Source rock thickness prediction method

Abstract

The invention provides a hydrocarbon source rock thickness prediction method. The method comprises the steps that target observation seismic data, logging data and auxiliary geological data in a research area are acquired; performing spectrum analysis on the target observation seismic data to obtain a near-well seismic channel spectrum, and performing TOC curve extraction on the logging data to obtain a logging TOC curve; constructing a TOC-seismic frequency response relation library according to the near-well seismic channel frequency spectrum and the logging TOC curve; dividing a research area into different sedimentary facies areas, calculating hydrocarbon source rock thickness at each position and generating a prior thickness distribution model; using the target observation seismic data, the logging data, the auxiliary geological data and the TOC-seismic frequency response relation library to continuously iteratively update to continuously reduce the target function to obtain an optimal thickness prediction model and output the hydrocarbon source rock prediction thickness; and carrying out compaction correction treatment and denudation recovery treatment on the predicted thickness of the hydrocarbon source rock to obtain the actual effective thickness of the hydrocarbon source rock. The method can more accurately predict the thickness of the hydrocarbon source rock.

Description

Technical Field

[0001] This application relates to the technical field of geophysical exploration, and more specifically, to a method for predicting the thickness of source rocks. Background Art

[0002] Predicting the thickness of Cambrian source rocks is of great significance in many aspects in the fields of oil and gas resource exploration, geological research, and energy development. At present, the prediction of Cambrian source rock thickness mainly relies on methods such as geochemical methods, well log curve inversion, and seismic inversion and attribute analysis. Among them, geochemical methods rely on sparse sampling points, are difficult to extrapolate horizontally, and have high costs; well log curve inversion uses the statistical relationship between well log responses and the TOC of source rocks to predict thickness, is greatly affected by the wellbore environment, and errors will occur in extrapolation due to lateral heterogeneity; when seismic inversion and attribute analysis indirectly predict the distribution of source rocks, due to the ancient age of the Cambrian strata, low seismic resolution, small physical property differences between source rocks and surrounding rocks, and weak signals; the above defects easily lead to inaccurate prediction of the thickness of source rocks. Summary of the Invention

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

[0004] In a first aspect, an embodiment of this application provides a method for predicting the thickness of source rocks, and the method includes: Obtain target observed seismic data, well log data, and auxiliary geological data within the study area, where the auxiliary geological data includes paleo-water depth grade, thermal maturity, and subsidence history data; Perform spectral analysis on the seismic wave data in the target observed seismic data to obtain the spectrum of the seismic trace next to the well, and extract the TOC curve from the well log data to obtain the well log TOC curve; Construct a TOC-seismic frequency response relationship library according to the corresponding relationship between the seismic frequency characteristics of the seismic trace next to the well and the TOC values of the well log TOC curve; Divide the study area into different sedimentary facies areas according to the paleo-water depth grade, and calculate the thickness of the source rocks at each location and generate a prior thickness distribution model based on the geological sedimentation laws of each sedimentary facies area as the initial model of the thickness prediction model; Use the target observed seismic data, the well log data, the auxiliary geological data, and the TOC-seismic frequency response relationship library to continuously iterate and update the model parameters of the prior thickness distribution model to continuously reduce the objective function, so as to obtain the optimal thickness prediction model and output the predicted thickness of the source rocks; The predicted thickness of the source rock is subjected to compaction correction to obtain the original thickness of the source rock at the initial stage of sedimentation, and the original thickness of the source rock at the initial stage of sedimentation is subjected to erosion restoration using the thermal maturity to obtain the actual effective thickness of the source rock.

[0005] In an alternative embodiment, the target observed seismic data is obtained through the following steps: Collect the observed seismic data within the research area, where the observed seismic data includes pre-stack observed seismic data and post-stack observed seismic data; Preprocess the observed seismic data to obtain the target observed seismic data with compensated seismic wave energy, where the time domain of the target observed seismic data matches the depth domain of the logging data.

[0006] In an alternative embodiment, constructing a TOC-seismic frequency response relationship library according to the corresponding relationship between the seismic frequency characteristics of the seismic trace beside the well and the TOC values of the logging TOC curve includes: Use the matching pursuit algorithm to match the seismic frequency characteristics of the seismic trace beside the well and the TOC values of the logging TOC curve to obtain the TOC-seismic frequency response relationship library.

[0007] In an alternative embodiment, dividing the research area into different sedimentary facies regions according to the paleo-water depth level, and calculating the thickness of the source rock at each location and generating a prior thickness distribution model based on the geological sedimentation laws of each sedimentary facies region as the initial model of the thickness prediction model includes: Divide the research area into different sedimentary facies regions according to the paleo-water depth level; Within each sedimentary facies region, based on the geological sedimentation laws of each sedimentary facies region, use the Kriging interpolation method to interpolate and calculate the thickness of the source rock to obtain the thickness of the source rock at each location and generate a prior thickness distribution model as the initial model of the thickness prediction model.

[0008] In an alternative embodiment, using the target observed seismic data, the logging data, the auxiliary geological data, and the TOC-seismic frequency response relationship library to continuously iterate and update the model parameters of the prior thickness distribution model to continuously reduce the objective function to obtain the optimal thickness prediction model and output the predicted thickness of the source rock includes: Based on the prior thickness distribution model, initialize the impedance model, and the impedance model contains the thickness parameter of the source rock; Based on the impedance model, perform forward modeling of the seismic wave equation to generate a synthetic seismic record; Construct an objective function, which includes a seismic data fitting term, a geological prior 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 well logging data is used to constrain the geological prior constraint term, and the auxiliary geological data and the TOC-seismic frequency response relationship library are used to constrain the lateral smoothing term; Adopt the conjugate gradient method to continuously iterate and update the model parameters of the prior thickness distribution model and the thickness parameters in the wave impedance model, so as to continuously reduce the objective function until the residual of the seismic data fitting term converges, and inversely obtain the optimal thickness prediction model and output the predicted thickness of the source rock.

[0009] In an optional embodiment, the objective function includes: ; Wherein, represents the objective function of the thickness prediction model, constitutes the seismic data fitting term, constitutes the geological prior constraint term, represents the lateral smoothing term; m represents the thickness prediction model, represents the target observed seismic data, G represents the forward operator, represents the seismic data weight matrix, represents the prior thickness distribution model, represents the paleo-water depth grade confidence weight matrix, L represents the Laplace smoothing operator, represents the geological prior weight coefficient, represents the lateral smoothing weight coefficient; Wherein, the paleo-water depth grade confidence weight matrix is calculated by the following formula: ; Wherein, represents the paleo-water depth grade confidence weight matrix, U represents the mass content of uranium in the rock or sediment, represents the mass content of thorium in the rock or sediment.

