, pore pressure, and maximum horizontal principal stress of the source rock in the target well satisfy the following formula:

[0087]

[0088] Where P0 is the overlying formation pressure, P p For pore pressure, SH max The maximum horizontal principal stress corresponds to the depth point in the maximum horizontal principal stress curve, and the unit is MPa; Z represents depth, TVD represents vertical depth, and the unit is m; ρ b This is density logging, with units in g / cm³. 3 g is the gravitational constant, which is 9.8 N / kg; P pn Normal compaction pore pressure, in MPa; α is the Biot coefficient, usually taken as 1; Δt0 is the sonic transit time of mudstone logging at the calculation point, in μs / ft; Δt n The sonic transit time is given by: n = (1 / 2) * (μs / ft) * (E / t) * (n) * (E / t) * (3–9) * (υ) * (Poisson's ratio, dimensionless) * (E / GPa) * (ε / t) H ε is the correlation coefficient for the maximum horizontal principal stress. h The correlation coefficient is the minimum horizontal principal stress.

[0089] For example, based on the bulk density curve and P-wave transit time curve, the overlying formation pressure and pore pressure of the source rock in the target well are determined according to a one-dimensional rock mechanics model and density extrapolation method. Furthermore, based on Poisson's ratio, Young's modulus, overlying formation pressure, and pore pressure, the maximum horizontal principal stress curve of the source rock in the target well is determined according to a combined spring model. It can be understood that the process of determining Poisson's ratio and Young's modulus based on the P-wave transit time curve, S-wave transit time curve, and bulk density curve is the same as in related technologies and will not be elaborated here. It should be noted that S-waves are also a type of seismic wave, with a slower propagation speed, mainly propagating through shearing material. The S-wave transit time curve records the propagation time of S-waves (S-waves) in the formation.

[0090] Correspondingly, the minimum horizontal principal stress Sh min Satisfy the following formula:

[0091]

[0092] In some embodiments, obtaining logging data of source rocks in a target well includes: obtaining logging data of source rocks in a target well, the logging data including dipole sonic array logging data and conventional logging data; extracting P-wave transit time curves and S-wave transit time curves of source rocks in the target well based on dipole sonic array logging data; and obtaining resistivity curves and bulk density curves of source rocks in the target well based on conventional logging data.

[0093] Among them, dipole sonic array logging is an advanced sonic logging technology. This technology generates sound waves in downhole tools and records the propagation time and attenuation characteristics of the sound waves in the formation through an array receiver. Correspondingly, based on the best practices and experience in the processing of logging data and geological analysis of source rocks in a target well, high-resolution P-wave and S-wave transit time curves of source rocks in the target well are extracted. Conventional logging includes resistivity logging, density logging, natural gamma logging, etc. The resistivity curves and bulk density curves of source rocks in the target well are directly obtained through conventional logging data of source rocks in the target well.

[0094] Considering that the evaluation of deep or ultra-deep source rocks in related technologies still relies on laboratory rock pyrolysis experiments, and that traditional methods for predicting organic carbon content, such as ΔLogR and multiple regression, are ineffective in predicting the organic carbon content of deep and ultra-deep source rocks, this application addresses this issue by combining rock pyrolysis experiments to verify the predicted organic carbon content considering the influence of geostress, thereby ensuring the accuracy of the predicted organic carbon content considering geostress.

[0095] Therefore, based on the above embodiments, in some implementations, the well logging prediction method for source rock organic carbon content further includes: obtaining core measured data of source rock in a target well, the core measured data including measured values ​​of organic carbon content of source rock at multiple depth points in the target well; based on the core measured data and the well logging prediction results of source rock organic carbon content in the target well, generating a cross-plot of measured organic carbon content and predicted organic carbon content of source rock in the target well, the cross-plot being used to characterize the accuracy of well logging prediction results of source rock organic carbon content in the target well.

[0096] Among them, the core measured data is obtained through rock pyrolysis experiments. Rock pyrolysis experiments are experimental techniques used to study the decomposition behavior and products of rocks under high temperature conditions. By pyrolyzing the source rock samples, 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 rock pyrolysis experiments, the measured values ​​of organic carbon content of source rocks at multiple depth points in the target single well can be obtained.

