Quantitative calibration method, medium and device for diffraction wave imaging data energy

Abstract

The present invention discloses a method, medium, and electronic device for quantitatively calibrating the energy of diffraction wave imaging data. The method comprises: obtaining a first energy ratio between diffraction wave imaging data and full-wavefield imaging data at an anomalous reflection; calibrating the energy of the diffraction wave imaging data based on the first energy ratio; and correcting the energy of the calibrated diffraction wave imaging data. The method for quantitatively calibrating the energy of diffraction wave imaging data achieves quantitative calibration of the seismic waveform energy of diffraction wave imaging data, matches the energy of full-wavefield imaging stacked deviation data, and ensures that the inversion impedance of the diffraction wave imaging data volume is consistent with the inversion impedance of the full-wavefield imaging data, thereby better enabling comparison of imaging characteristics of special geological bodies under different imaging methods.

Description

Technical Field

[0001] The invention belongs to the technical field of oil exploration seismic reservoir prediction, and in particular relates to a quantitative calibration method, medium and equipment for diffraction wave imaging data energy. Background Art

[0002] In conventional seismic data processing of clastic reservoirs, diffraction wave reflection information is often suppressed as interference. However, in seismic processing of fracture zones, faults, pinchouts, reef boundaries, subvolcanic rocks, small-scale intrusive bodies, irregular rock mounds, and other discontinuous geological bodies, these small-scale and irregular geological bodies can provide higher resolution than reflection waves. The use of diffraction wave imaging to identify fractures and caves is particularly prominent in fracture-cavity reservoirs. For fracture-cavity bodies, diffraction wave imaging can effectively suppress seismic reflection signals, highlighting the diffraction signal.

[0003] Diffraction waves have weak energy and are easily affected by noise. For a long time, research on seismic diffraction waves has primarily focused on seismic processing and imaging, studying how to separate diffraction wave information from conventional reflection seismic records. These methods can be further categorized into: dip filtering, plane wave deconstruction filtering, offset filtering, Radon transform, focusing, common reflection plane separation, offset dip domain separation, and other separation methods.

[0004] Many researchers have conducted similar research on the application of diffraction wave imaging in fracture-vuggy reservoir prediction. Since large-scale fracture-vuggy reservoirs, which initially exhibited "beaded reflections," have been highly utilized with oilfield development, small-scale fracture-vuggy reservoirs are crucial for increasing reserve utilization. Some researchers have applied a combined inversion method of local dip filtering and predictive inversion to separate and image diffraction waves. These images are then fused with conventional prestack time migration seismic data to highlight the non-beaded seismic anomalies of small-scale fracture-vuggy reservoirs, achieving promising results. Other researchers have used forward modeling to analyze the diffraction wave field characteristics under cave conditions of varying scales. They concluded that when small-scale caves develop, post-diffraction seismic waves dominate, while the morphology of large-scale caves strongly influences the amplitude of diffraction waves. In general, diffraction wave energy is stronger in uniformly filled caves than in heterogeneously filled ones, and the "beaded" energy of the reflection image convergence is stronger. This demonstrates the theoretical basis for the identification of small-scale fracture-vuggy reservoirs using diffraction wave imaging.

[0005] Currently, the application of seismic inversion technology in well deployment for fracture-cavity reservoir development has become a consensus. Inverted wave impedance can eliminate seismic wavelet sidelobes, improve vertical resolution, and convert formation interface information into lithologic information, making it more comparable with wellpoints. Calibration with wellpoints allows for semi-quantitative to quantitative prediction of fracture-cavity reservoir volume. However, the basic data for seismic inversion is primarily full-wavefield imaging data, and no literature has been published on inversion using diffraction wave imaging data volumes. Due to the difference in reflected energy between diffraction waves and full-wavefield imaging data, it is often difficult to obtain stable wavelets when directly using diffraction wave inversion for wavelet calibration. Therefore, the basis for seismic inversion using diffraction wave imaging data volumes is to rescale the energy of the diffraction wave imaging data to match conventional full-wavefield imaging data.

[0006] Therefore, a method is particularly needed to make the inversion impedance of the diffraction wave imaging data volume consistent with the inversion impedance of the full wavefield imaging data. Summary of the Invention

[0007] The purpose of the present invention is to provide a quantitative calibration method for diffraction wave imaging data energy, which makes the inversion impedance of diffraction wave imaging data consistent with the inversion impedance of full wavefield imaging data.

[0008] The present invention provides a quantitative calibration method for diffraction wave imaging data energy, comprising: obtaining a first energy ratio of diffraction wave imaging data to full-wavefield imaging data at an abnormal reflection; calibrating the energy of the diffraction wave imaging data based on the first energy ratio; and correcting the energy of the scaled diffraction wave imaging data.

