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

Abstract

The present invention relates to the technical field of oil and gas geophysical exploration, and provides a method and system for interpreting complex seismic horizons under the strong shielding effect of shale oil. The method comprises: preprocessing well logging data and actual seismic data; performing fine well-seismic calibration to clarify the geological information contained in the seismic complex wave, and determining the seismic interpretation target horizon in combination with actual production needs; performing seismic forward modeling to obtain forward modeled seismic data with different main frequencies, and determining the best seismic main frequency for identifying the seismic interpretation target horizon; performing empirical mode decomposition on the actual seismic data to obtain multiple eigenmode components representing different frequency components; performing correlation analysis on the multiple eigenmode components and the target forward modeled seismic data respectively to determine the preferred eigenmode component; and performing automatic interpretation of the seismic interpretation target horizon using the preferred eigenmode component to obtain the seismic interpretation target horizon interpretation result. The present invention can improve the efficiency and accuracy of interpretation and reduce the cost of seismic interpretation work.

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 horizons under the strong shielding effect of shale oil. Background Art

[0002] Oil shale has low velocity and density, while the surrounding rock above and below has higher velocity and density. The impedance difference between the oil shale and the surrounding rock causes seismic waves to reflect and refract between the oil shale and the surrounding rock, resulting in complex wave characteristics of low-frequency and chaotic phase axis reflections in seismic data. These complex waveforms can complicate seismic horizon interpretation because they can mask the true reflection signals of the target horizon.

[0003] Common methods for interpreting complex-wave seismic horizons include improving seismic resolution and acquiring multi-component data. Improving seismic data resolution typically involves frequency division or deconvolution, yielding high-frequency seismic data. High-frequency seismic data contains more detailed information and can provide more detailed information about stratigraphic formations. Multi-component seismic data, which uses a three-component seismic recorder to simultaneously acquire longitudinal and shear wave data, provides more waveform information, helping to accurately interpret complex stratigraphic structures.

[0004] Among these methods, it is difficult to determine the appropriate amount of resolution to improve seismic data. Too high a resolution can lead to aliasing, while too low a resolution makes it difficult to accurately interpret complex waves. Multi-component seismic data acquisition is also expensive, making it difficult to apply on a large scale in production. Therefore, a method for interpreting complex-wave seismic horizons caused by the strong shielding effect of oil shale is urgently needed. Summary of the Invention

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

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

[0007] Preprocessing of 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] Use the pre-processed logging data to carry out seismic forward modeling, obtain forward modeling seismic data of different main frequencies, determine the best seismic main frequency for identifying the target horizon for seismic interpretation, and use the corresponding forward modeling seismic data as 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] Perform correlation analysis on multiple eigenmode components and target forward seismic data, and select the eigenmode component with the highest correlation with the target forward seismic data as the preferred eigenmode component;

[0012] The optimized eigenmode components are used to automatically interpret the target layer of seismic interpretation and obtain the interpretation results of the target layer of seismic interpretation.

[0013] According to one aspect of the present invention, pre-processing 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 performing fine well-seismic calibration using pre-processed well logging data and actual seismic data to clarify the geological information contained in the seismic complex wave and determine the target horizon for seismic interpretation in combination with actual production needs includes:

[0016] The pre-processed logging data is used to calculate the wave impedance curve and convolve the wavelet 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 optimal 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 well logging data to perform seismic forward modeling, obtaining forward modeled seismic data of different dominant frequencies, and determining the optimal dominant 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] During the forward calculation process, the wave velocity and density of each simulation space point are determined based on 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 dominant frequency for identifying the target horizon 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 multiple 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 the multiple eigenmode components and the target forward seismic data, and the eigenmode component with the highest correlation with the target forward seismic data is selected as the preferred eigenmode component:

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

[0030] To achieve the above-mentioned object, the present invention further provides a complex seismic horizon 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 seismic interpretation target layer determination module uses pre-processed well 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 seismic interpretation target layer based on actual production needs;

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

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

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

[0036] The interpretation result acquisition module uses the optimized eigenmode 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 capable of running 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.

