Effective hydrocarbon source rock distribution prediction method and device, electronic equipment and storage medium

Abstract

According to the effective hydrocarbon source rock distribution prediction method and device, the electronic equipment and the storage medium provided by the invention, the relational expression between the key parameters corresponding to the target logging curve and the total organic carbon content is determined, and then the time-depth relation is determined; constructing a hydrocarbon source rock stratum framework model of the target area based on the time-depth relationship and the two-dimensional seismic survey line data, and performing inversion calculation on the key parameters through the two-dimensional seismic survey line data and the hydrocarbon source rock stratum framework model to obtain distribution characteristics of the key parameters in the hydrocarbon source rock stratum framework model, and the distribution characteristics of the effective hydrocarbon source rocks in the target area are determined through the relational expression and the distribution characteristics, so that the distribution of the effective hydrocarbon source rocks can be predicted under the condition that basic data (such as core data) are relatively lacking.

Description

Technical Field

[0001] The present application relates to the technical field of oil and gas exploration, and in particular to a prediction method, device, electronic equipment and storage medium for effective hydrocarbon source rock distribution. Background Art

[0002] Source rock refers to rock that is rich in organic matter and produces and discharges a large amount of oil and gas. For the evaluation of the oil and gas resources in a basin, the first research work to be carried out is the evaluation of the basin's source rocks. At the same time, the evaluation results of the basin's source rocks are also the key indicators for measuring the basin's oil and gas resource potential. There are usually three parameters for evaluating source rocks, namely organic matter abundance, organic matter type and organic matter maturity. Among them, the organic matter type and organic matter maturity are first sampled from the source rocks of field outcrops or drilled cores, and then targeted laboratory analysis and testing are carried out on the source rock samples, and finally the corresponding organic matter type and organic matter maturity parameters are obtained. The evaluation of organic matter abundance in source rocks can be divided into two categories according to the research scope: for single-point research, such as the evaluation of organic matter abundance in field outcrops or a well-drilled source rock, it is necessary to first collect samples of source rocks, and then conduct laboratory analysis and testing on the source rock samples to obtain parameters characterizing organic matter abundance, such as total organic carbon content, soluble hydrocarbons in source rock pyrolysis parameters, pyrolysis hydrocarbons, chloroform asphalt and total hydrocarbon content; for the evaluation of organic matter abundance in a certain basin, the prediction of effective source rock distribution is generally the main focus. Specifically, the prediction of effective source rock distribution in a basin usually includes two aspects: first, the calculation of total organic carbon content of source rocks, and second, the calculation of effective source rock thickness, where effective source rocks refer to source rocks with a total organic carbon content greater than 0.5%. Conventional prediction research on effective source rocks in basins uses laboratory sample analysis and testing technology to obtain the total organic carbon content of source rocks; on the other hand, the thickness of effective source rocks in underground strata is estimated by observing the thickness of source rocks in field outcrops. However, field outcrops are usually at the edge of basins, and when risk exploration wells are deployed in the early stage of exploration, there is relatively little drilling core data. In this case, there is no way to predict the distribution of effective source rocks. Summary of the invention

[0003] In response to the above problems, the present application provides a method, device, electronic device and storage medium for predicting the distribution of effective source rocks, which can predict the distribution of effective source rocks when basic data (such as core data) is relatively scarce.

[0004] The present application provides a method for predicting the distribution of effective hydrocarbon source rocks, including:

[0005] Acquire total organic carbon content data, drilling layer data, well logging curves and two-dimensional seismic line data of the target area;

[0006] Performing intersection analysis on the total organic carbon content data and the well logging curve to determine a target well logging curve, and determining a relationship between a key parameter corresponding to the target well logging curve and the total organic carbon content;

[0007] Based on the drilling layer data and the characteristic stratigraphic interface features on the stratigraphic section of the target area, the target well logging curve and the two-dimensional seismic line data are calibrated to determine the time-depth relationship;

[0008] Performing a stratum structural interpretation based on the two-dimensional seismic line data and the time-depth relationship to obtain a structural interpretation stratum, and determining a source rock stratum framework model of the target area based on the structural interpretation stratum;

[0009] Performing inversion calculation on the key parameters based on the two-dimensional seismic line data and the source rock stratum framework model to obtain distribution characteristics of the key parameters in the source rock stratum framework model;

[0010] The distribution characteristics of effective source rocks in the target area are determined based on the relationship and the distribution characteristics.

[0011] In some embodiments, the method further comprises:

[0012] Obtaining initial well logging curves of wells drilled in the target area;

[0013] The initial logging curve is standardized to obtain various logging curves, wherein the amplitude data of various logging curves are within the same value range.

[0014] In some embodiments, the method further comprises:

[0015] Acquire initial two-dimensional seismic line data of different areas in the target area;

[0016] Determine the time depth values ​​corresponding to the two landmark stratigraphic interfaces on each initial two-dimensional seismic line data based on at least two landmark stratigraphic interfaces in the target area;

[0017] Determine the closure errors corresponding to each initial two-dimensional seismic line data based on each time-depth value;

[0018] Moving each of the initial two-dimensional seismic line data based on the closure errors corresponding to each of the initial two-dimensional seismic line data to eliminate the closure errors of each of the initial two-dimensional seismic line data and obtain intermediate two-dimensional seismic line data;

[0019] Amplitude consistency processing is performed on the intermediate two-dimensional seismic line data to obtain the two-dimensional seismic line data.

[0020] In some embodiments, performing amplitude consistency processing on the intermediate two-dimensional seismic line data to obtain the two-dimensional seismic line data includes:

[0021] Count the range of amplitude values ​​of all intermediate two-dimensional seismic line data;

[0022] Determine a standard value range of the amplitude value based on the value range;

[0023] Determining a correction coefficient for the amplitude of each intermediate two-dimensional seismic line data based on the standard value range;

[0024] The amplitude value of the corresponding two-dimensional seismic line data is corrected based on the correction coefficient to obtain the two-dimensional seismic line data, wherein the value range of the amplitude value of each two-dimensional seismic line data is within the standard value range.