[0009] Optionally, obtaining the first energy ratio of the diffraction wave imaging data and the full-wavefield imaging data at the abnormal reflection includes: obtaining the maximum seismic anomaly in the diffraction wave imaging data, and obtaining the first reflection energy of the maximum seismic anomaly; determining the seismic anomaly corresponding to the position of the maximum seismic anomaly in the diffraction wave imaging data in the full-wavefield imaging data, and obtaining the second reflection energy of the corresponding seismic anomaly; and using the ratio of the first reflection energy to the second reflection energy as the first energy ratio.

[0010] Optionally, scaling the energy of the diffraction wave imaging data based on the first energy ratio includes: dividing the diffraction wave imaging data by the first energy ratio to obtain scaled diffraction wave imaging data.

[0011] Optionally, the correcting the energy of the scaled diffraction wave imaging data includes: determining the number of target areas of the diffraction wave imaging data; when the number of target locations of the diffraction wave imaging data is greater than a first threshold value of the number of locations and less than a second threshold value of the number of locations, obtaining an average energy ratio of all seismic anomaly bodies of the diffraction wave imaging data and the full-wavefield imaging data, and re-calibrating the energy of the scaled diffraction wave imaging data based on the average energy ratio; when the number of target locations of the diffraction wave imaging data is greater than the second threshold value of the number of locations, obtaining a second energy ratio of each seismic anomaly body of the diffraction wave imaging data to the seismic anomaly body of the full-wavefield imaging data corresponding to its position, and re-calibrating the energy of the scaled diffraction wave imaging data based on each second energy ratio.

[0012] Optionally, obtaining the second energy ratio of each seismic anomaly in the diffraction wave imaging data and the seismic anomaly in the full-wavefield imaging data corresponding to its position includes: obtaining each seismic anomaly in the diffraction wave imaging data; obtaining the third reflection energy of the seismic anomaly for each seismic anomaly respectively, determining the seismic anomaly corresponding to the position of the seismic anomaly in the diffraction wave imaging data in the full-wavefield imaging data, and obtaining the fourth reflection energy of the corresponding seismic anomaly, and using the ratio of the third reflection energy to the fourth reflection energy as the second energy ratio of the seismic anomaly.

[0013] Optionally, the re-calibrating the energy of the scaled diffraction wave imaging data based on each second energy ratio includes: dividing the diffraction wave imaging data into multiple sub-locations based on the position of each seismic anomaly body in the diffraction wave imaging data, wherein each sub-location corresponds to a seismic anomaly body; and re-calibrating the scaled diffraction wave imaging data in each sub-location based on the second energy ratio of the seismic anomaly body corresponding to the sub-location.

[0014] Optionally, the re-calibration of the scaled diffraction wave imaging data based on the second energy ratio of the seismic anomaly body corresponding to the sub-location includes: taking the structural layer of the target location of the diffraction wave imaging data as a constraint condition, and establishing a second energy ratio trend correction surface according to the second energy ratio of the seismic anomaly body corresponding to each sub-location, wherein the trend correction surface meets the requirement of the second energy ratio of each sub-location; longitudinally interpolating the trend correction surface within the same range as the scaled diffraction wave imaging data to obtain a spatial correction data volume of the diffraction wave imaging data; and dividing the scaled diffraction wave imaging data by the spatial correction data volume of the diffraction wave imaging data to obtain corrected diffraction wave imaging data.

[0015] Optionally, re-scaling the energy of the scaled diffraction wave imaging data based on the average energy ratio includes: dividing the scaled diffraction wave imaging data by the average energy ratio to obtain corrected diffraction wave imaging data.

[0016] The present invention also provides an electronic device, comprising: a memory storing executable instructions; and a processor running the executable instructions in the memory to implement the above-mentioned quantitative calibration method for diffraction wave imaging data energy.

[0017] The present invention also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the computer program implements the above-mentioned quantitative calibration method for the energy of diffraction wave imaging data.

[0018] The beneficial effects of the present invention are as follows: the quantitative calibration method of diffraction wave imaging data energy of the present invention realizes the quantitative calibration of seismic waveform energy of diffraction wave imaging data, matches the energy of full-wavefield imaging stacking data, makes the inversion impedance of diffraction wave imaging data consistent with the inversion impedance of full-wavefield imaging data, and better realizes the comparison of imaging characteristics of special geological bodies under different imaging methods.

[0019] The present invention has other features and advantages that will be apparent from or will be described in detail in the accompanying drawings and the following specific examples incorporated herein, which together serve to explain the specific principles of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The above and other objects, features and advantages of the present invention will become more apparent through a more detailed description of exemplary embodiments of the present invention with reference to the accompanying drawings, wherein like reference numerals generally represent like components throughout the exemplary embodiments of the present invention.

[0021] Figure 1 A flow chart of a method for quantitatively calibrating energy of diffraction wave imaging data according to an embodiment of the present invention is shown.

