Automatic seismic facies identification method based on region growing technique

Abstract

The application provides a seismic facies automatic identification method based on a region growing technique, comprising the following steps: step 1, converting original seismic data into stratum domain seismic data through Wheeler domain conversion; step 2, determining seismic attributes sensitive to seismic facies classification; step 3, selecting a seed point; step 4, setting a seismic facies plane and a vertical cutoff condition; step 5, performing a seed region growing process according to the cutoff condition; step 6, forcibly fusing regions still without a category into adjacent most similar regions with a category; step 7, obtaining a seismic facies identification result of each 1ms isochronous slice in a target layer based on sensitive attributes; and step 8, synthesizing each slice identification result to synthesize a three-dimensional seismic facies boundary. The seismic facies automatic identification method based on the region growing technique can quickly and accurately identify a seismic facies boundary in actual seismic data, is simple to operate, and has high three-dimensional seismic facies picking efficiency and accuracy, and has a practical application significance.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of oil and gas exploration, and particularly to a seismic facies automatic identification method based on region growing technology. BACKGROUND

[0002] In geological research, the definition of sedimentary facies is the sum of the generation environment, generation condition and characteristics of sediments, and the sedimentary facies analysis is an important basic work in the field of geological research and oil and gas exploration, and its reliability even directly determines the success or failure of oil and gas exploration. In the period when three-dimensional seismic data are not popular, the overall understanding of the sedimentary facies of a region is often based on the analysis of well logging facies, and the development of interwell sedimentary facies is inferred according to the geological understanding of the region. Due to the limitation of the "one-hole view" on the well, there is a lack of actual evidence, and the interwell sedimentary facies analysis has many problems.

[0003] With the wide application of three-dimensional seismic data, the regional sedimentary facies understanding can be obtained more through the analysis and transformation of seismic facies on the basis of well-seismic fine calibration, and the seismic facies can be understood as the sum of the sedimentary facies on the seismic profile. Through the analysis of the seismic facies in the three-dimensional seismic data, combined with the drilling and source direction information, the spatial distribution characteristics of the sedimentary facies can be established.

[0004] The current seismic facies analysis methods mainly include waveform classification method, seismic attribute characteristic mapping method, and seismic geomorphology facies classification method. Among them, the waveform classification method is to classify and use in the analysis of seismic facies according to the multiple attributes of the arrangement and combination of the same phase axis in the seismic reflection interface, such as chaotic, wavy and compound waveform, and the method has the advantages of simple calculation method and fast calculation speed, but is easily affected by the noise in the seismic data, so that the obtained seismic facies map is distributed in disorder and has large difference with the existing geological rules; the seismic attribute characteristic mapping method is to generate a seismic facies based on different advantage seismic attribute characteristics on the basis of a large number of seismic attribute calculations, and the method has the advantages of higher accuracy and soft constraint of wells, but is still affected by lithology, fracture and fluid, and has multiple solutions for the interpretation of sedimentary facies; the seismic geomorphology facies classification method is a method of characterizing and researching the sedimentary facies characteristics in different geological history periods on the high-resolution seismic data body stratum slice by combining high-precision three-dimensional seismic exploration technology with sedimentology and geomorphology, and the method also needs to use a large number of seismic attributes for calculation but is not constrained by wells, and has high prediction accuracy, but the calculation is relatively complex and is relatively difficult to realize compared with other methods.

[0005] In addition, the above three current methods all have two common problems: first, the analysis of three-dimensional seismic facies needs to be specifically implemented through a large amount of manual interpretation, and the nonlinear representation ability of human beings is limited, and the interpretation experience varies from person to person, which inevitably leads to the reduction of reliability and efficiency of the interpretation results; second, all of them only stay in the stage of two-dimensional plane seismic facies analysis and cannot realize the analysis and mapping of three-dimensional seismic facies.

[0006] Therefore, how to obtain the seismic facies characteristics faster and more accurately is an urgent demand of researchers, especially in the face of various geological phenomena in the late exploration period, which are staggered and superimposed in a short time and a small range, and this problem becomes more prominent and needs to introduce new data analysis techniques.

