A method for predicting the thickness of hydrocarbon source rocks

Abstract

This application provides a method for predicting source rock thickness, including: obtaining target observation seismic data, well logging data, and auxiliary geological data within a study area; performing spectral analysis on the target observation seismic data to obtain a near-hole seismic trace spectrum, and extracting TOC curves from the well logging data to obtain a well logging TOC curve; constructing a TOC-seismic frequency response relationship library based on the near-hole seismic trace spectrum and the well logging TOC curve; dividing the study area into different sedimentary facies regions, calculating the source rock thickness at each location, and generating a priori thickness distribution model; utilizing the target observation seismic data, well logging data, auxiliary geological data, and the TOC-seismic frequency response relationship library, iteratively updating the target seismic data, well logging data, auxiliary geological data, and the TOC-seismic frequency response relationship library to continuously reduce the objective function, thereby obtaining an optimal thickness prediction model and outputting the predicted source rock thickness; and performing compaction correction and denudation restoration on the predicted source rock thickness to obtain the actual effective thickness of the source rock. This application can more accurately predict source rock thickness.

Description

Technical Field

[0001] The present application relates to the field of geophysical exploration technology, and in particular to a method for predicting the thickness of hydrocarbon source rocks. Background Art

[0002] Predicting the thickness of Cambrian source rocks is of great significance in many areas, including oil and gas exploration, geological research, and energy development. Currently, Cambrian source rock thickness prediction is primarily based on geochemical methods, well log inversion, and seismic inversion and attribute analysis. Geochemical methods rely on sparse sampling points, making lateral extrapolation difficult and costly. Well log inversion, which predicts thickness based on the statistical relationship between logging responses and source rock TOC, is significantly affected by the wellbore environment, and lateral heterogeneity can lead to errors in extrapolation. Seismic inversion and attribute analysis, when indirectly predicting source rock distribution, suffers from weak signals due to the ancient age of the Cambrian strata, low seismic resolution, and minimal differences in physical properties between source rocks and surrounding rocks. These deficiencies can easily lead to inaccurate predictions of source rock thickness. Summary of the Invention

[0003] In view of this, the purpose of this application is to provide a method for predicting hydrocarbon source rock thickness, which comprehensively considers observed seismic data, well logging data, auxiliary geological data and TOC-seismic frequency response relationship library, and can improve the accuracy and reliability of hydrocarbon source rock thickness prediction.

[0004] In a first aspect, an embodiment of the present application provides a method for predicting the thickness of a source rock, the method comprising:

[0005] Acquire target observation seismic data, well logging data, and auxiliary geological data within the study area, the auxiliary geological data including paleowater depth level, thermal maturity, and sedimentation history data;

[0006] Performing spectrum analysis on the seismic wave data in the target observed seismic data to obtain a wellside seismic trace spectrum, and performing TOC curve extraction on the well logging data to obtain a well logging TOC curve;

[0007] Constructing a TOC-seismic frequency response relationship library based on the correspondence between the seismic frequency characteristics of the wellside seismic trace spectrum and the TOC value of the well logging TOC curve;

[0008] Dividing the study area into different sedimentary facies regions according to the paleo-water depth level, calculating the thickness of the source rock at each location based on the geological depositional laws of each sedimentary facies region and generating a priori thickness distribution model to serve as an initial model for the thickness prediction model;

[0009] Utilizing the target observed seismic data, the well logging data, the auxiliary geological data, and the TOC-seismic frequency response relationship library, the model parameters of the priori thickness distribution model are continuously iteratively updated to continuously reduce the objective function, thereby obtaining an optimal thickness prediction model and outputting the predicted thickness of the source rock;

[0010] The predicted thickness of the source rock is subjected to compaction correction processing to obtain the original thickness of the source rock in the early stage of deposition, and the original thickness of the source rock in the early stage of deposition is subjected to denudation recovery processing using the thermal maturity to obtain the actual effective thickness of the source rock.

[0011] In an optional embodiment, the target observed seismic data is obtained by the following steps:

[0012] Collecting observation seismic data in the study area, wherein the observation seismic data includes pre-stack observation seismic data and post-stack observation seismic data;

[0013] The observed seismic data is preprocessed to obtain target observed seismic data with seismic wave energy compensated, wherein the time domain of the target observed seismic data matches the depth domain of the well logging data.

[0014] In an optional embodiment, the TOC-seismic frequency response relationship library is constructed based on the correspondence between the seismic frequency characteristics of the near-well seismic trace spectrum and the TOC value of the well logging TOC curve, including:

[0015] The seismic frequency characteristics of the near-well seismic trace spectrum and the TOC value of the well logging TOC curve are matched using a matching pursuit algorithm to obtain a TOC-seismic frequency response relationship library.

[0016] In an optional embodiment, the study area is divided into different sedimentary facies regions according to the paleo-water depth level, and based on the geological depositional laws of each sedimentary facies region, the thickness of the source rock at each location is calculated and a priori thickness distribution model is generated to serve as an initial model of the thickness prediction model, including:

[0017] The study area is divided into different sedimentary facies areas according to paleo-water depth levels;

[0018] In each sedimentary facies area, based on the geological sedimentary laws of each sedimentary facies area, the Kriging interpolation method is used to interpolate the thickness of the source rock to obtain the thickness of the source rock at each location and generate a priori thickness distribution model as the initial model of the thickness prediction model.

[0019] In an optional embodiment, the method of continuously iteratively updating the model parameters of the prior thickness distribution model using the target observed seismic data, the well logging data, the auxiliary geological data, and the TOC-seismic frequency response relationship library to continuously reduce the objective function, thereby obtaining an optimal thickness prediction model and outputting the predicted thickness of the source rock, includes:

[0020] Initially constraining a wave impedance model based on a priori thickness distribution model, wherein the wave impedance model includes thickness parameters of the source rock;

[0021] Performing seismic wave equation forward modeling based on the wave impedance model to generate synthetic seismic records;

[0022] Constructing an objective function, the objective function including a seismic data fitting term, a geological priori constraint term, and a lateral smoothing term, the seismic data fitting term characterizing the difference between the synthetic seismic record and the target observed seismic data, the well logging data being used to constrain the geological priori constraint term, and the auxiliary geological data and the TOC-seismic frequency response relationship library being used to constrain the lateral smoothing term;

[0023] The conjugate gradient method is used to iteratively update the model parameters of the prior thickness distribution model and the thickness parameters in the wave impedance model, so that the objective function is continuously reduced until the residual error of the seismic data fitting term converges. The optimal thickness prediction model is obtained by inversion and the predicted thickness of the source rock is output.