[0038] 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.

[0039] According to the solution 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 different main frequency forward seismic data obtained by seismic forward modeling, the best seismic main frequency for accurately identifying the oil layer can be determined, and the standard for oil layer interpretation can be clarified. This method analyzes the correlation between multiple eigenmode components and target forward seismic data to select the most relevant eigenmode components, thereby improving 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 work. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0041] Figure 2 Carry out fine well-seismic calibration result diagram for pre-processed well logging data and actual seismic data;

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

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

[0044] Figure 5 The correlation coefficient diagram between the 10 eigenmode components and the 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 for enabling those skilled in the art to better understand and 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 The flowchart schematically shows a method for interpreting complex seismic horizons under strong shielding 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] Preprocessing of 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] Use the pre-processed logging data to carry out seismic forward modeling, obtain forward modeling seismic data of different main frequencies, determine the best seismic main frequency for identifying the target horizon for seismic interpretation, and use the corresponding forward modeling seismic data as 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] Perform correlation analysis on multiple eigenmode components and target forward seismic data, and select the eigenmode component with the highest correlation with the target forward seismic data as the preferred eigenmode component;

[0055] The optimized eigenmode components are used to automatically interpret the target layer of seismic interpretation and obtain the interpretation results of the target layer of seismic interpretation.

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

[0057] Correcting well logging curve distortion caused by wellbore collapse: Distortion of well logging curves caused by wellbore collapse can adversely affect subsequent seismic reservoir prediction. Therefore, it is necessary to correct the distorted well logging curves and restore their true physical characteristics.

[0058] Normalization: Because formation parameters and logging conditions may vary across well sections, the amplitude and scale of logging curves may also vary. For effective comparison and analysis, logging curves need to be normalized and converted into relative units.

[0059] Preprocessing well logging data, including correction for wellbore collapse-induced distortion and normalization, is a crucial step in seismic reservoir prediction. This preprocessing aims to eliminate distortion caused by factors like wellbore collapse, thereby more accurately reflecting the physical properties of the subsurface reservoir. This preprocessing improves the log data's reliability and provides reliable logs for subsequent well-seismic calibration.

[0060] Preprocessing of actual seismic data includes:

[0061] Outlier processing: Seismic data may contain outliers, 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: Seismic data often contains various types of noise, such as surface noise and electromagnetic interference. These noises can reduce the quality and reliability of seismic data, so appropriate methods are needed 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 subsurface reservoirs. To improve the resolution of seismic data, methods such as filtering in the time and frequency domains and waveform transformation can be used to enhance the details of the 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 achieve 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 horizon interpretation.

[0066] Furthermore, according to one embodiment of the present invention, fine well-seismic calibration is performed using pre-processed well logging data and actual seismic data to clarify the geological information contained in the seismic complex wave and determine the target horizon for seismic interpretation in combination with actual production needs, including:

[0067] The pre-processed logging data is used to calculate the wave impedance curve and convolve the wavelet 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 optimal 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 well logging curve measurement is the depth domain result, and the actual seismic data logging is the time domain result. Therefore, fine well seismic calibration is to determine the relationship between the seismic record and the underground reservoir properties by comparing the well logging data and the actual seismic data, thereby improving the accuracy of seismic interpretation. The pre-processed well 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 well logging data and the actual seismic data is achieved to ensure that the two are consistent in depth. Figure 2 As shown, the first track TVD is depth, the second track Time is time, the third track is the density curve RHOB, the fourth track is the acoustic time difference curve A, and the fifth track is the synthetic seismic record projected onto the actual seismic data, with a correlation coefficient of 0.6. The seventh track is the sandstone interpretation conclusion from the well logging interpretation. Simultaneously, the oil test results indicate two oil layers (Oil Layer 1 and Oil Layer 2). After fine well-seismic calibration, the three sandstones and two oil layers are accurately projected onto the actual seismic data, precisely located on the complex wave with a chaotic waveform, and the geological information such as the sandstone and oil layer corresponding to the complex wave is also clearly defined. In actual production, it is necessary to accurately characterize the distribution of the two oil layers, so the target horizon in the seismic interpretation needs to be the top surface of the two oil layers.