[0022] Figure 2 This is a comparison chart of the range histogram analysis of conventional PSTM full-wavefield imaging data and diffraction wave imaging data.

[0023] Figure 3 A schematic diagram of well-seismic calibration and wavelet extraction of conventional PSTM full-wavefield imaging data is shown.

[0024] Figure 4The figure shows a window energy attribute ratio λ when conventional PSTM full-wavefield imaging data and diffraction wave imaging data are the same, according to a quantitative calibration method of diffraction wave imaging data energy according to an embodiment of the present invention.

[0025] Figure 5 A histogram of the statistical probability distribution of lambda values of a quantitative calibration method for diffraction wave imaging data energy according to an embodiment of the present invention is shown.

[0026] Figure 6 The diagram shows a synthetic seismic record display after scaling the diffraction wave data energy with a specified λ value according to a quantitative scaling method for diffraction wave imaging data energy according to an embodiment of the present invention.

[0027] Figure 7 A waveform comparison diagram of PSTM full-wavefield imaging data and diffraction wave imaging data after energy calibration according to a quantitative calibration method of diffraction wave imaging data energy according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0028] The preferred embodiments of the present invention will be described in more detail below. Although the preferred embodiments of the present invention are described below, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0029] The present invention provides a quantitative calibration method for diffraction wave imaging data energy, comprising: obtaining a first energy ratio of diffraction wave imaging data to full-wavefield imaging data at an abnormal reflection; calibrating the energy of the diffraction wave imaging data based on the first energy ratio; and correcting the energy of the scaled diffraction wave imaging data.

[0030] There are two methods for calibrating the energy of diffraction imaging data. One is to scale the wavelet energy, scaling the seismic wavelet according to a specific scaling factor. This allows synthetic records generated from the scaled seismic wavelet to achieve a near energy match between the synthetic seismic record and the diffraction imaging data volume. The other method is to perform energy calibration directly on the data volume based on the energy ratio between full-wavefield imaging and diffraction imaging. This eliminates the need to modify the seismic wavelet energy. The calibration of well logging data and diffraction data, as well as subsequent inversion, can be performed using the wavelet extracted during well-seismic calibration using conventional full-wavefield imaging data volumes. To better compare the attributes of full-wavefield and diffraction data in subsequent work, a data volume energy calibration method is employed. This involves re-calibrating the diffraction wave reflection energy based on the first energy ratio λ of the diffraction wave reflection energy to the conventional full-wavefield data, achieving a near match between the diffraction wave and full-wavefield imaging data over the range. The energy of the scaled diffraction imaging data is then corrected based on the energy difference between the scaled diffraction imaging data and the full-wavefield imaging data.

[0031] According to an exemplary embodiment, a quantitative calibration method for diffraction wave imaging data energy quantitatively scales the seismic waveform energy of diffraction wave imaging data, matching the energy of full-wavefield imaging stacked offset data. This ensures that the inverted impedance of the diffraction wave imaging data volume is consistent with the inverted impedance of the full-wavefield imaging data, thereby better enabling comparison of the imaging characteristics of specific geological bodies using different imaging methods. The wave impedance data obtained based on the energy-scaled diffraction wave imaging data is more consistent with the inverted impedance of the full-wavefield imaging data, facilitating comparative analysis between wellpoint logging interpretation and seismic response, as well as quantitative interpretation such as reservoir physical property evaluation.

[0032] The purpose of using conventional full-wavefield imaging data volumes for well-seismic calibration and wavelet extraction is to determine the seismic wavelet energy that matches this data volume after seismic acquisition and processing. Typical full-wavefield imaging data types include prestack time migration (PSTM) and prestack depth migration (PSDM). They all share the commonality of utilizing full-wavefield information to image fracture-vuggy reservoirs. Fracture-vuggy reservoirs exhibit distinct seismic reflection characteristics such as beading and scrambling on the imaged profile, along with layered information derived from formation variations. Seismic reflections such as beading and scrambling contain some diffraction information. For nearly layered original formations on the profile, the primary information is reflection waves. Full-wavefield imaging data can integrate reflections from formation boundaries with anomalous reservoir reflections, playing a crucial role in seismic characterization of fracture-vuggy reservoirs. However, a challenge is the interference between diffraction wave imaging of fracture-vuggy reservoirs and layered reflections. This is particularly true at locations with strong seismic reflections, such as large unconformities, which can affect reservoir imaging.