[0024] In an optional embodiment, the objective function includes:

[0025] ;

[0026] in, represents the objective function of the thickness prediction model, Constitute the seismic data fitting term, Constitutes geological prior constraints, represents the horizontal smoothing term;

[0027] m represents the thickness prediction model, represents the target observed seismic data, G represents the positive calculation element, represents the seismic data weight matrix, represents the prior thickness distribution model, represents the paleo-water depth confidence weight matrix, L represents the Laplace smoothing operator, represents the geological prior weight coefficient, represents the horizontal smoothing weight coefficient;

[0028] The paleowater depth confidence weight matrix is ​​calculated using the following formula:

[0029] ;

[0030] in, represents the paleowater depth confidence weight matrix, U represents the mass content of uranium in rocks or sediments, Indicates the mass content of thorium in rocks or sediments.

[0031] In an optional embodiment, the method of performing compaction correction processing on the predicted thickness of the source rock to obtain the original thickness of the source rock at the initial stage of deposition, and performing denudation recovery processing on the original thickness of the source rock at the initial stage of deposition using the thermal maturity to obtain the actual effective thickness of the source rock includes:

[0032] Based on the pre-established relationship between porosity and depth, the thickness loss ratio of the source rock due to burial compaction is calculated;

[0033] Correcting the predicted thickness of the source rock according to the thickness loss ratio to obtain the original thickness of the source rock at the initial stage of deposition;

[0034] Compaction correction and cross-validation are performed on the missing formation thickness and erosion thickness to determine the actual effective thickness of the source rock; wherein the missing formation thickness is determined based on the comparison between the angular unconformity surface identified on the seismic profile and the logging curve, and the erosion thickness is determined based on the mutation point of the vitrinite reflectance, and the vitrinite reflectance is used to characterize the thermal maturity.

[0035] In an optional embodiment, the compaction correction and cross-verification of the missing thickness and the denuded thickness of the formation to determine the actual effective thickness of the source rock includes:

[0036] Comparing the angular unconformity surface identified on the seismic profile with the well logging curve to obtain the missing strata above and below the angular unconformity surface;

[0037] Using a time-depth conversion velocity model, the two-way travel time difference corresponding to the missing formation segment is converted into the missing formation thickness;

[0038] drawing a target variation curve of vitrinite reflectance as a function of depth, and identifying a mutation point of vitrinite reflectance from the target variation curve;

[0039] The erosion thickness is calculated by the difference between the measured value and the theoretical value of the vitrinite reflectance mutation point;

[0040] Using a compaction correction model, the missing thickness of the stratum and the denuded thickness are converted to the original thickness of the stratum and the original thickness of the denuded thickness at the initial stage of deposition, respectively;

[0041] The error value between the original thickness of the formation and the original thickness after denudation is calculated. If the error value is greater than a preset error threshold, iterative inversion is performed until the error value is no greater than the preset error threshold, thereby obtaining the actual effective thickness of the source rock.

[0042] In an optional embodiment, the original thickness of the source rock at the initial stage of deposition is calculated using the following formula:

[0043] ;

[0044] in, represents the original thickness of the source rock at the initial stage of deposition, represents the predicted thickness of source rock, Indicates the thickness loss ratio;

[0045] And / or, the missing thickness of the stratum is calculated by the following formula:

[0046] ;

[0047] in, Indicates the two-way time difference. Indicates the average velocity of the formation;

[0048] And / or, the erosion thickness is calculated by the following formula:

[0049] ;

[0050] in, represents the measured value of the vitrinite reflectance mutation point, It represents the theoretical value when there is no erosion. Represents the gradient with depth.

[0051] In an optional embodiment, the method further includes:

[0052] Generate result maps for exploration deployment, including source rock thickness contour maps, TOC and thickness intersection maps, and thickness comparison profiles before and after correction.

[0053] In a second aspect, an embodiment of the present application further provides a device for predicting the thickness of a source rock, the device comprising:

[0054] A data acquisition module, used to acquire target observation seismic data, well logging data and auxiliary geological data in the study area, wherein the auxiliary geological data includes paleowater depth level, thermal maturity and sedimentation history data;

[0055] A data processing module is used to perform spectrum analysis on the seismic wave data in the target observed seismic data to obtain a wellside seismic trace spectrum, and to extract a TOC curve from the well logging data to obtain a well logging TOC curve;

[0056] A relationship building module, configured to build a TOC-seismic frequency response relationship library based on the corresponding relationship between the seismic frequency characteristics of the wellside seismic trace spectrum and the TOC value of the well logging TOC curve;

[0057] a model determination module for dividing the study area into different sedimentary facies regions according to the paleo-water depth level, calculating the thickness of the source rock at each location based on the geological depositional laws of each sedimentary facies region, and generating a priori thickness distribution model to serve as an initial model for the thickness prediction model;

[0058] a model updating module for iteratively updating the model parameters of the prior thickness distribution model using the target observed seismic data, the well logging data, the auxiliary geological data, and the TOC-seismic frequency response relationship library to continuously reduce the objective function, thereby obtaining an optimal thickness prediction model and outputting the predicted thickness of the source rock;

[0059] The thickness correction module is used to perform compaction correction processing on the predicted thickness of the source rock to obtain the original thickness of the source rock in the early stage of deposition, and to perform denudation recovery processing on the original thickness of the source rock in the early stage of deposition using the thermal maturity to obtain the actual effective thickness of the source rock.

[0060] In a third aspect, an embodiment of the present application further provides an electronic device comprising: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor and the memory communicate via the bus, and when the machine-readable instructions are executed by the processor, the steps of the hydrocarbon source rock thickness prediction method as described above are performed.

[0061] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, which executes the steps of the above-mentioned method for predicting the thickness of hydrocarbon source rock when the computer program is executed by a processor.

[0062] The method for predicting the thickness of hydrocarbon source rocks provided in the embodiments of the present application has at least the following technical effects:

[0063] The embodiment of the present application obtains target observation seismic data and logging data to construct a TOC-seismic frequency response relationship library, generates a priori thickness distribution model in combination with paleowater depth levels, uses multiple parameters for iterative inversion, and performs compaction correction and erosion restoration processing on the predicted results. This solves the problems of the existing technology such as reliance on sparse sampling points, significant influence of wellbore environment, and low seismic resolution, improves the accuracy and reliability of source rock thickness prediction, and can provide more accurate data support for oil and gas exploration.

[0064] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0066] Figure 1 A flow chart of a method for predicting the thickness of a source rock provided in an embodiment of the present application;

[0067] Figure 2 A schematic diagram of the structure of a device for predicting the thickness of hydrocarbon source rocks provided in an embodiment of the present application;

[0068] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for which protection is claimed, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, each other embodiment obtained by those skilled in the art without making creative work falls within the scope of protection of the present application.

[0070] Research has found that Cambrian source rock thickness prediction is primarily based on geochemical methods, well log inversion, and seismic inversion and attribute analysis. Geochemical methods rely on sparse sampling points, making lateral extrapolation difficult and costly. Well log inversion, which uses the statistical relationship between logging responses and source rock TOC to predict thickness, is significantly affected by the wellbore environment, and lateral heterogeneity can lead to errors in extrapolation. Seismic inversion and attribute analysis, when indirectly predicting source rock distribution, suffers from weak signals due to the ancient age of the Cambrian strata, low seismic resolution, and minimal differences in physical properties between source rock and surrounding rock. These deficiencies can easily lead to inaccurate source rock thickness predictions.