[0007] In the Chinese patent application with the application number CN201611198747.8, a method for quantitatively representing growth fault activity intensity is involved. The method includes: constructing a high-precision sequence stratigraphic framework in a target layer section; based on the data of the rotated fault polygon and the sequence interface, the spatial data is rotated to obtain the rotated fault polygon and the sequence interface data; according to the rotated fault polygon and the sequence interface data, and combining the spatial geometric relationship, the total vector slip of the fault is determined; the three-dimensional spatial positioning of the same fault at the top and bottom interfaces of the same sequence is realized by using a spatial multiple cross-correlation algorithm; according to the three-dimensional spatial positioning result of the same fault at the top and bottom interfaces of the same sequence, the total vector slip of the fault at the corresponding breakpoint is matched, and the matching difference operation is performed to obtain the sequence vector slip of the same sedimentary fault system in the sequence formation process.

[0008] In the Chinese patent application with the application number CN201510696919.3, a method for depicting growth reverse faults in a small and medium-sized extensional fault-depression basin is involved, which belongs to the technical field of geological exploration. The method includes: data collection and processing of a to-be-identified area; sequence division is performed under the guidance of sequence stratigraphy theory to establish an isochronous sequence stratigraphic framework of the to-be-identified area; under the constraint of the isochronous sequence stratigraphic framework of the to-be-identified area, structural-stratigraphic linkage interpretation is performed to identify and divide normal faults and reverse faults; the normal faults and the reverse faults divided are subjected to stratigraphic-sedimentary linkage interpretation to eliminate the pseudo faults misidentified and establish a fault framework of the to-be-identified area; the normal faults and the reverse faults in the fault framework of the to-be-identified area are subjected to structural-sedimentary linkage interpretation to establish a growth fault structural framework and determine the growth reverse faults therein; a structural geological model of the growth reverse faults and a derivative structural geological model thereof are constructed to analyze the causes of the growth reverse faults in each structural geological model and guide oil and gas exploration in the to-be-identified area.

[0009] In the Chinese patent application No. CN202110092388.2, a method for identifying hidden faults based on fault growth mechanism and seismic attribute prediction is disclosed. The method comprises the following steps: (a) performing seismic data structural interpretation, statistically analyzing the growth characteristics of regional faults, and determining the main fault; (b) according to the distance-position variation relationship of the fault throw of the main fault determined in step (a), a "throw-distance" curve is prepared; (c) finding the throw value reduction point of the "throw-distance" curve to preliminarily predict the development position of the hidden fault; (d) scanning and extracting the fault attribute feature map for the post-stack spatial three-dimensional seismic body, and judging the fault path according to the abnormal response in the map; (e) if the position predicted in step (c) simultaneously satisfies the fault path response identified in step (d), it is explained as a hidden fault.

[0010] The above prior art is quite different from the present application, and cannot solve the technical problems we want to solve. Therefore, we have invented a new seismic facies automatic identification method based on region growing technology. SUMMARY

[0011] The purpose of the present application is to provide a seismic facies automatic identification method based on region growing technology, which has good application effect on three-dimensional seismic facies identification and division.

[0012] The purpose of the present application can be achieved by the following technical measures: a seismic facies automatic identification method based on region growing technology, which comprises:

[0013] Step 1: converting the original seismic data to stratigraphic domain seismic data by Wheeler domain conversion;

[0014] Step 2: determining the seismic attribute sensitive to seismic facies classification;

[0015] Step 3: selecting a seed point;

[0016] Step 4: setting the seismic facies plane and vertical cutoff condition;

[0017] Step 5: executing the seed region growing process according to the cutoff condition;

[0018] Step 6: forcibly merging the regions without class into the adjacent most similar regions with class;

[0019] Step 7: obtaining the seismic facies identification result of each 1ms isochronous slice in the target layer based on the sensitive attribute;

[0020] Step 8: integrating the identification results of each slice to synthesize the three-dimensional seismic facies boundary.

[0021] The purpose of the present application can also be achieved by the following technical measures:

[0022] Step 1 includes:

[0023] (1) On the basis of automatic interpretation or manual interpretation of the main horizon tracking of the target interval in seismic data, an isochronous stratigraphic framework of the working area is established;

[0024] (2) The internal part of the above stratigraphic framework is finely tracked, and each sedimentary event in the target interval is further divided;

[0025] (3) The horizon is flattened according to a certain geological horizon above the target interval, so as to obtain the stratigraphic domain seismic data.

