CN109782337B - Favorable reservoir boundary automatic picking method based on seismic attribute map - Google Patents
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Abstract
The invention provides a favorable reservoir boundary automatic picking method based on a seismic attribute map, which comprises the steps of carrying out selective transparent processing on a color seismic attribute map input in a bitmap format by adopting an accumulative windowing color filtering technology and only reserving a user attention area; smoothing the boundary of the user attention area by using a boundary pre-smoothing technology; automatically screening out the favorable area according to the characteristics of the area size, the shape, the extending direction and the like; and automatically picking up the favorable area boundary by applying a boundary lasso technique. The favorable reservoir boundary automatic picking method based on the seismic attribute map can automatically extract a user attention area according to pixel color value characteristics, screen a favorable target area, finely depict a favorable reservoir distribution range by picking the favorable area boundary, accurately calculate the favorable reservoir development area, and improve the work effect and research precision of oil and gas exploration researchers.
Description
Technical Field
The invention relates to the technical field of oil and gas exploration and development and computer graphic image processing, in particular to a favorable reservoir boundary automatic picking method based on a seismic attribute map.
Background
With the continuous deepening of the oil and gas exploration and development degree and the continuous increasing of the difficulty, the adopted technical means are continuously renovated, and the requirements on the multi-dimensional analysis and the comprehensive utilization of the seismic attribute map are increasingly urgent. On the basis of the seismic attribute map, how to more finely depict the favorable reservoir boundary and calculate the favorable reservoir area more accurately is a problem which needs to be solved by researchers urgently. At present, two common methods are available, one method is to perform experience-based range selection manually according to colors on a seismic attribute map, and the defects of the method are that the influence of human factors is large, the boundary cannot be picked automatically, the working efficiency is low, and the area calculation error is large. The other method is to use an edge detection method in the conventional image processing technology to obtain the brightness gradient value or the change rate of the original data by calculating a derivative, and the method does not consider the geological significance represented by the seismic attribute map, has large error and is sensitive to noise points in actual work and poor in practicability.
Therefore, how to accurately and efficiently depict favorable reservoir boundaries and accurately calculate the favorable reservoir areas is a difficult problem which needs to be solved urgently, such as the geological researchers performing quantitative fusion analysis and evaluation based on multi-attribute graphs and screening favorable exploration and development targets. The invention discloses a favorable reservoir boundary automatic picking method based on a seismic attribute map, which well solves the technical problems.
Disclosure of Invention
The invention aims to provide a favorable reservoir boundary automatic picking method based on a seismic attribute map, which can be more accurate and efficient, aiming at the problems in the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme that:
a favorable reservoir boundary automatic picking method based on a seismic attribute map comprises the following steps:
step 3, automatically screening the favorable target area for the user attention area with the smooth boundary obtained in the step 2 according to a specific rule;
and 4, automatically picking up the favorable boundary by applying a boundary lasso technology to the favorable target area screened in the step 3.
The above scheme further comprises:
in step 1, the input color seismic attribute map needs to be subjected to coordinate correction before being subjected to selective transparency processing, so that the input color seismic attribute map has accurate real coordinates, and the area of any closed area can be calculated conveniently.
In step 1, the cumulative windowing color filtering technique used means: one or more color windows are selected on the seismic attribute map by a mouse, one or more threshold value ranges are calculated by accumulating and counting color values contained in the windows, and selective transparent processing is carried out on the seismic attribute map according to the counted threshold value ranges.
In step 1, the cumulative window filtering technique includes two processing modes of cumulative window removal and cumulative window retention. The removing mode is to count all color values accumulated by windowing in the seismic attribute graph, calculate a threshold range, filter out color values in the graph which belong to the threshold range according to the threshold range and reserve color values outside the lower threshold range; the retention mode is to count all color values accumulated by windowing in the seismic attribute map, calculate a threshold range, then retain color values in the map which belong to the threshold range, and only filter out color values outside the threshold range.
In step 2, for the result obtained in step 1, smoothing is performed on the boundary of the user attention area by using a boundary pre-smoothing technique, so as to eliminate noise points such as burrs, convex-concave points and the like on the contour of the user attention area, so as to improve the smoothness of the boundary, and facilitate the subsequent pickup of the boundary of the interest area.
In step 3, for the user attention area with smooth boundary obtained in step 2, the favorable target area is automatically screened according to specific rules, wherein the specific rules comprise conditions such as area size, shape and extending direction.