[0071] Based on this, an embodiment of the present application provides a method for predicting the thickness of hydrocarbon source rocks, which comprehensively considers observed seismic data, well logging data, auxiliary geological data and the TOC-seismic frequency response relationship library, and can more accurately predict the thickness of hydrocarbon source rocks.

[0072] See also Figure 1 , Figure 1 This is a flow chart of a method for predicting the thickness of hydrocarbon source rocks provided in an embodiment of the present application. Figure 1 As shown in , the method for predicting the thickness of source rock provided in the embodiment of the present application includes:

[0073] S101. Obtain target observation seismic data, well logging data and auxiliary geological data in the study area. The auxiliary geological data include paleo-water depth level, thermal maturity and sedimentation history data. S102. Perform spectrum analysis on the seismic wave data in the target observation seismic data to obtain the spectrum of the wellside seismic trace, and extract the TOC curve of the well logging data to obtain the well logging TOC curve. S103. Construct a TOC-seismic frequency response relationship library based on the correspondence between the seismic frequency characteristics of the wellside seismic trace spectrum and the TOC value of the well logging TOC curve. S104. Divide the study area into different sedimentary facies according to the paleo-water depth level, and The geological sedimentary laws of the region are used to calculate the thickness of the source rock at each location and generate a priori thickness distribution model as the initial model of the thickness prediction model; S105, using the target observation seismic data, well logging data, auxiliary geological data and TOC-seismic frequency response relationship library, the model parameters of the priori thickness distribution model are continuously iteratively updated to continuously reduce the objective function, so as to obtain the optimal thickness prediction model and output the predicted thickness of the source rock; S106, the predicted thickness of the source rock is compacted and corrected to obtain the original thickness of the source rock in the early stage of deposition, and the original thickness of the source rock in the early stage of deposition is eroded and restored using thermal maturity to obtain the actual effective thickness of the source rock.

[0074] The embodiment of the present application obtains target observation seismic data and logging data to construct a TOC-seismic frequency response relationship library, generates a priori thickness distribution model in combination with paleowater depth levels, uses multiple parameters for iterative inversion, and performs compaction correction and erosion restoration processing on the predicted results. This solves the problems of the existing technology such as reliance on sparse sampling points, significant influence of wellbore environment, and low seismic resolution, improves the accuracy and reliability of source rock thickness prediction, and can provide more accurate data support for oil and gas exploration.

[0075] The following is an exemplary description through specific embodiments:

[0076] In step S101, target observation seismic data, well logging data, and auxiliary geological data are acquired within the study area. The auxiliary geological data include paleowater depth grade, thermal maturity, and sedimentation history data. Here, the study area refers to the specific geographic area for source rock thickness prediction, whose boundaries can be defined based on factors such as geological structural units and oil and gas exploration blocks. Target observation seismic data refers to data obtained by preprocessing observation seismic data. Observation seismic data is collected within the study area using seismic exploration equipment and contains information such as subsurface stratigraphic structure and lithologic variations. Well logging data refers to information reflecting the physical properties of the strata acquired during drilling using logging instruments. Auxiliary geological data supplements observation seismic data and well logging data to provide more comprehensive geological background information. Paleowater depth grade is a quantitative classification of paleowater depth in the study area based on biofossil assemblages, which can reflect the sedimentary environment. Thermal maturity is used to characterize the thermal evolution of organic matter in source rocks and is typically measured using indicators such as vitrinite reflectance. Sedimentation history data is information on the sedimentation of strata over geological periods, derived through tectonic sedimentation history simulation. For example, observational seismic data can be collected through three-dimensional seismic exploration technology, employing a multi-shot, multi-detector acquisition method to obtain seismic data covering the entire study area. After processing, these data can reveal the structural characteristics of strata thousands of meters below the surface. Well logging data can be obtained through various logging operations such as resistivity logging, sonic logging, and natural gamma logging in wells drilled within the study area. These logging operations can provide detailed records of physical properties such as resistivity, sonic velocity, and radioactivity. Paleowater depth classification can be determined through the identification and statistical analysis of fossils in drill cores. For example, if the concentration of a particular deep-water fossil is high, the area is classified as belonging to a paleowater depth corresponding to a deep-water sedimentary environment. Thermal maturity can be determined by measuring the vitrinite reflectance in drill core samples. The thermal maturity directly reflects the maturity of the source rock, which in turn affects its hydrocarbon generation capacity. Sedimentation history data can be obtained using basin simulation software, combining regional geological data with seismic stratigraphic information, to simulate the sedimentation process and magnitude of strata during different geological periods.

[0077] In practical applications, for a new exploration area, the scope of the study area can first be preliminarily delineated based on geological survey data, and then high-precision seismic exploration equipment can be used to collect observation seismic data. At the same time, several wells are arranged in the area for logging operations and core sampling, and auxiliary geological data are obtained through laboratory analysis of core samples. In this process, the determination of the paleo-water depth level may require reference to the paleogeographic map and biological fossil distribution patterns of the surrounding known areas. The thermal maturity test can use an automated vitrinite reflectance test instrument to improve the efficiency and accuracy of data acquisition. The simulation of sedimentation history data needs to comprehensively consider multiple factors such as regional tectonic movements and sea level changes to ensure that the simulation results can truly reflect the sedimentation history of the strata. In an optional embodiment, step S101 obtains the target observation seismic data through the following steps:

[0078] Step S1011: Acquire observed seismic data in the study area, where the observed seismic data includes pre-stack observed seismic data and post-stack observed seismic data.

[0079] In the above steps, pre-stack seismic data refers to the raw seismic data that has not been processed by migration and stacking. It retains information about seismic waves incident at different angles and contains richer underground structural details (such as lithologic interfaces and fault locations), but the data volume is large and noisy. Post-stack seismic data refers to the results of processing pre-stack seismic data such as correction and horizontal stacking. This suppresses some random noise, making the reflection wave groups more continuous and facilitating the intuitive identification of large-scale geological structures. Here, pre-stack and post-stack seismic data complement each other: pre-stack seismic data is used for fine inversion, while post-stack seismic data is used for macrostructural interpretation, jointly supporting subsequent geological analysis.

[0080] Step S1012: pre-process the observed seismic data to obtain target observed seismic data with seismic wave energy compensated, wherein the time domain of the target observed seismic data matches the depth domain of the well logging data.

[0081] Here, the observed seismic data are preprocessed by inverse Q filtering and horizon calibration to obtain target observed seismic data in which seismic wave energy is compensated and the time domain matches the depth domain of the well logging data.

[0082] This method can reversely compensate for attenuated energy and dispersion, restoring the original amplitude and frequency characteristics of seismic waves. It also solves the coordinate conversion problem between the seismic time domain (seconds) and the logging depth domain (meters), providing a unified reference framework for subsequent geological interpretation and inversion. This can significantly improve the quality of observed seismic data, thereby optimizing the accuracy and reliability of source rock thickness predictions. This not only helps improve the efficiency of oil and gas exploration, but also provides strong technical support for geological research and reservoir development.