[0025] In some embodiments, the determining a target well logging curve by performing intersection analysis based on the total organic carbon content data and the well logging curve comprises:

[0026] Performing intersection analysis on the total organic carbon content data and the well logging curve to determine the correlation between the total organic carbon content and each well logging curve;

[0027] A target well log curve is determined based on the correlation.

[0028] In some embodiments, determining the distribution characteristics of effective source rocks in the target area based on the relationship and the distribution characteristics includes:

[0029] Determine the threshold value of total organic carbon content of effective source rocks;

[0030] Determining a threshold value of a key parameter based on the relationship and the threshold value;

[0031] Determining a target key parameter distribution characteristic based on the threshold value of the key parameter and the distribution characteristic;

[0032] Determining the distribution range of the total organic carbon content in the target area based on the target key parameter distribution characteristics and the relationship;

[0033] The distribution characteristics of effective source rocks are determined based on the total organic carbon content distribution range.

[0034] The present application embodiment provides a prediction device for effective source rock distribution, comprising:

[0035] An acquisition module, used to acquire total organic carbon content data, drilling layer data, well logging curves and two-dimensional seismic line data of the target area that have been drilled;

[0036] A first determination module is used to perform intersection analysis on the total organic carbon content data and the well logging curve to determine a target well logging curve, and determine a relationship between a key parameter corresponding to the target well logging curve and the total organic carbon content;

[0037] A second determination module is used to perform well-seismic calibration on the target well logging curve and the two-dimensional seismic line data based on the drilling layer data and the characteristic stratigraphic interface features on the stratigraphic section of the target area to determine the time-depth relationship;

[0038] A third determination module is used to perform a stratum structural interpretation based on the two-dimensional seismic line data and the time-depth relationship to obtain a structural interpretation stratum, and determine a source rock stratum framework model of the target area based on the structural interpretation stratum;

[0039] An inversion module, used for performing inversion calculation on the key parameters based on the two-dimensional seismic line data and the source rock stratum framework model, and obtaining distribution characteristics of the key parameters in the source rock stratum framework model;

[0040] The fourth determination module is used to determine the distribution characteristics of effective source rocks in the target area based on the relationship and the distribution characteristics.

[0041] In some embodiments, the prediction device for effective source rock distribution is further used to:

[0042] Obtaining initial well logging curves of wells drilled in the target area;

[0043] The initial logging curve is standardized to obtain various logging curves, wherein the amplitude data of various logging curves are within the same value range.

[0044] An embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, any one of the above-mentioned methods for predicting the distribution of effective source rocks is executed.

[0045] An embodiment of the present application provides a storage medium, which stores a computer program that can be executed by one or more processors and can be used to implement any of the above-mentioned methods for predicting the distribution of effective source rocks.

[0046] The present application provides a prediction method, device, electronic device and storage medium for the distribution of effective source rocks. The method determines the relationship between the key parameters corresponding to the target logging curve and the total organic carbon content, and then determines the time-depth relationship. Based on the time-depth relationship and two-dimensional seismic line data, a source rock formation framework model of the target area is constructed. The key parameters are inverted and calculated through the two-dimensional seismic line data and the source rock formation framework model to obtain the distribution characteristics of the key parameters in the source rock formation framework model. The distribution characteristics of the effective source rocks in the target area are determined through the relationship and the distribution characteristics. This method can realize the prediction of the distribution of effective source rocks in the case where basic data (such as core data) are relatively scarce. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The present application will be described in more detail below based on embodiments and with reference to the accompanying drawings.

[0048] Figure 1 A schematic diagram of the implementation process of a method for predicting the distribution of effective hydrocarbon source rocks provided in an embodiment of the present application;

[0049] Figure 2 A schematic diagram of the implementation flow of another method for predicting the distribution of effective hydrocarbon source rocks provided in an embodiment of the present application;

[0050] Figure 3 A comparison chart before and after calibration provided in an embodiment of the present application;

[0051] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0052] In the drawings, the same reference numerals are used for the same components and the drawings are not drawn to scale. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0054] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0055] If similar descriptions of "first\second\third" appear in the application documents, the following instructions will be added. In the following description, the terms "first\second\third" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0056] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0057] Before introducing the embodiments of the present application, a brief introduction to the related technologies is given:

[0058] At present, there are few patents and many literatures on the prediction methods of effective source rock distribution in basins. The specific technical methods are mainly divided into three categories:

[0059] The first category: using the analysis and testing data of the source rocks that have been drilled in the region to generally evaluate the distribution of effective source rocks. This method uses a combination of geological statistics and laboratory analysis and testing to establish a method system for evaluating and predicting high-quality source rocks, and predict the distribution of effective source rocks in a basin or a certain region. This method has two problems: (1) It requires a sufficient number of drilled wells and corresponding analysis and testing data to build a method system for evaluating and predicting high-quality source rocks, but there is often not enough analysis and testing data in the early stages of exploration; (2) The distribution characteristics of effective source rocks obtained by this method do not have sufficient data constraints in areas without wells, and its prediction accuracy is greatly affected by drilling density.

[0060] The second category: using the logging curve fitting method to predict the distribution of effective source rocks. This method predicts the distribution characteristics of effective source rocks through logging curves, analytical test data and a small range of three-dimensional seismic data in a certain study area. However, this type of method is not applicable when solving the problem of predicting the distribution of effective source rocks in a large area such as a basin.

