Method, device, electronic device and storage medium for predicting effective hydrocarbon source rock distribution

Abstract

The present application provides a method, device, electronic device and storage medium for predicting the distribution of effective source rocks. The method determines the relationship between the key parameters corresponding to the target well 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 using 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 using the relationship and the distribution characteristics. This method can predict the distribution of effective source rocks in the case of a lack of basic data (such as core data).

Description

Technical Field

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

[0002] Source rock refers to rocks rich in organic matter that generate and expel large quantities of oil and gas. When evaluating a basin's oil and gas resources, the first step is to evaluate the basin's source rocks. This evaluation is also a key indicator of the basin's oil and gas resource potential. Three parameters are typically used to evaluate source rock: organic matter abundance, organic matter type, and organic matter maturity. For both organic matter type and organic matter maturity, the organic matter type and organic matter maturity are determined by first collecting source rock samples from field outcrops or drilled cores. Targeted laboratory analysis and testing of these source rock samples is then performed to determine the corresponding organic matter type and organic matter maturity parameters. The evaluation of organic matter abundance in source rocks can be divided into two categories based on the scope of the study. For single-point studies, such as those at a field outcrop or a drilled well, source rock samples must be collected and then analyzed in the laboratory to determine parameters characterizing organic matter abundance, such as total organic carbon content, soluble hydrocarbons in the source rock pyrolysis parameters, pyrolytic hydrocarbons, chloroform bitumen, and total hydrocarbon content. For regional organic matter abundance evaluation within a basin, the focus is generally on predicting the distribution of effective source rocks. Specifically, predicting the distribution of effective source rocks in a basin typically involves two aspects: first, calculating the total organic carbon content of the source rocks, and second, calculating the thickness of the effective source rocks. Effective source rocks are defined as those with a total organic carbon content greater than 0.5%. Conventional research on predicting effective source rocks in a basin relies on, on the one hand, using laboratory sample analysis and testing techniques to determine the total organic carbon content of the source rocks; on the other hand, by observing the thickness of source rocks in field outcrops, the thickness of the effective source rocks in the underground strata is estimated. However, field outcrops are often located at the basin margins, and during the initial stages of exploration, when risk-based exploratory wells are deployed, relatively little drill core data is available. Therefore, it is impossible 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 equipment and storage medium for predicting the distribution of effective hydrocarbon source rocks, which can predict the distribution of effective hydrocarbon source rocks when basic data (such as core data) is relatively scarce.

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

[0005] Obtaining total organic carbon content data, drilling layer data, well logging curves, and two-dimensional seismic line data of wells drilled in 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 horizon structural interpretation based on the two-dimensional seismic line data and the time-depth relationship to obtain a structural interpretation horizon, and determining a source rock stratum framework model of the target area based on the structural interpretation horizon;

[0009] Performing inversion calculation on the key parameters based on the two-dimensional seismic line data and the source rock formation framework model to obtain distribution characteristics of the key parameters in the source rock formation 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] Acquiring initial two-dimensional seismic line data for different areas in the target area;

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

[0017] Determining the closure errors corresponding to the initial two-dimensional seismic line data based on the respective time-depth values;

[0018] Moving each of the initial two-dimensional seismic line data based on the closure errors corresponding to the initial two-dimensional seismic line data to eliminate the closure errors 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 2D seismic line data;

[0022] Determining 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 values ​​of the corresponding two-dimensional seismic line data are corrected based on the correction coefficient to obtain the two-dimensional seismic line data, wherein the amplitude value range of each two-dimensional seismic line data is within the standard value range.

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

[0026] Performing intersection analysis on the total organic carbon content data and the well logging curves 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 target key parameter distribution characteristics based on the threshold value of the key parameter and the distribution characteristics;

[0032] Determining a distribution range of 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 provides a device for predicting the distribution of effective hydrocarbon source rocks, comprising:

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

[0036] A first determination module is configured 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 configured 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 configured to perform a horizon structural interpretation based on the two-dimensional seismic line data and the time-depth relationship to obtain a structural interpretation horizon, and determine a source rock stratum framework model of the target area based on the structural interpretation horizon;

[0039] an inversion module, configured to perform inversion calculation on the key parameters based on the two-dimensional seismic line data and the source rock formation framework model, and obtain distribution characteristics of the key parameters in the source rock formation 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 device for predicting the distribution of effective source rocks 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 hydrocarbon 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 hydrocarbon source rocks.

