Automatic continuous processing method for seismic attributes of multiple three-dimensional seismic work areas

Abstract

The invention relates to a method for automatically connecting seismic attributes of a plurality of three-dimensional seismic work areas. The method comprises the following steps: merging seismic attributes extracted from the plurality of three-dimensional seismic work areas in a research area; dividing cells, calculating the mean value of the seismic attributes of each cell, carrying out segmented region identification on the research region by using a flooding method, carrying out statistics on the seismic attribute distribution characteristics of each identified segmented region, carrying out standardization processing, and completing the equalization processing of the seismic attributes of each segmented region; the problems that at present, the seismic attribute continuous processing steps of multiple three-dimensional seismic work areas are tedious, and the precision is greatly influenced by human factors are effectively solved.

Description

Technical Field

[0001] This disclosure relates to the field of seismic reservoir prediction, specifically to a method for processing seismic attributes of multiple three-dimensional seismic zones in a contiguous manner. Background Technology

[0002] The statements in this section provide only background information in connection with this disclosure and do not constitute prior art.

[0003] Seismic exploration technology is an essential and widely used geophysical technique for oil and gas resource exploration. Seismic exploration mainly includes two-dimensional (2D) seismic exploration and three-dimensional (3D) seismic exploration. 3D seismic exploration technology was developed based on 2D seismic exploration technology. By applying 3D seismic exploration technology, accurate prediction and analysis of the geological structure of oil and gas reservoir areas and the distribution range of reservoirs can be achieved.

[0004] In recent years, seismic attribute analysis technology has been widely used in stratigraphic lithology interpretation, structural interpretation, reservoir evaluation, reservoir characteristic description, and reservoir fluid dynamics monitoring, playing an increasingly important role in oil and gas exploration and development. Seismic attribute technology can extract useful information hidden in seismic data, improving the accuracy of reservoir-favorable area prediction. Therefore, research on the application of seismic attribute technology in reservoir prediction is of great importance.

[0005] In seismic attribute extraction and analysis, it is often necessary to perform seismic attribute extraction and analysis on multiple 3D seismic work areas that overlap with each other, in order to complete the overall geological interpretation of multiple 3D seismic work areas. Multiple 3D seismic work areas are 3D seismic data collected in different years, with different equipment, and processed by different units. Therefore, there are significant differences in the seismic attribute values ​​between work areas.

[0006] To complete the seismic attribute extraction and analysis of multiple contiguous 3D seismic survey areas, there are generally three processing methods. The first is pre-stack stitching of multiple 3D seismic datasets. Pre-stack stitching typically uses field data directly for stitching. Stitching the raw field data requires the processing personnel to design a holistic stitching scheme, considering various differences globally, applying unified survey line directions, pixel sizes, unified final reference surfaces, unified processing flows and parameters, and unified velocity fields for unified stacking and migration processing, thereby obtaining a unified data volume and achieving a perfect stitching effect. Pre-stack stitching yields good results, but involves a large data volume, long processing time, and high cost. The second method is post-stack stitching of multiple 3D seismic datasets. Post-stack stitching generally does not involve direct stitching on the segmented migration data volumes, but rather performs matching processing on the individual stacked data, and finally applies a unified velocity field for a one-step full 3D migration. Its advantages are speed and relatively small workload. The main problem it addresses is the various inconsistencies caused by the acquisition system, differences in the original pre-stack processing steps, and the influence of DMO stacking boundaries. The first method is difficult to stitch together, and the stitching effect is not optimal. To a certain extent, there are still differences in energy and other properties between the stitched seismic data blocks. The second method is to extract seismic attributes from multiple 3D seismic work areas separately, and then stitch the extracted seismic attributes together as a whole. This method does not require reprocessing or stitching multiple seismic data, and is the most economical and has the shortest cycle time.