[0026] In step 2, the well logging facies of the actual drilling well is compared with different seismic attributes to select seismic attributes sensitive to seismic facies classification.

[0027] In step 3, the seed point is selected based on the following two basic principles:

[0028] (1) The well point on each slice must be a seed point, because on the basis of fine calibration, the well logging facies on the well can give the sedimentary facies label to the seismic facies beside the well;

[0029] (2) Non-well points that can reflect the typical form of sedimentary facies can also be seed points.

[0030] In step 4, the seismic facies planar cutoff condition is:

[0031] (1) The gray mean value m of the seed point area is calculated, and the seed point image area R is set, wherein the number of pixel points in the seed point area R is N, and the gray mean value of the seed point area is represented as:

[0032]

[0033] (2) The gray mean value of the neighborhood area is compared with the gray value of the seed point area, and if the absolute value of the two is less than the threshold value, they are merged:

[0034] max | f(x, y) - m | < K (x，y)∈0 <K

[0035] Wherein, f(x, y) is the gray mean value of the neighborhood area, m is the gray mean value of the seed point area, and K is the set threshold value.

[0036] In step 4, the vertical cutoff condition of the seismic facies is:

[0037] (1) Fine well-seismic calibration is carried out, and the well logging data on the well is converted from the depth domain to the time domain and matched with the seismic;

[0038] (2) Using well logging facies interpretation data on the well to determine the cutoff condition for the vertical growth of the seismic facies beside the well.

[0039] Step 5 comprises:

[0040] (1) Taking the seed point selected in step 3 as the growth starting point, judging whether there is a pixel point meeting the growth criterion in the neighborhood of the current region according to the growth cutoff condition provided in step 4, and if so, merging it into the grown region, thereby completing one iteration;

[0041] (2) Starting the second iteration according to the principle and method of the first iteration until no neighborhood pixel point meeting the condition is divided into the grown region, and the region growing algorithm ends.

[0042] In step 6, for the pixel points missed or excessively segmented in the merging process, the threshold K in step 4 is appropriately reduced, and the pixel points are re-merged and calculated, thereby being forced to fuse into the adjacent most similar and classed region.

[0043] Step 7 comprises:

[0044] (1) Obtaining a large number of isochronous slices at an interval of 1 ms for the stratum domain seismic data obtained through the Wheeler domain conversion in step 1;

[0045] (2) Using the seed point provided in step 3, the cutoff condition provided in step 4 and the region growing method provided in step 5 to identify the seismic facies in each isochronous slice according to the sensitive attribute.

[0046] In step 8, the identification results of the seismic facies boundary in different slices are integrated to be synthesized into a three-dimensional seismic facies boundary in space.

[0047] The automatic seismic facies identification method based on the region growing technology in the application, on the basis of fully utilizing well data as a hard constraint, gives the planar cutoff condition and the vertical cutoff condition of growth according to the data characteristics of the actual work area, and realizes the automatic identification and division of the three-dimensional seismic facies through the advanced region growing algorithm. The method can quickly and accurately identify the seismic facies boundary in the actual seismic data by simply giving the seed point, the growth criterion and the growth stopping condition, and has the advantages of simple operation, high three-dimensional seismic facies picking efficiency and accuracy, improved precision and greatly reduced workload, and has practical popularization and application significance. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 The flowchart of a specific embodiment of the automatic seismic facies identification method based on the region growing technology of the application;

[0049] Figure 2 The distribution diagram of 23 seed points in a specific embodiment of the application;

[0050] Figure 3 Fig. 1 is a schematic diagram of vertical cutoff conditions for central beach microfacies and underwater distributary channel microfacies according to an embodiment of the present application;

[0051] Figure 4 Fig. 2 is a three-dimensional seismic facies distribution map automatically constructed by a region growing technique according to an embodiment of the present application. DETAILED DESCRIPTION

[0052] It should be noted that the following detailed description is exemplary in nature and is intended to provide further description of the application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0053] It is also important to note that the terms used herein are not intended to limit the exemplary embodiments to the specific embodiments which can be illustrated by the examples, but rather, exemplary embodiments are meant to provide illustrative examples by using the terms. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof.