[0010] In an optional embodiment, performing a compaction correction process on the predicted thickness of the source rock to obtain the original thickness at the initial stage of source rock deposition, and using the thermal maturity to perform an erosion restoration process on the original thickness at the initial stage of source rock deposition to obtain the actual effective thickness of the source rock, including: According to the pre-established correspondence between porosity and depth, calculate the thickness loss ratio of the source rock caused by burial compaction; Correct the predicted thickness of the source rock according to the thickness loss ratio to obtain the original thickness at the initial stage of source rock deposition; Compaction correction and cross-validation are performed on the missing thickness and erosion thickness of the formation to determine the actual effective thickness of the source rock. Among them, the missing thickness of the formation is determined by comparing the angular unconformity surface identified on the seismic profile with the well log curve, the erosion thickness is determined according to the abrupt change point of the vitrinite reflectance, and the vitrinite reflectance is used to characterize the thermal maturity.

[0011] In an alternative embodiment, the compaction correction and cross-validation of the missing thickness and erosion thickness of the formation to determine the actual effective thickness of the source rock includes: Compare the angular unconformity surface identified on the seismic profile with the well log curve to obtain the missing formation segments above and below the angular unconformity surface; Using the time-depth conversion velocity model, convert the two-way travel time difference corresponding to the missing formation segment into the missing thickness of the formation; Draw the target change curve of the vitrinite reflectance changing with depth, and identify the abrupt change point of the vitrinite reflectance from the target change curve; Calculate the erosion thickness through the difference between the measured value and the theoretical value of the abrupt change point of the vitrinite reflectance; Using the compaction correction model, convert the missing thickness of the formation and the erosion thickness into the original thickness of the formation and the original erosion thickness at the initial stage of sedimentation respectively; Calculate the error value between the original thickness of the formation and the original erosion thickness. If the error value is greater than the preset error threshold, perform iterative inversion until the error value is not greater than the preset error threshold to obtain the actual effective thickness of the source rock.

[0012] In an alternative embodiment, the original thickness of the source rock at the initial stage of sedimentation is calculated by the following formula: ; Among them, represents the original thickness of the source rock at the initial stage of sedimentation, represents the predicted thickness of the source rock, represents the thickness loss ratio; And / or, the missing thickness of the formation is calculated by the following formula: ; Among them, represents the two-way travel time difference, represents the average velocity of the formation; And / or, the erosion thickness is calculated by the following formula: ; Among them, represents the measured value of the abrupt change point of the vitrinite reflectance, represents the theoretical value when there is no erosion, represents the gradient with depth.

[0013] In an alternative embodiment, the method further includes: Generating a result map for exploration deployment, where the result map includes an isopach map of the source rock thickness, a crossplot of TOC and thickness, and a thickness comparison profile before and after correction.

[0014] In a second aspect, an embodiment of the present application further provides a device for predicting the thickness of a source rock, where the device includes: A data acquisition module, configured to acquire target observed seismic data, well logging data, and auxiliary geological data within a research area, where the auxiliary geological data includes paleo - water depth level, thermal maturity, and subsidence history data; A data processing module, configured to perform spectral analysis on seismic wave data in the target observed seismic data to obtain the spectrum of the seismic trace beside the well, and extract the TOC curve from the well logging data to obtain the well logging TOC curve; A relationship construction module, configured to construct a TOC - seismic frequency response relationship library according to the correspondence between the seismic frequency characteristics of the seismic trace beside the well and the TOC values of the well logging TOC curve; A model determination module, configured to divide the research 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 sedimentation rules of each sedimentary facies region, and generate a prior thickness distribution model as the initial model of the thickness prediction model; A model update module, configured to continuously iterate and update the model parameters of the prior thickness distribution model by 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, so as to obtain an optimal thickness prediction model and output the predicted thickness of the source rock; A thickness correction module, configured to perform compaction correction processing on the predicted thickness of the source rock to obtain the original thickness at the initial stage of source rock deposition, and perform erosion restoration processing on the original thickness at the initial stage of source rock deposition by using the thermal maturity to obtain the actual effective thickness of the source rock.

[0015] In a third aspect, an embodiment of the present application further provides an electronic device, including: a processor, a memory, and a bus. The memory stores machine - readable instructions executable by the processor. When the electronic device runs, the processor communicates with the memory through the bus. When the machine - readable instructions are executed by the processor, the steps of the method for predicting the thickness of a source rock as described above are executed.

[0016] In a fourth aspect, an embodiment of the present application further provides a computer - readable storage medium, on which a computer program is stored. When the computer program is run by a processor, the steps of the method for predicting the thickness of a source rock as described above are executed.

[0017] The hydrocarbon source rock thickness prediction method provided by the embodiments of the present application has at least the following technical effects: In the embodiments of the present application, by obtaining target observed seismic data and logging data to construct a TOC-seismic frequency response relationship library, generating a prior thickness distribution model in combination with paleo-water depth levels, performing iterative inversion using multiple parameters, and performing compaction correction and erosion restoration processing on the prediction results, the problems in the prior art such as relying on sparse sampling points, being greatly affected by wellbore environments, and low seismic resolution are solved, the accuracy and reliability of hydrocarbon source rock thickness prediction are improved, and more accurate data support can be provided for oil and gas exploration.

[0018] To make the above objects, features, and advantages of the present application more obvious and understandable, the following specifically enumerates preferred embodiments and, in conjunction with the accompanying drawings, provides a detailed description as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] To more clearly illustrate the technical solutions of the embodiments of the present application, the accompanying drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present application and should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0020] Figure 1 It is a flowchart of a method for predicting the thickness of a hydrocarbon source rock provided by an embodiment of the present application; Figure 2 It is a schematic structural diagram of a device for predicting the thickness of a hydrocarbon source rock provided by an embodiment of the present application; Figure 3 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] To make the objects, 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 some embodiments of the present application, rather than all embodiments. Usually, the components of the embodiments of the present application 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 present application to be protected, but only represents the selected embodiments of the present application. Based on the embodiments of the present application, every other embodiment obtained by those of ordinary skill in the art without creative efforts belongs to the scope of protection of the present application.

[0022] It has been found through research that the prediction of the thickness of Cambrian source rocks mainly relies on methods such as geochemical methods, well logging curve inversion, seismic inversion, and attribute analysis. Among them, geochemical methods rely on sparse sampling points, are difficult to extrapolate laterally, and have high costs; well logging curve inversion uses the statistical relationship between well logging responses and the TOC of source rocks to predict thickness, is greatly affected by the wellbore environment, and errors will occur in extrapolation due to lateral heterogeneity; when seismic inversion and attribute analysis indirectly predict the distribution of source rocks, due to the ancient age of the Cambrian strata, low seismic resolution, small difference in physical properties between source rocks and surrounding rocks, and weak signals; the above defects easily lead to inaccurate prediction of the thickness of source rocks.

[0023] Based on this, the embodiments of the present application provide a method for predicting the thickness of source rocks, which comprehensively considers the 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 source rocks.