In step 4, for the beneficial target area screened in step 3, the system will accurately delineate and pick up the boundary of the beneficial target area according to a specific algorithm.
Through the processed seismic attribute map, the finally picked favorable target area boundary is smooth one or more closed curves, and the optimal work of the exploration target can be supported.
Another method for automatically picking favorable reservoir boundaries based on seismic attribute maps comprises the following steps:
in step 1, after the earthquake attribute map is input, coordinate correction is carried out on a target map to enable the target map to have real coordinates with practical significance, selective transparent processing is carried out on the input earthquake attribute map by adopting an accumulative windowing color filtering technology, irrelevant attributes are removed, and only a user attention area is reserved; the adopted accumulative windowing and filtering technology refers to that one or more color windows are selected on the seismic attribute map by a mouse, one or more threshold value ranges are calculated by accumulating and counting color values contained in the windows, and selective transparent processing is carried out on the seismic attribute map according to the counted threshold value ranges; the accumulated windowing color filtering technology also comprises two modes of accumulated windowing removal and accumulated windowing preservation, wherein the accumulated windowing removal mode is that all color values accumulated by windowing in the seismic attribute map are counted, a threshold range is calculated, color values in the threshold range are filtered, color values outside the threshold range are preserved, and the accumulated windowing preservation mode is just opposite to the accumulated windowing preservation mode; in practical application, the two modes are matched;
in step 2, on the basis of the processing result in step 1, utilizing a boundary pre-smoothing processing technology to trim convex-concave points and burr noise points on the outline of the user attention area; the boundary pre-smoothing technology adopted here refers to selecting and removing one or more small burrs or small areas which are considered to be locally small and need to be processed in a man-machine interaction mode by using an eraser or a frame pulling mode;
in step 3, the favorable target area is automatically screened on the basis of the processing result of step 2: setting an area threshold, wherein a reservoir area lower than the threshold is filtered as noise, and only a favorable reservoir area with a larger area is reserved, or a favorable target area is screened in an auxiliary manner through regular shapes and extension directions;
in step 4, a boundary lasso technology is applied to the beneficial target area screened in step 3, the boundary of the beneficial target area is accurately drawn and picked up, the area of any area is calculated and overlapped with the geological structure map, and finally the picked-up boundary of the beneficial target area is one or more smooth closed curves.
The favorable reservoir boundary automatic picking method based on the seismic attribute map firstly adopts the accumulative windowing color filtering technology to transparently process the seismic attribute map, and the processing method has the advantages of high speed, high efficiency and thorough processing and can completely reserve the user attention area. The method has the advantages that the problem of burrs on the boundary of the user attention area can be solved by utilizing the pre-smoothing boundary picking technology, the functions of removing meaningless noise points and the like are achieved, and a foundation is laid for picking up smooth boundaries subsequently. And the beneficial target area is automatically screened according to the factors such as the area size, the shape, the extending direction and the like of the area, so that the working efficiency of researchers is improved. The boundary of the favorable area accurately picked by the boundary lasso technology lays a solid foundation for subsequent work such as oil and gas resource quantity evaluation, exploration target optimization and the like, and obviously reduces the calculation error of the favorable reservoir area. The reservoir boundary picking method based on the bitmap accumulation windowing color filtering processing technology can greatly improve the working efficiency and comprehensive research level of researchers.
Drawings
FIG. 1 is a flow chart of one embodiment of a method for favorable reservoir boundary automatic pickup based on a seismic attribute map of the present invention;
FIG. 2 is an original seismic attribute map used in the present invention;
FIG. 3 is a diagram of the effect of the present invention after filtering by windowing, the fields shown being regions of interest to a user;
FIG. 4 is an enlarged view of a portion of a region of interest to a user in accordance with the present invention;
FIG. 5 is an enlarged view of a local area in a user's region of interest, after a pre-smoothing process;
FIG. 6 is an automatically screened advantageous target zone of the present invention;
fig. 7 is an advantageous reservoir boundary for automatic retrieval of the present invention.
Detailed Description
The invention is further described below with reference to the drawings and the preferred embodiments.
Example 1:
a favorable reservoir boundary automatic picking method based on a seismic attribute map comprises the following steps:
step 3, automatically screening the favorable target area for the user attention area with the smooth boundary obtained in the step 2 according to a specific rule;
and 4, automatically picking up the favorable boundary by applying a boundary lasso technology to the favorable target area screened in the step 3.
