Complex seismic horizon interpretation method and system under strong shale oil shielding effect

Abstract

The invention relates to the technical field of oil and gas geophysical exploration, and provides a complex seismic horizon interpretation method and system under a shale oil strong shielding effect, and the method comprises the steps: carrying out the preprocessing of logging data and actual seismic data; fine well seismic calibration is carried out, geological information contained in seismic complex waves is determined, and a seismic interpretation target horizon is determined in combination with actual production needs; carrying out seismic forward modeling to obtain different dominant frequency forward modeling seismic data, and determining and identifying the optimal seismic dominant frequency of the seismic interpretation target horizon; carrying out empirical mode decomposition on the actual seismic data to obtain a plurality of intrinsic mode components representing different frequency components; performing correlation analysis on the plurality of intrinsic mode components and the target forward modeling seismic data, and determining an optimal intrinsic mode component; and carrying out automatic interpretation of the seismic interpretation target horizon by using the optimized intrinsic mode component, and obtaining an interpretation result of the seismic interpretation target horizon. According to the method, the interpretation efficiency and precision can be improved, and the seismic interpretation work cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas geophysical exploration, and in particular to a method and system for interpreting complex seismic layers under the strong shielding effect of shale oil. Background Art

[0002] Oil shale has lower velocity and density, while the upper and lower surrounding rocks have higher velocity and density. The impedance difference between oil shale and the upper and lower surrounding rocks will cause seismic waves to be reflected and refracted between oil shale and surrounding rocks, resulting in complex wave characteristics of low-frequency and chaotic event axis reflections in seismic data. In seismic horizon interpretation, such complex waveforms will bring difficulties to interpretation because they may mask the true reflection signals of the target horizon.

[0003] Common methods for solving complex wave seismic horizon interpretation include improving seismic resolution and collecting multi-component data. Improving seismic data resolution usually uses methods such as frequency division or deconvolution to improve the resolution of seismic data to obtain high-frequency seismic data. High-frequency seismic data contains more detailed information and can provide more stratigraphic details. Multi-component seismic data uses a three-component seismic recorder to simultaneously collect longitudinal and shear wave data, which can provide more waveform information and help accurately interpret complex stratigraphic structures.

[0004] Among these methods, it is difficult to determine the appropriate amount for improving the resolution of seismic data. If the resolution is too high, false frequencies will appear, and if it is too low, it is difficult to achieve the accuracy of complex wave interpretation. The cost of multi-component seismic data acquisition is too high, and it is difficult to apply it on a large scale in production practice. Therefore, there is an urgent need for a complex wave seismic horizon interpretation method suitable for the strong shielding effect of oil shale. Summary of the invention

[0005] The purpose of the present invention is to solve at least one technical problem in the background technology and to provide a method and system for interpreting complex seismic layers under the strong shielding effect of shale oil.

[0006] To achieve the above object, the present invention provides a complex seismic horizon interpretation method under the strong shielding effect of shale oil, comprising:

[0007] Preprocess the well logging data and actual seismic data;

[0008] Use pre-processed logging data and actual seismic data to perform fine well-seismic calibration, clarify the geological information contained in the seismic complex wave, and determine the target layer for seismic interpretation based on actual production needs;

[0009] The pre-processed logging data is used to carry out seismic forward modeling, and forward modeling seismic data with different main frequencies are obtained. The best seismic main frequency for identifying the target layer of seismic interpretation is determined, and the corresponding forward modeling seismic data is the target forward modeling seismic data;

[0010] Perform empirical mode decomposition on actual seismic data to obtain multiple eigenmode components representing different frequency components;

[0011] Performing correlation analysis on multiple eigenmode components and target forward modeling seismic data respectively, and selecting the eigenmode component with the highest correlation with the target forward modeling seismic data as the preferred eigenmode component;

[0012] The target layer of seismic interpretation is automatically interpreted using the selected eigenmode components to obtain the interpretation results of the target layer of seismic interpretation.

