A method and device for predicting effective hydrocarbon source rocks, a storage medium and an electronic device

Abstract

The present application provides a kind of effective hydrocarbon source rock prediction method, device, storage medium and electronic equipment.The effective hydrocarbon source rock prediction method comprises: determining the target layer section of potential hydrocarbon source rock development in the sequence stratigraphic framework of target area;Potential hydrocarbon source rock thickness in the target layer section is predicted;Organic carbon content and vitrinite reflectance in the target layer section are predicted;Based on the potential hydrocarbon source rock thickness, organic carbon content and vitrinite reflectance in the target layer section, the distribution area of effective hydrocarbon source rock in the target layer section is determined.The problem that effective hydrocarbon source rock cannot be accurately predicted in low exploration degree area in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of oil and gas geology, specifically to a method, apparatus, storage medium, and electronic equipment for predicting effective source rocks. Background Technology

[0002] Currently, low-exploration areas include deep strata, deep water, and peripheral new areas. These areas have few wells and limited measured data, with only some 2D seismic survey lines. Whether effective source rocks are developed is the main basis for future exploration decisions. Effective source rocks generally refer to rocks that can generate and expel hydrocarbons and contribute to oil and gas accumulation.

[0003] In the prediction of potential source rocks, due to the limited number of wells, it is difficult to obtain a large amount of analytical data through planar uniform sample collection and experimental testing instruments. Therefore, statistical methods based on organic geochemistry are not feasible, and it is necessary to fully explore the information on the seismic response to source rocks. Previous studies have mostly traced the bottom interface of source rock terms through seismic reflection characteristics and seismic equivalence, but there is a relative lack of division and comparison of the overall sequence stratigraphic framework and identification of the initial marine (lacustral) flooding surface, the maximum lacustrine flooding surface, and condensation intervals (dense intervals). Methods such as velocity-lithology calculation models and multivariate seismic attribute regression models have been explored, but they are still insufficient in terms of drawing on data from adjacent areas with similar geological backgrounds.

[0004] In TOC (Total Organic Carbon) prediction, based on the specific responses of different logging techniques to source rocks, various effective prediction models have been explored according to the actual geological conditions of the specific study area. The ΔLogR method and the multiple regression method are relatively mature. The ΔLogR method requires a large amount of measured TOC data and is relatively cumbersome to operate, making it unsuitable for areas with low exploration levels. The multiple regression method can establish a full-well-section prediction model using shallow data and has high accuracy, but there are instances where the accuracy of a single-well model is higher than that of the comprehensive model. Regarding seismic prediction methods, amplitude and frequency seismic attributes are relatively sensitive to the response of lithology and organic matter. Reliable prediction models can be established through attribute combination optimization.

[0005] Regarding Ro (vitrinite reflectance) prediction, estimations can be made based on the relationship between shallow burial depth and measured Ro, as well as empirical formulas relating methane carbon isotopes and Ro from discovered oil and gas in adjacent areas. Basin simulation studies can also be conducted. In well logging technology, various maturity geochemical indices do not provide direct responses, and no relevant quantitative prediction models have been found. In seismic technology, maturity and mudstone porosity are generally considered to be related to burial history. Based on this, prediction models between seismic layer velocity and maturity have been explored. However, source rocks often exhibit undercompaction due to fluid retention, leading to deviations in prediction results.

[0006] In summary, existing technologies are mainly aimed at predicting potential source rocks in areas with high exploration levels or under special geological backgrounds. They rely on organic geochemical analysis of a large amount of experimental data and are not applicable to areas with low exploration levels. There are also no relevant records on the prediction of effective source rocks in areas with low exploration levels. Summary of the Invention

[0007] To address the problem that existing technologies cannot accurately predict effective source rocks in areas with low exploration levels, this invention provides a method, apparatus, storage medium, and electronic device for predicting effective source rocks.

[0008] In a first aspect, embodiments of the present invention provide a method for predicting effective source rocks, comprising:

[0009] Identify the target stratigraphic intervals within the sequence stratigraphic framework of the target area to determine the potential source rock development;

[0010] Predict the thickness of potential source rocks in the target strata;

[0011] Predict the organic carbon content and vitrinite reflectance in the target layer;

[0012] Based on the thickness of potential source rocks, organic carbon content, and vitrinite reflectance in the target stratigraphic section, the distribution area of ​​effective source rocks in the target stratigraphic section is determined.

[0013] In some embodiments, identifying the target interval for potential source rock development within the sequence stratigraphic framework includes:

[0014] The stratigraphic interval defined between the initial sea / lake flooding surface (FFS) and the maximum sea / lake flooding surface (MFS) was identified as the target interval for potential source rock development.

[0015] In some implementations, predicting the thickness of potential source rocks in the target stratum includes:

[0016] Calculate the thickness of the target segment;

[0017] Determine the mud-to-soil ratio of the target layer;

[0018] Based on the target layer thickness and the mudstone ratio, the thickness of potential source rocks in the target layer is predicted.

[0019] In some implementations, calculating the target segment thickness includes:

[0020] The thickness of the target layer is calculated using a first formula, which is as follows:

[0021] H = V2t2 - V1t1

[0022] Where H is the target layer thickness;

[0023] V2 is the root mean square velocity of the initial sea / lake flooding surface FFS reflection in phase axis;

[0024] t2 is the one-way travel time of the initial sea / lake flooding surface FFS reflection in phase axis;

[0025] V1 is the root mean square velocity of the reflection in phase axis of the maximum sea / lake flooding surface MFS;

[0026] t1 is the one-way travel time of the reflection phase axis of the maximum sea / lake flooding surface MFS.

[0027] In some implementations, determining the mud-to-soil ratio of the target layer includes:

[0028] Based on the Dix formula and the root mean square velocity of each interface in the target segment, the layer velocity of the target segment is calculated.

[0029] Based on the layer velocity of the target layer, the root mean square velocity of the mudstone, and the root mean square velocity of the sandstone, the mud-to-soil ratio of the target layer is determined.

[0030] The mud-soil ratio of the target layer is corrected based on existing statistical patterns of mud-soil ratio.

[0031] In some implementations, each root mean square velocity is determined in the following manner:

[0032] For horizontal formations, the root mean square velocity is equal to the superposition velocity;

[0033] For inclined strata, the stacking velocity is converted into the root mean square velocity using a preset conversion formula, which is as follows:

[0034]

[0035] Among them, V r The root mean square velocity;

[0036] V s For superposition speed;

[0037] L is the horizontal distance between two adjacent seismic traces on the same reflection phase axis;

[0038] Δt0 is the time difference between the same reflection phase axis on two adjacent seismic traces with a horizontal spacing of L.