[0046] The present application provides a method, device, electronic device and storage medium for predicting the distribution of effective source rocks. The method determines the relationship between the key parameters corresponding to the target well 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 using 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 using the relationship and the distribution characteristics. This method can predict the distribution of effective source rocks in the case of a lack of basic data (such as core data). BRIEF DESCRIPTION OF THE DRAWINGS

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

[0048] Figure 1 A schematic diagram of a process flow for implementing 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 process 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, like components are given like reference numerals, and the drawings are not drawn to scale. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this 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 describes 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 explanation will be added. In the following description, the terms "first\second\third" are only used to distinguish similar objects and do not represent a specific order for the objects. It can be understood that "first\second\third" can be interchanged with 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 commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only 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] Currently, there are relatively few patents and a large number of literature on methods for predicting the distribution of effective source rocks in basins. Specific technical methods can be divided into three categories:

[0059] The first category: using the analytical test data of the source rocks drilled in the region to generally evaluate the distribution of effective source rocks. This method uses a combination of geological statistics and laboratory analytical tests 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 analytical test data to construct a method system for evaluating and predicting high-quality source rocks, but there is often not enough analytical test data in the early stages of exploration; (2) The distribution characteristics of effective source rocks obtained using this method do not have sufficient data constraints in areas without wells, and its prediction accuracy is greatly affected by the 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 three-dimensional seismic data in a small area of ​​a 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 uses geological methods to determine the distribution characteristics of favorable sedimentary facies of source rocks to characterize the distribution of effective source rocks. This method uses analytical testing to establish the relationship between sedimentary facies parameters such as sedimentation rate and total organic carbon content, and determines the distribution characteristics of favorable source rocks through the distribution of sedimentary facies. However, this method does not consider the variation of sedimentary facies. Different regions within a basin often develop different sedimentary facies during the same geological period, a phenomenon known as isochronous anisotropy. Therefore, this method cannot effectively solve the problem of predicting the distribution of effective source rocks over a large basin.

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

[0063] To address the problems existing in the related art, embodiments of the present application provide a method for predicting the distribution of effective hydrocarbon source rocks, which is applicable to electronic devices such as computers and mobile terminals. The functions implemented by the method for predicting the distribution of effective hydrocarbon source rocks provided in embodiments of the present application can be implemented by a processor of the electronic device calling program code, wherein the program code can be stored in a computer storage medium.

[0064] Example 1

[0065] The present invention 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 hydrocarbon source rocks provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, including:

[0066] Step S1, obtaining total organic carbon content data of wells drilled in a target area, 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 ray curve, a porosity curve, a resistivity curve, etc.

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

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

[0070] In some embodiments, the electronic device can be connected to a database for communication to obtain total organic carbon content data, drilling layer data, logging curves, and two-dimensional seismic line data of wells drilled in 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 relatively little 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 at 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 of the initial two-dimensional seismic line data based on the at least two landmark stratigraphic interfaces in the target area.

[0082] In the examples of this application, due to the varying surface conditions of these different tectonic units or specific study areas within these units, the 2D seismic line data for these different tectonic units or specific study areas within these units exhibits certain time differences, known as closure errors. When using these 2D seismic line data to conduct basin-wide source rock evaluation, these closure errors must first be eliminated to improve the accuracy of the source rock evaluation. Therefore, it is necessary to perform closure error elimination processing on the initial 2D seismic line data.

[0083] In an 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 easily identifiable 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 the initial two-dimensional seismic line data based on the time-depth values.

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

[0088] In the embodiment of the present application, each initial seismic data can be overall time-shifted based on the closure error, thereby eliminating the closure error of two-dimensional seismic 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 embodiments of the present application, when seismic data is collected and processed for two-dimensional seismic line data in different areas of a basin, different acquisition times, different acquisition instruments and equipment, different seismic data processing systems used, and different processing technical methods will cause the amplitude value ranges of the two-dimensional seismic line data of different-level structural units or specific study areas within the structural units to be different. This 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, or the largest value range can 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 an enlargement 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 amplitude value range of each two-dimensional seismic line data is within the standard value 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 areas 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 key parameters 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 and the acoustic, 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 greatest, 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: performing 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.

[0104] In the embodiments of the present application, the well 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 can be calibrated with seismic data combined with the landmark stratigraphic interface characteristics on the seismic profile to determine the accurate time-depth relationship.