[0097] Furthermore, based on core measurement data and well logging predictions of organic carbon content in the source rocks of the target well, a cross-plot of the measured and predicted organic carbon content in the source rocks of the target well is generated. For example, Figure 3 A cross-plot of measured organic carbon content versus predicted organic carbon content provided for an exemplary embodiment of this application. (See diagram below.) Figure 3 As shown, the x-axis represents the measured organic carbon content obtained through rock pyrolysis experiments, in percentage (%); the y-axis represents the well logging prediction result of the organic carbon content of the source rock in the target well, in percentage (%); the diagonal (dashed line) represents the ideal situation where the measured organic carbon content is equal to the predicted organic carbon content. In other words, if all data points fall on this line, the prediction model's prediction is completely accurate. By observing the position of the data points relative to this line, the accuracy of the prediction model can be evaluated. If most data points are concentrated near this line, the model's prediction is relatively accurate; if the data points deviate significantly from this line, the model's prediction has a large error. R 2 The correlation coefficient quantifies the strength and direction of the linear relationship between measured and predicted organic carbon content. For example, an absolute value close to 1 (e.g., 0.8 to 1 or -0.8 to -1) indicates a strong correlation; an absolute value between 0.5 and 0.8 indicates a moderate correlation; an absolute value between 0.3 and 0.5 indicates a weak correlation; and an absolute value less than 0.3 indicates almost no correlation. Figure 3 The correlation coefficient R shown 2 =0.8313, indicating a strong correlation between the measured organic carbon content and the predicted organic carbon content, thus demonstrating the high accuracy of well logging prediction results for the source rock organic carbon content in the target well.

[0098] Figure 4 Another schematic diagram of the well logging prediction method for source rock organic carbon content provided as an exemplary embodiment of this application. (See attached diagram.) Figure 4 As shown, the well logging prediction method for source rock organic carbon content in this application embodiment includes the following steps:

[0099] S401. Obtain well logging data and core measurement data of the source rock in the target well. The well logging data includes dipole sonic array logging data and conventional logging data. The core measurement data includes the measured values ​​of organic carbon content of the source rock in the target well at multiple depth points.

[0100] For example, a deep-buried well in a certain block of the Tarim Oilfield is selected as the target well. Correspondingly, well logging data and core measurement data of the source rock in the target well are obtained.

[0101] S402. Based on dipole acoustic array logging data, extract the P-wave and S-wave time difference curves of the source rock in the target well.

[0102] S403. Based on conventional logging data, obtain the resistivity curve and bulk density curve of the source rock in the target well.

[0103] S404. Based on the P-wave transit time curve, S-wave transit time curve, and bulk density curve, the maximum horizontal principal stress curve of the source rock in the target well is determined according to the one-dimensional rock mechanics model and the combined spring model.

[0104] For example, the overlying formation pressure P0 and the pore pressure P p Maximum horizontal principal stress SH max and minimum horizontal principal stress Sh min They respectively satisfy the following formulas:

[0105]

[0106] Where Z represents depth, TVD represents vertical depth, and the unit is m; ρ b This is density logging, with units in g / cm³. 3 g is the gravitational constant, which is 9.8 N / kg; P pn Normal compaction pore pressure, in MPa; α is the Biot coefficient, usually taken as 1; Δt0 is the sonic transit time of mudstone logging at the calculation point, in μs / ft; Δt n The sonic transit time is given by: n = (1 / 2) * (μs / ft) * (E / t) * (n) * (E / t) * (3–9) * (υ) * (Poisson's ratio, dimensionless) * (E / GPa) * (ε / t) H ε is the correlation coefficient of the maximum horizontal principal stress corresponding to the depth point in the maximum horizontal principal stress curve; h The correlation coefficients of the minimum horizontal principal stresses corresponding to the depth points in the minimum horizontal principal stress curve; P0, P p SH max and Sh min The unit is MPa.

[0107] S405. Based on the resistivity curve and the maximum horizontal principal stress curve, determine the resistivity curve after considering the influence of geostress.

[0108] That is, 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 resistivity and maximum horizontal principal stress to obtain the resistivity corresponding to the depth point after considering the influence of ground stress, and obtain the resistivity curve after considering the influence of ground stress based on the resistivity corresponding to each depth point contained in the resistivity curve.

[0109] The resistivity considering the influence of geostress satisfies the following formula:

[0110]

[0111] Where Rt is the original resistivity at a certain depth point in the resistivity curve, and SH max R represents the maximum horizontal principal stress at that depth point in the maximum horizontal principal stress curve, and R represents the resistivity at that depth point after considering the stress effect.