[0083] In step S102, spectrum analysis is performed on the seismic wave data in the target observed seismic data to obtain a near-well seismic trace spectrum, and TOC curve extraction is performed on the well logging data to obtain a well logging TOC curve.

[0084] Seismic wave data is information recorded in the target seismic data, recording the temporal variations in ground vibration during seismic wave propagation. It contains seismic waves with different frequency components. Spectral analysis is a mathematical process that converts time-domain signals into frequency-domain signals. By performing spectral analysis on seismic wave data, the energy distribution of seismic waves at different frequencies can be determined, known as the borehole seismic trace spectrum. A TOC curve, a curve in well logging data that reflects the variation of total organic carbon content in a formation with depth, is extracted using specific logging instruments and data processing methods. For example, seismic wave data is typically stored as a time series, with each sampling point recording the amplitude of ground vibration at a specific moment. Spectral analysis can be performed using a fast Fourier transform (FFT) algorithm to convert seismic wave data from the time domain to the frequency domain, resulting in a borehole seismic trace spectrum. This spectrum displays the energy distribution of seismic waves across different frequency bands, such as low, medium, and high frequencies. For example, near a formation interface, higher low-frequency energy may reflect a significant difference in wave impedance at that interface. The extraction of TOC curves is generally done during the drilling process. Natural gamma ray logging, resistivity logging, and other logging data are combined with TOC content data obtained from laboratory analysis. Through mathematical modeling and data fitting, a relationship between logging response and TOC content is established. The TOC content at different depths is then calculated to form a TOC curve. This curve can intuitively demonstrate the vertical variation trend of TOC content in the formation and is of great significance for identifying source rock layers and assessing source rock quality. In specific applications, after acquiring the target observation seismic data and well logging data, the seismic wave data and well logging data in the target observation seismic data are processed as follows: first, a denoising algorithm is used to remove noise from the data. Then, a fast Fourier transform algorithm is used to perform spectral analysis on the denoised seismic wave data to obtain spectral information for the seismic trace near each well. At the same time, in-depth analysis of well logging data, combined with TOC content data measured in the laboratory on drill core samples, established a relationship model between well logging response and TOC content that is appropriate for the geological characteristics of the region. This model was used to calculate TOC content at different depths and plot well logging TOC curves. These near-well seismic trace spectra and well logging TOC curves provide the foundational data for the subsequent construction of a TOC-seismic frequency response relationship library.

[0085] In step S103, a TOC-seismic frequency response relationship library is constructed based on the correspondence between the seismic frequency characteristics of the near-well seismic trace spectrum and the TOC value of the well logging TOC curve.

[0086] The seismic frequency characteristics of the near-well seismic trace spectrum refer to information such as the energy distribution and frequency peak position of different frequency components in the spectrum, which reflects the propagation characteristics of seismic waves at different frequencies and the physical properties of the formation. The TOC value of the well logging TOC curve is the total organic carbon content corresponding to different depths on the curve, which represents the abundance of organic matter in the formation. The TOC-seismic frequency response relationship library is a data set that associates and stores the seismic frequency characteristics of the near-well seismic trace spectrum with the TOC values ​​of the well logging TOC curve. It is used to establish a corresponding relationship between seismic attributes and the organic carbon content of hydrocarbon source rocks.

[0087] Specifically, the seismic frequency characteristics of the near-well seismic trace spectrum can be characterized by calculating parameters such as the peak frequency, bandwidth, and low-frequency energy content. For example, a high proportion of low-frequency energy in the spectrum may indicate high wave impedance in the formation, and this wave impedance characteristic may be associated with a specific TOC content range. The TOC values ​​of the well logging TOC curve are obtained by processing and calculating well logging data, and their values ​​exhibit different variations in different stratigraphic intervals. When constructing a TOC-seismic frequency response relationship database, the near-well seismic trace spectrum characteristics of each well can be matched with the well logging TOC value at the corresponding depth to form data pairs. These pairs are then stored in a database in a specific format, such as a table, with each row representing a data point containing information such as well name, depth, seismic frequency characteristic parameters, and TOC value. By analyzing and statistically analyzing a large number of data points, potential relationships between seismic frequency characteristics and TOC values ​​can be identified, providing a basis for subsequent source rock thickness prediction.

[0088] In an optional embodiment, a matching pursuit algorithm is used to match the seismic frequency characteristics of the near-well seismic trace spectrum with the TOC value of the well logging TOC curve to obtain a TOC-seismic frequency response relationship library.

[0089] Here, since the relationship between TOC and seismic frequency has nonlinear and non-stationary characteristics, the matching pursuit algorithm is used. The matching pursuit algorithm can construct a TOC-seismic frequency response relationship library by adaptively decomposing the nonlinear relationship between seismic frequency characteristics and logging TOC.

[0090] In step S104, the study area is divided into different sedimentary facies regions according to the paleo-water depth level. Based on the geological depositional laws of each sedimentary facies region, the thickness of the source rock at each location is calculated and a priori thickness distribution model is generated to serve as the initial model of the thickness prediction model. Among them, the paleo-water depth level is a quantitative classification of paleo-water depth based on information such as the biofossil assemblage in the study area, and different paleo-water depth levels correspond to different sedimentary environments. Sedimentary facies regions are areas with similar geological depositional laws that are divided based on factors such as paleo-water depth level, sedimentary environment, and sedimentary characteristics. Geological depositional laws refer to the regular characteristics of sediment type, deposition mode, deposition rate, etc. under a specific sedimentary environment. The priori thickness distribution model is a model obtained after a preliminary estimation of the thickness of the source rock at each location in the study area based on the geological depositional laws of each sedimentary facies region. It provides initial constraints for the subsequent thickness prediction model.

[0091] In an optional embodiment, step S104 specifically includes:

[0092] Step S1041: Divide the study area into different sedimentary facies regions according to paleo-water depth levels;

[0093] Step S1042: Within each sedimentary facies region, the source rock thickness is interpolated using the Kriging interpolation method based on the geological depositional patterns of each sedimentary facies region. This method obtains the source rock thickness at each location and generates a priori thickness distribution model, which serves as the initial model for the thickness prediction model. Here, after the study area is divided into different sedimentary facies regions according to paleo-water depth levels, the geological depositional patterns of each sedimentary facies region can be analyzed using existing geological research results and sedimentary experience from similar regions. For example, in a shallow-water sedimentary facies region, historical sedimentary data and geological surveys indicate that the sedimentation rate in this region was relatively stable during a specific geological period, and that the source rock was primarily distributed in specific stratigraphic intervals. Based on these patterns, geostatistical methods, such as the Kriging interpolation method, can be used to interpolate the source rock thickness at each location within the region. By dividing the study area into several small grid cells, the Kriging interpolation formula is used to calculate an estimated source rock thickness at the center of each grid cell based on the source rock thickness data at known well locations, thereby generating a priori thickness distribution model. The model displays the preliminary distribution of source rock thickness in the study area in the form of a three-dimensional data body, providing initial constraints for subsequent precise inversion. Specifically, in practical applications, the study area is divided into four sedimentary facies regions, including shallow sea and semi-deep sea, based on the previously determined paleo-water depth level. For each sedimentary facies region, a large amount of geological data, including regional geological maps, sedimentary facies maps, and drill core descriptions, is collected to analyze its geological sedimentary laws. Then, the Kriging interpolation method is used to grid the study area based on the existing source rock thickness data of 8 wells, and the estimated value of the source rock thickness of each grid unit is calculated, ultimately generating a priori thickness distribution model. This model intuitively displays the preliminary distribution trend of source rock thickness in the study area, providing an important initial model for subsequent multi-parameter constrained inversion, and helping to improve the accuracy and efficiency of source rock thickness prediction.