[0007] For the consolidation of seismic attributes across multiple 3D seismic zones, a common method is to assign a correction coefficient to the seismic attributes of each zone, thereby achieving coordination and unification of seismic attribute parameters across the zones. There are two methods for calculating the correction coefficient. One method uses a single 3D seismic zone as a standard, unifying the numerical range of seismic attributes in other zones to the standard zone's range. For example, patent CN202011213463.8 discloses a method for consolidating seismic attributes across 3D seismic zones, which utilizes the technique of normalizing the seismic attributes of multiple zones before consolidation. The second method uses normalization or histogram methods to unify the numerical range of seismic attributes across all zones. For example, patent CN201410374636.2 discloses a method for consolidating attributes based on a histogram matching algorithm, which uses histogram methods to unify the numerical range of seismic attributes across multiple zones to the same scale.

[0008] Current methods for processing seismic attributes across multiple 3D seismic zones require pre-determining the extent of each zone and then scaling the extracted seismic attributes from each zone to the same scale. The extent and scaling factor of each zone need to be calculated manually, leading to cumbersome procedures, long processing times, and significant susceptibility to human error in accuracy when processing seismic attributes across multiple 3D seismic zones, thus increasing the difficulty of this process.

[0009] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art. Summary of the Invention

[0010] In view of this, this disclosure provides a method for processing seismic attributes of multiple three-dimensional seismic work areas in contiguous areas, which solves the problems of cumbersome steps and significant impact of human factors on the accuracy of current methods for processing seismic attributes of multiple three-dimensional seismic work areas in contiguous areas.

[0011] The technical concept of the method for processing seismic attributes of multiple three-dimensional seismic zones in this invention is: to identify different regions using the flooding method and to balance the seismic attributes of multiple regions using the feature distribution method.

[0012] Based on the technical concept of this invention, and to achieve the above-mentioned objective, the method for automatically connecting seismic attributes of multiple three-dimensional seismic work areas includes: The seismic attributes extracted from multiple 3D seismic zones within the study area were merged. Divide the area into cells and calculate the mean value of the seismic attributes in each cell. Apply the flooding method to segment and identify the study area. The distribution characteristics of seismic attributes in each segmented region are statistically identified and standardized to achieve a balance of seismic attributes in each segmented region.

[0013] In this disclosure and possible embodiments, the method for dividing the study area using the flooding method includes: The region is segmented based on the mean of the earthquake attributes of each cell and according to a set threshold value.

[0014] In this disclosure and possible embodiments, the threshold value is set based on the fact that the differences in seismic attribute values ​​between different three-dimensional seismic work areas are relatively large, while the differences in seismic attribute values ​​within the same three-dimensional seismic work area are relatively small.

[0015] In this disclosure and possible embodiments, the method for statistically analyzing the seismic attribute distribution characteristics of each segmented region includes: Calculate the seismic attribute histogram for each segmented region, and calculate the mean and standard deviation of the seismic attributes for each segmented region based on the histogram.

[0016] In this disclosure and possible embodiments, the method for performing the standardization conversion process includes: Based on the mean and standard deviation, the seismic attributes of each segmented region are standardized and transformed. After the transformation, the seismic attributes follow a standard normal distribution with a mean of 0 and a standard deviation of 1.

[0017] In this disclosure and possible embodiments, the method for calculating the mean value of the seismic attribute for each cell includes: The mean value of the seismic attribute for each cell is obtained by dividing the sum of all seismic attributes in the cell by the total number of sample points in the cell.

[0018] In this disclosure and possible embodiments, the method for dividing the study area into cells includes: The direction and size of the cells are selected based on the size of the surface elements in the study area and the direction of the measurement network. The study area is then gridded using these cells, dividing it into several cells of equal size.

[0019] In this disclosure and possible embodiments, the grid direction is consistent with the direction of the measurement mesh, and the size of the grid is an integer multiple of the surface element.

[0020] In this disclosure and possible embodiments, a method for extracting seismic attributes of multiple three-dimensional seismic zones includes: Seismic attributes are extracted from the three-dimensional seismic data volumes for each of the multiple three-dimensional seismic work areas. The extraction method uses the top and bottom layers of the target layer for fine seismic interpretation as the upper and lower time windows to extract attributes from the three-dimensional seismic data volumes. The seismic interpretation density is 1×1 and the entire area is closed.