[0039] In some embodiments, predicting the organic carbon content and vitrinite reflectance in the target layer includes:

[0040] Based on the seismic properties of the target segment and the pre-established relationship between organic carbon content and seismic properties, the organic carbon content in the target segment is predicted.

[0041] The vitrinite reflectance in the target layer is predicted by using a pre-established relationship between vitrinite reflectance and burial depth.

[0042] In some embodiments, determining the distribution area of ​​effective source rocks in the target stratigraphic segment based on the thickness of potential source rocks, organic carbon content, and vitrinite reflectance in the target stratigraphic segment includes:

[0043] The region within the target stratigraphic segment that simultaneously meets the preset conditions is identified as the distribution area of ​​effective source rocks;

[0044] The preset conditions include:

[0045] To reach the potential thickness of the source rock;

[0046] The organic carbon content is not less than the first threshold; and

[0047] The reflectance of the vitrinite group is not less than the second threshold.

[0048] Secondly, embodiments of the present invention provide an effective hydrocarbon source rock prediction device, comprising:

[0049] The first determining module is used to determine the target stratigraphic intervals for potential source rock development within the sequence stratigraphic framework of the target area;

[0050] The first prediction module is used to predict the thickness of potential source rocks in the target layer;

[0051] The second prediction module is used to predict the organic carbon content and vitrinite reflectance in the target layer.

[0052] The second determining module is used to determine the distribution area of ​​effective source rocks in the target layer based on the thickness of potential source rocks, organic carbon content and vitrinite reflectance in the target layer.

[0053] Thirdly, embodiments of the present invention provide a storage medium storing a computer program, which, when executed by one or more processors, implements the method for predicting effective source rocks as described in the first aspect.

[0054] Fourthly, embodiments of the present invention provide 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, it implements the method for predicting effective source rocks as described in the first aspect.

[0055] Compared with the prior art, one or more embodiments of the present invention can bring at least the following beneficial effects:

[0056] Addressing the current situation of limited drilling and measured data in low-exploration areas, this invention provides a method for predicting effective source rocks based on seismic data and existing domestic exploration knowledge. This method utilizes predictions of three indicators: potential source rock distribution, total organic carbon (TOC) content, and vitrinite reflectance (Ro). This provides support for comprehensive hydrocarbon accumulation research and exploration deployment decisions. The method involves identifying target stratigraphic intervals within the sequence stratigraphic framework of the target area; predicting the thickness of potential source rocks within these intervals; predicting the TOC content and vitrinite reflectance within these intervals; and determining the distribution area of ​​effective source rocks within these intervals based on the TOC thickness, TOC content, and vitrinite reflectance. This invention integrates information from seismic velocity, seismic properties, geostatistics, and empirical formulas, possessing a sound theoretical and practical foundation for hydrocarbon accumulation research and exploration deployment decisions. Attached Figure Description

[0057] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 This is a flowchart of an effective method for predicting source rocks of hydrocarbons provided in an embodiment of the present invention;

[0059] Figure 2 This is a flowchart of the process for predicting the thickness of potential source rocks in a target layer, provided in an embodiment of the present invention.

[0060] Figure 3 This is the identification result of the sequence stratigraphic framework and FFS / MFS interfaces in a certain region provided by an embodiment of the present invention;

[0061] Figure 4 This is existing statistical data on the mud-soil ratio of different facies zones in terrestrial lacustrine basins provided in the embodiments of the present invention;

[0062] Figure 5 These are the relative value change characteristics analysis data of various seismic attributes provided in the embodiments of the present invention;

[0063] Figure 6 This is a fitting diagram of the burial depth and Ro in a certain area provided by an embodiment of the present invention;

[0064] Figure 7 This is a predicted distribution map of effective source rocks in a certain region and a certain stratum provided in an embodiment of the present invention;

[0065] Figure 8This is a block diagram of an effective hydrocarbon source rock prediction device provided in an embodiment of the present invention. Detailed Implementation

[0066] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0067] Example 1

[0068] Figure 1 A flowchart of a method for predicting effective source rocks is shown, as follows: Figure 1 As shown, this embodiment provides a method for predicting effective source rocks, applicable to the prediction of effective source rocks in areas with low exploration levels. The method may include steps S110 to S140:

[0069] Step S110: Identify the target stratigraphic intervals for potential source rock development within the sequence stratigraphic framework of the target area.

[0070] Based on existing exploration, source rocks typically develop in deeper-water sedimentary environments. On the one hand, deeper-water sedimentary environments have higher paleoproductivity and are rich in organic matter; on the other hand, the hydrodynamic conditions in deeper-water sedimentary environments are weak, creating oxygen-deficient and reducing conditions, which are conducive to the preservation of organic matter.

[0071] Within the sequence stratigraphic framework, the basal portions of the marine / lacustral systems tract (EST) and the highstand systems tract (HST) belong to relatively deep-water sedimentary environments, which are most favorable locations for hydrocarbon source rock development. On seismic profiles, the basal boundary of these relatively deep-water sedimentary environments is the initial marine (lacustral) flooding surface (FFS), characterized by an overlapping phase axis that first crosses the littoral undulation zone; the apex boundary is the maximum marine (lacustral) flooding surface (MFS), which develops condensed sections and is characterized by a basal phase axis of the underlapping surface (see attached diagram). Figure 1 The two interfaces are characterized by strong to moderate amplitude, high to moderate frequency, good to moderate continuity, and disordered weak reflection. Accordingly, in some embodiments, target intervals for potential source rock development are determined within the sequence stratigraphic framework, including: defining the interval defined between the initial sea / lacustrine flooding surface (FFS) and the maximum sea / lacustrine flooding surface (MFS) as the target interval for potential source rock development.

[0072] Step S120: Predict the thickness of potential source rocks in the target layer.

[0073] Figure 2 A flowchart for predicting the thickness of potential source rocks in a target stratigraphic segment is shown, such as... Figure 2 As shown, in some embodiments, predicting the thickness of potential source rocks in the target layer may include steps S120-1 to S120-3:

[0074] Step S120-1: Calculate the thickness of the target layer.