[0094] In step S105, the target observed seismic data, well logging data, auxiliary geological data, and the TOC-seismic frequency response relationship library are used to iteratively update the model parameters of the priori thickness distribution model, continuously reducing the objective function to obtain the optimal thickness prediction model and output the predicted source rock thickness. The target observed seismic data contains seismic wave information on the structure and lithologic variations of the subsurface strata, serving as a crucial data foundation for the inversion process. Well logging data provides detailed information, such as the physical properties and organic carbon content of the strata, which is used to constrain the inversion process. The auxiliary geological data supplements the regional geological background information, facilitating a more accurate inversion model construction. The TOC-seismic frequency response relationship library establishes a correspondence between seismic attributes and the organic carbon content of the source rock, providing additional constraints for the inversion process. The priori thickness distribution model is an initial model of source rock thickness derived from previous geological analysis. Its model parameters include estimated source rock thickness values ​​at each location. The objective function is a mathematical expression used to measure the difference between the inversion model prediction and the actual observed data. By iteratively updating the model parameters of the priori thickness distribution model, the objective function value is reduced, resulting in an optimal thickness prediction model that better reflects the actual situation.

[0095] In an optional embodiment, step S105 specifically includes:

[0096] Step S1051: Initially constraining a wave impedance model based on the priori thickness distribution model, where the wave impedance model includes thickness parameters of the source rock.

[0097] Here, the wave impedance model is initially constrained based on the prior thickness distribution model. This model includes the thickness parameters of the source rock. For example, the source rock thickness at each location in the prior thickness distribution model is used as the initial value. The wave impedance model is then constructed by substituting the wave impedance calculation formula (wave impedance = density × velocity) into the density and velocity information obtained from well logging data. This wave impedance model reflects the stratum's resistance to seismic wave propagation and serves as the basis for forward modeling of the seismic wave equation.

[0098] Step S1052: Perform seismic wave equation forward modeling based on the wave impedance model to generate synthetic seismic records.

[0099] For example, numerical calculation methods such as the finite difference method and the finite element method are used to solve the seismic wave equation. The wave impedance model is used as input to simulate the propagation of seismic waves in the stratum. The seismic wave responses at different locations and times are calculated to generate synthetic seismic records. These synthetic seismic records are seismic data predicted by the model and can be compared with actual target observed seismic data to evaluate the accuracy of the model.

[0100] Forward modeling of the seismic wave equation can more accurately simulate the propagation of seismic waves in the strata, generating synthetic seismic records that better match the actual seismic data. This helps improve the accuracy of inversion results, especially in complex geological conditions such as strong lateral velocity variations and complex structures.

[0101] Step S1053: construct an objective function, which includes a seismic data fitting term, a geological priori constraint term, and a lateral smoothing term. The seismic data fitting term represents the difference between the synthetic seismic record and the target observed seismic data. The logging data is used to constrain the geological priori constraint term. The auxiliary geological data and the TOC-seismic frequency response relationship library are used to constrain the lateral smoothing term.

[0102] Specifically, the objective function includes:

[0103] ;

[0104] in, represents the objective function of the thickness prediction model, Constitute the seismic data fitting term, Constitutes geological prior constraints, represents the horizontal smoothing term;

[0105] m represents the thickness prediction model, represents the target observed seismic data, G represents the positive calculation element, represents the seismic data weight matrix, represents the prior thickness distribution model, represents the paleo-water depth confidence weight matrix, L represents the Laplace smoothing operator, represents the geological prior weight coefficient, represents the horizontal smoothing weight coefficient;

[0106] Among them, the paleowater depth confidence weight matrix is ​​calculated by the following formula:

[0107] ;

[0108] in, represents the paleowater depth confidence weight matrix, U represents the mass content of uranium in rocks or sediments, Indicates the mass content of thorium in rocks or sediments.

[0109] Among them, the U / Th ratio is introduced as an indicator factor of anoxic environment, which is directly involved in the calculation of the paleowater depth confidence weight matrix. It can also be set that when U / Th>1.2, the weight is doubled, so that the inversion results in the key areas of source rock development are more consistent with geological laws, significantly improving the reliability of the ancient stratum thickness prediction.

[0110] Here, the seismic data fitting term is used to measure the difference between the synthetic seismic record and the target observed seismic data. By minimizing this difference, the synthetic seismic record generated by the inversion model can be made closer to the actual observed data, thereby improving the accuracy of the inversion results. Well logging data is used to constrain the geological prior constraints. Well logging data reflects the actual physical properties of the formation and information such as organic carbon content. Incorporating this information into the objective function can make the inversion results more consistent with the actual geological conditions. Auxiliary geological data and the TOC-seismic frequency response relationship library are used to constrain the lateral smoothing term. The role of the lateral smoothing term is to ensure the spatial continuity and smoothness of the inversion results and avoid unreasonable mutations.

[0111] Furthermore, by comprehensively considering the observed seismic data, the prior thickness model, the paleo-water depth classification confidence weight matrix and the lateral smoothing term, the inversion problems under complex geological conditions, such as strong lateral velocity variations and complex structures, can be better handled, which helps to improve the applicability and reliability of the inversion results under complex geological conditions.

[0112] Step S1054: Use the conjugate gradient method to iteratively update the model parameters of the prior thickness distribution model and the thickness parameters in the wave impedance model, so that the objective function is continuously reduced until the residual error of the seismic data fitting term converges, and the optimal prediction model is obtained by inversion and the predicted thickness of the source rock is output.

[0113] In this step, in each iteration, the update direction and step size of the model parameters are calculated by the conjugate gradient method based on the gradient information of the objective function, the parameters in the prior thickness distribution model and the wave impedance model are updated, the seismic wave equation forward modeling and the objective function calculation are re-performed, and this process is repeated. When the residual of the seismic data fitting term is less than the preset threshold (such as 0.01), the inversion is considered to have converged, and the optimal thickness prediction model is obtained, and the source rock thickness predicted by the model is output.

[0114] For example, in the actual inversion process, after 50 iterations, the objective function gradually decreases from the initial value of 10 to 0.008, the residual converges, and the predicted thickness of the source rock is 60 meters on average and 120 meters at maximum.