[0061] The third category: Determine the distribution characteristics of favorable sedimentary phases of source rocks by geological means to characterize the distribution of effective source rocks. This method constructs the relationship between sedimentary phase parameters such as sedimentation rate and total organic carbon content through analytical testing methods, and determines the distribution characteristics of favorable source rocks through the distribution of sedimentary phases; however, this method does not take into account the changes in sedimentary phases, and different regions in the basin often develop different sedimentary phases in the same geological period, that is, the phenomenon of isochronous anisotropy. Therefore, this method cannot effectively solve the distribution prediction problem of effective source rocks in a large area such as a basin.

[0062] From the perspective of relevant technical methods, under the premise of relatively scarce basic data in the early stage of exploration, there is no effective technical method to solve the problem of predicting the distribution of effective source rocks in the basin.

[0063] Based on the problems existing in the related art, the embodiment of the present application provides a method for predicting the distribution of effective source rocks, which is applied to electronic devices, such as computers, mobile terminals, etc. The functions implemented by the method for predicting the distribution of effective source rocks provided in the embodiment of the present application can be implemented by calling program codes by a processor of the electronic device, wherein the program codes can be stored in a computer storage medium.

[0064] Embodiment 1

[0065] The present application embodiment provides a method for predicting the distribution of effective hydrocarbon source rocks. Figure 1 A schematic diagram of the implementation process of a method for predicting the distribution of effective source rocks provided in the embodiment of the present application is shown in FIG. Figure 1 As shown, including:

[0066] Step S1, obtaining total organic carbon content data, drilling layer data, well logging curves and two-dimensional seismic line data of the target area.

[0067] In the embodiment of the present application, the logging curve may include: an acoustic logging curve, a density logging curve, a natural gamma curve, a porosity curve, a resistivity curve, etc.

[0068] In the embodiment of the present application, the drilling stratification data may represent the stratification of the strata in the target area.

[0069] In the embodiment of the present application, the total organic carbon content data of the wells drilled in the target area, the drilling layer data, the logging curve and the two-dimensional seismic line data of the target area can be obtained through the input of the testing equipment, the storage device, etc. The testing equipment can be the total organic carbon content data analysis equipment, the logging equipment and the seismic data testing equipment, etc.

[0070] In some embodiments, the electronic device can be connected to a database for communication, and obtain total organic carbon content data, drilling layer data, logging curves, and two-dimensional seismic line data of the target area from the database.

[0071] In the embodiment of the present application, the target area may be a basin. The basin may be a low-exploration area with less basic drilling data.

[0072] In the embodiment of the present application, the amplitude data of each logging curve is within the same value range.

[0073] In the embodiment of the present application, the amplitude range of the two-dimensional seismic survey line data is the same and has passed the closure error processing.

[0074] In some embodiments, before step S1, the method further includes:

[0075] Step S11, obtaining initial logging curves of wells drilled in the target area.

[0076] Step S12, performing standardization processing on the initial logging curve to obtain various logging curves, wherein the amplitude data of various logging curves are within the same value range.

[0077] In the embodiment of the present application, since the initial logging curves are usually measured by different logging instruments in different measurement periods, it is necessary to eliminate the influence of different measurement periods and different logging instruments on the logging curves. The initial logging curves can be standardized so that the amplitude data of these initial logging curves are within the same value range.

[0078] In some embodiments, before step S1, the method further includes:

[0079] Step S13, acquiring initial two-dimensional seismic line data of different areas in the target area.

[0080] In the embodiment of the present application, the different regions may include: structural units of different levels or specific research areas within structural units.

[0081] Step S14, determining the time depth values ​​corresponding to the two landmark stratigraphic interfaces on each initial two-dimensional seismic line data based on the at least two landmark stratigraphic interfaces in the target area.

[0082] In the embodiment of the present application, due to the different surface conditions of these different levels of structural units or specific study areas within the structural units, there is a certain time difference in the two-dimensional seismic line data of different levels of structural units or specific study areas within the structural units, that is, the closure error of the two-dimensional seismic line data. When using these two-dimensional seismic line data to carry out the evaluation of source rocks in the basin, these closure errors must first be eliminated to improve the accuracy of source rock evaluation. Therefore, it is necessary to eliminate the closure error processing of the initial two-dimensional seismic line data.

[0083] In the embodiment of the present application, at least two landmark stratigraphic interfaces in the basin can be determined through geological knowledge in the target area. For example, the at least two landmark stratigraphic interfaces can include: the top interface of the Cambrian Qiongzhusi Formation and the bottom interface of the Silurian Formation as marker layers.

[0084] In the embodiment of the present application, at least two landmark stratigraphic interfaces are relatively easy to identify on the seismic profile, and therefore, the time-depth values ​​corresponding to the landmark stratigraphic interfaces can be read on the two-dimensional seismic line data.

[0085] Step S15, determining the closure errors corresponding to each initial two-dimensional seismic line data based on each time-depth value.

[0086] In the embodiment of the present application, after each time-depth value is determined, the difference can be calculated to determine the closure difference corresponding to each initial two-dimensional seismic line data.

[0087] Step S16, moving each initial seismic line data based on the closure errors corresponding to each initial two-dimensional seismic line data to eliminate the closure errors of each initial two-dimensional seismic line data to obtain intermediate two-dimensional seismic line data.

[0088] In the embodiment of the present application, each initial seismic data can be overall shifted in time based on the closure error, thereby eliminating the closure error of two-dimensional seismic survey line data in different regions.

[0089] Step S17, performing amplitude consistency processing on the intermediate two-dimensional seismic line data to obtain the two-dimensional seismic line data.

[0090] In the embodiment of the present application, when seismic data are collected and processed for two-dimensional seismic line data in different areas of the basin, due to different collection times, different collection instruments and equipment, different seismic data processing systems used, and different processing technical methods, the amplitude value ranges of the two-dimensional seismic line data of different levels of structural units or specific study areas within the structural units will be different, which will greatly affect the prediction accuracy of effective source rock evaluation using the two-dimensional seismic line data. Therefore, it is necessary to perform amplitude consistency processing on the intermediate two-dimensional seismic line data to obtain the two-dimensional seismic line data so that the amplitude value of the two-dimensional seismic line data is within the standard value range.