[0024] Please refer to Figure 1 , Figure 1 which is a flowchart of a method for predicting the thickness of source rocks provided by the embodiments of the present application. As Figure 1 shown in the method for predicting the thickness of source rocks provided by the embodiments of the present application includes:

[0025] S101. Obtain the target observed seismic data, well logging data, and auxiliary geological data in the study area, where the auxiliary geological data includes paleo-water depth level, thermal maturity, and subsidence history data; S102. Perform spectral analysis on the seismic wave data in the target observed seismic data to obtain the well-side seismic trace spectrum, and extract the TOC curve from the well logging data to obtain the well logging TOC curve; S103. Construct a TOC-seismic frequency response relationship library according to the corresponding relationship between the seismic frequency characteristics of the well-side seismic trace spectrum and the TOC values of the well logging TOC curve; S104. Divide the study area into different sedimentary facies regions according to the paleo-water depth level, and calculate the thickness of the source rocks at each location and generate a prior thickness distribution model based on the geological sedimentation laws of each sedimentary facies region as the initial model of the thickness prediction model; S105. Use the target observed seismic data, well logging data, auxiliary geological data, and the TOC-seismic frequency response relationship library to continuously iterate and update the model parameters of the prior thickness distribution model to continuously reduce the objective function, so as to obtain the optimal thickness prediction model and output the predicted thickness of the source rocks; S106. Perform compaction correction processing on the predicted thickness of the source rocks to obtain the original thickness at the initial stage of source rock deposition, and use the thermal maturity to perform denudation restoration processing on the original thickness at the initial stage of source rock deposition to obtain the actual effective thickness of the source rocks.

[0025] In the embodiments of the present application, by obtaining target observed seismic data and logging data to construct a TOC-seismic frequency response relationship library, generating a prior thickness distribution model in combination with paleo-water depth levels, performing iterative inversion using multiple parameters, and performing compaction correction and erosion restoration processing on the prediction results, the problems in the prior art such as relying on sparse sampling points, being greatly affected by the wellbore environment, and low seismic resolution are solved, the accuracy and reliability of hydrocarbon source rock thickness prediction are improved, and more accurate data support can be provided for oil and gas exploration.

[0026] The following is an exemplary description through specific embodiments: In step S101, target observed seismic data, logging data, and auxiliary geological data within the study area are obtained. The auxiliary geological data includes paleo-water depth levels, thermal maturity, and subsidence history data. Here, the study area refers to the specific geographical scope for hydrocarbon source rock thickness prediction, and its boundary can be delimited according to factors such as geological tectonic units and oil and gas exploration blocks; the target observed seismic data refers to the data obtained by preprocessing the observed seismic data, and the observed seismic data is the data collected by seismic exploration equipment within the study area, which contains information such as the underground stratigraphic structure and lithology changes; the logging data refers to the information reflecting the physical properties of the formation obtained by logging instruments during the drilling process; the auxiliary geological data is used to supplement the observed seismic data and logging data to provide more comprehensive geological background information; among them, the paleo-water depth level is the data obtained by quantitatively grading the paleo-water depth of the study area based on the fossil assemblage, which can reflect the sedimentary environment; the thermal maturity is used to characterize the thermal evolution degree of organic matter in the hydrocarbon source rock, and is commonly measured by indicators such as vitrinite reflectance; the subsidence history data is the subsidence information of the formation in different geological historical periods obtained by simulating the tectonic subsidence history. Exemplarily, the observed seismic data can be collected by three-dimensional seismic exploration technology, adopting a multi-shotpoint and multi-geophone acquisition method to obtain seismic data covering the entire study area, and these data can reflect the structural characteristics of the underground strata thousands of meters after being processed. The logging data can be obtained by performing various logging operations such as resistivity logging, acoustic logging, and natural gamma logging in the wells within the study area, and can record in detail the physical properties such as the resistivity, acoustic velocity, and radioactivity of the formation. The paleo-water depth level can be obtained by identifying and statistically analyzing the fossils in the drilling cores. For example, when the content of a certain specific deep-water fossil is relatively high, the area is classified into the paleo-water depth level corresponding to the deep-water sedimentary environment. The thermal maturity can be obtained by testing the vitrinite reflectance in the drilling core samples, and its numerical value directly reflects the maturity degree of the hydrocarbon source rock, thereby affecting the hydrocarbon generation ability of the hydrocarbon source rock. The subsidence history data can use basin simulation software, combined with regional geological data and seismic stratigraphy information, to simulate the subsidence process and subsidence amplitude of the formation in different geological historical periods.

[0027] In practical applications, for a new exploration area, first, the scope of the research area can be preliminarily delimited based on geological survey data. Then, high-precision seismic exploration equipment is used to collect observed seismic data. At the same time, several wells are arranged in the area for well logging operations and core sampling, and auxiliary geological data is obtained through laboratory analysis of the core samples. In this process, the determination of the paleo-water depth level may need to refer to the paleogeographic maps and the distribution laws of biological fossils in the surrounding known areas. The test of thermal maturity can use an automated vitrinite reflectance test instrument to improve the efficiency and accuracy of data acquisition. The simulation of subsidence history data needs to comprehensively consider various factors such as regional tectonic movements and sea-level changes to ensure that the simulation results can truly reflect the subsidence history of the strata. In an alternative embodiment, step S101 obtains the target observed seismic data through the following steps: Step S1011: Collect the observed seismic data in the research area. The observed seismic data includes pre-stack observed seismic data and post-stack observed seismic data.

[0028] In the above steps, the pre-stack observed seismic data refers to the original seismic data that has not been migrated and stacked, retaining the information of seismic waves incident from different angles and containing richer details of the underground structure (such as lithological interfaces and fault positions), but with a large amount of data and much noise. The post-stack observed seismic data refers to the result after processing the pre-stack observed seismic data, such as correction and horizontal stacking, suppressing some random noise, and making the reflection wave groups more continuous, which is convenient for intuitively identifying large-scale geological structures. Here, the pre-stack observed seismic data and the post-stack observed seismic data complement each other. The pre-stack observed seismic data is used for fine inversion, and the post-stack observed seismic data is used for macroscopic structure interpretation, jointly supporting subsequent geological analysis.

[0029] Step S1012: Preprocess the observed seismic data to obtain the target observed seismic data with compensated seismic wave energy, where the time domain of the target observed seismic data matches the depth domain of the well logging data.

[0030] Here, the pre-stack observed seismic data is preprocessed by inverse Q filtering and horizon calibration to obtain the target observed seismic data with compensated seismic wave energy and a time domain that matches the depth domain of the well logging data. Through the above method, the attenuated energy and dispersion can be compensated reversely, and the original amplitude and frequency characteristics of the seismic waves can be restored. In addition, the coordinate conversion problem between the seismic time domain (seconds) and the well logging depth domain (meters) can be solved, providing a unified reference framework for subsequent geological interpretation and inversion. It can significantly improve the quality of the observed seismic data, thereby optimizing the accuracy and reliability of hydrocarbon source rock thickness prediction. This not only helps to improve the efficiency of oil and gas exploration but also provides strong technical support for geological research and oil and gas reservoir development.