[0115] In step S106, the predicted thickness of the source rock is subjected to compaction correction processing to obtain the original thickness of the source rock in the early stage of deposition, and the original thickness of the source rock in the early stage of deposition is subjected to denudation recovery processing using thermal maturity to obtain the actual effective thickness of the source rock.

[0116] Here, the predicted source rock thickness is an estimate of the source rock's current thickness, obtained through multi-parameter constrained inversion. Compaction correction takes into account the reduction in thickness of source rocks due to burial compaction during geological history and restores their original thickness at the initial stage of deposition through certain methods. Erosion restoration uses information such as thermal maturity to restore the lost thickness of strata later affected by tectonic movements, thereby determining the actual effective thickness of the source rock, which better reflects its true thickness during geological history.

[0117] In an optional embodiment, step S106 specifically includes:

[0118] Step S1061: Calculate the thickness loss ratio of the source rock due to burial compaction based on the pre-established correspondence between porosity and depth;

[0119] Here, the proportion of source rock thickness loss due to burial compaction can be calculated based on the pre-established relationship between porosity and depth. For example, by analyzing logging data from multiple wells in the study area, an empirical formula for the variation of porosity with depth can be established, such as porosity = ab × depth (a and b are fitting coefficients). Based on compaction theory (such as the Smith compaction formula), the proportion of source rock thickness loss at different depths can be calculated.

[0120] Step S1062: Correct the predicted thickness of the source rock according to the thickness loss ratio to obtain the original thickness of the source rock at the initial stage of deposition.

[0121] Specifically, the original thickness of the source rock at the initial stage of deposition is calculated using the following formula:

[0122] ;

[0123] in, represents the original thickness of the source rock at the initial stage of deposition, represents the predicted thickness of source rock, Indicates the thickness loss ratio.

[0124] Compaction correction involves calculating the percentage of source rock thickness lost due to burial compaction based on a pre-established relationship between porosity and depth. This percentage is then used to correct the predicted source rock thickness. For example, using the porosity-depth relationship for diagenetic compaction correction can increase source rock thickness by an average of 10%.

[0125] Step S1063: Compaction correction and cross-validation are performed on the missing formation thickness and the eroded thickness to determine the actual effective thickness of the source rock; wherein, the missing formation thickness is determined by comparing the angular unconformity surface identified on the seismic profile with the logging curve, and the eroded thickness is determined based on the vitrinite reflectance mutation point, and the vitrinite reflectance is used to characterize thermal maturity.

[0126] Here, denudation restoration involves estimating the amount of denudation using stratigraphic contact relationships and cross-validating this by matching vitrinite reflectance mutation points with denudation events. For example, using stratigraphic contact relationships to estimate the amount of denudation, the estimated average denudation is 30 meters. Then, by matching vitrinite reflectance mutation points with denudation events and performing cross-validation, the denudation thickness is calculated and the actual thickness of the source rock is restored. The restored average thickness is 70 meters.

[0127] Specifically, step S1063 includes:

[0128] Comparing the angular unconformity identified on the seismic profile with the well logging curves reveals the missing segments above and below the angular unconformity. Using the time-depth conversion velocity model, the two-way travel time difference corresponding to the missing segment is converted into the missing thickness of the formation.

[0129] Specifically, the missing stratum thickness is calculated using the following formula:

[0130] ;

[0131] in, Indicates the two-way time difference. Indicates the average velocity of the formation;

[0132] By comparing the angular unconformity surface identified on the seismic profile with the logging curve to determine the missing section of the formation, and using the time-depth conversion velocity model and the formation missing thickness formula to calculate the formation missing thickness, the amount of formation missing caused by tectonic movement can be accurately quantified, providing direct data support for erosion restoration and avoiding the missing thickness estimation bias caused by relying solely on single seismic or logging data.

[0133] Draw a target variation curve of vitrinite reflectance versus depth, identify the vitrinite reflectance mutation point from the target variation curve; calculate the erosion thickness by the difference between the measured value and the theoretical value of the vitrinite reflectance mutation point;

[0134] Specifically, the erosion thickness is calculated by the following formula:

[0135] ;

[0136] in, represents the measured value of the vitrinite reflectance mutation point, It represents the theoretical value when there is no erosion. Represents the gradient with depth.

[0137] By plotting the curve of vitrinite reflectance changing with depth to identify mutation points, combining the erosion thickness formula to calculate the erosion thickness, and using the quantitative relationship between thermal maturity index and depth gradient, the organic matter evolution characteristics are directly linked to the erosion events, effectively solving the problem of lack of time-depth constraints in traditional erosion volume calculations and improving the reliability of erosion thickness calculations.

[0138] Using the compaction correction model, the missing thickness and eroded thickness of the formation are converted to the original thickness and original eroded thickness of the formation at the initial stage of deposition, respectively. The error value between the original thickness of the formation and the original eroded thickness is calculated. If the error value is greater than the preset error threshold, iterative inversion is performed until the error value is no greater than the preset error threshold, thereby obtaining the actual effective thickness of the source rock.

[0139] The compaction correction model is used to convert the missing thickness and erosion thickness of the strata into the original thickness in the early stage of deposition, and the consistency of the two is ensured through error iterative inversion, eliminating the superimposed effects of diagenetic compaction and tectonic erosion on the thickness of the source rock, making the final actual effective thickness of the source rock closer to the true value of geological history, providing high-precision basic data for oil and gas resource assessment, ancient sedimentary environment reconstruction and tectonic evolution analysis.

[0140] In an optional embodiment, the method provided in the embodiment of the present application further includes:

[0141] Generate result maps for exploration deployment, including source rock thickness contour maps, TOC and thickness intersection maps, and thickness comparison profiles before and after correction.

[0142] The source rock thickness contour map uses contour lines to represent the distribution of source rock thickness within the study area. The TOC-thickness cross-plot intersects source rock thickness with organic carbon content (TOC). The pre- and post-correction thickness comparison profile shows the change in source rock thickness after correction for diagenetic compaction and restoration of tectonic erosion.

[0143] First, source rock thickness contour maps are generated using contour plotting methods based on the predicted source rock thickness data output by the optimal thickness prediction model. For example, in this study, the generated source rock thickness contour map illustrates the spatial distribution of thickness, with high thickness values ​​concentrated in the central part of the study area.

[0144] Here, the source rock thickness contour map can intuitively show the spatial distribution characteristics of source rock thickness in the entire study area, clearly revealing the thickness variation trend and favorable zones. This provides an intuitive visual basis for the optimization and deployment of exploration targets, and helps to quickly identify potential oil and gas enrichment areas.

[0145] Second, a TOC vs. thickness crossplot plots the TOC values ​​at each well location against the corresponding source rock thickness in the same coordinate system to analyze the correlation between the two. For example, in this study, the generated TOC vs. thickness crossplot showed a positive correlation between TOC values ​​and thickness.