[0033] This process utilizes wellpoint logging data and full-wavefield data for borehole seismic calibration. Calibration requires extracting and evaluating seismic wavelets based on information such as seismic energy and spectrum. However, it is feasible to estimate the seismic wavelet energy from seismic reflection interfaces. Synthetic seismic records are typically generated using acoustic transit time and density curves obtained from conventional well logging. During this process, the waveform characteristics of the synthetic seismic records are compared with those of the actual seismic reflection data. When the two show good consistency in waveform phase and energy, the borehole seismic calibration is considered essentially complete, establishing a time-depth correspondence between the well logging and seismic data. During the borehole seismic calibration process, in addition to acoustic transit time and density curves, a seismic wavelet is also required to generate the synthetic seismic record. This is achieved through a seismic trace convolution model. Calibration typically uses a Ricker wavelet corresponding to the main seismic frequency as the initial wavelet. During the borehole seismic time-depth calibration process, the initial wavelet undergoes multiple rounds of energy and phase calibration. When the wavelet can achieve a satisfactory match between the synthetic seismic record and the actual seismic trace calibration, it can be determined that the current seismic wavelet is suitable for the current seismic data volume. It is important to emphasize that when performing well-seismic calibration, high-quality wellpoint data with no obvious anomalies in the well logging curves must be selected. Furthermore, the seismic profile at the wellpoint location must be free of significant seismic anomalies and the seismic events must be relatively stable. This is to prevent the strong energy of seismic anomalies from affecting the quality of the wavelet assessment. A seismic wavelet is a hypothetical energy pulse in the seismic data generation process. This pulse propagates downward from the surface and returns to the surface, where it is processed to form seismic data.

[0034] For seismic data volumes of the same type with different reflection energies, even using the same geophysical inversion method and the same seismic wavelet, the inverted wave impedance values can vary significantly. This makes it essential to perform energy calibration for different data volumes. Because diffraction wave imaging anomalous reflections are primarily concentrated in fracture-cavity reservoirs and exhibit characteristics such as beaded or chaotic reflections, when comparing data volume energies, it is necessary to select the location of the seismic anomaly on the diffraction wave imaging data volume and calculate its energy value. This energy value is then compared with the reflection energy of the seismic anomaly at the corresponding location in the full-wavefield imaging data to determine the energy ratio range between the two.

[0035] As an optional solution, obtaining a first energy ratio of the diffraction wave imaging data to the full-wavefield imaging data at the abnormal reflection includes: obtaining a maximum seismic anomaly in the diffraction wave imaging data, and obtaining a first reflection energy of the maximum seismic anomaly; determining a seismic anomaly in the full-wavefield imaging data corresponding to a position of the maximum seismic anomaly in the diffraction wave imaging data, and obtaining a second reflection energy of the corresponding seismic anomaly; and using the ratio of the first reflection energy to the second reflection energy as the first energy ratio.

[0036] Specifically, the maximum energy plane attributes within the same window of the target layer are obtained, namely attribute a and attribute b. a is the first reflection energy of the largest seismic anomaly in the diffraction wave imaging data, and b is the second reflection energy of the seismic anomaly on the full-wavefield imaging data volume that corresponds to the location of the seismic anomaly on the diffraction wave imaging data volume.

[0037] Calculation of the attribute first energy ratio: λ value = attribute a / attribute b. The calculated λ can be a level attribute or an interval value.

[0038] As an optional solution, scaling the energy of the diffraction wave imaging data based on the first energy ratio includes: dividing the diffraction wave imaging data by the first energy ratio to obtain scaled diffraction wave imaging data.

[0039] Specifically, the number of target locations of the diffraction wave imaging data that need to be initially corrected is estimated; and data volume calibration is performed according to the first energy ratio. Specifically, the diffraction wave imaging data needs to be divided by the first energy ratio so that the energy of the diffraction wave imaging data is substantially matched with the energy of the full-wavefield imaging data, thereby obtaining the diffraction wave imaging data after initial calibration.

[0040] As an optional solution, correcting the energy of the scaled diffraction wave imaging data includes: determining the number of target locations of the diffraction wave imaging data; when the number of target locations of the diffraction wave imaging data is greater than a first threshold value of the number of locations and less than a second threshold value of the number of locations, obtaining an average energy ratio of all seismic anomaly bodies of the diffraction wave imaging data and the full-wavefield imaging data, and re-calibrating the energy of the scaled diffraction wave imaging data based on the average energy ratio; when the number of target locations of the diffraction wave imaging data is greater than the second threshold value of the number of locations, obtaining a second energy ratio of each seismic anomaly body in the diffraction wave imaging data to a seismic anomaly body in the full-wavefield imaging data corresponding to its position, and re-calibrating the energy of the scaled diffraction wave imaging data based on each second energy ratio.

[0041] Specifically, after the initial calibration, if the calibration data area is small or if analysis of energy differences at a single wellpoint is required, further calibration of the diffraction wave energy is necessary, based on the specific wellpoint calibration results. The key concept is to select wells with good calibration results for wellpoint synthetic records using full-wavefield imaging data to conduct well-to-seismic relationship analysis of the diffraction wave energy volume. Because diffraction wave imaging energy varies significantly across reservoirs of varying scales and types under complex conditions, further energy correction based on wellpoint data is necessary to improve the accuracy of wellpoint reservoir prediction.