[0091] In the embodiment of the present application, step S17 can be implemented by the following steps:

[0092] Step S171, counting the range of amplitude values ​​of all intermediate two-dimensional seismic line data.

[0093] Step S172: determining a standard value range of the amplitude value based on the value range.

[0094] In the embodiment of the present application, the standard value range can be set. Any value range in the value range can be determined as the standard value range. The standard value range can also be the middle value of the value range. The largest value range can also be determined as the standard value range.

[0095] Step S173: determining a correction coefficient for the amplitude of each intermediate two-dimensional seismic line data based on the standard value range.

[0096] In the embodiment of the present application, the correction coefficient can be considered as a magnification or reduction ratio.

[0097] Step S174, correcting the amplitude value of the corresponding two-dimensional seismic line data based on the correction coefficient to obtain the two-dimensional seismic line data, wherein the range of the amplitude value of each two-dimensional seismic line data is within the standard range.

[0098] In the embodiment of the present application, the amplitude value of each two-dimensional seismic line data may be multiplied by a correction coefficient, thereby achieving correction of the amplitude value of the two-dimensional seismic line data.

[0099] In the embodiment of the present application, after correction, amplitude consistency processing of two-dimensional seismic line data in different regions is achieved.

[0100] Step S2, performing intersection analysis on the total organic carbon content data and the well logging curve to determine a target well logging curve, and determining a relationship between a key parameter corresponding to the target well logging curve and the total organic carbon content.

[0101] For example, an intersection analysis of the total organic carbon content of the source rock with the acoustic wave, density, natural gamma and other logging curves can be carried out to produce an intersection analysis diagram, so that the logging curve with the highest correlation with the total organic carbon content of the source rock or the calculation combination of several logging curves can be determined as the target logging curve, and the parameters corresponding to the target logging curve are the key parameters. For example, if the correlation between the acoustic wave and the total organic carbon content of the source rock is the largest, the key parameter is the acoustic wave.

[0102] In the embodiment of the present application, the relationship between the key parameters for source rock identification and the total organic carbon content can also be determined through intersection analysis.

[0103] Step S3, based on the drilling layer data and the characteristic stratigraphic interface features on the stratigraphic section of the target area, the target logging curve and the two-dimensional seismic line data are calibrated to determine the time-depth relationship.

[0104] In the embodiments of the present application, the logging curves are all depth domain data, while the seismic data has both time domain and depth domain. The two-dimensional seismic line data is usually time domain data. Before performing seismic-key parameter inversion, it is necessary to make full use of the few well drilling data in the basin for well-seismic calibration to establish an accurate time-depth relationship (i.e., time-depth relationship).

[0105] In the embodiment of the present application, a number of landmark stratigraphic interfaces can be determined based on geological knowledge, and then the drilled wells are calibrated by combining drilling stratification data and landmark stratigraphic interface characteristics on the seismic profile to determine an accurate time-depth relationship.

[0106] Step S4, performing a stratum structural interpretation based on the two-dimensional seismic line data and the time-depth relationship to obtain a structural interpretation stratum, and determining a source rock stratum framework model of the target area based on the structural interpretation stratum.

[0107] In the embodiment of the present application, the stratigraphic interpretation of the source rock strata can be carried out based on the two-dimensional seismic line data on the basis of the time-depth relationship, so as to determine the structural interpretation horizon, and the structural interpretation horizon of the two-dimensional seismic line data can be used to construct a source rock stratigraphic framework model of the basin. In the embodiment of the present application, the source rock stratigraphic framework model can standardize the stratigraphic structure of the target area, and the two-dimensional seismic line data can correspond to the position in each source rock stratigraphic framework model.

[0108] Step S5, performing inversion calculation on the key parameters based on the two-dimensional seismic line data and the source rock stratum framework model to obtain distribution characteristics of the key parameters in the source rock stratum framework model.

[0109] In the embodiment of the present application, since there is a correlation between the two-dimensional seismic line data and the key parameters, the key parameters can be inverted and calculated based on the two-dimensional seismic line data and the source rock formation framework model to obtain the distribution characteristics of the key parameters in the source rock formation framework model.

[0110] Step S6, determining the distribution characteristics of effective source rocks in the target area based on the relationship and the distribution characteristics.

[0111] In the embodiments of the present application, the distribution characteristics of the effective source rocks include: distribution range and thickness.

[0112] In the embodiment of the present application, step S6 can be implemented by the following steps:

[0113] Step S61, determining a threshold value of the total organic carbon content of the effective source rock.

[0114] In the embodiment of the present application, effective source rock refers to source rock with a total organic carbon content greater than 0.5%, and the threshold value is a total organic carbon content of 0.5%.

[0115] Step S62: determining a threshold value of a key parameter based on the relationship and the threshold value.

[0116] In the embodiment of the present application, the threshold value of the key parameter can be calculated by the threshold value and the relational expression, that is, the threshold value of the key parameter when the total organic carbon content of the source rock is 0.5% is obtained.

[0117] Step S63, determining the target key parameter distribution characteristics based on the threshold value of the key parameter and the distribution characteristics.

[0118] In the embodiment of the present application, data in the distribution characteristics can be eliminated based on the threshold value of the key parameter, so as to determine the target key parameter distribution characteristics.

[0119] The key parameter corresponding to a total organic carbon content greater than 0.5% may be determined as a target key parameter distribution characteristic.

[0120] Step S64: determining the distribution range of the total organic carbon content in the target area based on the target key parameter distribution characteristics and the relationship.