[0112] S406. Based on the resistivity curve and P-wave time difference 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.

[0113] The resistivity curve and P-wave transit time curve after considering the influence of geostress are superimposed in reverse. The difference between the resistivity curve and the P-wave transit time curve after considering the influence of geostress is calculated to obtain the amplitude difference of the source rock at each depth point in the target well. The amplitude difference of the source rock at each depth point in the target well is then substituted into the ΔLogR model to obtain the well logging prediction results of the organic carbon content of the source rock in the target well.

[0114] For example, the amplitude difference ΔlogR of the source rock at various depth points in the target well satisfies the following formula:

[0115] ΔlogR=log(R / R Baseline )+K(Δt-Δt Baseline )

[0116] Among them, R Baseline Baseline resistivity, in Ω·m; Δt is the P-wave transit time, in μs / ft; Δt Baseline The baseline P-wave transit time is expressed in μs / ft; K is the superposition coefficient, which can be 0.02; the baseline refers to the overlapping portion of the non-source rock resistivity and P-wave transit time.

[0117] Furthermore, the amplitude difference of the source rock in the target well at each depth point is substituted into the ΔLogR model to obtain the well logging prediction results of the organic carbon content of the source rock in the target well. The well logging prediction results of the organic carbon content (TOC) at each depth point satisfy the following formula:

[0118] TOC = Δlog R × 10 (2.297-0.1688LOM)

[0119] Among them, LOM is a parameter related to maturity.

[0120] S407. Based on the core measurement data and the well logging prediction results of the source rock organic carbon content in the target well, generate a cross-plot of the measured organic carbon content and the predicted organic carbon content in the source rock of the target well.

[0121] Based on core data and well logging predictions of organic carbon content in the source rock of the target well, a cross-plot of the 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 the organic carbon content of the source rock in the target well is generated.

[0122] For example, Figure 5 A well logging prediction result diagram of the organic carbon content of the source rock in a target single well, provided as an exemplary embodiment of this application. (See diagram for reference.) Figure 5 As shown, 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 deep resistivity in ohms·m and RM represents medium resistivity in ohms·m. The third channel is the three-porosity curve, where DT represents P-wave transit time in μm / ft, CNC represents neutron porosity in %, and DEN represents 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 is the amplitude difference ΔLogR between the acoustic transit time and the resistivity after considering the stress effect; the sixth channel is 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 Figure 5As shown in the graph of the measured organic carbon content and the predicted organic carbon content in the core in the sixth track, the two are in good agreement, which further illustrates that the logging prediction results for the organic carbon content of source rocks in the target single well are highly accurate.

[0123] In summary, this application has at least the following advantages:

[0124] 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.

[0125] Second, the prediction results of organic carbon content after considering the influence of geostress were verified by combining rock pyrolysis experiments, thereby ensuring the accuracy of the prediction results of organic carbon content after considering the influence of geostress.

[0126] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0127] Figure 6 A schematic diagram of a well logging prediction device for source rock organic carbon content provided as an exemplary embodiment of this application. Figure 6 As shown, the source rock organic carbon content logging prediction device 60 includes an acquisition module 61, a first determination module 62, a second determination module 63, and a prediction module 64, wherein:

[0128] The acquisition module 61 is used to acquire logging data of source rocks in the target single well;

[0129] The first determining module 62 is used to determine the maximum horizontal principal stress curve of the source rock in the target single well based on well logging data;

[0130] The second determining module 63 is used to determine the resistivity curve after considering the influence of geostress based on well logging data and the maximum horizontal principal stress curve.

[0131] The prediction module 64 is used to predict the organic carbon content of the source rock in the target well at each depth point based on the resistivity curve after considering the influence of geostress, using the ΔLogR model, and obtain the well logging prediction results of the organic carbon content of the source rock in the target well.

[0132] In one possible implementation, the logging data includes a resistivity curve, and the second determining module 63 can be specifically used to: determine the maximum horizontal principal stress corresponding to the depth point in the maximum horizontal principal stress curve for the resistivity contained in the resistivity curve; calculate the quotient of resistivity and maximum horizontal principal stress to obtain the resistivity corresponding to the depth point after considering the influence of geostress; and obtain the resistivity curve after considering the influence of geostress based on the resistivity corresponding to each depth point in the resistivity curve.