[0072] Furthermore, according to one embodiment of the present invention, seismic forward modeling is performed using pre-processed well logging data to obtain forward modeled seismic data of different dominant frequencies, and the optimal dominant frequency for identifying the target horizon for 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] During the forward calculation process, the wave velocity and density of each simulation space point are determined based on 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 dominant frequency for identifying the target horizon for seismic interpretation.

[0077] In this embodiment, the acoustic wave 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 wave time difference curve and the density curve. During the forward calculation process, the wave velocity and density of each simulation space point are determined based on the values ​​of the acoustic wave 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 The following table shows forward seismic data with dominant frequencies ranging from 20 Hz to 65 Hz. The first trace for each frequency is the sandstone interpretation conclusion, and the second trace is the synthetic record (i.e., forward seismic data). It can be seen that at dominant frequencies of 20 Hz, 25 Hz, 30 Hz, and 35 Hz, the synthetic records corresponding to reservoirs 1 and 2 lack obvious event reflections. At 40 Hz, strong event reflections appear. At 45 Hz, reservoirs 1 and 2 reflect strong events, and both reservoirs are located at the locations with the strongest amplitudes, making it easier to accurately interpret their corresponding seismic horizons. As the dominant frequency of the wavelet increases, at 50 Hz, 55 Hz, 60 Hz, and 65 Hz, although the reflection events corresponding to reservoirs 1 and 2 strengthen further, the reservoirs are located below the strongest amplitudes, making it difficult to accurately interpret their corresponding seismic horizons. Therefore, it can be determined that the optimal dominant frequency for identifying target horizons for seismic interpretation is 45 Hz, and the corresponding forward seismic data are the target forward seismic data.

[0078] Furthermore, according to one 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 (IMFs) 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 features of seismic data. The specific steps are as follows:

[0082] 1. Sort the original seismic wave data into 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 functions corresponding to the rock structure to obtain seismic wave data containing rich frequency components;

[0085] 4. Extract different characteristic information of seismic wave data based on the different frequency components of the eigenmode function, such as the amplitude and arrangement of different frequencies;

[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 Figure 1, the results of EMD extraction are 10 different intrinsic mode components, of which the first track is the sandstone interpretation conclusion, the second track is the synthetic record (i.e., the target forward seismic data), which is used as a standard to select the best eigenmode component for the final seismic interpretation, and the third track is different eigenmode components. The value in the brackets after each eigenmode component is the correlation coefficient between the eigenmode component and the target forward seismic data, indicating the correlation between the two.

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

[0089] Correlation analysis is performed on multiple eigenmode components and target forward seismic data to determine the correlation coefficients between different eigenmode components and target forward seismic data. The eigenmode component with the highest correlation coefficient is selected as the preferred eigenmode component for complex wave seismic horizon 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] Furthermore, 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 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 fluctuations, which also causes abnormal interpretation results. Figure 7 The effect of using the preferred eigenmode components for automatic interpretation of oil layer 1 and oil layer 2 can be seen. Due to the improvement of the resolution of the eigenmode component results, the results of automatic tracking 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 different main frequency forward modeling seismic data obtained by seismic forward modeling, the best seismic main frequency for accurately identifying the oil layer can be determined, and the standard for oil layer interpretation is clarified. This method analyzes the correlation between multiple eigenmode components and target forward modeling seismic data to select the most relevant eigenmode components, thereby improving 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 work.