[0031] In step S102, spectral analysis is performed on the seismic wave data in the target observed seismic data to obtain the spectral information of the seismic trace near the well, and the TOC curve is extracted from the logging data to obtain the logging TOC curve.

[0032] Among them, the seismic wave data is the information recorded in the target observed seismic data about the ground vibration changing with time during the propagation of seismic waves, which contains seismic waves with different frequency components. Spectral analysis is a mathematical processing method that converts a time-domain signal into a frequency-domain signal. By performing spectral analysis on the seismic wave data, the energy distribution of seismic waves at different frequencies can be obtained, that is, the spectral information of the seismic trace near the well. The logging TOC curve is a curve in the logging data that reflects the change of total organic carbon content in the formation with depth, and it is obtained through specific logging instruments and data processing methods. Exemplarily, the seismic wave data is usually stored in the form of a time series, and each sampling point records the amplitude of the ground vibration at a certain moment. When performing spectral analysis, the fast Fourier transform (FFT) algorithm can be used to convert the seismic wave data from the time domain to the frequency domain to obtain the spectral information of the seismic trace near the well. This spectrum shows the energy distribution of seismic waves in different frequency bands such as low frequency, medium frequency, and high frequency. For example, near a certain formation interface, the low-frequency energy is stronger, which may reflect a large impedance difference at this interface. For the extraction of the logging TOC curve, generally during the drilling process, using various logging data such as natural gamma logging and resistivity logging, combined with the TOC content data obtained from laboratory analysis, a relationship between the logging response and the TOC content is established through mathematical modeling and data fitting methods, and then the TOC content at different depths is calculated to form the logging TOC curve. This curve can intuitively show the longitudinal change trend of the TOC content in the formation and is of great significance for identifying source rock horizons and evaluating the quality of source rocks. In specific applications, after obtaining the target observed seismic data and logging data, the following processing is performed on the seismic wave data and logging data in the target observed seismic data: First, the noise in the data is removed using a denoising algorithm, and then the fast Fourier transform algorithm is used to perform spectral analysis on the denoised seismic wave data to obtain the spectral information of each seismic trace near the well. At the same time, in-depth analysis is performed on the logging data, and combined with the TOC content data measured on the drilling core samples in the laboratory, a relationship model between the logging response and the TOC content suitable for the geological characteristics of this area is established. Through this model, the TOC content at different depths is calculated and the logging TOC curve is drawn. These spectral information of the seismic trace near the well and the logging TOC curve provide basic data for subsequent construction of the TOC-seismic frequency response relationship library.

[0033] In step S103, a TOC-seismic frequency response relationship library is constructed according to the correspondence between the seismic frequency characteristics of the spectral information of the seismic trace near the well and the TOC values of the logging TOC curve.

[0034] Among them, the seismic frequency characteristics of the seismic trace beside the well refer to information such as the energy distribution of different frequency components and the position of the frequency peak in the spectrum, which reflect the propagation characteristics of seismic waves at different frequencies and the physical properties of the formation. The TOC value of the logging TOC curve is the total organic carbon content value 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 correlates and stores the seismic frequency characteristics of the seismic trace beside the well with the TOC value of the logging TOC curve, and is used to establish the corresponding relationship between seismic attributes and the organic carbon content of source rocks.

[0035] Specifically, the seismic frequency characteristics of the seismic trace beside the well can be characterized by calculating parameters such as the peak frequency, frequency band width, and low-frequency energy ratio of the spectrum. For example, when the low-frequency energy ratio in the spectrum is relatively high, it may mean that the formation has a relatively high wave impedance, and this wave impedance characteristic may be related to a specific TOC content range. The TOC value of the logging TOC curve is obtained through processing and calculation of logging data, and its numerical variation law will be different in different formation sections. When constructing the TOC-seismic frequency response relationship library, the seismic frequency characteristics of the seismic trace beside each well can be matched one by one with the logging TOC value at the corresponding depth to form data pairs. Then, these data pairs are stored in the database in a certain format, such as in the form of a table, where each row represents a data point and contains information such as the well name, depth, seismic frequency characteristic parameters, and TOC value. Through the analysis and statistics of a large number of data points, the potential relationship pattern between the seismic frequency characteristics and the TOC value can be found, providing a basis for subsequent prediction of the thickness of source rocks.

[0036] In an alternative embodiment, the matching pursuit algorithm is used to match the seismic frequency characteristics of the seismic trace beside the well with the TOC value of the logging TOC curve to obtain the TOC-seismic frequency response relationship library.

[0037] Here, since the relationship between TOC and seismic frequency has the characteristics of non-linearity and non-stationarity, the matching pursuit algorithm is adopted. The matching pursuit algorithm can adaptively decompose the non-linear relationship between the seismic frequency characteristics and the logging TOC to construct the TOC-seismic frequency response relationship library.

[0038] In step S104, the study area is divided into different sedimentary facies regions according to the paleo - water depth grades. Based on the geological sedimentation laws of each sedimentary facies region, the thickness of the source rocks at each location is calculated and a prior thickness distribution model is generated to serve as the initial model of the thickness prediction model. Among them, the paleo - water depth grade is a quantitative classification of the paleo - water depth based on information such as the combination of biological fossils in the study area, and different paleo - water depth grades correspond to different sedimentary environments. The sedimentary facies region is a region with similar geological sedimentation laws divided according to factors such as the paleo - water depth grade, sedimentary environment, and sedimentary characteristics. The geological sedimentation law refers to the regular characteristics of the type, sedimentation mode, sedimentation rate, etc. of sediments under a specific sedimentary environment. The prior thickness distribution model is a model obtained by preliminarily estimating the thickness of the source rocks at each location in the study area on the basis of considering the geological sedimentation laws of each sedimentary facies region, and it provides initial constraint conditions for the subsequent thickness prediction model.