[0021] In this disclosure and possible embodiments, during the extraction of seismic attributes, for overlay areas simultaneously covered by two or more three-dimensional seismic survey areas, the seismic data of the overlay areas with relatively good seismic data quality are selected for attribute extraction.

[0022] In this disclosure and possible embodiments, the seismic attributes of the extracted multiple three-dimensional seismic zones are smoothed.

[0023] The beneficial effects of this invention are as follows: This disclosed method for processing seismic attributes across multiple 3D seismic work zones utilizes the flooding method to calculate the extent of each work zone, thereby identifying different areas. Simultaneously, it employs the characteristic distribution method to balance the seismic attribute energy across multiple regions, achieving the goal of calculating the proportion coefficient for each work zone. Therefore, this method calculates the extent and proportion coefficient of each work zone using a non-human-based approach, ensuring accuracy unaffected by human factors. Furthermore, it boasts fast processing speed, low consolidation difficulty, and excellent consolidation results. It effectively solves the problems of cumbersome steps and significant accuracy susceptibility to human intervention in current methods for processing seismic attributes across multiple 3D seismic work zones. Attached Figure Description

[0024] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the specification, serve to illustrate the technical solutions of this disclosure.

[0025] Figure 1 This is a flowchart of the method for processing seismic attributes of multiple three-dimensional seismic zones according to the present invention; Figure 2 This is a three-dimensional seismic work area distribution map of the study area in this embodiment of the invention; Figure 3 This is a grid division diagram of the study area in an embodiment of the present invention; Figure 4 This is a diagram showing the distribution of segmented regions according to an embodiment of the present invention; Figure 5-1 , 5-2 These are histograms of root mean square amplitude properties before and after standardized transformation in an embodiment of the present invention. Figure 6 This is a property distribution diagram after amplitude equalization in an embodiment of the present invention. Detailed Implementation

[0026] The present disclosure is described below based on embodiments; however, it is worth noting that the present disclosure is not limited to these embodiments. In the detailed description of the present disclosure below, certain specific details are described in detail. However, those skilled in the art will fully understand the present disclosure for the parts not described in detail.

[0027] Furthermore, unless the context explicitly requires it, the words "comprising," "including," and similar terms throughout the specification and claims should be interpreted as including rather than exclusive or exhaustive; that is, meaning "including but not limited to."

[0028] Current methods for processing seismic attributes in multiple 3D seismic zones require pre-determining the extent of each zone and then scaling the extracted seismic attributes from each zone to the same scale. However, the extent and scaling factor of each zone need to be calculated manually, making the process of consolidating seismic attributes from multiple zones cumbersome and highly susceptible to human intervention in terms of accuracy. To address these issues, this disclosure provides a method for calculating the extent of each zone using a flooding method to identify different areas. For calculating the scaling factor of each zone, this disclosure utilizes a characteristic distribution method to balance the seismic attribute energy across multiple areas. Therefore, the consolidation processing method of this disclosure calculates the extent and scaling factor of each zone in a non-human-driven manner, ensuring accuracy unaffected by human intervention. Furthermore, it offers fast processing speed, low consolidation difficulty, and good consolidation results.

[0029] The technical solution of the method for processing seismic attributes of multiple 3D seismic work areas disclosed herein is as follows: First, attributes are extracted from the 3D seismic data volume of multiple 3D seismic work areas in the target area. Then, smoothing processing is performed. The study area is divided into multiple cells, and the mean value of the seismic attribute of each cell is calculated. The segmented regions are calculated using the flooding method. The distribution characteristics of seismic attributes in multiple segmented regions are calculated. The seismic attributes of multiple segmented regions are standardized and transformed to complete the equalization processing of seismic attributes in multiple segmented regions, thereby obtaining the result of processing seismic attributes of multiple 3D seismic work areas in a contiguous manner.

[0030] To make the objectives, technical solutions, and advantages of the present invention clearer, the prediction method of the present invention will be further described in detail below with reference to the accompanying drawings and examples.