[0013] According to one aspect of the present invention, preprocessing the well logging data includes: correcting the curve distortion caused by well wall collapse and normalizing the well logging curve;

[0014] Preprocessing of actual seismic data includes outlier processing, noise attenuation, resolution improvement and data interpolation.

[0015] According to one aspect of the present invention, the method of using the pre-processed logging data and the actual seismic data to perform fine well-seismic calibration, clarifying the geological information contained in the seismic complex wave, and determining the seismic interpretation target layer in combination with actual production needs includes:

[0016] The pre-processed logging data is used to calculate the wave impedance curve and the wavelet convolution to obtain the synthetic seismic record;

[0017] By comparing the synthetic seismic records with the actual seismic data, the correct correspondence between the two is determined, and the depth matching between the logging data and the actual seismic data is achieved to ensure that the two are consistent in depth;

[0018] Conduct correlation analysis between synthetic seismic records and actual seismic data to determine the best time-depth relationship and achieve fine well-seismic calibration;

[0019] After fine well-seismic calibration, the sandstone and oil layer above the well are accurately projected onto the actual seismic data, clarifying the geological information of the sandstone and oil layer corresponding to the complex wave.

[0020] According to one aspect of the present invention, the method of using the pre-processed logging data to carry out seismic forward modeling, obtaining forward modeling seismic data of different main frequencies, and determining the best seismic main frequency for identifying the target horizon for seismic interpretation includes:

[0021] Input the acoustic time difference curve and density curve in the pre-processed logging data into the seismic wave propagation forward simulation program, and use the acoustic time difference curve and density curve to perform forward calculation;

[0022] In the forward calculation process, the wave velocity and density of each simulation space point are determined according to the values ​​of the acoustic time difference curve and density curve;

[0023] Set the main frequencies of the Ricker wavelet to 20Hz, 25Hz, 30Hz, 35Hz, 40Hz, 45Hz, 50Hz, 55Hz, 60Hz, and 65Hz in sequence;

[0024] The synthetic seismic records are carefully matched with the actual seismic data to determine the best seismic main frequency for identifying the target layer for seismic interpretation.

[0025] According to one aspect of the present invention, the empirical mode decomposition is performed on the actual seismic data to obtain a plurality of eigenmode components representing different frequency components:

[0026] The actual seismic data is decomposed into a set of intrinsic mode functions and a residual term through empirical mode decomposition;

[0027] Among them, the intrinsic mode function is the component separated in the time domain according to different frequencies, and the residual term is the remaining high-frequency noise.

[0028] According to one aspect of the present invention, the correlation analysis is performed on a plurality of eigenmode components and target forward modeling seismic data respectively, and the eigenmode component with the highest correlation with the target forward modeling seismic data is selected as the preferred eigenmode component:

[0029] Correlation analysis is performed on multiple eigenmode components and target forward modeling seismic data to determine the correlation coefficients between different eigenmode components and target forward modeling seismic data, and the eigenmode component with the highest correlation coefficient is selected as the preferred eigenmode component for complex wave seismic layer interpretation.

[0030] To achieve the above object, the present invention also provides a complex seismic layer interpretation system under the strong shielding effect of shale oil, comprising:

[0031] Data preprocessing module, which preprocesses well logging data and actual seismic data;

[0032] The module for determining the target layer of seismic interpretation uses the pre-processed logging data and actual seismic data to perform fine well-seismic calibration, clarify the geological information contained in the seismic complex wave, and determine the target layer of seismic interpretation in combination with actual production needs;

[0033] The seismic main frequency determination module uses the pre-processed logging data to carry out seismic forward modeling, obtains forward modeling seismic data with different main frequencies, and determines the best seismic main frequency for identifying the target layer of seismic interpretation. The corresponding forward modeling seismic data is the target forward modeling seismic data;

[0034] The intrinsic mode component acquisition module performs empirical mode decomposition on actual seismic data to obtain multiple intrinsic mode components representing different frequency components;

[0035] A preferred eigenmode component selection module performs correlation analysis on multiple eigenmode components and target forward modeling seismic data, and selects the eigenmode component with the highest correlation with the target forward modeling seismic data as the preferred eigenmode component;

[0036] The interpretation result acquisition module uses the optimal intrinsic mode components to automatically interpret the seismic interpretation target layer and obtain the seismic interpretation target layer interpretation results.