[0039] In an optional embodiment, step S104 specifically includes: Step S1041: Divide the study area into different sedimentary facies regions according to the paleo - water depth grades; Step S1042: Within each sedimentary facies area, based on the geological sedimentation laws of each sedimentary facies area, use the Kriging interpolation method to interpolate and calculate the thickness of the source rock, obtain the thickness of the source rock at each position, and generate a prior thickness distribution model as the initial model of the thickness prediction model. Here, after dividing the study area into different sedimentary facies areas according to the paleo-water depth grade, for each sedimentary facies area, the existing geological research results and sedimentation experience of similar areas can be used to analyze its geological sedimentation laws. For example, in a certain shallow sea sedimentary facies area, according to historical sedimentation data and geological surveys, it is known that the sedimentation rate in this area was relatively stable during a specific geological period, and the source rock was mainly distributed in a specific stratigraphic section. Based on these laws, geostatistical methods such as the Kriging interpolation method can be used to interpolate and calculate the thickness of the source rock at each position in this area. By dividing the study area into several small grid units, and based on the source rock thickness data at known drilling positions, use the Kriging interpolation formula to calculate the estimated thickness of the source rock at the center position of each grid unit, thereby generating a prior thickness distribution model. This model shows the preliminary distribution of the source rock thickness in the study area in the form of a three-dimensional data volume, providing initial constraints for subsequent precise inversion. Specifically, in practical applications, according to the previously determined paleo-water depth grade, the study area is divided into 4 sedimentary facies areas such as shallow sea and bathyal. For each sedimentary facies area, a large amount of geological data is collected, including regional geological maps, sedimentary facies maps, drilling core descriptions, etc., to analyze its geological sedimentation laws. Then, using the Kriging interpolation method, based on the source rock thickness data of 8 existing wells, the study area is gridded, and the estimated thickness of the source rock for each grid unit is calculated, and finally a prior thickness distribution model is generated. This model intuitively shows the preliminary distribution trend of the source rock thickness in the study area, provides an important initial model for subsequent multi-parameter constrained inversion, and helps to improve the accuracy and efficiency of source rock thickness prediction.

[0040] In step S105, using the target observed seismic data, well logging data, auxiliary geological data, and the TOC-seismic frequency response relationship library, the model parameters of the prior 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. Among them, the target observed seismic data contains seismic wave information on the underground stratigraphic structure and lithology changes, which is an important data basis for inversion. The well logging data provides detailed information such as the physical properties and organic carbon content of the formation, and is used to constrain the inversion process. The auxiliary geological data supplements the regional geological background information, which helps to more accurately construct the inversion model. The TOC-seismic frequency response relationship library establishes the corresponding relationship between seismic attributes and the organic carbon content of the source rock, providing additional constraint conditions for inversion. The prior thickness distribution model is an initial model of the source rock thickness obtained based on previous geological analysis, and its model parameters include the estimated values of the source rock thickness at each location, etc. The objective function is a mathematical expression used to measure the difference between the prediction results of the inversion model and the actual observed data. By continuously iteratively updating the model parameters of the prior thickness distribution model, the value of the objective function is reduced, so as to obtain an optimal thickness prediction model that is more in line with the actual situation.

[0041] In an optional embodiment, step S105 specifically includes: Step S1051: Initially constrain the impedance model based on the prior thickness distribution model, and the impedance model includes the thickness parameter of the source rock.

[0042] Here, initially constrain the impedance model based on the prior thickness distribution model, and the impedance model includes the thickness parameter of the source rock. For example, take the source rock thickness at each location in the prior thickness distribution model as the initial value, substitute it into the impedance calculation formula (impedance = density × velocity), and combine the density and velocity information obtained from the well logging data to construct the impedance model. This impedance model reflects the obstruction of the formation to the propagation of seismic waves and is the basis for forward modeling of the seismic wave equation.

[0043] Step S1052: Conduct forward modeling of the seismic wave equation based on the impedance model to generate a synthetic seismic record.

[0044] Exemplarily, use numerical calculation methods such as the finite difference method and the finite element method to solve the seismic wave equation. Take the impedance model as the input, simulate the propagation process of seismic waves in the formation, calculate the seismic wave responses at different positions and different times, and generate a synthetic seismic record. This synthetic seismic record is the seismic data predicted based on the model and can be compared with the actual target observed seismic data to evaluate the accuracy of the model.

[0045] By forward modeling using the seismic wave equation, the propagation process of seismic waves in the formation can be more accurately simulated, generating synthetic seismic records that better match the actual seismic data. This helps improve the accuracy of the inversion results, especially under complex geological conditions such as strong lateral velocity variation and complex structures.

[0046] Step S1053: Construct an objective function, which includes a seismic data fitting term, a geological prior 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. Well logging data is used to constrain the geological prior constraint term, and auxiliary geological data and the TOC-seismic frequency response relationship library are used to constrain the lateral smoothing term.

[0047] Specifically, the objective function includes: ; Among them, represents the objective function of the thickness prediction model, constitutes the seismic data fitting term, constitutes the geological prior constraint term, represents the lateral smoothing term; m represents the thickness prediction model, represents the target observed seismic data, G represents the forward operator, represents the seismic data weight matrix, represents the prior thickness distribution model, represents the paleo-water depth grade confidence weight matrix, L represents the Laplace smoothing operator, represents the geological prior weight coefficient, represents the lateral smoothing weight coefficient; Among them, the paleo-water depth grade confidence weight matrix is calculated by the following formula: ; Among them, represents the paleo-water depth grade confidence weight matrix, U represents the mass content of uranium in rocks or sediments, represents the mass content of thorium in rocks or sediments.

[0048] Among them, the U / Th ratio is introduced as an indicator factor for anoxic environments, directly participating in the calculation of the paleo-water depth grade confidence weight matrix. It can also be set that when U / Th > 1.2, the weight is doubled, making the inversion results more in line with geological laws in the key areas of hydrocarbon source rock development and significantly improving the reliability of thickness prediction in ancient strata.

[0049] 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 constraint term because well logging data reflects information such as the actual physical properties and organic carbon content of the formation. Introducing this information into the objective function can make the inversion results more consistent with the geological actual situation. 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 continuity and smoothness of the inversion results in space and avoid unreasonable mutations.

[0050] Furthermore, by comprehensively considering the observed seismic data, prior thickness model, paleo-water depth classification confidence weight matrix, and lateral smoothing term, etc., the inversion problem under complex geological conditions, such as strong lateral velocity variation, complex structures, etc., can be better handled, which helps to improve the applicability and reliability of the inversion results under complex geological conditions.

[0051] Step S1054: Continuously iterate and 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 continuously decreases until the residual of the seismic data fitting term converges, and the optimal prediction model is inverted and the predicted thickness of the source rock is output.

[0052] In this step, in each iteration, according to the gradient information of the objective function, the update direction and step size of the model parameters are calculated by the conjugate gradient method, the parameters in the prior thickness distribution model and the wave impedance model are updated, the forward modeling of the seismic wave equation and the calculation of the objective function are performed again. Repeat this process. When the residual of the seismic data fitting term is less than a preset threshold (such as 0.01), it is considered that the inversion converges, the optimal thickness prediction model is obtained, and the thickness of the source rock predicted by this model is output.

[0053] 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 average predicted thickness of the source rock is 60 meters, and the maximum thickness is 120 meters.

[0054] In step S106, the predicted thickness of the source rock is subjected to compaction correction to obtain the original thickness at the initial stage of source rock deposition, and the original thickness at the initial stage of source rock deposition is subjected to erosion restoration using the thermal maturity to obtain the actual effective thickness of the source rock.