[0054] The method for automatic identification of seismic facies based on the region growing technique according to the present application comprises the following steps:

[0055] Step 1, converting the original seismic data to strata domain seismic data by Wheeler domain conversion; Step 2, determining seismic attributes sensitive to seismic facies classification; Step 3, selecting seed points; Step 4, setting seismic facies plane and vertical cutoff conditions; Step 5, executing seed region growing process according to the cutoff conditions; Step 6, forcibly merging regions without a class into adjacent regions with a similar class; Step 7, obtaining the identification result of seismic facies of each 1ms isochronous slice in the target layer based on the sensitive attributes; and Step 8, synthesizing the identification results of each slice to form a three-dimensional seismic facies boundary.

[0056] The present application can quickly and accurately identify the three-dimensional seismic facies boundary in actual seismic data, greatly improving the accuracy and efficiency of three-dimensional seismic facies analysis, and has practical application significance.

[0057] The following are several specific embodiments of the application, and different embodiments all adopt the technical process provided by the present application, but the specific calculation parameters are different according to the actual situation of different work areas.

[0058] Embodiment 1:

[0059] In the specific embodiment 1 of the application, as shown in Fig. 1, the vertical cutoff conditions for central beach microfacies and underwater distributary channel microfacies are set as follows: Figure 1 Figure 1 ​A flow chart of a seismic facies automatic identification method based on region growing technique of the present application includes the following eight steps and related parameter settings in this embodiment:

[0060] Firstly, the original seismic data is converted to stratigraphic domain seismic data by Wheeler domain conversion. In this case, the identification of seismic facies in a certain 3D seismic work area in western China is taken as an example. The main target interval of this block is Jurassic Sangonghe Formation. Six interpreted horizons, i.e., K1q, J2x, J1s3, J1s2 2-2 , J1s2 2-1 , J1s2 1 , are selected to construct the isochronous stratigraphic framework of the work area. On the basis of fine tracking of the internal sublayers in the framework, the layer is flattened along K1q, thereby obtaining the stratigraphic domain seismic data of the work area.

[0061] Secondly, the seismic attributes sensitive to the classification of seismic facies are determined. In this case, the seismic attributes sensitive to the classification of seismic facies are selected by comparing the well logging facies of the drilled wells with different seismic attributes, including root mean square amplitude and arc length.

[0062] Thirdly, the seed points are selected. According to two principles for selecting seed points: (1) the well points on each slice must be seed points; and (2) the non-well points that can reflect the typical morphology of sedimentary facies can also be seed points, Figure 2 for the distribution of the 23 seed points selected in this case, in addition to the well points of 20 drilled wells in the work area, three non-well seed points are selected for channel sand dam, sheet sand and shore shallow lake.

[0063] Fourthly, the planar and vertical cutoff conditions of seismic facies are set. The planar cutoff condition is that whether the difference between the gray mean value of the seed point region and the gray mean value of the adjacent region is less than the set threshold K is determined to judge whether to merge with the seed point. In this case, the threshold K is set to 0.02. Figure 3 for the vertical cutoff condition diagram of the heart beach microfacies and underwater distributary channel microfacies in this embodiment, on the basis of well-seismic fine calibration, the well logging facies interpretation data of the drilled well are used to determine the cutoff condition for the vertical growth of the seismic facies beside the well.

[0064] Fifthly, the seed region growing process is executed according to the cutoff condition. In this case, the 20 well points and three typical non-well points selected in the third step are taken as the starting points of growth, and the cutoff condition provided in the fourth step is used to judge whether there is a pixel point in the adjacent region of the current region that meets the growth criterion. Through multiple iterations, until there is no adjacent pixel point that meets the condition to be divided into the grown region, the region growing algorithm ends.

[0065] Step 6, the regions without class are forced to merge into the adjacent most similar regions with class. This step is mainly used to deal with the pixels missed or over-segmented in the merging process, in this case, the threshold K in step 4 is adjusted to 0.015, and the missed pixels are re-merged.