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

[0107] In an embodiment of the present application, a stratigraphic interpretation of the source rock formation can be performed based on the time-depth relationship and two-dimensional seismic line data, thereby determining the structural interpretation horizon. Using the structural interpretation horizons of the two-dimensional seismic line data, a source rock stratigraphic framework model of the basin can be constructed. In an embodiment of the present application, the source rock stratigraphic framework model can be used to standardize the stratigraphic structure of the target area, and the two-dimensional seismic line data can correspond to the positions of 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 embodiment of the present application, the distribution characteristics of the effective source rock 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: Determine the threshold value of the 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 using the threshold value and the relationship, 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 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, thereby determining the target key parameter distribution characteristics.

[0119] The key parameter corresponding to a total organic carbon content greater than 0.5% can be determined as the 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 formula 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 from the distribution range of total organic carbon content in 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, drilling layer data, logging curves and two-dimensional seismic line data of the target area, which have been drilled in the target area; perform intersection analysis on the total organic carbon content data and the logging curves 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. relationship; performing a stratigraphic interpretation based on the two-dimensional seismic line data and the time-depth relationship to obtain a structural interpretation horizon, and determining a source rock stratum framework model of the target area based on the structural interpretation horizon; performing an 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; 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] Example 2

[0126] Based on the above embodiments, the present invention 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 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 collation and source rock identification key parameters determination.

[0129] Step S1011: collecting and organizing source rock data.

[0130] In areas with low exploration depth, basic drilling data is often scarce. Therefore, it is necessary to collect analytical test data, drilling data, and logging data from wells drilled in and around the basin. This includes analytical test data on the total organic carbon content of the source rocks (similar to the total organic carbon content data in the above embodiment), drilling layer data, and acoustic, density, and natural gamma ray logging data. Considering that these logging data were obtained at different measurement times and using different logging instruments, it is necessary to standardize the collected acoustic, density, and natural gamma ray logging data to eliminate the effects of different measurement times and logging instruments on the logging data and ensure that the logging data are within the same value range.

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

[0132] Based on the analytical test data of the drilled source rocks and the standardized acoustic, density, natural gamma and other logging curves, an intersection analysis of the total organic carbon content of the source rocks and the acoustic, density, natural gamma and other logging curves is carried out to determine the logging curve or the calculation combination of several logging curves with the highest correlation with the total organic carbon content of the source rocks 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 2D seismic line data correction mainly includes two steps: 2D seismic line closure error elimination and amplitude consistency processing, wherein:

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

[0136] In the early stages of exploration, 2D seismic data acquisition and processing within a basin are typically targeted at specific tectonic units at different levels or within specific study areas. Due to varying surface conditions within these units or within specific study areas, the 2D seismic data from these units or within specific study areas exhibit time differences, known as closure errors. When using these 2D seismic data for source rock evaluation within the basin, these errors must be eliminated. Specifically, based on geological understanding, at least two landmark stratigraphic boundaries within the basin are identified. These landmark boundaries are typically easily identified on seismic profiles. Then, the time-depth values ​​corresponding to these landmark stratigraphic boundaries are read from the 2D seismic data for each tectonic unit or within a specific study area, and the specific closure errors are calculated. Finally, the 2D seismic data are time-shifted to eliminate these errors.

[0137] Step S1022: amplitude consistency processing step.

[0138] When acquiring and processing 2D seismic data from different tectonic units within a basin or specific study areas within a tectonic unit, the amplitude ranges of the 2D seismic data vary due to differences in acquisition time, instrumentation, seismic data processing systems, and processing techniques. This can significantly impact the accuracy of effective source rock evaluation using 2D seismic data. Therefore, amplitude consistency processing is necessary for 2D seismic data from different tectonic units or specific study areas within a tectonic unit. Specifically, the amplitude ranges of all 2D seismic data used in the study are statistically analyzed. Then, a standard amplitude range is determined based on the amplitude ranges of all 2D seismic data. Finally, amplitude coefficient correction is used to bring the amplitude ranges of the different 2D seismic data into the same standard range, achieving amplitude consistency for 2D seismic data from different tectonic units or specific study areas within a tectonic unit.

[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 drilled wells, establishment of a framework model of source rock formations in the basin, and inversion of key parameters for identifying source rock using 2D seismic data. Each step is described as follows:

[0141] Step S1031: calibrate the seismic data of the drilled well.