[0121] In the embodiment of the present application, the distribution characteristics of the target key parameters can be input into the relationship to determine the distribution range of the total organic carbon content in the target area.

[0122] Step S65, determining the distribution characteristics of effective source rocks based on the total organic carbon content distribution range.

[0123] The thickness of effective source rocks can be calculated through the distribution range of total organic carbon content of source rocks, thereby predicting the distribution characteristics of effective source rocks in the basin.

[0124] The method for predicting the distribution of effective source rocks provided in the embodiment of the present application is to obtain the total organic carbon content data of the wells drilled in the target area, the drilling layer data, the logging curve and the two-dimensional seismic line data of the target area; perform intersection analysis on the total organic carbon content data and the logging curve to determine the target logging curve, and determine the relationship between the key parameters corresponding to the target logging curve and the total organic carbon content; perform well-seismic calibration on the target logging curve and the two-dimensional seismic line data based on the drilling layer data and the characteristic stratigraphic interface characteristics on the stratigraphic section of the target area to determine the time-depth relationship; performing stratigraphic structural interpretation based on the two-dimensional seismic line data and the time-depth relationship to obtain structural interpretation stratigraphic positions, and determining the source rock stratigraphic framework model of the target area based on the structural interpretation stratigraphic positions; performing inversion calculation on the key parameters based on the two-dimensional seismic line data and the source rock stratigraphic framework model to obtain the distribution characteristics of the key parameters in the source rock stratigraphic framework model; determining the distribution characteristics of effective source rocks in the target area based on the relationship and the distribution characteristics, so as to realize the prediction of the distribution of effective source rocks in the case of a lack of basic data (such as core data).

[0125] Embodiment 2

[0126] Based on the above embodiments, the present application provides a method for predicting the distribution of effective source rocks. Combining drilling, logging and seismic data, a method for predicting the distribution of effective source rocks in a basin is proposed. Figure 2 The steps shown include four steps: source rock data analysis, 2D seismic line data correction, seismic key parameter inversion, and source rock distribution feature prediction:

[0127] Step S101, source rock data analysis.

[0128] In the embodiment of the present application, source rock data analysis mainly includes two steps: source rock data collection and organization and source rock identification key parameter determination.

[0129] Step S1011, collecting and collating source rock data.

[0130] In low-exploration areas, there is often less basic drilling data. Therefore, it is necessary to collect the analysis and test data of the source rocks that have been drilled in the basin and surrounding areas, as well as the drilling and logging data, including the total organic carbon content analysis and test data of the source rocks (the same as the total organic carbon content data in the above embodiment), drilling stratification data, and acoustic wave, density, natural gamma and other logging curve data. Considering that these logging curve data are measured by different logging instruments at different measurement periods, it is necessary to standardize the collected acoustic wave, density, natural gamma and other logging curves to eliminate the influence of different measurement periods and different logging instruments on the logging curve data, so that these logging curve data are within the same value range.

[0131] Step S1012: determining key parameters for source rock identification.

[0132] Based on the analysis and test data of the drilled source rocks and the standardized acoustic wave, density, natural gamma and other logging curves, an intersection analysis of the total organic carbon content of the source rocks and the acoustic wave, density, natural gamma and other logging curves is carried out to determine the logging curve with the highest correlation with the total organic carbon content of the source rocks or the calculation combination of several logging curves as the key parameters for source rock identification; at the same time, the relationship between the key parameters for source rock identification and the total organic carbon content and the threshold value of the key parameters for effective source rock identification are determined, that is, the threshold value of the key parameters when the total organic carbon content of the source rocks is greater than 0.5%.

[0133] Step S102, 2D seismic line data correction.

[0134] In the embodiment of the present application, the two-dimensional seismic line data correction mainly includes two steps: two-dimensional seismic line closure error elimination and amplitude consistency processing, wherein:

[0135] Step S1021, a step of eliminating closure errors of two-dimensional seismic line data.

[0136] In the early stage of exploration, the two-dimensional seismic data acquisition and processing in the basin are usually carried out for specific structural units of different levels or specific study areas in the basin. Due to the different surface conditions of these structural units of different levels or specific study areas in the structural units, there is a certain time difference in the two-dimensional seismic line data of the structural units of different levels or specific study areas in the structural units, that is, the closure error of the two-dimensional seismic line data. When using these two-dimensional seismic line data to carry out the evaluation of source rocks in the basin, these closure errors must be eliminated first. Specifically, firstly, at least two landmark stratigraphic interfaces in the basin are determined based on geological knowledge. Such landmark stratigraphic interfaces are usually easy to identify on seismic profiles; then, the time depth values ​​corresponding to the landmark stratigraphic interfaces are read on the two-dimensional seismic line data of the structural units of different levels or specific study areas in the structural units, and the specific value of the closure error is calculated; finally, the closure error of the two-dimensional seismic line data of the structural units of different levels or specific study areas in the structural units is eliminated by performing overall time shift on the two-dimensional seismic line data.

[0137] Step S1022, amplitude consistency processing step.

[0138] When collecting and processing seismic data of two-dimensional seismic line data of different levels of structural units or specific study areas in the basin, due to different collection time, different collection instruments and equipment, different seismic data processing systems used, and different processing technical methods, the amplitude value ranges of two-dimensional seismic line data of different levels of structural units or specific study areas in the structural units will be different, which will greatly affect the prediction accuracy of effective source rock evaluation using two-dimensional seismic line data. Therefore, it is necessary to carry out amplitude consistency processing on two-dimensional seismic line data of different levels of structural units or specific study areas in the structural units. Specifically, first, the amplitude value ranges of all two-dimensional seismic line data used in the study are counted respectively, and then, according to the amplitude value ranges of all two-dimensional seismic line data, a standard amplitude value range is determined; finally, the amplitude value ranges of different two-dimensional seismic line data are processed into the same standard value range by the amplitude coefficient correction processing method, so as to achieve amplitude consistency processing of two-dimensional seismic line data of different levels of structural units or specific study areas in the structural units.