[0037] To achieve the above-mentioned purpose, the present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the complex seismic layer interpretation method under the strong shielding effect of shale oil as described above is implemented.

[0038] To achieve the above objectives, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the complex seismic layer interpretation method under the strong shielding effect of shale oil as described above is implemented.

[0039] According to the scheme of the present invention, the present invention can clarify the geological information contained in the seismic complex wave and determine the target layer of seismic interpretation by preprocessing and well-seismic calibration of logging data and actual seismic data. At the same time, by using the forward seismic data with different main frequencies obtained by seismic forward modeling, the best seismic main frequency for accurately identifying the oil layer can be determined, and the standard of oil layer interpretation is clarified. The method analyzes the correlation between multiple eigenmode components and target forward seismic data, selects the most relevant eigenmode components, and improves the reliability and accuracy of seismic interpretation. Automatic interpretation of the target layer of seismic interpretation using the selected eigenmode components can improve the efficiency and accuracy of interpretation and reduce the cost of seismic interpretation. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A flowchart schematically showing a method for interpreting complex seismic horizons under strong shielding of shale oil according to an embodiment of the present invention;

[0041] Figure 2 Develop fine well-seismic calibration result diagrams for pre-processed well logging data and actual seismic data;

[0042] Figure 3a-3j They are the forward modeling seismic data graphs corresponding to the main wave frequencies of 20Hz, 25Hz, 30Hz, 35Hz, 40Hz, 45Hz, 50Hz, 55Hz, 60Hz and 65Hz respectively;

[0043] Figure 4a-4j They are 10 different eigenmode component diagrams obtained by empirical mode decomposition;

[0044] Figure 5 It is the correlation coefficient diagram between 10 eigenmode components and target forward modeling seismic data;

[0045] Figure 6 This is the effect diagram of automatic interpretation of oil layer 1 and oil layer 2 using original seismic data;

[0046] Figure 7 This is the effect diagram of automatic interpretation of oil layer 1 and oil layer 2 using the preferred eigenmode analysis. DETAILED DESCRIPTION

[0047] The present invention will now be discussed with reference to exemplary embodiments. It should be understood that the embodiments discussed are only to enable those skilled in the art to better understand and thus implement the present invention, rather than implying any limitation on the scope of the present invention.

[0048] As used herein, the term “including” and variations thereof are to be interpreted as open-ended terms meaning “including, but not limited to.” The term “based on” is to be interpreted as “based, at least in part, on.” The terms “one embodiment” and “an embodiment” are to be interpreted as “at least one embodiment.”

[0049] Figure 1 A flowchart schematically shows a complex seismic layer interpretation method under the strong shielding effect of shale oil according to an embodiment of the present invention. Figure 1 As shown, in this embodiment, the complex seismic layer interpretation method under the strong shielding effect of shale oil includes:

[0050] Preprocess the well logging data and actual seismic data;

[0051] Use pre-processed logging data and actual seismic data to perform fine well-seismic calibration, clarify the geological information contained in the seismic complex wave, and determine the target layer for seismic interpretation based on actual production needs;

[0052] The pre-processed logging data is used to carry out seismic forward modeling, and forward modeling seismic data with different main frequencies are obtained. The best seismic main frequency for identifying the target layer of seismic interpretation is determined, and the corresponding forward modeling seismic data is the target forward modeling seismic data;

[0053] Perform empirical mode decomposition on actual seismic data to obtain multiple eigenmode components representing different frequency components;

[0054] Performing correlation analysis on multiple eigenmode components and target forward modeling seismic data respectively, and selecting the eigenmode component with the highest correlation with the target forward modeling seismic data as the preferred eigenmode component;

[0055] The target layer of seismic interpretation is automatically interpreted using the selected eigenmode components to obtain the interpretation results of the target layer of seismic interpretation.