[0031] Figure 1 The method for processing seismic attributes of multiple three-dimensional seismic work areas as described in the embodiments of the present invention is as follows: Figure 1 As shown, the method includes the following steps: Step 1: Extract seismic attributes from the 3D seismic survey area: Using the top and bottom horizons of the target layer for fine seismic interpretation as the upper and lower time windows, attribute extraction is performed on the 3D seismic data volume. The seismic interpretation density should be 1x1 and closed across the entire area. For multiple 3D seismic work areas, seismic attributes should be extracted separately for each work area. During the seismic attribute extraction process, for overlay areas simultaneously covered by two or more 3D seismic work areas, the seismic data with better quality should be selected for attribute extraction in the overlay areas.

[0032] Step 2: Smooth the seismic attributes of the 3D seismic survey area: Selecting appropriate smoothing algorithms and parameters is crucial for smoothing seismic attributes extracted from seismic data volumes. The purpose of smoothing is to eliminate the influence of individual outliers on the attribute distribution characteristics. Different smoothing algorithms and parameters should be used for different seismic attributes extracted from different 3D seismic survey areas.

[0033] Step 3: Cell division: First, the seismic attributes of multiple 3D seismic zones are merged to obtain a single seismic attribute for the entire region. Then, based on the line and trace intervals (cell size) and orientation of the multiple seismic networks in the study area, appropriate cell lengths and widths are selected to divide the study area into multiple equal-sized cells. The cell orientation should be as consistent as possible with the orientation of the seismic network, and the cell size should ideally be an integer multiple of the cell size.

[0034] Step 4: Calculate the mean value of the earthquake attribute for each cell: For all grids in the study area, the mean value of the seismic attribute of each cell is calculated. The calculation method is to sum all the seismic attributes in the cell, obtain the sum, and then divide the sum by the total number of sample points to obtain the mean value of the seismic attribute of each cell.

[0035] Step 5: Based on the mean of the seismic attributes of each cell, calculate the segmented region using the flooding method: The Flood Fill Algorithm, also known as the Seed Fill Algorithm, is a method that uses a seed found within a region as a basis and then expands outwards through a neighborhood search to fill the region. In image processing, this algorithm can identify contiguous areas with similar color or pattern attributes within an image. Following conventional techniques in this field, and considering the significant differences in seismic attribute values ​​between different 3D seismic zones but relatively small differences within the same 3D seismic zone, an appropriate threshold value is set, and the Flood Fill Algorithm is applied to calculate the distribution areas of 3D seismic zones with large numerical differences, resulting in multiple segmented regions.

[0036] Step 6: Statistically analyze the distribution characteristics of seismic attributes in multiple segmented regions: The range of seismic attribute values ​​generally follows a Gaussian normal distribution. To statistically analyze the distribution characteristics of seismic attributes in multiple segmented regions, the specific statistical method is to calculate the seismic attribute histogram for each segmented region calculated using the water-flooding method, and then calculate the mean and standard deviation of the seismic attribute distribution in each segmented region based on the histogram.

[0037] Step 7: Equalize the seismic attributes of multiple segmented regions: The seismic attributes of multiple segmented regions are standardized and transformed to achieve seismic attribute equilibration across these regions.

[0038] The standard normal distribution is a special type of normal distribution (mean 0, standard deviation 1). Based on the mean and standard deviation of each segmented region, the seismic attributes of multiple segmented regions are standardized and transformed. After the transformation, the seismic attributes follow a standard normal distribution with a mean of 0 and a standard deviation of 1.

[0039] The following uses the seismic attribute processing of a certain block of 3D seismic work area to illustrate the specific process of the present invention.

[0040] The study area in this embodiment is the Beizhong Oilfield in the Hailar Basin of Daqing Oilfield, with a total area of ​​147.6 km². 2 The seismic data for this oilfield is composed of six 3D seismic work areas stitched together. The numbering of each 3D seismic work area is as follows: Figure 2 The target stratum in this study block is the Nantun Formation, the main stratum in the Hailar Oilfield. Among the six 3D seismic work areas in the study block, the seismic grid cells numbered 1 and 3 are 20×10 meters, and those numbered 2, 4, and 6 are 20×20 meters. The acquisition years and processing methods for these three 3D seismic work areas are basically the same. The seismic grid cell numbered 5 is 20×25 meters.