[0139] Step S103: inversion of key seismic parameters.

[0140] In the embodiment of the present application, the inversion of key seismic parameters includes three steps: seismic calibration of the drilled wells, establishment of a stratigraphic framework model of the basin source rock, and inversion of key parameters for identifying the two-dimensional seismic source rock. Each step is described as follows:

[0141] Step S1031, seismic calibration of the drilled well.

[0142] Well logging curves are all depth domain data, while seismic data has both time domain and depth domain. Two-dimensional seismic line data are usually time domain data. Therefore, before inverting the key seismic parameters, it is necessary to make full use of the few wells that have been drilled in the basin for well-seismic calibration to establish an accurate time-depth relationship. Specifically, first determine a number of landmark stratigraphic interfaces based on geological knowledge, and then combine the drilling layering data and the landmark stratigraphic interface characteristics on the seismic profile to perform well-seismic calibration of the drilled wells to determine the accurate time-depth relationship.

[0143] Step S1032: Establishing a stratigraphic framework model of the basin source rock.

[0144] Firstly, on the basis of the established time-depth relationship, the stratigraphic structural interpretation of the source rock strata was carried out based on the two-dimensional seismic data. Then, the stratigraphic framework model of the source rock strata in the basin was constructed using the structural interpretation stratigraphic layers of the two-dimensional seismic data.

[0145] Step S1033: Inversion of key parameters for 2D seismic source rock identification.

[0146] Based on the two-dimensional seismic survey line data in the basin, combined with the key parameters for source rock identification and the stratigraphic framework model of the source rock in the basin, the two-dimensional seismic key parameter inversion for source rock identification was carried out to obtain the distribution characteristics of the key parameters for source rock identification in the basin.

[0147] Step S104: predicting the distribution characteristics of source rocks.

[0148] In the embodiments of the present application, the distribution range of the total organic carbon content of the source rock in the basin can be calculated, and the thickness of the effective source rock can be statistically calculated, thereby realizing the prediction of the distribution characteristics of the effective source rock in the basin.

[0149] The method provided in the embodiment of the present application can realize the prediction of the distribution characteristics of effective source rocks in the basin when basic data are relatively scarce in the early stage of exploration, which will be conducive to breakthroughs in oil and gas exploration in areas with low exploration degree and improve the economic benefits of oil and gas exploration.

[0150] Based on the above embodiments, the present application provides an application example of a method for predicting the distribution of effective source rocks. In a basin, relatively few wells have been drilled to the Cambrian Qiongzhusi Formation. Before the use of this technology, the distribution characteristics of effective source rocks in the Cambrian Qiongzhusi Formation in a basin were unclear. The following steps are implemented in the specific application:

[0151] (1) Collect and organize analytical test data such as the total organic carbon content of source rocks in the basin, drilling stratification data, and logging curve data, and standardize the logging curves of the drilled wells; then, conduct an intersection analysis of the total organic carbon content of source rocks with acoustic wave, density, natural gamma and other logging curves, and finally determine that longitudinal wave impedance is the key parameter for source rock identification, and obtain the relationship between total organic carbon content and longitudinal wave impedance, as well as the longitudinal wave impedance threshold value of effective source rocks.

[0152] (2) The top interface of the Cambrian Qiongzhusi Formation and the bottom interface of the Silurian Formation were selected as marker layers, and closure error correction was performed on the 2D seismic line data of the basin. Figure 3 A comparison chart before and after calibration is provided in an embodiment of the present application, such as Figure 3 As shown, Figure 3 The upper figure in the figure is a schematic diagram of the seismic section before closure error correction. Figure 3 The figure below is a schematic diagram of the seismic profile after closure error correction. Then, the amplitude value range of all two-dimensional seismic line data is statistically calculated; then, the amplitude values ​​of all two-dimensional seismic line data are processed into the same amplitude value range using the amplitude coefficient correction processing method.

[0153] (3) Carry out seismic calibration of the drilled wells and interpret the top and bottom interfaces of the Cambrian Qiongzhusi Formation on the 2D seismic survey lines. Combined with the drilled well data, 2D seismic P-wave impedance inversion is carried out to obtain the distribution characteristics of P-wave impedance in the basin.

[0154] (4) Using the relationship between total organic carbon content and longitudinal wave impedance, the longitudinal wave impedance threshold value of effective source rocks, and the distribution characteristics of longitudinal wave impedance in the basin, the distribution range of total organic carbon content in the source rocks of the basin is calculated, and the thickness of the effective source rocks is statistically calculated, thereby realizing the prediction of the distribution characteristics of effective source rocks in the Cambrian Qiongzhusi Formation in a certain basin.

[0155] The prediction method of the effective source rock distribution provided by the embodiment of the present application can realize the prediction of the effective source rock distribution in the basin when the basic data is relatively scarce in the early stage of exploration. It will be conducive to the breakthrough of oil and gas exploration in the low exploration degree area and improve the economic benefits of oil and gas exploration. The method includes: sorting and analyzing the source rock data in the basin, standardizing the well logging curves of the drilled wells, combining the analysis and test data of the source rock samples to determine the key parameters for source rock identification, the relationship between the key parameters and the total organic carbon content, and the key parameter threshold value of the effective source rock; using the two-dimensional seismic data in the basin to carry out two-dimensional seismic line data correction; then, carrying out the basin-wide two-dimensional seismic source rock identification key parameter inversion to obtain the distribution characteristics of the key parameters for source rock identification in the basin; finally, based on the relationship between the key parameters for source rock identification and the total organic carbon content, the key parameter threshold value of the effective source rock, and the distribution characteristics of the key parameters in the basin, the distribution characteristics and thickness of the effective source rock in the basin are statistically calculated, thereby realizing the accurate prediction of the distribution of the effective source rock in the basin in the low exploration degree area.