[0075] In some implementations, calculating the target segment thickness may include:

[0076] The thickness of the target layer is calculated using the first formula, which is as follows:

[0077] H = V2t2 - V1t1

[0078] Where H is the target layer thickness, in meters;

[0079] V2 is the root mean square velocity of the initial sea / lake flooding surface FFS reflection in phase axis, in m / s;

[0080] t2 is the one-way travel time of the initial sea / lake flooding surface FFS reflection phase axis, in seconds;

[0081] V1 is the root mean square velocity of the reflection phase axis of the maximum sea / lake flooding surface MFS, in m / s;

[0082] t1 is the one-way travel time of the reflection phase axis of the maximum sea / lake flooding surface MFS, in seconds.

[0083] In some implementations, each root mean square velocity is determined in the following manner:

[0084] (1) For horizontal strata, the root mean square velocity is equal to the stacking velocity, which can be obtained directly from seismic data.

[0085] (2) For inclined strata, the stacking velocity is converted into the root mean square velocity using a preset conversion formula, which is as follows:

[0086]

[0087] Among them, V r The root mean square velocity is in m / s;

[0088] V s The velocity is the superposition velocity, in m / s;

[0089] L is the horizontal distance between two adjacent seismic traces on the same reflection phase axis;

[0090] Δt0 is the time difference, in seconds, between the same reflection phase axis on two adjacent seismic traces with a horizontal spacing of L.

[0091] Step S120-2: Determine the mud-to-soil ratio of the target layer.

[0092] In some implementations, determining the mud-to-soil ratio of the target layer may include steps S120-2a to S120-2c:

[0093] Step S120-2a: Calculate the layer velocity of the target layer based on the Dix formula and the root mean square velocity of each interface in the target layer.

[0094] The Dix formula is as follows:

[0095]

[0096] Among them, V int The velocity is the layer velocity, in m / s;

[0097] v r，n Let be the root mean square velocity of the nth interface in the target layer, in m / s;

[0098] v r，n-1 The root mean square velocity (RMS) of the (n-1)th interface in the target layer, in m / s;

[0099] t 0，n Let be the round-trip travel time (s) for the nth interface in the target layer.

[0100] t 0，n-1 Let s be the round-trip travel time of the (n-1)th interface in the target layer.

[0101] Step S120-2b: Determine the mudstone-to-soil ratio of the target layer based on the layer velocity of the target layer, the root mean square velocity of the mudstone, and the root mean square velocity of the sandstone.

[0102] Based on the time-averaged equation, the relationships between layer velocity and relative mudstone content, root-mean-square velocity of pure mudstone, and root-mean-square velocity of pure sandstone (e.g., carbonate rock) are determined:

[0103]

[0104] Among them, V int The velocity is the layer velocity, in m / s;

[0105] P m This refers to the relative mudstone content in a two-phase medium, also known as the mudstone-to-land ratio, such as the ratio of mudstone layer thickness to stratum thickness.

[0106] V m is the root mean square velocity of pure mudstone, in m / s;

[0107] V n denoted as the root mean square velocity of pure sandstone, in m / s.

[0108] Based on the above relationship and the calculated layer velocity, the P of the target layer can be calculated. m .

[0109] It should be understood that the root mean square velocity (RMS) of each interface in the target layer (including the RMS velocity of the nth interface and the (n-1)th interface), as well as the RMS velocity of pure mudstone and pure sandstone, can also be determined in the following way:

[0110] (1) For horizontal strata, the root mean square velocity is equal to the stacking velocity, which can be obtained directly from seismic data.

[0111] (2) For inclined strata, the transformation relationship is preset. Convert the superposition velocity into the root mean square velocity.

[0112] Step S120-2c: Correct the mud-soil ratio of the target layer by combining the existing statistical laws of mud-soil ratio.

[0113] In some cases, the mud-to-soil ratio of the target layer calculated above may not accurately reflect the mud-to-soil ratio of the target layer. Therefore, by combining the geological background, the calculated mud-to-soil ratio of the target layer can be further corrected so that the corrected mud-to-soil ratio conforms to the geological background.

[0114] In practical applications, the mud-to-soil ratio of the target stratigraphic segment can be corrected by combining existing statistical patterns of mud-to-soil ratio. These existing patterns can be obtained from regions with the same or similar geological background as the target stratigraphic segment, such as regions with the same or similar sedimentary facies. By analyzing the planar distribution of mud-to-soil ratios in regions with the same or similar geological background, the mud-to-soil ratio of the target stratigraphic segment can be determined. The mud-to-soil ratio at the corresponding position in the existing statistical patterns can be used to replace the calculated mud-to-soil ratio, or the mud-to-soil ratio at the corresponding position in the existing statistical patterns can be summed with the calculated mud-to-soil ratio, and the average value can be taken as the corrected mud-to-soil ratio. This will result in a mud-to-soil ratio that more accurately reflects the true geological conditions.

[0115] Step S120-3: Based on the target layer thickness and mud-land ratio, predict the thickness of potential source rocks in the target layer.

[0116] h = H × P m

[0117] Where h is the thickness of the potential source rock, in meters.

[0118] Step S130: Predict the organic carbon content and vitrinite reflectance in the target layer.

[0119] In some implementations, predicting the organic carbon content and vitrinite reflectance in the target layer includes:

[0120] (a) Based on the seismic properties of the target segment and the pre-established relationship between organic carbon content and seismic properties, predict the total organic carbon (TOC) content in the target segment.

[0121] Significant differences exist in seismic properties between source rocks and non-source rocks, as well as between source rocks at different stratigraphic levels, especially in amplitude and frequency seismic properties, which can reflect the level of total organic carbon (TOC). In areas with low exploration levels, the relationship between TOC and seismic properties of source rocks can be established.

[0122] In practical applications, the extracted seismic attributes can be normalized first, and then the relative values ​​of seismic attributes at different station numbers along the seismic survey line can be statistically analyzed. The n seismic attributes that best match the actual geological conditions can be selected. Combined with a small amount of measured organic carbon content data from shallow layers, the following multiple linear regression model can be constructed:

[0123] TOC=a·A1+b·A2+c·A3+…+n·A n +C

[0124] Where: TOC represents organic carbon content, %;

[0125] A1, A2, A3…A n These represent the first to nth earthquake attributes, respectively.

[0126] a, b, c, and n are the fitting coefficients;

[0127] C is a constant.

[0128] Furthermore, by using the seismic attributes of the target segment obtained from seismic data and the pre-established relationship between organic carbon content and seismic attributes (i.e., the aforementioned multiple linear regression model), the organic carbon content in the target segment can be predicted.