[0066] Step 7, based on the sensitive attributes, the seismic facies recognition results of each 1ms isochronous slice in the target layer are obtained. In this case, a total of 322 isochronous slices are obtained in the stratigraphic domain data, each slice contains two sensitive attributes of root mean square amplitude and arc length, based on the above sensitive attributes, using the 23 seed points in step 3, according to the growth stopping condition provided in step 4, the seismic facies recognition results of each 1ms isochronous slice are obtained.

[0067] Step 8, the three-dimensional seismic facies boundary is synthesized by integrating the recognition results of each slice. The three-dimensional work area of this case is 280Km 2 , excluding data preparation and other work, the whole seismic facies recognition process takes 3 hours, greatly improving the efficiency of seismic facies analysis, Figure 4 The three-dimensional seismic facies distribution map automatically constructed by the region growing technique is consistent with the existing geological understanding of the area, and also proves the accuracy of the seismic facies automatic recognition method based on the region growing technique.

[0068] Example 2

[0069] In the specific embodiment 2 of the application, similar to embodiment 1, the following eight implementation steps are also included, but different parameter settings are used:

[0070] Step 1, the original seismic data is converted to stratigraphic domain seismic data by Wheeler domain conversion. In this case, the seismic facies recognition of a three-dimensional seismic work area in western China is taken as an example, the main target layer of this block is Jurassic Xishanyao Formation, the isochronous stratigraphic framework of the work area is established by selecting K1q, J2x2 2 , J2x2 1 , J2x1 2 , J2x1 1 , J1s3 six interpreted horizons, on the basis of fine tracking of small layers in the framework, the layer is flattened along K1q, thereby obtaining the stratigraphic domain seismic data of the work area.

[0071] Step 2, determine the seismic attributes sensitive to seismic facies classification. In this case, the seismic attribute sensitive to seismic facies classification is the maximum amplitude, which is selected by comparing the well logging facies of the actual drilling well with different seismic attributes.

[0072] Third step, select seed points. According to two principles of seed point selection: (1) each layer slice on the well point must be as seed point; (2) non-well point which can reflect the typical form of sedimentary facies can also be as seed point, 13 seed points are selected in this embodiment, including 10 well points in the work area and 3 non-well point seed points selected for the sheet sand, peat bog and shore shallow lake.

[0073] Fourth step, set the seismic facies plane and vertical cutoff condition. The plane cutoff condition is: whether to merge with the seed point is judged by calculating the difference between the gray mean value of the seed point area and the gray mean value of the neighborhood area and whether it is less than the set threshold K, in this embodiment, the threshold K is set to 0.03; the vertical cutoff condition is: on the basis of well-seismic fine calibration, the well logging facies interpretation data of the drilled well is used to determine the cutoff condition for the vertical growth of the seismic facies beside the well.

[0074] Fifth step, execute the seed region growing process according to the cutoff condition. In this case, 10 well point positions and 3 typical non-well point positions selected in the third step, a total of 13 seed points are taken as the growing starting point, according to the growing cutoff condition provided in the fourth step, whether there is a pixel point in the neighborhood of the current region that meets the growing criteria is judged, through multiple iterations, until there is no neighborhood pixel point that meets the condition is divided into the grown region, the region growing algorithm ends.

[0075] Sixth step, forcibly fuse the region which still has no class to the adjacent most similar and classed region. This step is mainly used to process the pixel points which are missed or over-divided in the merging process, in this case, the threshold K in the fourth step is adjusted to 0.035, and the missed pixel points are re-merged.

[0076] Seventh step, obtain the seismic facies recognition result of each 1ms isochronous slice in the target layer based on the sensitive attribute. In this embodiment, we obtain a total of 256 isochronous slices in the stratum domain data, each slice contains the maximum amplitude of the sensitive attribute, based on the above sensitive attribute, using the 13 seed points in the third step, according to the growing cutoff condition provided in the fourth step, the seismic facies recognition result of each 1ms isochronous slice is obtained.

[0077] Eighth step, synthesize the recognition results of each slice to synthesize the three-dimensional seismic facies boundary. The three-dimensional work area is 398Km 2 , which does not include data preparation and other work, the whole recognition process takes 6.2 hours, due to larger data volume, the calculation speed is reduced, but compared with manual picking of seismic facies, the speed still meets the expectation, and good technical implementation effect is also achieved.