[0156] Embodiment 3

[0157] Based on the foregoing embodiments, the embodiments of the present application provide a prediction device for the distribution of effective source rocks. The modules included in the device and the units included in each module can be implemented by a processor in a computer device; of course, they can also be implemented by a specific logic circuit; in the implementation process, the processor can be a central processing unit (CPU, Central Processing Unit), a microprocessor (MPU, Microprocessor Unit), a digital signal processor (DSP, Digital Signal Processing) or a field programmable gate array (FPGA, Field Programmable Gate Array), etc.

[0158] The embodiment of the present application provides a prediction device for effective hydrocarbon source rock distribution, and the prediction device for effective hydrocarbon source rock distribution includes:

[0159] An acquisition module, used to acquire total organic carbon content data, drilling layer data, well logging curves and two-dimensional seismic line data of the target area that have been drilled;

[0160] A first determination module is used to perform intersection analysis on the total organic carbon content data and the well logging curve to determine a target well logging curve, and determine a relationship between a key parameter corresponding to the target well logging curve and the total organic carbon content;

[0161] A second determination module is used to perform well-seismic calibration on the target well logging curve and the two-dimensional seismic line data based on the drilling layer data and the characteristic stratigraphic interface features on the stratigraphic section of the target area to determine the time-depth relationship;

[0162] A third determination module is used to perform a stratum structural interpretation based on the two-dimensional seismic line data and the time-depth relationship to obtain a structural interpretation stratum, and determine a source rock stratum framework model of the target area based on the structural interpretation stratum;

[0163] An inversion module, used for performing inversion calculation on the key parameters based on the two-dimensional seismic line data and the source rock stratum framework model, and obtaining distribution characteristics of the key parameters in the source rock stratum framework model;

[0164] The fourth determination module is used to determine the distribution characteristics of effective source rocks in the target area based on the relationship and the distribution characteristics.

[0165] In some embodiments, the prediction device for effective source rock distribution is further used to:

[0166] Obtaining initial well logging curves of wells drilled in the target area;

[0167] The initial logging curve is standardized to obtain various logging curves, wherein the amplitude data of various logging curves are within the same value range.

[0168] In some embodiments, the prediction device for effective source rock distribution is further used to:

[0169] Acquire initial two-dimensional seismic line data of different areas in the target area;

[0170] Determine the time depth values ​​corresponding to the two landmark stratigraphic interfaces on each initial two-dimensional seismic line data based on at least two landmark stratigraphic interfaces in the target area;

[0171] Determine the closure errors corresponding to each initial two-dimensional seismic line data based on each time-depth value;

[0172] Moving each of the initial two-dimensional seismic line data based on the closure errors corresponding to each of the initial two-dimensional seismic line data to eliminate the closure errors of each of the initial two-dimensional seismic line data and obtain intermediate two-dimensional seismic line data;

[0173] Amplitude consistency processing is performed on the intermediate two-dimensional seismic line data to obtain the two-dimensional seismic line data.

[0174] In some embodiments, performing amplitude consistency processing on the intermediate two-dimensional seismic line data to obtain the two-dimensional seismic line data includes:

[0175] Count the range of amplitude values ​​of all intermediate two-dimensional seismic line data;

[0176] Determine a standard value range of the amplitude value based on the value range;

[0177] Determining a correction coefficient for the amplitude of each intermediate two-dimensional seismic line data based on the standard value range;

[0178] The amplitude value of the corresponding two-dimensional seismic line data is corrected based on the correction coefficient to obtain the two-dimensional seismic line data, wherein the value range of the amplitude value of each two-dimensional seismic line data is within the standard value range.

[0179] In some embodiments, the determining a target well logging curve by performing intersection analysis based on the total organic carbon content data and the well logging curve comprises:

[0180] Performing intersection analysis on the total organic carbon content data and the well logging curve to determine the correlation between the total organic carbon content and each well logging curve;

[0181] A target well log curve is determined based on the correlation.

[0182] In some embodiments, determining the distribution characteristics of effective source rocks in the target area based on the relationship and the distribution characteristics includes:

[0183] Determine the threshold value of total organic carbon content of effective source rocks;

[0184] Determining a threshold value of a key parameter based on the relationship and the threshold value;

[0185] Determining a target key parameter distribution characteristic based on the threshold value of the key parameter and the distribution characteristic;

[0186] Determining the distribution range of the total organic carbon content in the target area based on the target key parameter distribution characteristics and the relationship;

[0187] The distribution characteristics of effective source rocks are determined based on the total organic carbon content distribution range.

[0188] It should be noted that in the embodiments of the present application, if the above-mentioned prediction method of the effective source rock distribution is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM, Read Only Memory), a disk or an optical disk. In this way, the embodiments of the present application are not limited to any specific combination of hardware and software.

[0189] Accordingly, an embodiment of the present application provides a storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps in the method for predicting the distribution of effective source rocks provided in the above embodiment are implemented.

[0190] Embodiment 4

[0191] An embodiment of the present application provides an electronic device; Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown in FIG. Figure 4 As shown, the electronic device 700 includes: a processor 701, at least one communication bus 702, a user interface 703, at least one external communication interface 704, and a memory 705. The communication bus 702 is configured to realize the connection and communication between these components. The user interface 703 may include a display screen, and the external communication interface 704 may include a standard wired interface and a wireless interface. The processor 701 is configured to execute the program of the prediction method of the effective source rock distribution stored in the memory to implement the steps in the prediction method of the effective source rock distribution provided in the above embodiment.

[0192] The description of the above electronic device and storage medium embodiments is similar to the description of the above method embodiments, and has similar beneficial effects as the method embodiments. For technical details not disclosed in the computer device and storage medium embodiments of this application, please refer to the description of the method embodiments of this application for understanding.