[0041] Step 1: Extract seismic attributes from the 3D seismic survey area. The method for extracting seismic attributes from a 3D seismic work area is to extract attributes from the 3D seismic data volume by using the top and bottom layers of the target layer for fine seismic interpretation as the upper and lower time window ranges.

[0042] Detailed structural interpretation was performed on six 3D seismic work areas within the study area, completing the detailed interpretation of the stratigraphic horizons corresponding to the top and bottom surfaces of the Nantun Formation. The seismic interpretation density was 1×1 and the entire area was closed. Figure 3 ).

[0043] For six 3D seismic work areas, root-mean-square (RMS) amplitude seismic attributes were extracted using the top and bottom time windows of the top and bottom of the Nantun Formation layers determined by seismic interpretation. The extraction of RMS amplitude attributes for the six 3D seismic work areas was completed.

[0044] Step 2: Smooth the seismic attributes of the 3D seismic survey area. Different filtering algorithms were used for the root mean square amplitude (RMS) attributes extracted from six 3D seismic zones to eliminate singular values ​​caused by faults and other factors. Three commonly used filtering methods in landmark seismic interpretation systems are smoothing, median filtering, and peaking filtering. Through experiments, for 3D seismic zone 1, due to slightly lower seismic data quality, a 7×7 peaking filter was first applied to remove singular values, followed by full-area interpolation of the filtered RMS amplitude (peaking filtering may remove some peak values). Median filtering with a 5×5 parameter was used for 3D seismic zones 2, 3, and 5. Median filtering with a 7×7 parameter was used for 3D seismic zones 5 and 6.

[0045] Step 3: Cell Division First, the seismic attributes of the six 3D seismic zones are merged to obtain a seismic attribute map of the entire region. Then, based on the line and trace intervals (cell size) and the orientation of the six seismic networks in the study area, appropriate cell lengths and widths are selected to divide the study area into multiple cells of equal size.

[0046] Based on the surface size of the six 3D seismic work areas and the area of ​​the study region, a cell size of 1000×1000 meters was used, resulting in a total of 35×53=1855 cells. The angles of the six seismic grids are all 90°, and the grid direction adopts the same angle as the seismic grid direction (parallel to the coordinate axis direction).

[0047] Step 4: Calculate the mean value of the grid cells The root mean square amplitude attribute values ​​within each 1000×1000 meter grid are summed to obtain the sum of the root mean square amplitude attributes for each grid. This sum is then divided by the total number of samples within the grid to obtain the mean value of the root mean square seismic attribute for each 1000×1000 meter cell. For grids without seismic attributes, the mean value is set to 0.

[0048] Step 5: Calculate the segmented region using the flooding method Using the flooding method, a total of 4 segmented regions were identified in the study area in this embodiment. Figure 4 The segmented region A is basically consistent with the range of 3D seismic zone 1, segmented region B is basically consistent with 3D seismic zone 5, and segmented region C is basically consistent with 3D seismic zone 3. Because the root mean square amplitude attribute range of 3D seismic zones numbered 2, 4, and 6 is basically the same, segmented region D covers the three 3D seismic zones numbered 2, 4, and 6.

[0049] Step 6: Statistical analysis of seismic attribute distribution characteristics in the four segmented regions According to statistics, the root mean square (RMS) seismic attribute values ​​of multiple segmented regions all follow a Gaussian normal distribution. For the four regions calculated using the water-flooding method, histograms of the RMS amplitude seismic attributes within each region are calculated. Then, the mean and standard deviation of the RMS amplitude seismic attributes for the four regions are calculated based on the histograms. Figure 5-1 ).

[0050] From the root mean square attribute distribution map of the four segmented regions, the mean and standard deviation of region A are 10927 and 1700, respectively; the mean and standard deviation of region B are 11525 and 2186, respectively; the mean and standard deviation of region C are 10665 and 1500, respectively; and the mean and standard deviation of region D are 12746 and 2341, respectively.