[0055] Here, the predicted thickness of the source rock is the estimated value of the thickness of the source rock in the current state obtained through multi-parameter constrained inversion. The compaction correction process takes into account the reduction in thickness of the source rock due to burial compaction during the geological history, and restores its original thickness at the initial stage of deposition through a certain method. The denudation restoration process aims at the denudation of the formation affected by factors such as tectonic movements in the later stage, and uses information such as thermal maturity to restore the denuded thickness, so as to obtain the actual effective thickness of the source rock, which can better reflect the true thickness of the source rock during the geological history.

[0056] In an alternative embodiment, step S106 specifically includes: Step S1061: Calculate the thickness loss ratio of the source rock due to burial compaction according to the pre-established correspondence between porosity and depth. Here, the thickness loss ratio of the source rock due to burial compaction can be calculated according to the pre-established correspondence between porosity and depth. For example, by analyzing the logging data of multiple wells in the study area, an empirical formula for the variation of porosity with depth is established, such as porosity = a - b×depth (a and b are fitting coefficients), and then according to the compaction theory (such as the Smith compaction formula), the thickness loss ratio of the source rock at different depths is calculated.

[0057] 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.

[0058] Specifically, the original thickness of the source rock at the initial stage of deposition is calculated by the following formula: ; Where represents the original thickness of the source rock at the initial stage of deposition, represents the predicted thickness of the source rock, represents the thickness loss ratio.

[0059] The compaction correction process is to calculate the thickness loss ratio of the source rock due to burial compaction according to the pre-established correspondence between porosity and depth, and then correct the predicted thickness of the source rock according to this ratio. For example, diagenetic compaction correction is carried out using the porosity-depth relationship, and the thickness of the source rock increases by an average of 10% after correction.

[0060] Step S1063: Perform compaction correction and cross-validation on the missing thickness and denuded thickness of the formation to determine the actual effective thickness of the source rock; among them, the missing thickness of the formation is determined by comparing the angular unconformity surface identified on the seismic profile with the logging curve, and the denuded thickness is determined according to the sudden change point of the vitrinite reflectance, and the vitrinite reflectance is used to characterize the thermal maturity.

[0061] Here, the denudation restoration process estimates the denudation amount using the stratigraphic contact relationship and conducts cross-verification based on the matching of the vitrinite reflectance mutation points with denudation events. For example, using the stratigraphic contact relationship, the denudation amount is estimated. The estimation result shows that the average denudation amount is 30 meters. Then, based on the matching of the vitrinite reflectance mutation points with denudation events, cross-verification is carried out. Finally, the denudation thickness is calculated to restore the actual thickness of the hydrocarbon source rock, and the average thickness after restoration is 70 meters.

[0062] Specifically, step S1063 specifically includes: Compare the angular unconformity surface identified on the seismic profile with the logging curve to obtain the missing formation segments above and below the angular unconformity surface; use the time-depth conversion velocity model to convert the two-way travel time difference corresponding to the missing formation segments into the missing formation thickness; Specifically, calculate the missing formation thickness through the following formula: ; Where, represents the two-way travel time difference, represents the average formation velocity; By comparing the angular unconformity surface identified on the seismic profile with the logging curve to determine the missing formation segments, and using the time-depth conversion velocity model and the missing formation thickness formula to calculate the missing formation thickness, the missing formation amount caused by tectonic movement can be accurately quantified, providing direct data support for denudation restoration and avoiding the estimation deviation of the missing thickness caused by relying solely on single seismic or logging data.

[0063] Draw the target change curve of vitrinite reflectance versus depth, and identify the vitrinite reflectance mutation points from the target change curve; calculate the denudation thickness through the difference between the measured value and the theoretical value of the vitrinite reflectance mutation points; Specifically, calculate the denudation thickness through the following formula: ; Where, represents the measured value of the vitrinite reflectance mutation point, represents the theoretical value without denudation, represents the gradient with depth.

[0064] By drawing the curve of vitrinite reflectance versus depth to identify the mutation points, combining with the denudation thickness formula to calculate the denudation thickness, and using the quantitative relationship between the thermal maturity index and the depth gradient, the organic matter evolution characteristics are directly related to the denudation events, effectively solving the problem of lack of time-depth constraint in traditional denudation amount calculation and improving the reliability of denudation thickness calculation.

[0065] Using the compaction correction model, the missing thickness and erosion thickness of the formation are respectively converted to the original thickness of the formation and the original erosion thickness at the initial stage of sedimentation; calculate the error value between the original thickness of the formation and the original erosion thickness. If the error value is greater than the preset error threshold, iterative inversion is performed until the error value is not greater than the preset error threshold to obtain the actual effective thickness of the source rock.

[0066] Using the compaction correction model to convert the missing thickness and erosion thickness of the formation into the original thickness at the initial stage of sedimentation, and ensuring their consistency through error iterative inversion, eliminates the superimposed effects of diagenetic compaction and tectonic erosion on the thickness of the source rock, making the finally obtained actual effective thickness of the source rock closer to the true value of geological history, and providing high-precision basic data for the evaluation of oil and gas resources, the reconstruction of paleo-sedimentary environment and the analysis of tectonic evolution.

[0067] In an optional embodiment, the method provided by the embodiments of the present application further includes: Generating result maps for exploration deployment, including isopach maps of source rock thickness, TOC-thickness cross plots, and thickness comparison profiles before and after correction.

[0068] Among them, the isopach map of source rock thickness is a map representing the thickness distribution of source rocks in the study area with isopachs. The TOC-thickness cross plot is a plot for cross-analysis of the source rock thickness and total organic carbon content (TOC). The thickness comparison profile before and after correction is a profile showing the thickness change of the source rock before and after diagenetic compaction correction and restoration of tectonic erosion amount.

[0069] First, the isopach map of source rock thickness is generated by using the isopach drawing method based on the predicted thickness data of the source rock output by the optimal thickness prediction model. For example, in this study, the generated isopach map of source rock thickness shows the spatial distribution characteristics of the thickness, and the map shows that the high-value areas of thickness are concentrated in the middle of the study area.

[0070] Here, the isopach map of source rock thickness can intuitively display the spatial distribution characteristics of the source rock thickness in the entire study area, clearly revealing the change trend of the thickness 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. Second, the TOC-thickness cross plot is to plot the TOC values at each drilling location and the corresponding source rock thickness values in the same coordinate system to analyze the correlation between the two. For example, in this study, the generated TOC-thickness cross plot shows a positive correlation between the TOC value and the thickness.

[0071] Here, the TOC vs. thickness crossplot analyzes the intersection of the thickness of the source rock and the total organic carbon (TOC) content, which can reveal the correlation between the two and identify the distribution areas of high-quality source rocks with high TOC values and large thicknesses. This is crucial for evaluating the quality and resource potential of source rocks and can guide the deployment of exploration wells and the formulation of development plans.