[0042] If the number of target locations of the diffraction wave imaging data is less than the first threshold of the target location, it means that the area is small and no further calibration is required. If the number of target locations of the diffraction wave imaging data is greater than the first threshold of the target location and less than the second threshold of the target location, it means that the area is slightly larger and further calibration is required. The average energy ratio of all seismic anomalies of the diffraction wave imaging data and the full-wavefield imaging data is obtained and the energy of the scaled diffraction wave imaging data is re-calibrated using the average energy ratio. If the number of target locations of the diffraction wave imaging data is greater than the second threshold of the target location, it means that the area of the study area is large. In this case, the second energy ratio (λ 1 ,λ 2 ,λ 3 ...), and use the second energy ratio to calibrate the corresponding sub-region.

[0043] As an optional solution, obtaining the second energy ratio of each seismic anomaly in the diffraction wave imaging data and the seismic anomaly in the full-wavefield imaging data corresponding to its position includes: obtaining each seismic anomaly in the diffraction wave imaging data; obtaining the third reflection energy of the seismic anomaly for each seismic anomaly, determining the seismic anomaly corresponding to the position of the seismic anomaly in the diffraction wave imaging data in the full-wavefield imaging data, and obtaining the fourth reflection energy of the corresponding seismic anomaly, and using the ratio of the third reflection energy to the fourth reflection energy as the second energy ratio of the seismic anomaly.

[0044] Specifically, the second energy ratio of each seismic anomaly body is obtained respectively. For the second energy ratio of a seismic anomaly body, the second energy ratio is the ratio of the third reflection energy of the seismic anomaly body in the diffraction wave imaging data to the fourth reflection energy of the seismic anomaly body corresponding to the position of the seismic anomaly body in the diffraction wave imaging data.

[0045] As an optional solution, re-calibrating the energy of the scaled diffraction wave imaging data based on each second energy ratio includes: dividing the diffraction wave imaging data into multiple sub-locations based on the position of each seismic anomaly body in the diffraction wave imaging data, wherein each sub-location corresponds to a seismic anomaly body; and re-calibrating the scaled diffraction wave imaging data within each sub-location based on the second energy ratio of the seismic anomaly body corresponding to the sub-location.

[0046] Specifically, within each sub-location, the sub-location is calibrated using the second energy ratio corresponding to the sub-location.

[0047] As an optional solution, re-scaling the scaled diffraction wave imaging data based on the second energy ratio of the seismic anomaly body corresponding to the sub-location includes: taking the structural layer of the target location of the diffraction wave imaging data as a constraint condition, establishing a second energy ratio trend correction surface according to the second energy ratio of the seismic anomaly body corresponding to each sub-location, the trend correction surface meeting the second energy ratio requirement of each sub-location; longitudinally interpolating the trend correction surface within the same range as the scaled diffraction wave imaging data to obtain a spatial correction data volume of the diffraction wave imaging data; and dividing the scaled diffraction wave imaging data by the spatial correction data volume of the diffraction wave imaging data to obtain corrected diffraction wave imaging data.

[0048] Specifically, with the regional structural layer as a constraint condition, a second energy ratio trend correction surface of the work area is established according to the second energy ratio of the seismic anomaly body recorded in each sub-location; the correction surface is checked to determine whether the correction surface can meet the differentiated requirements of the second energy ratio of each sub-location, and at the same time there are no obvious anomalies on the plane; according to the range of the re-corrected diffraction wave imaging data volume, the correction surface is longitudinally interpolated into a trend volume to establish a diffraction wave imaging data space correction data volume; the correction data volume is used to perform mathematical calculations on the diffraction wave imaging data, specifically, the diffraction wave imaging data volume is divided by the correction data volume, so as to achieve re-calibration of the diffraction wave imaging data.

[0049] As an optional solution, the scaled diffraction wave imaging data is divided by the average energy ratio to obtain corrected diffraction wave imaging data.

[0050] Specifically, the energy ratios of all seismic anomaly bodies in the work area between the diffraction wave imaging data and the full-wavefield imaging data are read; the average value of the energy ratios of all seismic anomaly bodies is calculated to obtain the average energy ratio; and mathematical calculations are performed on the scaled diffraction wave imaging data based on the average energy ratio, specifically, the diffraction wave imaging data volume is divided by the average energy ratio to achieve re-calibration.

[0051] The present invention also provides an electronic device, which includes: a memory storing executable instructions; and a processor running the executable instructions in the memory to implement the above-mentioned quantitative calibration method for diffraction wave imaging data energy.

[0052] The present invention also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the computer program implements the above-mentioned quantitative calibration method for the energy of diffraction wave imaging data.