[0142] Well logs are all depth-domain data, while seismic data is both time-domain and depth-domain. 2D seismic line data is typically time-domain data. Therefore, before inverting key seismic parameters, it is important to fully utilize the data from the few wells already drilled within the basin to perform well-seismic calibration to establish an accurate time-depth relationship. Specifically, several landmark stratigraphic boundaries are first identified based on geological understanding. Then, combining drilling layer data with the signature stratigraphic boundary features on seismic profiles, well-seismic calibration of the drilled wells is performed to determine an accurate time-depth relationship.

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

[0144] First, based on 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 basin's source rock strata was constructed using the structural interpretation layers of the two-dimensional seismic data.

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

[0146] Based on the 2D seismic survey line data in the basin, combined with the key parameters for source rock identification and the source rock stratigraphic framework model in the basin, 2D seismic inversion of key parameters for source rock identification was carried out to obtain the distribution characteristics of key parameters for source rock identification in the basin.

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

[0148] In the embodiment of the present application, the relationship between the obtained key parameters for source rock identification and the total organic carbon content, the threshold value of the key parameters for effective source rock identification, and the distribution characteristics of the obtained key parameters for source rock identification within the basin can be used to calculate the distribution range of the total organic carbon content of the source rocks in the basin, and the thickness of the effective source rocks can be statistically calculated, thereby realizing the prediction of the distribution characteristics of the effective source rocks in the basin.

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

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

[0151] (1) The total organic carbon content and other analytical test data, drilling stratification data, and logging curve data of the wells drilled in the basin were collected and organized, and the logging curves of the wells drilled were standardized; then, the intersection analysis of the total organic carbon content of the source rocks and the acoustic wave, density, natural gamma and other logging curves was carried out, and finally the longitudinal wave impedance was determined to be the key parameter for source rock identification. The relationship between total organic carbon content and longitudinal wave impedance, as well as the longitudinal wave impedance threshold value of effective source rocks were obtained.

[0152] (2) The top interface of the Cambrian Qiongzhusi Formation and the bottom interface of the Silurian Formation were selected as marker layers, and the closure error correction of the 2D seismic line data in the basin was performed. Figure 3 A comparison chart before and after calibration is provided in the 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 2D seismic line data is calculated; then, using the amplitude coefficient correction method, the amplitude values ​​of all 2D seismic line data are processed to the same amplitude value range.

[0153] (3) Seismic calibration of the drilled wells was carried out, and the top and bottom interfaces of the Cambrian Qiongzhusi Formation were interpreted on the 2D seismic survey line. Combined with the drilled well data, 2D seismic P-wave impedance inversion was 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 of source rocks in the basin is calculated, and the thickness of 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 for the distribution of effective source rocks provided in the embodiments of the present application can predict the distribution of effective source rocks in a basin when basic data is relatively scarce in the early stages of exploration. This will be beneficial to breakthroughs in oil and gas exploration in low-exploration areas and improve the economic benefits of oil and gas exploration. The method includes: organizing and analyzing source rock data in the basin, standardizing the logging curves of drilled wells, and determining key parameters for source rock identification, the relationship between key parameters and total organic carbon content, and the key parameter threshold values ​​of effective source rocks based on the analytical test data of source rock samples; using two-dimensional seismic data in the basin to perform two-dimensional seismic line data correction; then, performing basin-wide two-dimensional seismic inversion of key parameters for source rock identification to obtain the distribution characteristics of key parameters for source rock identification within the basin; finally, based on the relationship between key parameters for source rock identification and total organic carbon content, the key parameter threshold values ​​of effective source rocks, and the distribution characteristics of key parameters within the basin, the distribution characteristics and thickness of effective source rocks in the basin are statistically calculated, thereby achieving accurate prediction of the distribution of effective source rocks in the basin in low-exploration areas.

[0156] Example 3

[0157] Based on the foregoing embodiments, an embodiment of the present application provides a device for predicting 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), a microprocessor (MPU), a digital signal processor (DSP), or a field programmable gate array (FPGA), etc.