[0129] (b) Using the pre-established relationship between vitrinite reflectance and burial depth, predict the vitrinite reflectance (Ro) in the target layer.

[0130] Based on the existing burial depth of shallow source rocks and the measured vitrinite reflectance data, the following empirical formula can be established:

[0131] R o =α·exp (β·D)

[0132] Among them, R o The reflectance of the vitrinite group is expressed as a percentage.

[0133] D is the burial depth of the source rock, in meters;

[0134] α and β are fitting coefficients.

[0135] Based on the above empirical formula, the vitrinite reflectance R can be calculated according to the obtained burial depth. o .

[0136] In some cases, the calculated vitrinite reflectance R o It may not accurately reflect the true reflectance R of the outgoing image group. o Therefore, if oil and gas are discovered in a neighboring area, the vitrinite reflectance can be estimated using the following empirical formula to correct the calculated vitrinite reflectance and verify the aforementioned calculation results.

[0137] Saprophytic gas: δ 13 C1 = 21.71 × lgR o -43.31

[0138] Humic gas: δ 13 C1 = 40.49 × lgR o -34.00

[0139] Where, δ 13 C1 is a carbon isotope of methane, ‰;

[0140] R o —Vitreous reflectance, %.

[0141] In practical applications, the specific methods for correcting the calculated vitrinite reflectance can be as follows: Sum the vitrinite reflectances calculated using different empirical formulas and take the average value as the corrected vitrinite reflectance, which is then used as the predicted vitrinite reflectance in the target layer. Alternatively, the empirical formulas for saprophytic gas or humic gas can be used to back-calculate R. o Verify and revise the empirical formula R. o =α·exp (β·D) .

[0142] Step S140: Based on the thickness of potential source rocks, organic carbon content and vitrinite reflectance in the target layer, determine the distribution area of ​​effective source rocks in the target layer.

[0143] In some implementations, the distribution area of ​​effective source rocks in the target stratigraphic section is determined based on the thickness of potential source rocks, organic carbon content, and vitrinite reflectance, including:

[0144] The region within the target stratigraphic segment that simultaneously meets preset conditions is identified as the distribution area of ​​effective source rocks. In some implementations, the preset conditions include:

[0145] To reach the potential thickness of the source rock;

[0146] The organic carbon content is not less than the first threshold; and

[0147] The reflectance of the vitrinite group is not less than the second threshold.

[0148] For an effective source rock to be considered a valid hydrocarbon source rock, it must first have the potential for hydrocarbon generation, meaning it must have reached the stage of thermal maturity and evolution, activating its hydrocarbon generation potential, and the vitrinite reflectance (Ro) must be above 0.7%. Secondly, it must have the potential for hydrocarbon accumulation, meaning it must be able to overcome the binding effect of hydrocarbon adsorption and release hydrocarbons, and the total organic carbon (TOC) content must be above 1.0%. Therefore, the first threshold could be 1.0%, and the second threshold could be 0.7%. Based on the above predictions, contour maps with TOC ≥ 1.0% and vitrinite reflectance (Ro) ≥ 0.7% can be overlaid on a potential source rock distribution map, and the overlapping area can be identified as the effective source rock distribution area. It should be understood that the source rock distribution map is determined based on the thickness of the potential source rock.

[0149] The method provided in this embodiment, based on the principle of sequence stratigraphy, limits the favorable development interval of potential source rocks to the period between the initial sea / lake flooding surface (FFS) and the maximum sea / lake flooding surface (MFS); based on the principle of seismic exploration, it uses seismic velocity information to predict the mud-soil ratio; based on the correlation between seismic attributes and TOC, it uses multiple linear regression and parameter optimization to predict TOC; and combined with existing exploration knowledge and empirical formulas, it uses multiple methods to predict key parameters of potential source rocks, cross-validating each other to improve the reliability of the results.

[0150] Based on the prediction of the mud-to-soil ratio in earthquakes, and combined with the statistical regularity of the mud-to-soil ratio under similar geological backgrounds, the planar distribution characteristics of the mud-to-soil ratio are determined. In the prediction of TOC, a regression model between TOC and multiple seismic attributes is constructed by making full use of a small amount of shallow measured data to achieve high prediction accuracy. In the prediction of Ro, the planar distribution of Ro is determined by comprehensively using two sets of empirical formulas for Ro, depth, and isotopes.

[0151] The effective source rock distribution is determined by three indicators: potential source rock distribution, TOC (Total Hydrocarbon Content), and Ro (Ro content). A lower limit of 1.0% for TOC and 0.7% for Ro are used, both based on extensive existing exploration experience. This invention integrates information from various aspects, including seismic velocity, seismic properties, geostatistical laws, and empirical formulas, and has a sound theoretical and practical foundation.

[0152] Example 2

[0153] This embodiment illustrates an application example of the crack identification method described in the above embodiments:

[0154] Predicting the planar distribution of effective source rocks in a basin area, including:

[0155] First, using specialized seismic interpretation software, a sequence stratigraphic framework for the Yanchang Formation was established, and system tracts were delineated. Sequence boundaries, FFS, and MFS were identified, such as... Figure 3 As shown, SB1 to SB8 represent interfaces, and SQ1 to SQ5 represent hierarchical sequences.

[0156] Secondly, the thickness of the target segment is predicted through stratigraphic tracing interpretation and the time-depth conversion relationship (H = V2t2 - V1t1); the conversion between the root mean square velocity and the stacking velocity of horizontal / dipping strata is calculated, combined with... Figure 4 The existing statistical data on mud-soil ratios in different facies zones of terrestrial lacustrine basins (statistical histograms of mud-soil ratios within each systems tract of different sedimentary facies) are shown. The predicted mud-soil ratio is obtained by multiplying the target layer thickness by the mud-soil ratio, which is between 100 and 300 meters. Figure 7 As shown.

[0157] Furthermore, by extracting and normalizing nine seismic attributes (amplitude, frequency, and energy) within the target segment, three seismic attributes—average energy, maximum amplitude, and root mean square amplitude—that best match the distribution characteristics of the lacustrine basin facies were selected. Figure 5 As shown. The three seismic attributes—average energy, maximum amplitude, and root mean square amplitude—are combined with six measured TOC data points to construct the following multiple linear regression model:

[0158] TOC = a·A1 + b·A2 + c·A3 + C

[0159] Among them, A1, A2, and A3 are the three seismic attributes: average energy, maximum amplitude, and root mean square amplitude, respectively.