The above embodiment further includes:
in step 1, the input color seismic attribute map needs to be subjected to coordinate correction before being subjected to selective transparency processing, so that the input color seismic attribute map has accurate real coordinates, and the area of any closed area can be calculated conveniently.
In step 1, the cumulative windowing color filtering technique used means: one or more color windows are selected on the seismic attribute map by a mouse, one or more threshold value ranges are calculated by accumulating and counting color values contained in the windows, and selective transparent processing is carried out on the seismic attribute map according to the counted threshold value ranges.
In step 1, the cumulative window filtering technique includes two processing modes of cumulative window removal and cumulative window retention. The removing mode is to count all color values accumulated by windowing in the seismic attribute graph, calculate a threshold range, filter out color values in the graph which belong to the threshold range according to the threshold range and reserve color values outside the lower threshold range; the retention mode is to count all color values accumulated by windowing in the seismic attribute map, calculate a threshold range, then retain color values in the map which belong to the threshold range, and only filter out color values outside the threshold range.
In step 2, for the result obtained in step 1, smoothing is performed on the boundary of the user attention area by using a boundary pre-smoothing technique, so as to eliminate noise points such as burrs, convex-concave points and the like on the contour of the user attention area, so as to improve the smoothness of the boundary, and facilitate the subsequent pickup of the boundary of the interest area.
In step 3, for the user attention area with smooth boundary obtained in step 2, the favorable target area is automatically screened according to specific rules, wherein the specific rules comprise conditions such as area size, shape and extending direction.
In step 4, for the beneficial target area screened in step 3, the system will accurately delineate and pick up the boundary of the beneficial target area according to a specific algorithm.
Through the processed seismic attribute map, the finally picked favorable target area boundary is smooth one or more closed curves, and the optimal work of the exploration target can be supported.
Exemplary embodiment 2:
as shown in fig. 1, fig. 1 is a flow chart of an advantageous reservoir boundary automatic picking method based on a seismic attribute map according to the invention. The method comprises the steps of inputting a color seismic attribute map, extracting a user attention area, performing boundary pre-smoothing processing, automatically screening a favorable area, picking up a favorable area boundary, supporting exploration target optimization and the like.
In step 101, after the seismic attribute map is input, the coordinates of the target map are corrected to have actual coordinates, and the map with the actual coordinate information can lay a foundation for the application of subsequent area calculation, boundary data pickup and other related steps. The flow proceeds to step 102.
In step 102, the input seismic attribute map is selectively processed in a transparent manner by using an accumulative windowing color filtering technology, irrelevant attributes are removed, and only a user attention area is reserved. The adopted accumulative windowing and filtering technology refers to that one or more color windows are selected on the seismic attribute map by a mouse, one or more threshold value ranges are calculated by accumulating and counting color values contained in the windows, and selective transparent processing is carried out on the seismic attribute map according to the counted threshold value ranges. Fig. 3 shows the effect after the treatment.
The cumulative window filtering technique includes both cumulative window removal and cumulative window retention. The accumulated windowing removal mode is that all color values accumulated by windowing in the seismic attribute map are counted, a threshold range is calculated, color values in the threshold range are filtered out, color values outside the threshold range are reserved, and the accumulated windowing reservation mode is just opposite to the accumulated windowing reservation mode. In practical application, the two modes are matched for use, and the effect is better. The flow proceeds to step 103.
In step 103, based on the processing result in step 102, noise points such as convex-concave points, burrs, etc. on the contour of the user attention area are trimmed by using a boundary pre-smoothing processing technique to improve the smoothness of the boundary, so as to facilitate the subsequent picking up of a smooth boundary of the interest area. The boundary pre-smoothing technology adopted here refers to selecting and removing one or more local small burrs or small areas which need to be processed in a man-machine interaction mode by using an eraser or a frame pulling mode. Fig. 4 and 5 are graphs showing comparison between the effects before and after the boundary pre-smoothing. The flow proceeds to step 104.
In step 104, favorable target areas are automatically screened based on the results of the processing in step 103. An area threshold can be artificially set, and the reservoir area below the threshold can be filtered out as noise, and only the favorable reservoir area with larger area is reserved. In addition, the favorable target area can be screened in an auxiliary way through the rules of shape, extension direction and the like. Fig. 6 is a schematic diagram of the beneficial target area screened automatically. The flow proceeds to step 105.
In step 105, for the favorable target area screened in step 104, the system will accurately delineate and pick the favorable target area boundary according to a specific algorithm. Fig. 7 shows the effect diagram of the delineated boundary of the advantageous target area. The flow proceeds to step 106.