[0093] Furthermore, to achieve the above-mentioned purpose, the present invention also provides a complex seismic horizon 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 seismic interpretation target layer determination module uses pre-processed well 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 seismic interpretation target layer based on actual production needs;

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

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

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

[0099] The interpretation result acquisition module uses the optimized eigenmode 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 mentioned above. 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 runnable 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 using electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0104] Those skilled in the art will 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 this 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 merely illustrative. For example, the division of the modules is merely a logical function division. In actual implementation, there may be other division methods, 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 separate, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the objectives 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 as software modules and sold or used as standalone products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes 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 transmission / reception method according to various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, ROM, RAM, a magnetic disk, or an optical disk.

[0109] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the inventive concept. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this application.

[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 strong shielding of shale oil, characterized by: include: Preprocessing of 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; Use the pre-processed logging data to carry out seismic forward modeling, obtain forward modeling seismic data of different main frequencies, determine the best seismic main frequency for identifying the target horizon for seismic interpretation, and use the corresponding forward modeling seismic data as the target forward modeling seismic data; Perform empirical mode decomposition on actual seismic data to obtain multiple eigenmode components representing different frequency components; Perform correlation analysis on multiple eigenmode components and target forward seismic data, and select the eigenmode component with the highest correlation with the target forward seismic data as the preferred eigenmode component; Automatically interpret the target layer of seismic interpretation using the optimized intrinsic mode components to obtain the interpretation results of the target layer of seismic interpretation; The method of using the pre-processed well logging data to perform seismic forward modeling, obtaining forward modeled seismic data of different main frequencies, and determining the best seismic main frequency for identifying the target horizon for 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; During the forward calculation process, the wave velocity and density of each simulation space point are determined based on 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; Finely match the synthetic seismic records with the actual seismic data to determine the best seismic dominant frequency for identifying the target horizon for seismic interpretation; 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; The correlation analysis is performed on the multiple eigenmode components and the 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: Correlation analysis is performed on multiple eigenmode components and target forward seismic data to determine the correlation coefficients between different eigenmode components and target forward seismic data. The eigenmode component with the highest correlation coefficient is selected as the preferred eigenmode component for complex wave seismic horizon 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 well 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 for seismic interpretation in combination with actual production needs, includes: The pre-processed logging data is used to calculate the wave impedance curve and convolve the wavelet 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 optimal 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. 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 seismic interpretation target layer determination module uses pre-processed well 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 seismic interpretation target layer based on actual production needs; The seismic dominant frequency determination module uses the pre-processed logging data to carry out seismic forward modeling, obtains forward modeling seismic data with different dominant frequencies, and determines the best seismic dominant frequency for identifying the target horizon for seismic interpretation. The corresponding forward modeling seismic data is the target forward modeling seismic data. The eigenmode component acquisition module performs empirical mode decomposition on actual seismic data to obtain multiple eigenmode components representing different frequency components; The preferred eigenmode component selection module performs correlation analysis on multiple eigenmode components and target forward seismic data, and selects the eigenmode component with the highest correlation with the target forward seismic data as the preferred eigenmode component; The interpretation result acquisition module uses the optimized intrinsic mode components to automatically interpret the target layer of seismic interpretation and obtain the interpretation results of the target layer of seismic interpretation; The method of using the pre-processed well logging data to perform seismic forward modeling, obtaining forward modeled seismic data of different main frequencies, and determining the best seismic main frequency for identifying the target horizon for 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; During the forward calculation process, the wave velocity and density of each simulation space point are determined based on 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; Finely match the synthetic seismic records with the actual seismic data to determine the best seismic dominant frequency for identifying the target horizon for seismic interpretation; 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; The correlation analysis is performed on the multiple eigenmode components and the 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: Correlation analysis is performed on multiple eigenmode components and target forward seismic data to determine the correlation coefficients between different eigenmode components and target forward seismic data. The eigenmode component with the highest correlation coefficient is selected as the preferred eigenmode component for complex wave seismic horizon interpretation.

5. An electronic device, characterized in that The invention 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 3 is implemented.

6. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the complex seismic layer interpretation method under the strong shielding effect of shale oil as described in any one of claims 1-3.

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