[0160] Table 1 shows the fitted data of the relative values ​​of TOC and seismic attributes for a certain section in a certain region:

[0161] Table 1. Calculation of relative values ​​of TOC and seismic attributes for a certain section in a certain region.

[0162]

[0163] The constructed multiple linear regression model achieved a correlation of over 90%, and its application predicted that the total organic carbon (TOC) content would be between 0.5% and 2.0%. Figure 7 As shown.

[0164] Based on 40 measured Ro data points collected from shallow layers, an empirical formula R for the vitrinite reflectance Ro was established. o =α·exp (β·D) ,like Figure 6 As shown in Table 2, Ro was calculated by back-calculating methane carbon isotope data of oil and gas discovered in onshore areas. The calculated data are shown in Table 2. The empirical formula was verified and corrected, and the results show that Ro is mainly between 0.6% and 1.0%.

[0165] Table 2 Natural Gas δ in a Certain Region 13 C1 and Ro calculation data table

[0166]

[0167] Based on the source rock distribution map determined by the potential source rock thickness, overlapping areas with TOC ≥ 1.0% and Ro ≥ 0.7% are identified as effective source rock distribution areas, mainly distributed in the central and northern parts of the basin and some areas in the eastern part. Figure 7 As shown.

[0168] Example 3

[0169] Figure 8 A block diagram of a device for predicting effective source rocks is shown, applicable to the prediction of effective source rocks in areas with low exploration levels, such as... Figure 8 As shown, this embodiment provides an effective hydrocarbon source rock prediction device, including:

[0170] The first determining module 810 is used to determine the target stratigraphic intervals for potential source rock development within the sequence stratigraphic framework of the target area.

[0171] The first prediction module 820 is used to predict the thickness of potential source rocks in the target layer based on the stacking rate.

[0172] The second prediction module 830 is used to predict the organic carbon content and vitrinite reflectance in the target layer.

[0173] The second determining module 840 is used to determine the distribution area of ​​effective source rocks in the target layer based on the thickness of potential source rocks, organic carbon content and vitrinite reflectance in the target layer.

[0174] It should be understood that the first determining module 810 can be used to execute step S110 in Embodiment 1, the first predicting module 820 can be used to execute step S120 in Embodiment 1, the second predicting module 830 can be used to execute step S130 in Embodiment 1, and the second determining module 840 can be used to execute step S140 in Embodiment 1.

[0175] When using the apparatus of this embodiment to predict effective source rocks, the process includes:

[0176] The first determining module 810 is used to determine the target stratigraphic intervals for potential source rock development within the sequence stratigraphic framework of the target area.

[0177] In some implementations, identifying target intervals for potential source rock development within a sequence stratigraphic framework includes: defining the interval defined between the initial sea / lake flooding surface (FFS) and the maximum sea / lake flooding surface (MFS) as the target interval for potential source rock development.

[0178] The first prediction module 820 is used to predict the thickness of potential source rocks in the target layer.

[0179] In some implementations, when the first prediction module 820 predicts the thickness of potential source rocks in the target section, it is specifically used to: calculate the thickness of the target section, determine the mud-to-soil ratio of the target section, and predict the thickness of potential source rocks in the target section based on the thickness of the target section and the mud-to-soil ratio.

[0180] In practical applications, the thickness of the target layer can be calculated using the first formula, which is as follows:

[0181] H = V2t2 - V1t1

[0182] Where H is the target layer thickness, in meters;

[0183] V2 is the root mean square velocity of the initial sea / lake flooding surface FFS reflection in phase axis, in m / s;

[0184] t2 is the one-way travel time of the initial sea / lake flooding surface FFS reflection phase axis, in seconds;

[0185] V1 is the root mean square velocity of the reflection phase axis of the maximum sea / lake flooding surface MFS, in m / s;

[0186] t1 is the one-way travel time of the reflection phase axis of the maximum sea / lake flooding surface MFS, in seconds.

[0187] In some implementations, each root mean square velocity is determined in the following manner:

[0188] (1) For horizontal strata, the root mean square velocity is equal to the stacking velocity, which can be obtained directly from seismic data.

[0189] (2) For inclined strata, the stacking velocity is converted into the root mean square velocity using a preset conversion formula, which is as follows:

[0190]

[0191] Among them, V r The root mean square velocity is in m / s;

[0192] V s The velocity is the superposition velocity, in m / s;

[0193] L is the horizontal distance between two adjacent seismic traces on the same reflection phase axis;

[0194] Δt0 is the time difference, in seconds, between the same reflection phase axis on two adjacent seismic traces with a horizontal spacing of L.

[0195] In some implementations, when the first prediction module 820 determines the mud-soil ratio of the target layer, it may include:

[0196] Based on the Dix formula and the root mean square velocity of each interface in the target segment, the layer velocity of the target segment is calculated.

[0197] The Dix formula is as follows:

[0198]

[0199] Among them, V int The velocity is the layer velocity, in m / s;

[0200] v r，n Let be the root mean square velocity of the nth interface in the target layer, in m / s;

[0201] v r，n-1 The root mean square velocity (RMS) of the (n-1)th interface in the target layer, in m / s;

[0202] t 0，n Let be the round-trip travel time (s) for the nth interface in the target layer.

[0203] t 0，n-1 Let s be the round-trip travel time of the (n-1)th interface in the target layer.

[0204] The mudstone-to-soil ratio of the target layer is determined based on the layer velocity, root mean square velocity of mudstone, and root mean square velocity of sandstone.

[0205] Based on the time-averaged equation, the relationship between layer velocity and relative mudstone content, pure mudstone, and pure sandstone (e.g., carbonate rocks) is determined:

[0206]

[0207] Among them, V int The velocity is the layer velocity, in m / s;

[0208] P m This refers to the relative mudstone content in a two-phase medium, also known as the mudstone-to-land ratio, such as the ratio of mudstone layer thickness to stratum thickness.

[0209] V m is the root mean square velocity of pure mudstone, in m / s;

[0210] V n denoted as the root mean square velocity of pure sandstone, in m / s.

[0211] Based on the above relationship and the calculated layer velocity, the P of the target layer can be calculated. m .

[0212] The mud-soil ratio of the target layer is corrected based on existing statistical patterns of mud-soil ratio.

[0213] Based on the target layer thickness and mud-land ratio, the thickness of potential source rocks in the target layer is predicted.

[0214] h = H × P m

[0215] Where h is the thickness of the potential source rock, in meters.