[0078] Example 3:

[0079] In the specific embodiment 3 of the application, similar to embodiment 1, the following eight implementation steps are also included, but different parameter settings are adopted:

[0080] Firstly, the original seismic data is converted into stratigraphic domain seismic data through Wheeler domain conversion. In this case, the seismic facies identification of a 3D seismic work area in eastern China is taken as an example. The main target interval of the block is Paleogene Shahejie Formation. The isochronous stratigraphic framework of the work area is constructed by selecting Es3z, Es3x, Es4s 1 , Es4s 2 , Es4s 3 five interpretation horizons. On the basis of fine tracking of internal layers, layer flattening is performed along Es3z, so as to obtain the stratigraphic domain seismic data of the work area.

[0081] Secondly, the seismic attributes sensitive to seismic facies classification are determined. In this case, the seismic attributes sensitive to seismic facies classification are maximum absolute amplitude and arc length, which are selected by comparing the well logging facies of actual drilled wells with different seismic attributes.

[0082] Thirdly, seed points are selected. According to two principles of seed point selection: (1) the well points on each slice must be seed points; (2) the non-well points that can reflect the typical morphology of sedimentary facies can also be seed points. In this embodiment, a total of 35 seed points are selected, including 32 well points in the work area and 3 non-well point seed points selected for fan delta plain overbank, delta plain natural levee and lake floor fan.

[0083] Fourthly, the seismic facies plane and vertical cutoff conditions are set. The plane cutoff condition is: whether to merge with the seed point is determined by calculating the difference between the gray mean value of the seed point region and the gray mean value of the adjacent region and whether it is less than the set threshold K. In this embodiment, the threshold K is set to 0.015. The vertical cutoff condition is: on the basis of well-seismic fine calibration, the well logging facies interpretation data of drilled wells are used to determine the vertical growth cutoff condition of the seismic facies beside the well.

[0084] Fifthly, the seed region growth process is executed according to the cutoff condition. In this case, the 32 well point positions and 3 typical non-well point positions selected in the third step, a total of 35 seed points, are taken as the growth starting points. According to the growth cutoff condition provided in the fourth step, it is determined whether there is a pixel point in the adjacent region of the current region that meets the growth criterion. Through multiple iterations, until there is no adjacent pixel point that meets the condition to be divided into the grown region, the region growth algorithm ends.

[0085] In the sixth step, the region without class is forced to merge into the adjacent most similar region with class. This step is mainly used to deal with the missed or over-segmented pixels in the merging process. In this case, the threshold K in the fourth step is adjusted to 0.01, and the missed pixels are re-merged.

[0086] In the seventh step, the seismic facies identification result of each 1ms isochronous slice in the target layer is obtained based on the sensitive attribute. In this embodiment, a total of 302 isochronous slices are obtained in the stratum domain data, each slice contains the maximum absolute amplitude and arc length of the sensitive attribute. Based on the sensitive attribute, the 35 seed points in the third step are used, and the growth stopping condition provided in the fourth step is used to obtain the seismic facies identification result of each 1ms isochronous slice.

[0087] In the eighth step, the three-dimensional seismic facies boundary is synthesized based on the identification results of each slice. The three-dimensional work area is 223Km 2 , and does not contain data preparation and other work. The whole identification process takes 5.8 hours. Since the three-dimensional data is high-density seismic data, the bin is small, and the data volume is large, the calculation speed is affected, but compared with manual picking of seismic facies, the speed still meets the expectation, and good technical implementation effect is also achieved.

[0088] The present application can be applied to the field of oil and gas exploration, and can be applied to the field of seismic facies identification and analysis. The method fully utilizes well data as a hard constraint, gives the plane stopping condition and the vertical stopping condition of growth according to the data characteristics of the actual work area, and realizes automatic identification and division of three-dimensional seismic facies through an advanced region growing algorithm. The method can quickly and accurately identify the seismic facies boundary in the actual seismic data by simply giving seed points, growth criteria and growth stopping conditions. The method is simple to operate, and has high three-dimensional seismic facies picking efficiency and accuracy. The method improves the accuracy and greatly reduces the workload, and has practical application significance.