In step 106, the area of any region can be calculated by using the result in step 105, and the area can be overlaid with other related maps such as geological structure maps, so that the optimization work of the exploration target can be conveniently carried out. The flow ends.
Claims (5)
1. A favorable reservoir boundary automatic picking method based on a seismic attribute map is characterized by comprising the following steps: the method comprises the following steps:
step 1, inputting a colorful earthquake attribute map in a bitmap format, and performing selective transparent processing on the earthquake attribute map by adopting an accumulative windowing color filtering technology, wherein only a user attention area is reserved;
the accumulated windowing color filtering technology comprises the steps of using a mouse to screen out one or more color windows on a seismic attribute map, calculating one or more threshold value ranges by accumulating and counting color values contained in the windows, and carrying out selective transparent processing on the seismic attribute map according to the counted threshold value ranges;
step 2, smoothing the boundary of the user attention area obtained in the step 1 by utilizing a boundary pre-smoothing technology;
step 3, automatically screening the beneficial target area according to a specific rule for the user attention area with the smooth boundary obtained in the step 2, wherein the specific rule is that the beneficial target area is automatically screened out according to the characteristics of the area size, the shape and the extending direction;
and 4, automatically picking the boundary of the favorable area by applying a boundary lasso technology to the favorable target area screened in the step 3.
2. The method of claim 1 for automatically picking favorable reservoir boundaries based on seismic attribute maps, wherein: the cumulative windowing processing technique includes cumulative windowing removal and cumulative windowing retention; the removing mode is to count all color values accumulated by windowing in the seismic attribute map, calculate a threshold range, filter out color values in the threshold range and keep color values outside the threshold range; the retention mode is to count all color values accumulated by windowing in the seismic attribute map, calculate a threshold range, finally retain color values within the threshold range, and filter out color values outside the threshold range.
3. The method for automatically picking favorable reservoir boundaries based on seismic attribute maps according to claim 1 or 2, characterized by: in step 2, a pre-smoothing technology is introduced in the process of processing the boundary of the user attention area to process noise points on the boundary of the user attention area.
4. The method of claim 3 for automatically picking favorable reservoir boundaries based on seismic attribute maps, wherein: the boundary pre-smoothing is to trim convex-concave points and burr noisy points on the outline of the user attention area so as to improve the smoothness of the boundary and facilitate the pickup of the subsequent favorable area boundary.
5. A favorable reservoir boundary automatic picking method based on a seismic attribute map is characterized by comprising the following steps:
in step 1, after the earthquake attribute map is input, coordinate correction is carried out on a target map to enable the target map to have real coordinates with practical significance, selective transparent processing is carried out on the input earthquake attribute map by adopting an accumulative windowing color filtering technology, irrelevant attributes are removed, and only a user attention area is reserved; the adopted accumulative windowing and filtering technology refers to that one or more color windows are selected on the seismic attribute map by a mouse, one or more threshold value ranges are calculated by accumulating and counting color values contained in the windows, and selective transparent processing is carried out on the seismic attribute map according to the counted threshold value ranges; the accumulated windowing color filtering technology also comprises two modes of accumulated windowing removal and accumulated windowing preservation, wherein the accumulated windowing removal mode is that all color values accumulated by windowing in the seismic attribute map are counted, a threshold range is calculated, color values in the threshold range are filtered, color values outside the threshold range are preserved, and the accumulated windowing preservation mode is just opposite to the accumulated windowing preservation mode; in practical application, the two modes are matched;
in step 2, on the basis of the processing result in step 1, utilizing a boundary pre-smoothing processing technology to trim convex-concave points and burr noise points on the outline of the user attention area; the boundary pre-smoothing technology adopted here refers to selecting and removing one or more small burrs or small areas which are considered to be locally small and need to be processed in a man-machine interaction mode by using an eraser or a frame pulling mode;
in step 3, the favorable target area is automatically screened on the basis of the processing result of step 2: setting an area threshold, wherein a reservoir area lower than the threshold is filtered as noise, and only a favorable reservoir area with a larger area is reserved, or a favorable target area is screened in an auxiliary manner through regular shapes and extension directions;
in step 4, a boundary lasso technology is applied to the beneficial target area screened in step 3, the boundary of the beneficial target area is accurately drawn and picked up, the area of any area is calculated and overlapped with the geological structure map, and finally the picked-up boundary of the beneficial target area is one or more smooth closed curves.
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