[0216] In some embodiments, when the second prediction module 830 predicts the organic carbon content and vitrinite reflectance in the target layer, it includes:

[0217] (a) Based on the seismic properties of the target segment and the pre-established relationship between organic carbon content and seismic properties, predict the total organic carbon (TOC) content in the target segment.

[0218] Significant differences exist in seismic properties between source rocks and non-source rocks, as well as between source rocks at different stratigraphic levels, especially in amplitude and frequency seismic properties, which can reflect the level of total organic carbon (TOC). In areas with low exploration levels, the relationship between TOC and seismic properties of source rocks can be established.

[0219] In practical applications, the extracted seismic attributes can be normalized first, and then the relative values ​​of seismic attributes at different station numbers along the seismic survey line can be statistically analyzed. The n seismic attributes that best match the actual geological conditions can be selected. Combined with a small amount of measured organic carbon content data from shallow layers, the following multiple linear regression model can be constructed:

[0220] TOC=a·A1+b·A2+c·A3+…+n·A n +C

[0221] Where: TOC represents organic carbon content, %;

[0222] A1, A2, A3…A n These represent the first to nth earthquake attributes, respectively.

[0223] a, b, c, and n are the fitting coefficients;

[0224] C is a constant.

[0225] Furthermore, by using the seismic attributes of the target segment obtained from seismic data and the pre-established relationship between organic carbon content and seismic attributes (i.e., the aforementioned multiple linear regression model), the organic carbon content in the target segment can be predicted.

[0226] (b) Using the pre-established relationship between vitrinite reflectance and burial depth, predict the vitrinite reflectance (Ro) in the target layer.

[0227] Based on the existing burial depth of shallow source rocks and the measured vitrinite reflectance data, the following empirical formula can be established:

[0228] R o =α·exp (β·D)

[0229] Among them, R o The reflectance of the vitrinite group is expressed as a percentage.

[0230] D is the burial depth of the source rock, in meters;

[0231] α and B are the fitting coefficients.

[0232] Based on the above empirical formula, the vitrinite reflectance R can be calculated according to the obtained burial depth. o .

[0233] In some cases, the calculated vitrinite reflectance R o It may not accurately reflect the true reflectance R of the outgoing image group. o Therefore, if oil and gas are discovered in a neighboring area, the vitrinite reflectance can be estimated using the following empirical formula to correct the calculated vitrinite reflectance and verify the aforementioned calculation results.

[0234] Saprophytic gas: δ 13 C1 = 21.71 × lgR o -43.31

[0235] Humic gas: δ 13 C1 = 40.49 × lgR o -34.00

[0236] Where, δ 13 C1 is a carbon isotope of methane, ‰;

[0237] R o —Vitreous reflectance, %.

[0238] In practical applications, the specific methods for correcting the calculated vitrinite reflectance can be as follows: Sum the vitrinite reflectances calculated using different empirical formulas and take the average value as the corrected vitrinite reflectance, which is then used as the predicted vitrinite reflectance in the target layer. Alternatively, the empirical formulas for saprophytic gas or humic gas can be used to back-calculate R. o Verify and revise the empirical formula R. o =α·exp (β·D) .

[0239] In some embodiments, when the second determining module 840 determines the distribution area of ​​effective source rocks in the target layer based on the thickness of potential source rocks, organic carbon content, and vitrinite reflectance in the target layer, it includes:

[0240] The region within the target stratigraphic segment that simultaneously meets preset conditions is identified as the distribution area of ​​effective source rocks. In some implementations, the preset conditions include:

[0241] To reach the potential thickness of the source rock;

[0242] The organic carbon content is not less than the first threshold; and

[0243] The reflectance of the vitrinite group is not less than the second threshold.

[0244] For an effective source rock to be considered a valid hydrocarbon source rock, it must first have the potential for hydrocarbon generation, meaning it must have reached the stage of thermal maturity and evolution, activating its hydrocarbon generation potential, and the vitrinite reflectance (Ro) must be above 0.7%. Secondly, it must have the potential for hydrocarbon accumulation, meaning it must be able to overcome the binding effect of hydrocarbon adsorption and release hydrocarbons, and the total organic carbon (TOC) content must be above 1.0%. Therefore, the first threshold could be 1.0%, and the second threshold could be 0.7%. Based on the above predictions, contour maps with TOC ≥ 1.0% and vitrinite reflectance (Ro) ≥ 0.7% can be overlaid on a potential source rock distribution map, and the overlapping area can be identified as the effective source rock distribution area. It should be understood that the source rock distribution map is determined based on the thickness of the potential source rock.

[0245] Those skilled in the art will understand that the above-described modules or steps can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by the computing device, or fabricating them separately as individual integrated circuit modules, or fabricating multiple modules or steps into a single integrated circuit module. This invention is not limited to any specific hardware and software combination.

[0246] Example 4

[0247] This embodiment provides a storage medium storing a computer program that, when executed by one or more processors, implements the effective source rock prediction method of Embodiment 1.

[0248] In this embodiment, the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. For details of the method, please refer to Embodiment 1.

[0249] When the computer program is executed by one or more processors, the effective source rock prediction method implemented may include steps S110 to S140:

[0250] Step S110: Identify the target stratigraphic intervals for potential source rock development within the sequence stratigraphic framework of the target area.

[0251] In some implementations, identifying target intervals for potential source rock development within a sequence stratigraphic framework includes: defining the interval defined between the initial sea / lake flooding surface (FFS) and the maximum sea / lake flooding surface (MFS) as the target interval for potential source rock development.

[0252] Step S120: Predict the thickness of potential source rocks in the target layer.

[0253] In some implementations, predicting the thickness of potential source rocks in the target stratigraphic segment may include steps S120-1 to S120-3:

[0254] Step S120-1: Calculate the thickness of the target layer.

[0255] In some implementations, calculating the target segment thickness may include:

[0256] The thickness of the target layer is calculated using the first formula, which is as follows:

[0257] H = V2t2 - V1t1

[0258] Where H is the target layer thickness, in meters;

[0259] V2 is the root mean square velocity of the initial sea / lake flooding surface FFS reflection in phase axis, in m / s;

[0260] t2 is the one-way travel time of the initial sea / lake flooding surface FFS reflection phase axis, in seconds;

[0261] V1 is the root mean square velocity of the reflection phase axis of the maximum sea / lake flooding surface MFS, in m / s;

[0262] t1 is the one-way travel time of the reflection phase axis of the maximum sea / lake flooding surface MFS, in seconds.