[0053] Example 1

[0054] Figure 1 A flow chart of a method for quantitatively calibrating energy of diffraction wave imaging data according to an embodiment of the present invention is shown. Figure 2This is a comparison chart of the range histogram analysis of conventional PSTM full-wavefield imaging data and diffraction wave imaging data. Figure 3 A schematic diagram of well-seismic calibration and wavelet extraction of conventional PSTM full-wavefield imaging data is shown. Figure 4 The figure shows a window energy attribute ratio λ when conventional PSTM full-wavefield imaging data and diffraction wave imaging data are the same, according to a quantitative calibration method of diffraction wave imaging data energy according to an embodiment of the present invention. Figure 5 A histogram of the statistical probability distribution of lambda values of a quantitative calibration method for diffraction wave imaging data energy according to an embodiment of the present invention is shown. Figure 6 The diagram shows a synthetic seismic record display after scaling the diffraction wave data energy with a specified λ value according to a quantitative scaling method for diffraction wave imaging data energy according to an embodiment of the present invention. Figure 7 A waveform comparison diagram of PSTM full-wavefield imaging data and diffraction wave imaging data after energy calibration according to a quantitative calibration method of diffraction wave imaging data energy according to an embodiment of the present invention is shown.

[0055] Combine Figure 1 、 Figure 2 、 Figure 3 、 Figure 4 、 Figure 5 、 Figure 6 and Figure 7 As shown, the quantitative calibration method of the diffraction wave imaging data energy includes:

[0056] Step 1: Obtain a first energy ratio between the diffraction wave imaging data and the full wavefield imaging data at the abnormal reflection;

[0057] Wherein, obtaining the first energy ratio of the diffraction wave imaging data and the full-wavefield imaging data at the abnormal reflection includes: obtaining the maximum seismic anomaly body in the diffraction wave imaging data, and obtaining the first reflection energy of the maximum seismic anomaly body; determining the seismic anomaly body corresponding to the position of the maximum seismic anomaly body in the diffraction wave imaging data in the full-wavefield imaging data, and obtaining the second reflection energy of the corresponding seismic anomaly body; and using the ratio of the first reflection energy to the second reflection energy as the first energy ratio.

[0058] Step 2: calibrating the energy of the diffraction wave imaging data based on the first energy ratio;

[0059] The diffraction wave imaging data is divided by the first energy ratio to obtain scaled diffraction wave imaging data.

[0060] Step 3: Correct the energy of the scaled diffraction wave imaging data.

[0061] The correcting of the energy of the scaled diffraction wave imaging data includes: determining the number of target locations of the diffraction wave imaging data; when the number of target locations of the diffraction wave imaging data is greater than a first threshold value of the number of locations and less than a second threshold value of the number of locations, obtaining an average energy ratio of all seismic anomaly bodies of the diffraction wave imaging data and the full-wavefield imaging data, and re-calibrating the energy of the scaled diffraction wave imaging data based on the average energy ratio; when the number of target locations of the diffraction wave imaging data is greater than the second threshold value of the number of locations, obtaining a second energy ratio of each seismic anomaly body in the diffraction wave imaging data to a seismic anomaly body in the full-wavefield imaging data corresponding to its position, and re-calibrating the energy of the scaled diffraction wave imaging data based on each second energy ratio.

[0062] Wherein, obtaining the second energy ratio of each seismic anomaly body in the diffraction wave imaging data and the seismic anomaly body in the full-wavefield imaging data corresponding to its position includes: obtaining each seismic anomaly body in the diffraction wave imaging data; obtaining the third reflection energy of the seismic anomaly body for each seismic anomaly body, determining the seismic anomaly body corresponding to the position of the seismic anomaly body in the diffraction wave imaging data in the full-wavefield imaging data, and obtaining the fourth reflection energy of the corresponding seismic anomaly body, and using the ratio of the third reflection energy to the fourth reflection energy as the second energy ratio of the seismic anomaly body.

[0063] The re-calibrating of the energy of the scaled diffraction wave imaging data based on each second energy ratio includes: dividing the diffraction wave imaging data into a plurality of sub-locations based on the position of each seismic anomaly in the diffraction wave imaging data, wherein each sub-location corresponds to a seismic anomaly; and re-calibrating the scaled diffraction wave imaging data within each sub-location based on the second energy ratio of the seismic anomaly corresponding to the sub-location.

[0064] The method further comprises: taking the structural layer of the target location of the diffraction wave imaging data as a constraint condition, and establishing a second energy ratio trend correction surface according to the second energy ratio of the seismic anomaly body corresponding to each sub-location, wherein the trend correction surface meets the second energy ratio requirement of each sub-location; longitudinally interpolating the trend correction surface within the same range as the scaled diffraction wave imaging data to obtain a spatial correction data volume of the diffraction wave imaging data; and dividing the scaled diffraction wave imaging data by the spatial correction data volume of the diffraction wave imaging data to obtain corrected diffraction wave imaging data.