[0133] In one possible implementation, the logging data also includes a P-wave transit time curve and a bulk density curve. The prediction module 64 can be specifically used to: superimpose the resistivity curve and the P-wave transit time curve after considering the influence of geostress in reverse, calculate the difference between the resistivity curve and the P-wave transit time curve after considering the influence of geostress, and obtain the amplitude difference of the source rock at each depth point in the target well; substitute the amplitude difference of the source rock at each depth point in the target well into the ΔLogR model to obtain the logging prediction result of the organic carbon content of the source rock in the target well.

[0134] In one possible implementation, the logging data further includes shear wave transit time curves. The first determining module 62 can be specifically used to: determine the overlying formation pressure and pore pressure of the source rock in the target well based on the bulk density curve and the P-wave transit time curve, according to a one-dimensional rock mechanics model; and determine the maximum horizontal principal stress curve of the source rock in the target well based on Poisson's ratio, Young's modulus, overlying formation pressure, and pore pressure, according to a combined spring model. The Poisson's ratio and Young's modulus are determined based on the P-wave transit time curve, the shear wave transit time curve, and the bulk density curve. The overlying formation pressure, pore pressure, and maximum horizontal principal stress of the source rock in the target well satisfy the following formula:

[0135]

[0136]

[0137] Where P0 is the overlying formation pressure, P p For pore pressure, SH max Z represents the maximum horizontal principal stress at the depth point in the maximum horizontal principal stress curve; Z represents depth, TVD represents vertical depth, and ρ represents the maximum horizontal principal stress. b For density logging, g is the gravitational constant; P pn The normal compaction pore pressure is given by α, the Biot coefficient is given by Δt0, and the sonic transit time of the mudstone logging at the calculation point is given by Δt. n The sonic transit time is given by the calculation point corresponding to the normal compaction trend line of mudstone, where n is the Eaton coefficient, υ is Poisson's ratio, E is Young's modulus, and ε is... H ε is the correlation coefficient for the maximum horizontal principal stress. h The correlation coefficient is the minimum horizontal principal stress.

[0138] In one possible implementation, the acquisition module 61 can be specifically used to: acquire logging data of source rocks in a target well, including dipole sonic array logging data and conventional logging data; extract the P-wave transit time curve and S-wave transit time curve of source rocks in the target well based on the dipole sonic array logging data; and acquire the resistivity curve and bulk density curve of source rocks in the target well based on the conventional logging data.

[0139] In one possible implementation, the acquisition module 61 can also be used to: acquire core measured data of source rocks in the target well, the core measured data including measured values ​​of organic carbon content of source rocks at multiple depth points in the target well; based on the core measured data and well logging prediction results of organic carbon content of source rocks in the target well, generate a cross-plot of measured organic carbon content and predicted organic carbon content of source rocks in the target well, the cross-plot being used to characterize the accuracy of well logging prediction results of organic carbon content of source rocks in the target well.

[0140] The source rock organic carbon content logging prediction device provided in this application embodiment can execute the technical solution shown in the above-described source rock organic carbon content logging prediction method embodiment. Its implementation principle and beneficial effects are similar, and will not be repeated here.

[0141] It should be noted that the division of the various modules in the above device is merely a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, these modules can be implemented entirely in software via processing element calls; they can be fully implemented in hardware; or some modules can be implemented by processing element calls to software, while others are implemented in hardware. For example, the prediction module can be a separate processing element, or it can be integrated into a chip in the above device. Alternatively, it can be stored as program code in the memory of the above device, and its function can be called and executed by a processing element. The implementation of other modules is similar. Moreover, these modules can be fully or partially integrated together, or they can be implemented independently. The processing element here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each of the above modules can be completed through integrated logic circuits in the hardware of the processor element or through software instructions.

[0142] For example, these modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more Digital Signal Processors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs). As another example, when a module is implemented using processing element scheduler code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together as a System-On-a-Chip (SOC).

[0143] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., Digital Video Discs, DVDs), or semiconductor media (e.g., solid-state disks (SSDs)).

[0144] Figure 7 A schematic diagram of the structure of an electronic device provided as an exemplary embodiment of this application. For example... Figure 7 As shown, the electronic device 70 in this embodiment includes:

[0145] At least one processor 71; and a memory 72 communicatively connected to said at least one processor;

[0146] The memory 72 stores instructions that can be executed by the at least one processor 71 to cause the electronic device to perform the method as described in any of the above embodiments.