[0089] Finally, it should be noted that: the above only describes the preferred embodiments of the present application and is not used to limit the present application. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements to some technical features. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0090] In addition to the technical features described in the specification, they are known to those skilled in the art.

Claims

1. An automatic seismic facies identification method based on region growing technology, characterized in that, The method includes: Step 1: Convert the raw seismic data into stratigraphic domain seismic data using Wheeler domain transformation; Step 2: Determine seismic attributes that are sensitive to seismic facies classification; Step 3, select the seed point; Step 4: Set the seismic phase plane and vertical cutoff conditions; In step 4, the seismic phase plane cutoff condition is: 4.1 Calculate the mean gray value m of the seed point region. Let the seed point image region be R, and the number of pixels in it be N. Then the mean gray value of the seed point region is expressed as: ； 4.2 Compare the average gray value of the neighboring region with the gray value of the seed point region. If the absolute value of both is less than a threshold, then merge them. ； in, f(x,y) Let be the average gray level of the neighboring region, and m be the average gray level of the seed point region. K The set threshold; In step 4, the vertical cutoff condition for the seismic phase is: 4.3 Conduct fine well-seismic calibration, converting well logging data from the depth domain to the time domain and ensuring good matching with seismic data; 4.

4. Use well logging facies interpretation data to determine the cutoff conditions for the vertical growth of seismic facies near the well; Step 5: Perform the seed region growth process according to the vertical cutoff condition, including: 5.

1. Using the seed point selected in step 3 as the growth starting point, and based on the growth cutoff condition provided in step 4, determine whether there are any pixels in the neighborhood of the current region that meet the growth criteria. If so, merge them into the grown region to complete one iteration. 5.

2. Based on the principles and methods of the first iteration, the second iteration begins until no neighboring pixels that meet the conditions are included in the grown region, at which point the region growing algorithm ends. Step 6: Force the unclassified regions to merge into the nearest, most similar, and classified regions. In step 6, for pixels that are missed or oversegmented during the merging process, the threshold K in step 4 is appropriately reduced, and the above pixels are recalculated for merging, thereby forcibly merging into the nearest, most similar, and classified regions. Step 7: Based on the sensitive attributes, obtain the seismic facies identification results of the isochronous slices every 1ms within the target layer; Step 8: Combine the identification results of each slice to synthesize a three-dimensional seismic phase boundary.

2. The automatic seismic facies identification method based on region growing technology according to claim 1, characterized in that, Step 1 includes: 1.1 Based on the tracking of the main stratigraphic positions of the target segment through automatic or manual interpretation in the seismic data, establish an isochronous stratigraphic framework for the work area; 1.

2. Perform fine-grained tracing of the internal layers of the isochronous stratigraphic framework, and further subdivide each depositional event within the target interval; 1.

3. Stratigraphic flattening is performed on a geological stratum above the target stratum to obtain stratigraphic seismic data.

3. The automatic seismic facies identification method based on region growing technology according to claim 1, characterized in that, In step 2, the logging facies of the actual drilled well are compared with different seismic attributes to select seismic attributes that are sensitive to seismic facies classification.

4. The automatic seismic facies identification method based on region growing technology according to claim 1, characterized in that, In step 3, the selection of seed points is based on the following two fundamental principles: a. Well points on each slice must be used as seed points; b. Non-well points that can reflect the typical morphology of sedimentary facies can also be used as seed points.

5. The automatic seismic facies identification method based on region growing technology according to claim 1, characterized in that, Step 7 includes: 7.

1. Obtain a large number of isochronous slices from the stratigraphic domain seismic data obtained by Wheeler domain transformation in step 1 at 1ms intervals; 7.

2. Using the seed points provided in step 3, the cutoff conditions provided in step 4, and the region growing method provided in step 5, seismic facies identification is performed on the sensitive attributes in each isochronous slice.

6. The automatic seismic facies identification method based on region growing technology according to claim 1, characterized in that, In step 8, the identification results of seismic phase boundaries from different slices are combined and synthesized into three-dimensional seismic phase boundaries in space.

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