[0263] In some implementations, each root mean square velocity is determined in the following manner:

[0264] (1) For horizontal strata, the root mean square velocity is equal to the stacking velocity, which can be obtained directly from seismic data.

[0265] (2) For inclined strata, the stacking velocity is converted into the root mean square velocity using a preset conversion formula, which is as follows:

[0266]

[0267] Among them, V r The root mean square velocity is in m / s;

[0268] V s The velocity is the superposition velocity, in m / s;

[0269] L is the horizontal distance between two adjacent seismic traces on the same reflection phase axis;

[0270] Δt0 is the time difference, in seconds, between the same reflection phase axis on two adjacent seismic traces with a horizontal spacing of L.

[0271] Step S120-2: Determine the mud-to-soil ratio of the target layer.

[0272] In some implementations, determining the mud-to-soil ratio of the target layer may include steps S120-2a to S120-2c:

[0273] Step S120-2a: Calculate the layer velocity of the target layer based on the Dix formula and the root mean square velocity of each interface in the target layer.

[0274] The Dix formula is as follows:

[0275]

[0276] Among them, V int The velocity is the layer velocity, in m / s;

[0277] v r，n Let be the root mean square velocity of the nth interface in the target layer, in m / s;

[0278] v r，n-1The root mean square velocity (RMS) of the (n-1)th interface in the target layer, in m / s;

[0279] t 0，n Let be the round-trip travel time (s) for the nth interface in the target layer.

[0280] t 0，n-1 Let s be the round-trip travel time of the (n-1)th interface in the target layer.

[0281] Step S120-2b: Determine the mudstone-to-soil ratio of the target layer based on the layer velocity of the target layer, the root mean square velocity of the mudstone, and the root mean square velocity of the sandstone.

[0282] Based on the time-averaged equation, the relationship between layer velocity and relative mudstone content, pure mudstone, and pure sandstone (e.g., carbonate rocks) is determined:

[0283]

[0284] Among them, V int The velocity is the layer velocity, in m / s;

[0285] P m This refers to the relative mudstone content in a two-phase medium, also known as the mudstone-to-land ratio, such as the ratio of mudstone layer thickness to stratum thickness.

[0286] V m is the root mean square velocity of pure mudstone, in m / s;

[0287] V n denoted as the root mean square velocity of pure sandstone, in m / s.

[0288] Based on the above relationship and the calculated layer velocity, the P of the target layer can be calculated. m .

[0289] Step S120-2c: Correct the mud-soil ratio of the target layer by combining the existing statistical laws of mud-soil ratio.

[0290] Step S120-3: Based on the target layer thickness and mud-land ratio, predict the thickness of potential source rocks in the target layer.

[0291] h = H × P m

[0292] Where h is the thickness of the potential source rock, in meters.

[0293] Step S130: Predict the organic carbon content and vitrinite reflectance in the target layer.

[0294] In some implementations, predicting the organic carbon content and vitrinite reflectance in the target layer includes:

[0295] (a) Based on the seismic properties of the target segment and the pre-established relationship between organic carbon content and seismic properties, predict the total organic carbon (TOC) content in the target segment.

[0296] In practical applications, the extracted seismic attributes can be normalized first, and then the relative values ​​of seismic attributes at different station numbers along the seismic survey line can be statistically analyzed. The n seismic attributes that best match the actual geological conditions can be selected. Combined with a small amount of measured organic carbon content data from shallow layers, the following multiple linear regression model can be constructed:

[0297] TOC=a·A1+b·A2+c·A3+…+n·A n +C

[0298] Where: TOC represents organic carbon content, %;

[0299] A1, A2, A3…A n These represent the first to nth earthquake attributes, respectively.

[0300] a, b, c, and n are the fitting coefficients;

[0301] C is a constant.

[0302] Furthermore, by using the seismic attributes of the target segment obtained from seismic data and the pre-established relationship between organic carbon content and seismic attributes (i.e., the aforementioned multiple linear regression model), the organic carbon content in the target segment can be predicted.

[0303] (b) Using the pre-established relationship between vitrinite reflectance and burial depth, predict the vitrinite reflectance (Ro) in the target layer.

[0304] Based on the existing burial depth of shallow source rocks and the measured vitrinite reflectance data, the following empirical formula can be established:

[0305] R o =α·exp (β·D)

[0306] Among them, R o The reflectance of the vitrinite group is expressed as a percentage.

[0307] D is the burial depth of the source rock, in meters;

[0308] α and β are fitting coefficients.

[0309] Based on the above empirical formula, the vitrinite reflectance R can be calculated according to the obtained burial depth. o .

[0310] In some cases, the calculated vitrinite reflectance R o It may not accurately reflect the true reflectance R of the outgoing image group.o Therefore, if oil and gas are discovered in a neighboring area, the vitrinite reflectance can be estimated using the following empirical formula to correct the calculated vitrinite reflectance and verify the aforementioned calculation results.

[0311] Saprophytic gas: δ 13 C1 = 21.71 × lgR o -43.31

[0312] Humic gas: δ 13 C1 = 40.49 × lgR o -34.00

[0313] Where, δ 13 C1 is a carbon isotope of methane, ‰;

[0314] R o —Vitreous reflectance, %.

[0315] In practical applications, the specific methods for correcting the calculated vitrinite reflectance can be as follows: Sum the vitrinite reflectances calculated using different empirical formulas and take the average value as the corrected vitrinite reflectance, which is then used as the predicted vitrinite reflectance in the target layer. Alternatively, the empirical formulas for saprophytic gas or humic gas can be used to back-calculate R. o Verify and revise the empirical formula R. o =α·exp (β·D) .

[0316] Step S140: Based on the thickness of potential source rocks, organic carbon content and vitrinite reflectance in the target layer, determine the distribution area of ​​effective source rocks in the target layer.

[0317] In some implementations, the distribution area of ​​effective source rocks in the target stratigraphic section is determined based on the thickness of potential source rocks, organic carbon content, and vitrinite reflectance, including:

[0318] The region within the target stratigraphic segment that simultaneously meets preset conditions is identified as the distribution area of ​​effective source rocks. In some implementations, the preset conditions include:

[0319] To reach the potential thickness of the source rock;

[0320] The organic carbon content is not less than the first threshold; and

[0321] The reflectance of the vitrinite group is not less than the second threshold.