[0056] Further, according to an embodiment of the present invention, preprocessing the well logging data includes:

[0057] Correction of curve distortion caused by wellbore collapse: The distortion of logging curves caused by wellbore collapse will have an adverse effect on subsequent seismic reservoir prediction. Therefore, it is necessary to correct the distorted logging curves and restore their true physical characteristics.

[0058] Curve normalization: Since the formation parameters and logging conditions of different well sections may be different, the amplitude and scale of the logging curve may also be different. In order to make effective comparison and analysis, the logging curve needs to be normalized and converted into relative units.

[0059] Preprocessing of logging data such as curve distortion correction and curve normalization caused by wellbore collapse is an important part of seismic reservoir prediction. These preprocessing works are aimed at eliminating the distortion of logging curves caused by factors such as wellbore collapse, so as to more accurately reflect the physical characteristics of underground reservoirs. These preprocessing works can improve the logging curves and reliability, and provide reliable logging curves for subsequent well-seismic calibration.

[0060] Preprocessing of actual seismic data includes:

[0061] Outlier processing: There may be outliers in seismic data, such as abnormally high or low values ​​due to equipment failure or other reasons. These outliers will interfere with subsequent analysis and interpretation, so they need to be identified and processed.

[0062] Noise attenuation: Various types of noise often exist in seismic data, such as surface noise, electromagnetic interference, etc. These noises will reduce the quality and reliability of seismic data, so corresponding methods need to be taken to attenuate and remove them to improve the signal-to-noise ratio of seismic data.

[0063] Resolution improvement: The resolution of seismic data determines its ability to interpret underground reservoirs. In order to improve the resolution of seismic data, some methods can be used, such as filtering in the time domain and frequency domain, waveform transformation, etc., to enhance the detailed information of seismic data.

[0064] Data interpolation: The sampling interval of seismic data may be uneven, or there may be some missing data points. In order to ensure the continuity and integrity of the data, data interpolation is required to fill in the missing data points and reach a certain sampling interval.

[0065] These preprocessing tasks can improve the accuracy and reliability of seismic data and provide an accurate seismic data basis for subsequent well-seismic calibration and stratigraphic interpretation.

[0066] Further, according to an embodiment of the present invention, the pre-processed logging data and the actual seismic data are used to perform fine well-seismic calibration, clarify the geological information contained in the seismic complex wave, and determine the seismic interpretation target layer in combination with the actual production needs, including:

[0067] The pre-processed logging data is used to calculate the wave impedance curve and the wavelet convolution to obtain the synthetic seismic record;

[0068] By comparing the synthetic seismic records with the actual seismic data, the correct correspondence between the two is determined, and the depth matching between the logging data and the actual seismic data is achieved to ensure that the two are consistent in depth;

[0069] Conduct correlation analysis between synthetic seismic records and actual seismic data to determine the best time-depth relationship and achieve fine well-seismic calibration;

[0070] After fine well-seismic calibration, the sandstone and oil layer above the well are accurately projected onto the actual seismic data, clarifying the geological information of the sandstone and oil layer corresponding to the complex wave.

[0071] In this embodiment, the result of the logging curve measurement is the depth domain result, and the actual seismic data logging is the time domain result. Therefore, fine well seismic calibration determines the relationship between the seismic record and the underground reservoir properties by comparing the logging data and the actual seismic data, thereby improving the accuracy of seismic interpretation. The pre-processed logging data (density curve RHOB and acoustic wave time difference curve AC) is used to calculate the wave impedance curve and the sub-wave convolution to obtain a synthetic seismic record. By comparing it with the actual seismic data, the correct correspondence between the two is determined, and the depth matching of the logging data and the actual seismic data is achieved to ensure that the two are consistent in depth. Figure 2 As shown, the first TVD is depth, the second Time is time, the third is density curve RHOB, the fourth is acoustic time difference curve A, the fifth is synthetic seismic record projected onto actual seismic data, the correlation coefficient between the two is 0.6, and the seventh is the sandstone interpretation conclusion of well logging interpretation. At the same time, the oil test results obtained two oil layers (oil layer 1 and oil layer 2). After fine well-seismic calibration, 3 sandstones and 2 oil layers are accurately projected onto the actual seismic data, just located on the complex wave with messy waveforms, and the geological information such as sandstone and oil layer corresponding to the complex wave is also clarified. In actual production, it is necessary to accurately characterize the distribution of the two oil layers, so the target layer in the seismic interpretation needs to be the top surface of the two oil layers.