[0146] Here, a TOC-thickness crossplot, which intersects source rock thickness with organic carbon content (TOC), reveals the correlation between the two and identifies areas of high-quality source rock with high TOC values ​​and significant thickness. This is crucial for assessing source rock quality and resource potential, guiding the deployment of exploration wells and the development of development plans.

[0147] Third, the thickness comparison profiles before and after correction are selected from typical sections within the study area. These sections plot the source rock thickness before and after correction, visually demonstrating the impact of correction on thickness predictions. For example, in this study, the dynamic thickness comparison profiles generated before and after correction showed a significant increase in thickness after correction.

[0148] The thickness comparison sections before and after dynamic correction demonstrate the changes in source rock thickness before and after diagenetic compaction correction and tectonic denudation recovery, visually demonstrating the impact of dynamic correction on thickness predictions. This helps understand the burial history and compaction processes of source rocks over geological time, providing important insights for geological research and oil and gas reservoir development.

[0149] Furthermore, the method provided in the embodiments of the present application can also evaluate the model accuracy and quantify the credibility of the prediction results based on cross-validation strategy and uncertainty analysis.

[0150] Specifically, cross-validation allows you to divide a dataset into multiple subsets, alternating between using a portion as the training set and the remaining portion as the validation set. This effectively assesses the model's generalization ability, avoids overfitting or underfitting, and thus improves the reliability of predictions. Cross-validation provides a more objective and comprehensive assessment of model performance, ensuring that the model maintains stable prediction accuracy across different data subsets.

[0151] Uncertainty analysis quantifies the uncertainty of forecast results, for example by calculating confidence intervals or probability distributions. This helps identify possible sources of error in the forecast and assess the confidence level of the forecast. Uncertainty analysis can provide decision makers with more comprehensive information, helping them better understand the reliability and risk of the forecast results.

[0152] The embodiment of the present application constrains the inversion model by collecting multidimensional data, such as observational seismic data, well logging data, and auxiliary geological data. Since the model comprehensively considers information from seismic, well logging, and geological parameters, it can more accurately predict the thickness of the source rock; the matching pursuit algorithm is used to match the well logging TOC curve with the spectrum of the seismic trace near the well to construct a TOC-seismic frequency response relationship library, which helps to improve the correlation between seismic data and geological parameters, thereby enhancing the reliability of the prediction results. Based on the paleowater depth classification parameters, the Kriging interpolation method is used to generate a priori thickness distribution model, which helps to better constrain the prediction of the source rock thickness during the inversion process and improve the accuracy of the prediction results. The thickness of the Cambrian source rock obtained by the thickness prediction model is corrected for diagenesis and restored for erosion, which helps to more accurately assess the actual thickness of the source rock and provide more reliable data support for oil and gas resource evaluation. This application can not only be used to predict the thickness of the source rock, but also provide valuable data support for geological research and oil and gas reservoir development, and help to deeply understand geological processes such as sedimentary environment and tectonic evolution. In addition, it can also be used to predict the thickness of source rocks in different regions and under different geological conditions, and has strong versatility and adaptability.

[0153] Based on the same inventive concept, an embodiment of the present application also provides a source rock thickness prediction device corresponding to the source rock thickness prediction method. Since the principle of solving the problem by the device in the embodiment of the present application is similar to the above-mentioned source rock thickness prediction method in the embodiment of the present application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0154] See also Figure 2 , Figure 2 This is a schematic diagram of the structure of a device for predicting the thickness of a source rock provided in an embodiment of the present application. Figure 2 As shown in , the device 200 includes:

[0155] Data acquisition module 201, for acquiring target observation seismic data, well logging data and auxiliary geological data in the study area, wherein the auxiliary geological data includes paleo-water depth level, thermal maturity and sedimentation history data;

[0156] The data processing module 202 is configured to perform spectrum analysis on the seismic wave data in the target observed seismic data to obtain a near-well seismic trace spectrum, and to extract a TOC curve from the well logging data to obtain a well logging TOC curve;

[0157] A relationship building module 203 is configured to build a TOC-seismic frequency response relationship library based on the corresponding relationship between the seismic frequency characteristics of the wellside seismic trace spectrum and the TOC value of the well logging TOC curve;

[0158] A model determination module 204 is configured to divide the study area into different sedimentary facies regions according to the paleo-water depth level, calculate the thickness of the source rock at each location based on the geological depositional laws of each sedimentary facies region, and generate a priori thickness distribution model to serve as an initial model for the thickness prediction model;

[0159] A model updating module 205 is configured to utilize the target observed seismic data, the well logging data, the auxiliary geological data, and the TOC-seismic frequency response relationship library to continuously iteratively update the model parameters of the priori thickness distribution model so as to continuously reduce the objective function, thereby obtaining an optimal thickness prediction model and outputting the predicted thickness of the source rock;

[0160] The thickness correction module 206 is used to perform compaction correction processing on the predicted thickness of the source rock to obtain the original thickness of the source rock in the early stage of deposition, and to perform denudation recovery processing on the original thickness of the source rock in the early stage of deposition using the thermal maturity to obtain the actual effective thickness of the source rock.

[0161] The source rock thickness prediction device provided in the embodiment of the present application obtains target observation seismic data and logging data to construct a TOC-seismic frequency response relationship library, generates a priori thickness distribution model in combination with paleowater depth levels, uses multiple parameters for iterative inversion, and performs compaction correction and erosion restoration processing on the prediction results. It solves the problems of the existing technology such as reliance on sparse sampling points, significant influence of wellbore environment, and low seismic resolution, improves the accuracy and reliability of source rock thickness prediction, and can provide more accurate data support for oil and gas exploration.

[0162] See also Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 3 As shown in FIG, the electronic device 300 includes a processor 310 , a memory 320 and a bus 330 .

[0163] The memory 320 stores machine-readable instructions executable by the processor 310. When the electronic device 300 is running, the processor 310 communicates with the memory 320 via the bus 330. When the machine-readable instructions are executed by the processor 310, the above-mentioned Figure 1 The specific implementation of the steps of the method for predicting the thickness of hydrocarbon source rocks in the method embodiment shown can be found in the method embodiment and will not be repeated here.

[0164] The embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the computer program can execute the above-mentioned Figure 1 The steps of the method for predicting the thickness of hydrocarbon source rocks in the method embodiment shown are specifically implemented in accordance with the method embodiment and will not be described in detail here.

[0165] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0166] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. There may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed may be through some communication interface, indirect coupling or communication connection of devices or units, which may be electrical, mechanical or other forms.