[0072] Thirdly, the thickness comparison profiles before and after correction are to select typical profiles within the study area and draw the source rock thickness profiles before and after correction respectively, visually showing the impact of correction on the thickness prediction results. For example, in this study, the generated thickness comparison profiles before and after dynamic correction show that the thickness increases significantly after correction.

[0073] Here, the thickness comparison profiles before and after dynamic correction show the changes in the thickness of the source rock before and after diagenetic compaction correction and the restoration of tectonic erosion amount, which can visually show the impact of dynamic correction on the thickness prediction results. This helps to understand the burial history and compaction process of the source rock in the geological history period and provides important references for geological research and oil and gas reservoir development.

[0074] Furthermore, the method provided by the embodiments of the present application can also evaluate the model accuracy and quantify the credibility of the prediction results based on the cross-validation strategy and uncertainty analysis.

[0075] Specifically, through cross-validation, the dataset can be divided into multiple subsets, and one part is used as the training set in turn, and the rest is used as the validation set. This can effectively evaluate the generalization ability of the model, avoid overfitting or underfitting of the model, and thus improve the reliability of the prediction results. Cross-validation can provide a more objective and comprehensive evaluation of the model performance, ensuring that the model can maintain stable prediction accuracy on different data subsets.

[0076] Uncertainty analysis can quantify the uncertainty of the prediction results, such as by calculating the confidence interval or probability distribution of the prediction results. This helps to identify the possible error sources in the prediction results and evaluate the credibility of the prediction results. Uncertainty analysis can provide more comprehensive information for decision-makers, helping them better understand the reliability and risks of the prediction results.

[0077] In the embodiments of the present application, by collecting multi-dimensional data, such as observed seismic data, logging data, and auxiliary geological data, the inversion model is constrained. Since this model comprehensively considers the information of seismic, logging, and geological parameters, it can more accurately predict the thickness of the source rock. The matching pursuit algorithm is used to match the logging TOC curve with the spectrum of the seismic trace beside the well, and a TOC-seismic frequency response relationship library is constructed, which helps to improve the correlation between seismic data and geological parameters, thereby enhancing the reliability of the prediction results. Based on the paleo-water depth grading parameters, the Kriging interpolation method is used to generate a prior 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. Diagenetic compaction correction and erosion restoration processing are performed on the thickness of the Cambrian source rock obtained from the thickness prediction model, which helps to more accurately evaluate the actual thickness of the source rock and provide more reliable data support for oil and gas resource evaluation. The present application can not only be used for the prediction of the source rock thickness, but also provide valuable data support for geological research and oil and gas reservoir development, helping to deeply understand geological processes such as sedimentary environment and tectonic evolution. In addition, it can also be applied to the prediction of the source rock thickness in different regions and different geological conditions, with strong generality and adaptability.

[0078] Based on the same inventive concept, in the embodiments of the present application, there is also provided a source rock thickness prediction device corresponding to the source rock thickness prediction method. Since the principle of solving problems by the device in the embodiments of the present application is similar to the above source rock thickness prediction method in the embodiments of the present application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0079] Please refer to Figure 2 , Figure 2 which is a schematic structural diagram of a source rock thickness prediction device provided by the embodiments of the present application. As Figure 2 shown in, the device 200 includes: A data acquisition module 201, configured to acquire target observed seismic data, logging data, and auxiliary geological data within a research area, where the auxiliary geological data includes paleo-water depth grade, thermal maturity, and subsidence history data; A data processing module 202, configured to perform spectrum analysis on the seismic wave data in the target observed seismic data to obtain the spectrum of the seismic trace beside the well, and extract the TOC curve from the logging data to obtain the logging TOC curve; A relationship construction module 203, configured to construct a TOC-seismic frequency response relationship library according to the correspondence between the seismic frequency characteristics of the spectrum of the seismic trace beside the well and the TOC values of the logging TOC curve; The model determination module 204 is configured to divide the research 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 sedimentation laws of each sedimentary facies region, and generate a prior thickness distribution model as the initial model of the thickness prediction model. The model update module 205 is configured to continuously iterate and update the model parameters of the prior thickness distribution model by using the target observed seismic data, the logging data, the auxiliary geological data, and the TOC-seismic frequency response relationship library to continuously reduce the objective function, so as to obtain an optimal thickness prediction model and output the predicted thickness of the source rock. The thickness correction module 206 is configured to perform compaction correction processing on the predicted thickness of the source rock to obtain the original thickness at the initial stage of source rock deposition, and perform erosion restoration processing on the original thickness at the initial stage of source rock deposition by using the thermal maturity to obtain the actual effective thickness of the source rock.

[0080] The source rock thickness prediction device provided by the embodiments of the present application constructs a TOC-seismic frequency response relationship library by acquiring target observed seismic data and logging data, generates a prior thickness distribution model in combination with the paleo-water depth level, performs iterative inversion using multiple parameters, and performs compaction correction and erosion restoration processing on the prediction results, solving the problems in the prior art such as relying on sparse sampling points, being greatly affected by the wellbore environment, and low seismic resolution, improving the accuracy and reliability of source rock thickness prediction, and being able to provide more accurate data support for oil and gas exploration.

[0081] Please refer to Figure 3 , Figure 3 which is a schematic structural diagram of an electronic device provided by the embodiments of the present application. As Figure 3 shown in

[0082] the electronic device 300 includes a processor 310, a memory 320, and a bus 330. Figure 1 The memory 320 stores machine-readable instructions executable by the processor 310. When the electronic device 300 runs, the processor 310 communicates with the memory 320 through the bus 330. When the machine-readable instructions are executed by the processor 310, the steps of the source rock thickness prediction method in the method embodiment as shown above can be executed. The specific implementation manner can refer to the method embodiment and will not be elaborated here.

[0083] The embodiments of the present application further provide a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is run by a processor, the steps of the source rock thickness prediction method in the method embodiment as shown above can be executed. The specific implementation manner can refer to the method embodiment and will not be elaborated here. Figure 1 The embodiments of the present application further provide a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is run by a processor, the steps of the source rock thickness prediction method in the method embodiment as shown above can be executed. The specific implementation manner can refer to the method embodiment and will not be elaborated here.