[0072] Further, according to an embodiment of the present invention, seismic forward modeling is carried out using the preprocessed logging data to obtain forward modeling seismic data with different main frequencies, and the best seismic main frequency for identifying the target horizon of seismic interpretation is determined, including:

[0073] Input the acoustic time difference curve and density curve in the pre-processed logging data into the seismic wave propagation forward simulation program, and use the acoustic time difference curve and density curve to perform forward calculation;

[0074] In the forward calculation process, the wave velocity and density of each simulation space point are determined according to the values ​​of the acoustic time difference curve and density curve;

[0075] Set the main frequencies of the Ricker wavelet to 20Hz, 25Hz, 30Hz, 35Hz, 40Hz, 45Hz, 50Hz, 55Hz, 60Hz, and 65Hz in sequence;

[0076] The synthetic seismic records are carefully matched with the actual seismic data to determine the best seismic main frequency for identifying the target layer for seismic interpretation.

[0077] In this embodiment, the acoustic time difference curve and the density curve are input into the seismic forward simulation program. The seismic forward simulation program can be a seismic wave propagation simulation program based on the finite difference method, the finite element method or other numerical methods. Forward calculations are performed using the acoustic time difference curve and the density curve. During the forward calculation process, the wave velocity and density of each simulated space point are determined based on the values ​​of the acoustic time difference curve and the density curve, and the main frequency of the Ricker wavelet is set to 20Hz, 25Hz, 30Hz, 35Hz, 40Hz, 45Hz, 50Hz, 55Hz, 60Hz, and 65Hz in sequence. The higher the main frequency, the shorter the period of the wavelet and the sharper the waveform. The stronger the ability to identify sand bodies and oil layers. Figure 3a-3j As shown in the figure, they are forward modeling seismic data with main frequencies of 20Hz to 65Hz, respectively. The first track of each frequency data is the sandstone interpretation conclusion, and the second track is the synthetic record (i.e., forward modeling seismic data). It can be seen that when the main frequencies are 20Hz, 25Hz, 30Hz, and 35Hz, the synthetic records corresponding to oil layers 1 and 2 have no obvious event reflection; when it reaches 40Hz, a strong event reflection appears; when it reaches 45Hz, oil layers 1 and 2 strongly reflect the event, and the two oil layers are located at the position where the amplitude of the strong reflection event is the strongest, which makes it easier to accurately interpret the corresponding seismic layers; with the increase of the main frequency of the wavelet, when it reaches 50Hz, 55Hz, 60Hz, and 65Hz, although the reflection events corresponding to oil layers 1 and 2 are further strengthened, the position of the oil layers is located below the strongest amplitude, which makes it difficult to accurately interpret the corresponding seismic layers. Therefore, it can be determined that the best seismic main frequency for identifying the target layer of seismic interpretation is 45Hz, and its corresponding forward modeling seismic data is the target forward modeling seismic data.

[0078] Further, according to an embodiment of the present invention, empirical mode decomposition is performed on actual seismic data to obtain multiple eigenmode components representing different frequency components:

[0079] The actual seismic data is decomposed into a set of intrinsic mode functions (IMF) and a residual term through empirical mode decomposition (EMD);

[0080] Among them, IMF is the component separated in the time domain according to different frequencies, and the residual term is the remaining high-frequency noise.