[0051] Step 7: Seismic attribute equalization processing of 4 segmented regions, completing the seismic attribute consolidation processing of 6 3D seismic work areas. Based on the mean and standard deviation of the four segmented regions, the root mean square amplitude seismic attributes of the four regions were standardized. After the transformation, the root mean square amplitude seismic attributes of each block all followed a standard normal distribution with a mean of 0 and a standard deviation of 1. Figure 5-2 ).

[0052] The formula for standardized transformation is: ;

[0053] After equalization, there were no singularities in the root mean square amplitude properties within the study area, and the property variations conformed to geological sedimentary patterns. There were no obvious linear overlaps or abrupt change points at the boundaries of the six 3D seismic work areas. Figure 6 The root mean square amplitude properties of the six 3D seismic zones, when contiguous, can be used for the overall geological interpretation of the study area.

[0054] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, and are 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. The terminology used herein is chosen to best explain the principles, practical applications, or technical improvements to the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for automatically connecting seismic attributes of multiple three-dimensional seismic zones, characterized in that, The application relates to a method for automatically processing seismic attributes of multiple three-dimensional seismic work areas. The method comprises the following steps: merging seismic attributes extracted from multiple three-dimensional seismic work areas in a research area; dividing cells and calculating the average value of seismic attributes of each cell; applying a flood fill method to divide and identify regions in the research area; and statistically analyzing the distribution characteristics of seismic attributes of each divided region and performing standardization processing to complete the equalization of seismic attributes of each divided region. The method for dividing and identifying regions in the research area by applying the flood fill method comprises the following steps: based on the average value of seismic attributes of each cell, a threshold value is set to identify the divided regions.

3. The method according to claim 2, wherein the threshold value is set according to the relatively large difference in the values of seismic attributes of different three-dimensional seismic work areas and the relatively small difference in the values of seismic attributes of the same three-dimensional seismic work area.

2. The method according to claim 1, wherein, The method for statistically analyzing the distribution characteristics of seismic attributes of each divided region comprises the following steps: the histogram of seismic attributes in each divided region is calculated, and the mean value and standard deviation of seismic attributes of each divided region are calculated according to the histogram. The method for performing standardization conversion processing comprises the following steps: according to the mean value and standard deviation, the seismic attributes of each divided region are converted by standardization, and the converted seismic attributes conform to the standard normal distribution with a mean value of 0 and a standard deviation of 1. The method for calculating the average value of seismic attributes of each cell comprises the following steps: the average value of seismic attributes of each cell is obtained by dividing the sum of all seismic attributes in the cell by the total number of samples in the cell. The method for dividing cells in the research area comprises the following steps: the direction and size of the cells are selected according to the size of the bin and the direction of the survey network, the research area is gridded by using the cells, and the research area is divided into a plurality of cells with equal size.

4. The method according to any one of claims 1-3, wherein, 8. The method according to claim 7, wherein the grid direction is consistent with the direction of the survey network, and the size of the grid is an integer multiple of the size of the bin. The method for extracting seismic attributes of multiple three-dimensional seismic work areas comprises the following steps: seismic attributes in the range of each three-dimensional seismic work area are extracted, the extraction method is to set the top and bottom horizons of the target layer of seismic fine interpretation as the upper and lower time window ranges, and the seismic attribute extraction is performed on the three-dimensional seismic data body, and the seismic interpretation density is 1x1 and the whole area is closed.

5. The method of claim 4, wherein, 10. The method according to claim 9, wherein in the process of extracting seismic attributes, for the superimposed area covered by two or more three-dimensional seismic work areas simultaneously, seismic data of the superimposed area is selected to perform attribute extraction on the seismic data with relatively good seismic data quality; and / or, the extracted seismic attributes of multiple three-dimensional seismic work areas are subjected to smoothing processing. ​ 6. The method according to any one of claims 1-3 or 5, wherein, ​ ​ 7. The method of claim 6, wherein, ​ ​ ​ ​ 9. The method of claim 1-3, 5, 7 or 8, wherein, ​ ​ ​ ​ ​

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