[0065] The scaled diffraction wave imaging data is divided by the average energy ratio to obtain the corrected diffraction wave imaging data.

[0066] like Figure 2As shown in Figure 2, when processing seismic data, it is necessary to balance and compensate for the energy of different seismic reflections to make up for the energy loss of the seismic signal during the downward propagation process. At the same time, different processing methods and processing parameters lead to energy differences in the same batch of acquired data.

[0067] exist Figure 3 Conventional seismic calibration and wavelet extraction for PSTM full-wavefield imaging data utilizes wellpoint logging data and full-wavefield data for seismic calibration. Calibration requires extracting and evaluating seismic wavelets based on information such as seismic energy and spectrum. The resulting extracted wavelet must resemble the Ricker wavelet in morphology, and its energy must match the energy of the convolved synthetic record with that of the full-wavefield imaging PSTM data.

[0068] exist Figure 4 In the figure, the ratio of the two energy attribute layers is calculated. At different positions on the plane, the energy ratio of the full-wavefield imaging data and the diffraction wave imaging data is different. In general, the energy ratio of the two is less than 5, and the part above 5 is discretely distributed.

[0069] As can be seen from Figure 5, the energy ratio of the conventional PSTM full-wavefield imaging data and the diffraction wave imaging data in the reflected energy is not a typical normal distribution, but is concentrated below 5. The distribution values above 5 are relatively small, which means that the overall energy ratio of the PSTM full-wavefield imaging data and the diffraction wave imaging data is between 2 and 3. There are points in the data with large energy differences, which should be local reflection energy differences caused by different processing methods, and not strong reflection differences such as "beading" that require attention.

[0070] exist Figure 6 In the , based on the statistical energy ratio λ range, combined with the difference in reflection energy between the full wave field imaging data and the diffraction wave imaging data at the well point location, the λ1 value is comprehensively determined to be 3.5. By using the seismic data calculation function to increase the overall energy of the diffraction wave imaging data by 3.5 times, the reflection energy of the full wave field imaging data and the diffraction wave imaging data can be matched. Figure 3 The synthetic seismic records are produced by using the estimated seismic wavelets, and their reflected energy matches well with the full-wavefield imaging data and diffraction wave imaging data.

[0071] like Figure 7 As shown in the figure, by matching the reflected energy of the energy-scaled diffraction wave imaging data with the PSTM full-wavefield imaging data, the differences in the reflection characteristics of the two data sets can be intuitively analyzed. The same processing and extraction parameters can be used for correlation calculations and attribute extraction analysis, further facilitating subsequent seismic interpretation work.

[0072] This application uses the development of small fracture-cavity reservoirs characterized by weak beading and chaotic reflections. The data volume based on the diffraction wave energy scale is consistent with the full wavefield data volume in terms of energy, and has a good effect on identifying fracture-cavity anomalies. This provides a well-seismic calibration idea for the wave impedance inversion of diffraction wave imaging data, allowing the inversion of diffraction wave imaging data to be carried out according to conventional seismic data processes, and has a promising prospect for promotion.

[0073] Example 2

[0074] The present disclosure provides an electronic device, comprising: a memory storing executable instructions; and a processor executing the executable instructions in the memory to implement the above-mentioned quantitative calibration method for diffraction wave imaging data energy.

[0075] An electronic device according to an embodiment of the present disclosure includes a memory and a processor.

[0076] The memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), a hard disk, flash memory, etc.

[0077] The processor may be a central processing unit (CPU) or other form of processing unit having data processing capability and / or instruction execution capability, and may control other components in the electronic device to perform desired functions. In one embodiment of the present disclosure, the processor is used to execute the computer-readable instructions stored in the memory.

[0078] Those skilled in the art should understand that in order to solve the technical problem of how to obtain a good user experience, this embodiment may also include well-known structures such as a communication bus and an interface, and these well-known structures should also be included in the scope of protection of this disclosure.

[0079] For detailed description of this embodiment, please refer to the corresponding description in the aforementioned embodiments, which will not be repeated here.

[0080] Example 3

[0081] The present disclosure provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the computer program implements the above-mentioned quantitative calibration method for diffraction wave imaging data energy.

[0082] According to an embodiment of the present disclosure, a computer-readable storage medium stores non-transitory computer-readable instructions, which, when executed by a processor, execute all or part of the steps of the aforementioned methods of the embodiments of the present disclosure.

[0083] The above-mentioned computer-readable storage media include, but are not limited to, optical storage media (e.g., CD-ROMs and DVDs), magneto-optical storage media (e.g., MOs), magnetic storage media (e.g., magnetic tapes or mobile hard disks), media with built-in rewritable non-volatile memory (e.g., memory cards), and media with built-in ROM (e.g., ROM cartridges).