[0081] In this embodiment, in seismology, EMD can be used to extract the characteristics of seismic data, and the specific steps are as follows:

[0082] 1. Sort the original seismic wave data in time series to obtain the frequency information of the data;

[0083] 2. Determine the number of IMFs as needed, and use the EMD method to decompose the seismic wave data into several intrinsic mode functions and a residual term;

[0084] 3. Select the eigenmode function corresponding to the rock structure to obtain seismic wave data containing information-rich frequency components;

[0085] 4. Extract different characteristic information of seismic wave data, such as amplitude and arrangement of different frequencies, according to different frequency components of the intrinsic mode function;

[0086] 5. Use the extracted feature information as input and apply machine learning algorithms for analysis to obtain more accurate seismic horizon interpretation results.

[0087] like Figure 4a-4j As shown in the figure, the result of EMD extraction is 10 different intrinsic mode components, of which the first one is the sandstone interpretation conclusion, the second one is the synthetic record (i.e., the target forward modeling seismic data), which is used as the standard to select the best intrinsic mode component for the final seismic interpretation, and the third one is different intrinsic mode components. The value in the brackets after each intrinsic mode component is the correlation coefficient between the intrinsic mode component and the target forward modeling seismic data, indicating the correlation between the two.

[0088] Further, according to an embodiment of the present invention, correlation analysis is performed on multiple eigenmode components and target forward modeling seismic data, and the eigenmode component with the highest correlation with the target forward modeling seismic data is selected as the preferred eigenmode component:

[0089] Correlation analysis is performed on multiple eigenmode components and target forward modeling seismic data to determine the correlation coefficients between different eigenmode components and target forward modeling seismic data, and the eigenmode component with the highest correlation coefficient is selected as the preferred eigenmode component for complex wave seismic layer interpretation.

[0090] In this embodiment, correlation analysis is performed on multiple eigenmode components and target forward seismic data, such as Figure 5 As shown in the figure, the horizontal axis represents the 10 different eigenmode components obtained above, and the vertical axis is the correlation coefficient between the eigenmode components and the target forward seismic data. It can be seen from the figure that the correlation coefficient of eigenmode component 9 is the highest, reaching 0.65, and it is used as the preferred eigenmode component for complex wave seismic layer interpretation.

[0091] Further, in this embodiment, if Figure 6 The figure shows the effect of automatic interpretation of oil layer 1 and oil layer 2 using original seismic data. Due to the low resolution of complex wave seismic, the corresponding layers of oil layer 1 are weak reflections, and even the local weak reflections are not developed. Therefore, during automatic tracking, the interpreted layer jumps to the upper adjacent strong reflection, resulting in abnormal interpretation results. Although the overall phase axis of oil layer 2 is strong and the layer can be tracked to a certain extent, the lateral changes of the complex wave are drastic, and the layer has obvious ups and downs, which also causes abnormal interpretation results. Figure 7 In order to use the preferred intrinsic mode components to automatically interpret the effect of oil layer 1 and oil layer 2, it can be seen that due to the improvement of the resolution of the intrinsic mode component results, the automatic tracking results of oil layer 1 and oil layer 2 have good lateral continuity, which well solves the abnormal phenomenon of complex wave layer interpretation and improves the interpretation accuracy.

[0092] According to the above scheme of the present invention, the present invention can clarify the geological information contained in the seismic complex wave and determine the target layer of seismic interpretation by preprocessing and well-seismic calibration of logging data and actual seismic data. At the same time, by using the forward seismic data with different main frequencies obtained by seismic forward modeling, the best seismic main frequency for accurately identifying the oil layer can be determined, and the standard of oil layer interpretation is clarified. This method analyzes the correlation between multiple eigenmode components and target forward seismic data, selects the most relevant eigenmode components, and improves the reliability and accuracy of seismic interpretation. Automatic interpretation of the target layer of seismic interpretation using the selected eigenmode components can improve the efficiency and accuracy of interpretation and reduce the cost of seismic interpretation.