[0193] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present application, the size of the sequence number of the above-mentioned processes does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The above-mentioned sequence numbers of the embodiments of the present application are only for description and do not represent the advantages and disadvantages of the embodiments.

[0194] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0195] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0196] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0197] In addition, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be a separate unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0198] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read Only Memory), disks or optical disks, etc. Various media that can store program codes.

[0199] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application can essentially or in other words, the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for a controller to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0200] The above is only an implementation method of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for predicting the distribution of effective source rocks, characterized in that: include: Acquire total organic carbon content data, drilling layer data, well logging curves and two-dimensional seismic line data of the target area; Performing intersection analysis on the total organic carbon content data and the well logging curve to determine a target well logging curve, and determining a relationship between a key parameter corresponding to the target well logging curve and the total organic carbon content; Based on the drilling layer data and the characteristic stratigraphic interface features on the stratigraphic section of the target area, the target well logging curve and the two-dimensional seismic line data are calibrated to determine the time-depth relationship; Performing a stratum structural interpretation based on the two-dimensional seismic line data and the time-depth relationship to obtain a structural interpretation stratum, and determining a source rock stratum framework model of the target area based on the structural interpretation stratum; Performing inversion calculation on the key parameters based on the two-dimensional seismic line data and the source rock stratum framework model to obtain distribution characteristics of the key parameters in the source rock stratum framework model; The distribution characteristics of effective source rocks in the target area are determined based on the relationship and the distribution characteristics.

2. The method according to claim 1, characterized in that The method further comprises: Obtaining initial well logging curves of wells drilled in the target area; The initial logging curve is standardized to obtain various logging curves, wherein the amplitude data of various logging curves are within the same value range.

3. The method according to claim 1, characterized in that The method further comprises: Acquire initial two-dimensional seismic line data of different areas in the target area; Determine the time depth values ​​corresponding to the two landmark stratigraphic interfaces on each initial two-dimensional seismic line data based on at least two landmark stratigraphic interfaces in the target area; Determine the closure errors corresponding to each initial two-dimensional seismic line data based on each time-depth value; Moving each of the initial two-dimensional seismic line data based on the closure errors corresponding to each of the initial two-dimensional seismic line data to eliminate the closure errors of each of the initial two-dimensional seismic line data to obtain intermediate two-dimensional seismic line data; Amplitude consistency processing is performed on the intermediate two-dimensional seismic line data to obtain the two-dimensional seismic line data.

4. The method according to claim 3, characterized in that The performing amplitude consistency processing on the intermediate two-dimensional seismic line data to obtain the two-dimensional seismic line data comprises: Count the range of amplitude values ​​of all intermediate two-dimensional seismic line data; Determine a standard value range of the amplitude value based on the value range; Determining a correction coefficient for the amplitude of each intermediate two-dimensional seismic line data based on the standard value range; The amplitude value of the corresponding two-dimensional seismic line data is corrected based on the correction coefficient to obtain the two-dimensional seismic line data, wherein the value range of the amplitude value of each two-dimensional seismic line data is within the standard value range.

5. The method according to claim 1, characterized in that The determining of the target well logging curve by performing intersection analysis based on the total organic carbon content data and the well logging curve comprises: Performing intersection analysis on the total organic carbon content data and the well logging curve to determine the correlation between the total organic carbon content and each well logging curve; A target well log curve is determined based on the correlation.

6. The method according to claim 1, characterized in that The determining of the distribution characteristics of effective source rocks in the target area based on the relationship and the distribution characteristics includes: Determine the threshold value of total organic carbon content of effective source rocks; Determining a threshold value of a key parameter based on the relationship and the threshold value; Determining a target key parameter distribution characteristic based on the threshold value of the key parameter and the distribution characteristic; Determining the distribution range of the total organic carbon content in the target area based on the target key parameter distribution characteristics and the relationship; The distribution characteristics of effective source rocks are determined based on the total organic carbon content distribution range.

7. A prediction device for the distribution of effective hydrocarbon source rocks, characterized in that: include: An acquisition module, used to acquire total organic carbon content data, drilling layer data, well logging curves and two-dimensional seismic line data of the target area that have been drilled; A first determination module is used to perform intersection analysis on the total organic carbon content data and the well logging curve to determine a target well logging curve, and determine a relationship between a key parameter corresponding to the target well logging curve and the total organic carbon content; A second determination module is used to perform well-seismic calibration on the target well logging curve and the two-dimensional seismic line data based on the drilling layer data and the characteristic stratigraphic interface features on the stratigraphic section of the target area to determine the time-depth relationship; A third determination module is used to perform a stratum structural interpretation based on the two-dimensional seismic line data and the time-depth relationship to obtain a structural interpretation stratum, and determine a source rock stratum framework model of the target area based on the structural interpretation stratum; An inversion module, used for performing inversion calculation on the key parameters based on the two-dimensional seismic line data and the source rock stratum framework model, and obtaining distribution characteristics of the key parameters in the source rock stratum framework model; The fourth determination module is used to determine the distribution characteristics of effective source rocks in the target area based on the relationship and the distribution characteristics.

8. The prediction device for effective source rock distribution according to claim 7, characterized in that: The prediction device for the effective source rock distribution is also used for: Obtaining initial well logging curves of wells drilled in the target area; The initial logging curve is standardized to obtain various logging curves, wherein the amplitude data of various logging curves are within the same value range.

9. An electronic device, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the method for predicting the distribution of effective source rocks as claimed in any one of claims 1 to 7 is executed.

10. A storage medium, characterized in that: The computer program stored in the storage medium can be executed by one or more processors and can be used to implement the method for predicting the distribution of effective source rocks as described in any one of claims 1 to 7.

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