[0084] While various embodiments of the present invention have been described above, the above description is intended to be illustrative, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A quantitative calibration method for diffraction wave imaging data energy, characterized in that: include: Acquiring a first energy ratio of the diffraction wave imaging data to the full wavefield imaging data at the abnormal reflection location; calibrating the energy of the diffraction wave imaging data based on the first energy ratio; Correct the energy of the scaled diffraction wave imaging data; The correcting of the energy of the scaled diffraction wave imaging data includes: determining the number of target locations of the diffraction wave imaging data; When the number of target locations of the diffraction wave imaging data is greater than a first threshold value of the number of locations and less than a second threshold value of the number of locations, obtaining an average energy ratio of all seismic anomaly bodies of the diffraction wave imaging data and the full-wavefield imaging data, and re-calibrating the energy of the scaled diffraction wave imaging data based on the average energy ratio; When the number of target locations of the diffraction wave imaging data is greater than a second threshold value of the number of locations, a second energy ratio of each seismic anomaly body in the diffraction wave imaging data to the seismic anomaly body in the full-wavefield imaging data corresponding to its position is obtained, and the energy of the scaled diffraction wave imaging data is re-scaled based on each second energy ratio.

2. The quantitative calibration method for diffraction wave imaging data energy according to claim 1, wherein: The obtaining of a first energy ratio of diffraction wave imaging data to full wavefield imaging data at the abnormal reflection location comprises: Acquire a maximum seismic anomaly body in the diffraction wave imaging data, and acquire a first reflection energy of the maximum seismic anomaly body; Determining, in the full-wavefield imaging data, a seismic anomaly body corresponding to the location of the maximum seismic anomaly body in the diffraction wave imaging data, and acquiring second reflected energy of the corresponding seismic anomaly body; The ratio of the first reflected energy to the second reflected energy is used as a first energy ratio.

3. The quantitative calibration method of diffraction wave imaging data energy according to claim 1, wherein: The calibrating the energy of the diffraction wave imaging data based on the first energy ratio includes: The diffraction wave imaging data is divided by the first energy ratio to obtain scaled diffraction wave imaging data.

4. The quantitative calibration method for diffraction wave imaging data energy according to claim 1, wherein: The step of obtaining a second energy ratio of each seismic anomaly body in the diffraction wave imaging data to a seismic anomaly body in the full wavefield imaging data corresponding to its position comprises: Acquiring each seismic anomaly body in the diffraction wave imaging data; For each seismic anomaly, the third reflected energy of the seismic anomaly is obtained respectively, the seismic anomaly corresponding to the position of the seismic anomaly in the diffraction wave imaging data is determined in the full-wavefield imaging data, and the fourth reflected energy of the corresponding seismic anomaly is obtained, and the ratio of the third reflected energy to the fourth reflected energy is used as the second energy ratio of the seismic anomaly.

5. The quantitative calibration method of diffraction wave imaging data energy according to claim 1, wherein: The re-calibrating the energy of the calibrated diffraction wave imaging data based on each second energy ratio includes: Based on the position of each seismic anomaly body in the diffraction wave imaging data, the diffraction wave imaging data is divided into a plurality of sub-locations, wherein each sub-location corresponds to a seismic anomaly body; For each sub-location, the scaled diffraction wave imaging data is re-scaled in the sub-location based on the second energy ratio of the seismic anomaly body corresponding to the sub-location.

6. The quantitative calibration method for diffraction wave imaging data energy according to claim 5, wherein: The re-scaling of the scaled diffraction wave imaging data based on the second energy ratio of the seismic anomaly body corresponding to the sub-location includes: Taking the structural layer of the target location of the diffraction wave imaging data as a constraint condition, and according to the second energy ratio of the seismic anomaly body corresponding to each sub-location, establishing a second energy ratio trend correction surface, wherein the trend correction surface meets the second energy ratio requirement of each sub-location; performing longitudinal interpolation on the trend correction surface within the same range as the scaled diffraction wave imaging data to obtain a diffraction wave imaging data spatial correction data volume; The scaled diffraction wave imaging data is divided by the diffraction wave imaging data space correction data volume to obtain corrected diffraction wave imaging data.

7. The quantitative calibration method for diffraction wave imaging data energy according to claim 1, wherein: The re-calibrating of the energy of the scaled diffraction wave imaging data based on the average energy ratio includes: The scaled diffraction wave imaging data is divided by the average energy ratio to obtain corrected diffraction wave imaging data.

8. An electronic device, characterized in that: The electronic device comprises: a memory storing executable instructions; A processor, wherein the processor runs the executable instructions in the memory to implement the quantitative calibration method for diffraction wave imaging data energy according to any one of claims 1 to 7.

9. 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 quantitative calibration method for diffraction wave imaging data energy according to any one of claims 1 to 7.

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