[0084] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0085] In several embodiments provided in the present 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 illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For another example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some communication interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0086] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0087] In addition, in each embodiment of the present application, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0088] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0089] Finally, it should be noted that the above-described embodiments are only specific embodiments of the present application, used to illustrate the technical solutions of the present application, rather than limiting it. The protection scope of the present application is not limited thereto. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that any person skilled in the art within the technical scope disclosed by the present application can still modify the technical solutions recorded in the foregoing embodiments, or can easily think of changes, or perform equivalent replacements on some of the technical features; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for predicting the thickness of a hydrocarbon source rock, characterized in that, The method includes: Obtaining target observed seismic data, logging data, and auxiliary geological data within the research area, where the auxiliary geological data includes paleo-water depth level, thermal maturity, and subsidence history data; Performing spectral analysis on the seismic wave data in the target observed seismic data to obtain the spectral of the seismic trace adjacent to the well, and extracting the TOC curve from the logging data to obtain the logging TOC curve; Constructing a TOC-seismic frequency response relationship library based on the correspondence between the seismic frequency characteristics of the spectral of the seismic trace adjacent to the well and the TOC values of the logging TOC curve; Dividing the research area into different sedimentary facies regions according to the paleo-water depth level, and calculating the thickness of the source rock at each location and generating a prior thickness distribution model based on the geological sedimentation laws of each sedimentary facies region, as the initial model of the thickness prediction model; Using the target observed seismic data, the logging data, the auxiliary geological data, and the TOC-seismic frequency response relationship library to continuously iterate and update the model parameters of the prior thickness distribution model to continuously reduce the objective function, so as to obtain the optimal thickness prediction model and output the predicted thickness of the source rock; Performing compaction correction processing on the predicted thickness of the source rock to obtain the original thickness at the initial stage of source rock deposition, and using the thermal maturity to perform erosion restoration processing on the original thickness at the initial stage of source rock deposition 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 through the following steps: Collecting observed seismic data within the research area, where the observed seismic data includes pre-stack observed seismic data and post-stack observed seismic data; Performing preprocessing on the observed seismic data to obtain the target observed seismic data with compensated seismic wave energy, where the time domain of the target observed seismic data matches the depth domain of the logging data.

3. The method according to claim 1, characterized in that The constructing a TOC-seismic frequency response relationship library based on the correspondence between the seismic frequency characteristics of the spectral of the seismic trace adjacent to the well and the TOC values of the logging TOC curve includes: Using the matching pursuit algorithm to match the seismic frequency characteristics of the spectral of the seismic trace adjacent to the well and the TOC values of the logging TOC curve to obtain the TOC-seismic frequency response relationship library.

4. The method according to claim 1, wherein The dividing the research area into different sedimentary facies regions according to the paleo-water depth level, and calculating the thickness of the source rock at each location and generating a prior thickness distribution model based on the geological sedimentation laws of each sedimentary facies region, as the initial model of the thickness prediction model, includes: Dividing the research area into different sedimentary facies regions according to the paleo-water depth level; Within each sedimentary facies region, based on the geological sedimentation laws of each sedimentary facies region, using the Kriging interpolation method to interpolate and calculate the thickness of the source rock to obtain the thickness of the source rock at each location and generate a prior thickness distribution model, as the initial model of the thickness prediction model.

5. The method according to claim 1, characterized in that, The using the target observed seismic data, the logging data, the auxiliary geological data, and the TOC-seismic frequency response relationship library to continuously iterate and update the model parameters of the prior thickness distribution model to continuously reduce the objective function, so as to obtain the optimal thickness prediction model and output the predicted thickness of the source rock, includes: Based on a prior thickness distribution model to initially constrain a wave impedance model, the thickness parameter of a source rock is included in the wave impedance model; Based on the wave impedance model, forward modeling of seismic wave equations is performed to generate synthetic seismic records; Construct an objective function, which includes a seismic data fitting term, a geological prior 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. Well logging data is used to constrain the geological prior constraint term, and the auxiliary geological data and the TOC-seismic frequency response relationship library are used to constrain the lateral smoothing term; The conjugate gradient method is used to continuously 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 continuously decreases until the residual of the seismic data fitting term converges, and the optimal thickness prediction model is inversely obtained and the predicted thickness of the source rock is output.

6. The method according to claim 5, wherein The objective function includes: ； Among them, represents the objective function of the thickness prediction model, constitutes the seismic data fitting term, constitutes the geological prior constraint term, represents the lateral smoothing term; m represents the thickness prediction model, represents the target observed seismic data, G represents the forward operator, represents the seismic data weight matrix, represents the prior thickness distribution model, represents the paleo - water depth grade confidence weight matrix, L represents the Laplace smoothing operator, represents the geological prior weight coefficient, represents the lateral smoothing weight coefficient; Wherein, the paleo-water depth grade confidence weight matrix is calculated by the following formula: ； Among them, represents the confidence weight matrix of paleo-water depth grade, U represents the mass content of uranium in rocks or sediments, represents the mass content of thorium in rocks or sediments.

7. The method according to claim 1, wherein Performing compaction correction processing on the predicted thickness of the source rock to obtain the original thickness at the initial stage of source rock deposition, and using the thermal maturity to perform erosion restoration processing on the original thickness at the initial stage of source rock deposition to obtain the actual effective thickness of the source rock, including: According to the pre-established correspondence between porosity and depth, calculate the thickness loss ratio of the source rock due to burial compaction; Correct the predicted thickness of the source rock according to the thickness loss ratio to obtain the original thickness at the initial stage of source rock deposition; Perform compaction correction and cross-validation on the missing formation thickness and the eroded thickness to determine the actual effective thickness of the source rock. Among them, the missing formation thickness is determined by comparing the angular unconformity surface identified on the seismic profile with well logging curves, and the eroded thickness is determined by the vitrinite reflectance mutation point, and the vitrinite reflectance is used to represent the thermal maturity.

8. The method according to claim 7, wherein Performing compaction correction and cross-validation on the missing formation thickness and the eroded thickness to determine the actual effective thickness of the source rock, including: Compare the angular unconformity surface identified on the seismic profile with well logging curves to obtain the missing formation segments above and below the angular unconformity surface; Using the time-depth conversion velocity model, convert the two-way travel time difference corresponding to the missing formation segment into the missing formation thickness; Draw a target change curve of vitrinite reflectance changing with depth, and identify the vitrinite reflectance mutation point from the target change curve; Calculate the eroded thickness through the difference between the measured value and the theoretical value of the vitrinite reflectance mutation point; Using the compaction correction model, convert the missing formation thickness and the eroded thickness into the original formation thickness and the original eroded thickness at the initial stage of deposition respectively; Calculate the error value between the original formation thickness and the original eroded thickness. If the error value is greater than the preset error threshold, perform iterative inversion until the error value is not greater than the preset error threshold to obtain the actual effective thickness of the source rock.

9. The method according to claim 8, characterized in that Calculate the original thickness at the initial stage of source rock deposition by the following formula: ； Among them, represents the original thickness at the initial stage of hydrocarbon source rock deposition, represents the predicted thickness of the hydrocarbon source rock, represents the thickness loss ratio; And / or, calculate the missing formation thickness by the following formula: ； Among them, represents the two-way moveout time difference, represents the average formation velocity; And / or, calculate the eroded thickness by the following formula: ； Among them, represents the measured value of the vitrinite reflectance mutation point, represents the theoretical value when there is no erosion, represents the gradient with depth.

10. The method according to claim 1, wherein The method further includes: Generate achievement maps for exploration deployment, where the achievement maps include isopach maps of source rock thickness, cross plots of TOC vs. thickness, and thickness comparison profiles before and after correction.

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