[0322] For an effective source rock to be considered a valid hydrocarbon source rock, it must first have the potential for hydrocarbon generation, meaning it must have reached the stage of thermal maturity and evolution, activating its hydrocarbon generation potential, and the vitrinite reflectance (Ro) must be above 0.7%. Secondly, it must have the potential for hydrocarbon accumulation, meaning it must be able to overcome the binding effect of hydrocarbon adsorption and release hydrocarbons, and the total organic carbon (TOC) content must be above 1.0%. Therefore, the first threshold could be 1.0%, and the second threshold could be 0.7%. Based on the above predictions, contour maps with TOC ≥ 1.0% and vitrinite reflectance (Ro) ≥ 0.7% can be overlaid on a potential source rock distribution map, and the overlapping area can be identified as the effective source rock distribution area. It should be understood that the source rock distribution map is determined based on the thickness of the potential source rock.

[0323] Example 5

[0324] This embodiment provides an electronic device, including a memory and a processor. The memory stores a computer program, which, when executed by the processor, implements the effective source rock prediction method of Embodiment 1.

[0325] In this embodiment, the processor may be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic components, and is used to execute the methods in the above embodiments. The methods implemented when the computer program running on the processor is executed can be referred to the specific embodiments of the methods provided in the foregoing embodiments of this invention, and will not be repeated here.

[0326] In the several embodiments provided in this invention, it should be understood that the disclosed systems and methods can also be implemented in other ways. The system and method embodiments described above are merely illustrative.

[0327] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0328] While the embodiments disclosed in this invention are as described above, the content is merely for the purpose of facilitating understanding of the invention and is not intended to limit the invention. Any person skilled in the art to which this invention pertains may make any modifications and variations in form and detail of the implementation without departing from the spirit and scope disclosed herein; however, the scope of patent protection for this invention shall still be determined by the scope defined in the appended claims.

Claims

1. A method for predicting effective source rocks, characterized in that, include: Identify the target stratigraphic intervals within the sequence stratigraphic framework of the target area to determine the potential source rock development; The thickness of the target segment is calculated using a first formula, which is as follows: in, H The target layer thickness; V 2 represents the root mean square velocity of the initial sea / lake flooding surface FFS reflection phase axis; t 2 represents the one-way travel time of the initial sea / lake flooding surface FFS reflection phase axis; V 1 represents the root mean square velocity of the reflection in phase axis of the maximum sea / lake flooding surface MFS; t 1 represents the one-way travel time of the reflection phase axis of the maximum sea / lake flooding surface MFS; Based on the Dix formula and the root mean square velocity of each interface in the target layer, the layer velocity of the target layer is calculated; based on the layer velocity of the target layer, the root mean square velocity of mudstone and sandstone, the mud-to-soil ratio of the target layer is determined; the mud-to-soil ratio of the target layer is corrected by combining existing statistical laws of mud-to-soil ratio. Based on the target layer thickness and the mudstone ratio, the thickness of potential source rocks in the target layer is predicted; Predict the organic carbon content and vitrinite reflectance in the target layer; Based on the thickness of potential source rocks, organic carbon content, and vitrinite reflectance in the target stratigraphic section, the distribution area of ​​effective source rocks in the target stratigraphic section is determined.

2. The method for predicting effective source rocks according to claim 1, characterized in that, Identifying target intervals for potential source rock development within the sequence stratigraphic framework, including: The stratigraphic interval defined between the initial sea / lake flooding surface (FFS) and the maximum sea / lake flooding surface (MFS) was identified as the target interval for potential source rock development.

3. The method for predicting effective source rocks according to claim 1, characterized in that, The root mean square velocity is determined as follows: For horizontal formations, the root mean square velocity is equal to the superposition velocity; For inclined strata, the stacking velocity is converted into the root mean square velocity using a preset conversion formula, which is as follows: in, V r The root mean square velocity; V s For superposition speed; L The horizontal distance between two adjacent seismic traces is the same reflection phase axis. Δ t 0 represents the horizontal spacing. L The time difference between the same reflection phase axis on two adjacent seismic traces.

4. The method for predicting effective source rocks according to claim 1, characterized in that, The prediction of the organic carbon content and vitrinite reflectance in the target layer includes: Based on the seismic properties of the target segment and the pre-established relationship between organic carbon content and seismic properties, the organic carbon content in the target segment is predicted. The vitrinite reflectance in the target layer is predicted by using a pre-established relationship between vitrinite reflectance and burial depth.

5. The method for predicting effective source rocks according to claim 1, characterized in that, The determination of the distribution area of ​​effective source rocks in the target stratigraphic segment based on the thickness of potential source rocks, organic carbon content, and vitrinite reflectance includes: The region within the target stratigraphic segment that simultaneously meets the preset conditions is identified as the distribution area of ​​effective source rocks; The preset conditions include: To reach the potential thickness of the source rock; The organic carbon content is not less than the first threshold; and The reflectance of the vitrinite group is not less than the second threshold.

6. A device for predicting effective hydrocarbon source rocks, characterized in that, include: The first determining module is used to determine the target stratigraphic intervals for potential source rock development within the sequence stratigraphic framework of the target area; The first prediction module is used to calculate the thickness of the target section using a first calculation formula; calculate the layer velocity of the target section based on the Dix formula and the root mean square velocity of each interface in the target section; determine the mudstone-to-soil ratio of the target section based on the layer velocity, the root mean square velocity of the mudstone, and the root mean square velocity of the sandstone; correct the mudstone-to-soil ratio of the target section by combining existing statistical laws of mudstone-to-soil ratio; and predict the thickness of potential source rocks in the target section based on the thickness of the target section and the mudstone-to-soil ratio. The first calculation formula is as follows: in, H The target layer thickness; V 2 represents the root mean square velocity of the initial sea / lake flooding surface FFS reflection phase axis; t 2 represents the one-way travel time of the initial sea / lake flooding surface FFS reflection phase axis; V 1 represents the root mean square velocity of the reflection in phase axis of the maximum sea / lake flooding surface MFS; t 1 represents the one-way travel time of the reflection phase axis of the maximum sea / lake flooding surface MFS; The second prediction module is used to predict the organic carbon content and vitrinite reflectance in the target layer. The second determining module is used to determine the distribution area of ​​effective source rocks in the target layer based on the thickness of potential source rocks, organic carbon content and vitrinite reflectance in the target layer.

7. A storage medium, characterized in that, The storage medium stores a computer program, which, when executed by one or more processors, implements the method for predicting effective source rocks as described in any one of claims 1 to 5.

8. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, implements the method for predicting effective source rocks as described in any one of claims 1 to 5.

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