[0093] Furthermore, to achieve the above-mentioned purpose, the present invention also provides a complex seismic layer interpretation system under the strong shielding effect of shale oil, comprising:

[0094] Data preprocessing module, which preprocesses well logging data and actual seismic data;

[0095] The module for determining the target layer of seismic interpretation uses the pre-processed logging data and actual seismic data to perform fine well-seismic calibration, clarify the geological information contained in the seismic complex wave, and determine the target layer of seismic interpretation in combination with actual production needs;

[0096] The seismic main frequency determination module uses the pre-processed logging data to carry out seismic forward modeling, obtains forward modeling seismic data with different main frequencies, and determines the best seismic main frequency for identifying the target layer of seismic interpretation. The corresponding forward modeling seismic data is the target forward modeling seismic data;

[0097] The intrinsic mode component acquisition module performs empirical mode decomposition on actual seismic data to obtain multiple intrinsic mode components representing different frequency components;

[0098] A preferred eigenmode component selection module performs correlation analysis on multiple eigenmode components and target forward modeling seismic data, and selects the eigenmode component with the highest correlation with the target forward modeling seismic data as the preferred eigenmode component;

[0099] The interpretation result acquisition module uses the optimal intrinsic mode components to automatically interpret the seismic interpretation target layer and obtain the seismic interpretation target layer interpretation results.

[0100] The complex seismic layer interpretation system under the strong shielding effect of shale oil according to the present invention can realize the complex seismic layer interpretation method under the strong shielding effect of shale oil. The specific process steps are as described above and will not be repeated here.

[0101] Furthermore, to achieve the above-mentioned purpose, the present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the complex seismic layer interpretation method under the strong shielding effect of shale oil as described above is implemented.

[0102] Furthermore, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the complex seismic layer interpretation method under the strong shielding effect of shale oil as described above is implemented.

[0103] Those skilled in the art will appreciate that the modules and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0104] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and equipment can refer to the corresponding processes in the aforementioned method implementation methods, and will not be repeated here.

[0105] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0106] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.

[0107] In addition, each functional module in the embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0108] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. 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 perform all or part of the steps of the energy-saving signal sending / receiving method of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical disks.

[0109] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other.

[0110] It should be understood that the size of the serial numbers of each step in the content of the invention and the implementation methods of the present invention does not absolutely 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 implementation methods of the present invention.

Claims

1. A complex seismic horizon interpretation method under the strong shielding effect of shale oil, characterized by: include: Preprocess the well logging data and actual seismic data; Use pre-processed logging data and actual seismic data to perform fine well-seismic calibration, clarify the geological information contained in the seismic complex wave, and determine the target layer for seismic interpretation based on actual production needs; The pre-processed logging data is used to carry out seismic forward modeling, and forward modeling seismic data with different main frequencies are obtained. The best seismic main frequency for identifying the target layer of seismic interpretation is determined, and the corresponding forward modeling seismic data is the target forward modeling seismic data; Perform empirical mode decomposition on actual seismic data to obtain multiple eigenmode components representing different frequency components; Performing correlation analysis on multiple eigenmode components and target forward modeling seismic data respectively, and selecting the eigenmode component with the highest correlation with the target forward modeling seismic data as the preferred eigenmode component; The target layer of seismic interpretation is automatically interpreted using the selected eigenmode components to obtain the interpretation results of the target layer of seismic interpretation.

2. The complex seismic horizon interpretation method under the strong shielding effect of shale oil according to claim 1 is characterized in that: Preprocessing of logging data includes: correcting the curve distortion caused by well wall collapse and normalizing the logging curve; Preprocessing of actual seismic data includes outlier processing, noise attenuation, resolution improvement and data interpolation.