[0158] The present application provides an effective source rock distribution prediction device, which includes:

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

[0160] A first determination module is configured 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 configured 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 configured to perform a horizon structural interpretation based on the two-dimensional seismic line data and the time-depth relationship to obtain a structural interpretation horizon, and determine a source rock stratum framework model of the target area based on the structural interpretation horizon;

[0163] an inversion module, configured to perform inversion calculation on the key parameters based on the two-dimensional seismic line data and the source rock formation framework model, and obtain distribution characteristics of the key parameters in the source rock formation 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 device for predicting the distribution of effective source rocks 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 device for predicting the distribution of effective source rocks is further used to:

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

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

[0171] Determining the closure errors corresponding to the initial two-dimensional seismic line data based on the respective time-depth values;

[0172] Moving each of the initial two-dimensional seismic line data based on the closure errors corresponding to the initial two-dimensional seismic line data to eliminate the closure errors 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 2D seismic line data;

[0176] Determining 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 values ​​of the corresponding two-dimensional seismic line data are corrected based on the correction coefficient to obtain the two-dimensional seismic line data, wherein the amplitude value range of each two-dimensional seismic line data is within the standard value range.

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

[0180] Performing intersection analysis on the total organic carbon content data and the well logging curves 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 target key parameter distribution characteristics based on the threshold value of the key parameter and the distribution characteristics;

[0186] Determining a distribution range of 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 for the distribution of effective hydrocarbon source rocks 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 embodiments of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic 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] Example 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 facilitate 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 a program for a method for predicting the distribution of effective hydrocarbon source rocks stored in the memory, thereby implementing the steps of the method for predicting the distribution of effective hydrocarbon source rocks provided in the above-described 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 the 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 the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. 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 serial numbers of the embodiments of the present application are for description only and do not represent the advantages and disadvantages of the embodiments.

[0194] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0195] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, 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 across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

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

[0198] Those skilled in the art will understand that all or part of the steps of implementing the above-mentioned method embodiments 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 embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROMs), magnetic disks, optical disks, and other 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 functional 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, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling 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 merely an embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for predicting the distribution of effective source rocks, characterized in that: include: Obtaining total organic carbon content data, drilling layer data, well logging curves, and two-dimensional seismic line data of wells drilled in 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 horizon structural interpretation based on the two-dimensional seismic line data and the time-depth relationship to obtain a structural interpretation horizon, and determining a source rock stratum framework model of the target area based on the structural interpretation horizon; Performing inversion calculation on the key parameters based on the two-dimensional seismic line data and the source rock formation framework model to obtain distribution characteristics of the key parameters in the source rock formation 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: Acquiring initial two-dimensional seismic line data for different areas in the target area; Determining, based on at least two landmark stratigraphic interfaces in the target area, time-depth values ​​corresponding to the two landmark stratigraphic interfaces on each initial two-dimensional seismic line data; Determining the closure errors corresponding to the initial two-dimensional seismic line data based on the respective time-depth values; Moving each of the initial two-dimensional seismic line data based on the closure errors corresponding to the initial two-dimensional seismic line data to eliminate the closure errors of the initial two-dimensional seismic line data and 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 includes: Count the range of amplitude values ​​of all intermediate 2D seismic line data; Determining 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 values ​​of the corresponding two-dimensional seismic line data are corrected based on the correction coefficient to obtain the two-dimensional seismic line data, wherein the amplitude value range of each two-dimensional seismic line data is within the standard value range.

5. The method according to claim 1, wherein The performing intersection analysis on the total organic carbon content data and the well logging curve to determine the target well logging curve includes: Performing intersection analysis on the total organic carbon content data and the well logging curves 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, wherein 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 target key parameter distribution characteristics based on the threshold value of the key parameter and the distribution characteristics; Determining a distribution range of 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 device for predicting the distribution of effective hydrocarbon source rocks, characterized in that: include: An acquisition module is used to acquire total organic carbon content data, drilling layer data, well logging curves and two-dimensional seismic line data of the target area of ​​the wells drilled in the target area; A first determination module is configured 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 configured 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 configured to perform a horizon structural interpretation based on the two-dimensional seismic line data and the time-depth relationship to obtain a structural interpretation horizon, and determine a source rock stratum framework model of the target area based on the structural interpretation horizon; an inversion module, configured to perform inversion calculation on the key parameters based on the two-dimensional seismic line data and the source rock formation framework model, and obtain distribution characteristics of the key parameters in the source rock formation 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 device for predicting the distribution of effective hydrocarbon source rocks 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: The method comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the method for predicting the distribution of effective hydrocarbon 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 hydrocarbon source rocks as claimed in any one of claims 1 to 7.

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