[0147] Alternatively, the memory 72 can be either standalone or integrated with the processor 71.

[0148] The memory 72 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device.

[0149] The processor 71 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application. Specifically, when implementing the well logging prediction method for source rock organic carbon content described in the foregoing method embodiments, the electronic device may be, for example, an electronic device with processing capabilities such as a server.

[0150] Optionally, the electronic device may also include a communication interface 73. In specific implementations, if the communication interface 73, memory 72, and processor 71 are implemented independently, they can be interconnected via a bus to complete communication. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc., but this does not imply that there is only one bus or one type of bus.

[0151] Optionally, in a specific implementation, if the communication interface 73, memory 72 and processor 71 are integrated on a single chip, then the communication interface 73, memory 72 and processor 71 can communicate through an internal interface.

[0152] The implementation principle and technical effects of the electronic device provided in this embodiment can be found in the foregoing embodiments, and will not be repeated here.

[0153] This application also provides a computer-readable storage medium storing computer-executable instructions. When the computer-executable instructions are executed, they are used to implement the method steps as described in the above method embodiments. The specific implementation methods and technical effects are similar and will not be repeated here.

[0154] The aforementioned computer-readable storage media can be implemented from any type of volatile or non-volatile storage device 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 accessible to a general-purpose or special-purpose computer.

[0155] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside within an application-specific integrated circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in a source rock organic carbon content logging prediction device.

[0156] This application also provides a computer program product, including a computer program, which, when executed, implements the method steps as described in the above method embodiments. The specific implementation and technical effects are similar and will not be repeated here.

[0157] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0158] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for predicting organic carbon content of a hydrocarbon source rock from well logs, the method comprising: The method comprises the following steps: obtaining logging data of source rock in a target single well; determining a maximum horizontal principal stress curve of the source rock in the target single well based on the logging data; determining a resistivity curve considering the influence of geostress based on the logging data and the maximum horizontal principal stress curve; predicting 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, to obtain a logging prediction result of the organic carbon content of the source rock in the target single well.

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 logging data comprises a resistivity curve, and the determination of the resistivity curve considering the influence of geostress based on the logging data and the maximum horizontal principal stress curve comprises the following steps: determining the maximum horizontal principal stress corresponding to the depth point corresponding to the resistivity in the resistivity curve 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; obtaining the resistivity curve considering the influence of geostress according to the resistivity of 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 further comprises a compressional wave slowness curve and a bulk density curve, and the prediction of the organic carbon content of the source rock in the target single well at each depth point by the ΔLogR model based on the resistivity curve considering the influence of geostress to obtain the logging prediction result of the organic carbon content of the source rock in the target single well comprises the following steps: superimposing the resistivity curve considering the influence of geostress and the compressional wave slowness curve in reverse to calculate the difference between the resistivity curve considering the influence of geostress and the compressional wave slowness curve, to obtain the amplitude difference of the source rock in the target single well at each depth point; substituting the amplitude difference of the source rock 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 source rock in the target single well.

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 further comprises a shear wave slowness curve, and the determination of the maximum horizontal principal stress curve of the source rock in the target single well based on the logging data comprises the following steps: determining the overburden pressure and pore pressure of the source rock in the target single well according to a one-dimensional rock mechanics model based on the bulk density curve and the compressional wave slowness curve; determining the maximum horizontal principal stress curve of the source rock in the target single well according to a combined spring model based on Poisson's ratio, Young's modulus, the overburden pressure and the pore pressure, wherein the Poisson's ratio and Young's modulus are determined based on the compressional wave slowness curve, the shear wave slowness curve and the bulk density curve; the overburden pressure, pore pressure and maximum horizontal principal stress of the source rock in the target single well satisfy 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 method comprises the following steps: obtaining logging data of source rock in a target single well; 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; Based on the conventional logging data, a resistivity curve and a bulk density curve of the source rock in the target single 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, Further comprising: Core measured data of the source rock in the target single well is obtained, and the core measured data includes measured values of organic carbon content of the source rock in the target single well at multiple depth points; 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 represent 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, Comprising: An obtaining module is configured to obtain logging data of a source rock in a target single well; A first determining 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 determining 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, so as to obtain a logging prediction result of the organic carbon content of the source rock in the target single well.

8. An electronic device, comprising: Comprising: 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.

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