[0167] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0168] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0169] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0170] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solutions of the present application, rather than to limit them. The scope of protection of the present application is not limited thereto. Although the present application has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-mentioned embodiments within the technical scope disclosed in the present application, or perform equivalent replacements for some of the technical features thereof. These modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for predicting the thickness of hydrocarbon source rocks, characterized in that: The method comprises: Acquire target observation seismic data, well logging data, and auxiliary geological data within the study area, the auxiliary geological data including paleowater depth level, thermal maturity, and sedimentation history data; Performing spectrum analysis on the seismic wave data in the target observed seismic data to obtain a wellside seismic trace spectrum, and performing TOC curve extraction on the well logging data to obtain a well logging TOC curve; Constructing a TOC-seismic frequency response relationship library based on the correspondence between the seismic frequency characteristics of the wellside seismic trace spectrum and the TOC value of the well logging TOC curve; Dividing the study area into different sedimentary facies regions according to the paleo-water depth level, calculating the thickness of the source rock at each location based on the geological depositional laws of each sedimentary facies region and generating a priori thickness distribution model to serve as an initial model for the thickness prediction model; Utilizing the target observed seismic data, the well logging data, the auxiliary geological data, and the TOC-seismic frequency response relationship library, the model parameters of the priori thickness distribution model are continuously iteratively updated to continuously reduce the objective function, thereby obtaining an optimal thickness prediction model and outputting the predicted thickness of the source rock; The predicted thickness of the source rock is subjected to compaction correction processing to obtain the original thickness of the source rock in the early stage of deposition, and the original thickness of the source rock in the early stage of deposition is subjected to denudation recovery processing using the thermal maturity to obtain the actual effective thickness of the source rock.

2. The method according to claim 1, characterized in that The target observed seismic data is obtained by the following steps: Collecting observation seismic data in the study area, wherein the observation seismic data includes pre-stack observation seismic data and post-stack observation seismic data; The observed seismic data is preprocessed to obtain target observed seismic data with seismic wave energy compensated, wherein the time domain of the target observed seismic data matches the depth domain of the well logging data.

3. The method according to claim 1, characterized in that The TOC-seismic frequency response relationship library is constructed based on the correspondence between the seismic frequency characteristics of the wellside seismic trace spectrum and the TOC value of the well logging TOC curve, including: The seismic frequency characteristics of the near-well seismic trace spectrum and the TOC value of the well logging TOC curve are matched using a matching pursuit algorithm to obtain a TOC-seismic frequency response relationship library.

4. The method according to claim 1, wherein The method of dividing the study area into different sedimentary facies regions according to the paleo-water depth level and calculating the thickness of the source rock at each location based on the geological depositional laws of each sedimentary facies region and generating a priori thickness distribution model as an initial model of the thickness prediction model includes: The study area is divided into different sedimentary facies areas according to paleo-water depth levels; In each sedimentary facies area, based on the geological sedimentary laws of each sedimentary facies area, the Kriging interpolation method is used to interpolate the thickness of the source rock to obtain the thickness of the source rock at each location and generate a priori thickness distribution model as the initial model of the thickness prediction model.

5. The method according to claim 1, wherein The method of continuously iteratively updating the model parameters of the priori thickness distribution model by utilizing the target observed seismic data, the well logging data, the auxiliary geological data, and the TOC-seismic frequency response relationship library to continuously reduce the objective function, thereby obtaining an optimal thickness prediction model and outputting the predicted thickness of the source rock, includes: Initially constraining a wave impedance model based on a priori thickness distribution model, wherein the wave impedance model includes thickness parameters of the source rock; Performing seismic wave equation forward modeling based on the wave impedance model to generate synthetic seismic records; Construct an objective function, wherein the objective function includes: ； in, represents the objective function of the thickness prediction model, Constitute the seismic data fitting term, Constitutes geological prior constraints, represents the horizontal smoothing term; m represents the thickness prediction model, represents the target observed seismic data, G represents the positive calculation element, represents the seismic data weight matrix, represents the prior thickness distribution model, represents the paleo-water depth confidence weight matrix, L represents the Laplace smoothing operator, represents the geological prior weight coefficient, represents the horizontal smoothing weight coefficient; The paleowater depth confidence weight matrix is ​​calculated using the following formula: ； in, represents the paleowater depth confidence weight matrix, U represents the mass content of uranium in rocks or sediments, Indicates the mass content of thorium in rocks or sediments; The target observation seismic data, the well logging data, the auxiliary geological data and the TOC-seismic frequency response relationship library are used to iteratively update the model parameters of the prior thickness distribution model and the thickness parameters in the wave impedance model using the conjugate gradient method, so that the objective function is continuously reduced until the residual of the seismic data fitting term converges, and the optimal thickness prediction model is obtained by inversion and the predicted thickness of the source rock is output.

6. The method according to claim 1, characterized in that The method of performing compaction correction on the predicted thickness of the source rock to obtain the original thickness of the source rock at the initial stage of deposition, and performing denudation recovery on the original thickness of the source rock at the initial stage of deposition using the thermal maturity to obtain the actual effective thickness of the source rock, includes: Based on the pre-established relationship between porosity and depth, the thickness loss ratio of the source rock due to burial compaction is calculated; Correcting the predicted thickness of the source rock according to the thickness loss ratio to obtain the original thickness of the source rock at the initial stage of deposition; Compaction correction and cross-validation are performed on the missing formation thickness and erosion thickness to determine the actual effective thickness of the source rock; wherein the missing formation thickness is determined based on the comparison between the angular unconformity surface identified on the seismic profile and the logging curve, and the erosion thickness is determined based on the mutation point of the vitrinite reflectance, and the vitrinite reflectance is used to characterize the thermal maturity.

7. The method according to claim 6, characterized in that The compaction correction and cross-verification of the missing thickness and eroded thickness of the formation to determine the actual effective thickness of the source rock include: Comparing the angular unconformity surface identified on the seismic profile with the well logging curve to obtain the missing strata above and below the angular unconformity surface; Using a time-depth conversion velocity model, the two-way travel time difference corresponding to the missing formation segment is converted into the missing formation thickness; drawing a target variation curve of vitrinite reflectance as a function of depth, and identifying a mutation point of vitrinite reflectance from the target variation curve; The erosion thickness is calculated by the difference between the measured value and the theoretical value of the vitrinite reflectance mutation point; Using a compaction correction model, the missing thickness of the stratum and the denuded thickness are converted to the original thickness of the stratum and the original thickness of the denuded thickness at the initial stage of deposition, respectively; The error value between the original thickness of the formation and the original thickness after denudation is calculated. If the error value is greater than a preset error threshold, iterative inversion is performed until the error value is no greater than the preset error threshold, thereby obtaining the actual effective thickness of the source rock.

8. The method according to claim 7, characterized in that The original thickness of the source rock at the initial stage of deposition is calculated using the following formula: ； in, represents the original thickness of the source rock at the initial stage of deposition, represents the predicted thickness of source rock, Indicates the thickness loss ratio; And / or, the missing thickness of the stratum is calculated by the following formula: ； in, Indicates the two-way time difference. Indicates the average velocity of the formation; And / or, the erosion thickness is calculated by the following formula: ； in, represents the measured value of the vitrinite reflectance mutation point, It represents the theoretical value when there is no erosion. Represents the gradient with depth.

9. The method according to claim 1, characterized in that The method further comprises: Generate result maps for exploration deployment, including source rock thickness contour maps, TOC and thickness intersection maps, and thickness comparison profiles before and after correction.

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