3. The complex seismic horizon interpretation method under the strong shielding effect of shale oil according to claim 1 is characterized in that: The method of using the pre-processed logging data and actual seismic data to perform fine well-seismic calibration, clarifying the geological information contained in the seismic complex wave, and determining the target layer of seismic interpretation in combination with actual production needs includes: The pre-processed logging data is used to calculate the wave impedance curve and the wavelet convolution to obtain the synthetic seismic record; By comparing the synthetic seismic records with the actual seismic data, the correct correspondence between the two is determined, and the depth matching between the logging data and the actual seismic data is achieved to ensure that the two are consistent in depth; Conduct correlation analysis between synthetic seismic records and actual seismic data to determine the best time-depth relationship and achieve fine well-seismic calibration; After fine well-seismic calibration, the sandstone and oil layer above the well are accurately projected onto the actual seismic data, clarifying the geological information of the sandstone and oil layer corresponding to the complex wave.

4. The complex seismic horizon interpretation method under the strong shielding effect of shale oil according to claim 1 is characterized in that: The method of using the pre-processed logging data to carry out seismic forward modeling, obtaining forward modeling seismic data of different main frequencies, and determining the best seismic main frequency for identifying the target layer of seismic interpretation includes: Input the acoustic time difference curve and density curve in the pre-processed logging data into the seismic wave propagation forward simulation program, and use the acoustic time difference curve and density curve to perform forward calculation; In the forward calculation process, the wave velocity and density of each simulation space point are determined according to the values ​​of the acoustic time difference curve and density curve; Set the main frequencies of the Ricker wavelet to 20Hz, 25Hz, 30Hz, 35Hz, 40Hz, 45Hz, 50Hz, 55Hz, 60Hz, and 65Hz in sequence; The synthetic seismic records are carefully matched with the actual seismic data to determine the best seismic main frequency for identifying the target layer for seismic interpretation.

5. The complex seismic layer interpretation method under the strong shielding effect of shale oil according to claim 1 is characterized in that: The empirical mode decomposition is performed on the actual seismic data to obtain multiple eigenmode components representing different frequency components: The actual seismic data is decomposed into a set of intrinsic mode functions and a residual term through empirical mode decomposition; Among them, the intrinsic mode function is the component separated in the time domain according to different frequencies, and the residual term is the remaining high-frequency noise.

6. The complex seismic horizon interpretation method under the strong shielding effect of shale oil according to any one of claims 1 to 5, characterized in that: The correlation analysis is performed on the multiple eigenmode components and the target forward modeling seismic data respectively, and the eigenmode component with the highest correlation with the target forward modeling seismic data is selected as the preferred eigenmode component: Correlation analysis is performed on multiple eigenmode components and target forward modeling seismic data to determine the correlation coefficients between different eigenmode components and target forward modeling seismic data, and the eigenmode component with the highest correlation coefficient is selected as the preferred eigenmode component for complex wave seismic layer interpretation.

7. Complex seismic layer interpretation system under strong shielding effect of shale oil, characterized by: include: Data preprocessing module, which preprocesses well logging data and actual seismic data; The module for determining the target layer of seismic interpretation uses the pre-processed logging data and actual seismic data to perform fine well-seismic calibration, clarify the geological information contained in the seismic complex wave, and determine the target layer of seismic interpretation in combination with actual production needs; The seismic main frequency determination module uses the pre-processed logging data to carry out seismic forward modeling, obtains forward modeling seismic data with different main frequencies, and determines the best seismic main frequency for identifying the target layer of seismic interpretation. The corresponding forward modeling seismic data is the target forward modeling seismic data; The intrinsic mode component acquisition module performs empirical mode decomposition on actual seismic data to obtain multiple intrinsic mode components representing different frequency components; A preferred eigenmode component selection module performs correlation analysis on multiple eigenmode components and target forward modeling seismic data, and selects the eigenmode component with the highest correlation with the target forward modeling seismic data as the preferred eigenmode component; The interpretation result acquisition module uses the optimal intrinsic mode components to automatically interpret the seismic interpretation target layer and obtain the seismic interpretation target layer interpretation results.

8. An electronic device, characterized in that The method comprises a processor, a memory and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the method for interpreting complex seismic layers under the strong shielding effect of shale oil as described in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the method for interpreting complex seismic layers under the strong shielding effect of shale oil as described in any one of claims 1-6 is implemented.

Images