CN108072898B - Geological boundary identification method based on planar data density estimation - Google Patents
Geological boundary identification method based on planar data density estimation Download PDFInfo
- Publication number
- CN108072898B CN108072898B CN201810141072.6A CN201810141072A CN108072898B CN 108072898 B CN108072898 B CN 108072898B CN 201810141072 A CN201810141072 A CN 201810141072A CN 108072898 B CN108072898 B CN 108072898B
- Authority
- CN
- China
- Prior art keywords
- data
- data density
- boundary
- geological
- density
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
- G01V1/30—Analysis
Abstract
The invention provides a geological boundary identification method based on planar data density estimation, which comprises the following steps: step 1, preprocessing seismic attribute data output by a seismic interpretation system according to a data sampling interval and a plane distribution range to generate an input data matrix; step 2, estimating the planar data density of the attribute data by using an eight-neighbor Sobel deformation operator for the input data matrix obtained in the step 1; step 3, solving a first derivative of the planar data density obtained in the step 2, and calculating a data segmentation boundary by using the first derivative as input data and adopting a watershed algorithm; and step 4, taking the knowledge of geologists as guidance, and extracting the boundary of the target geologic body by adopting a supervision mode. The geological boundary identification method based on the plane data density estimation achieves the purpose of accurately and efficiently identifying various geological boundaries, further improves the seismic description capacity and assists oil field exploration and development.
Description
Technical Field
The invention relates to the field of geological and geophysical exploration, in particular to a geological boundary identification method based on planar data density estimation.
Background
With the continuous development of seismic exploration theory, technology and equipment, abundant underground seismic information is provided by massive three-dimensional seismic data. The seismic attribute analysis is a very common and effective means and plays a very important role in lithology interpretation, geologic body identification and description. Particularly, in the case of lithologic objects with increasing specific gravity, not only the thickness, physical properties, etc. of the lithologic body need to be described, but also the boundary of the lithologic body needs to be accurately depicted.
The application of data density analysis to identification of geologic body boundaries still belongs to a new idea in the field of seismic description. The method can be applied to recognition of geologic body boundaries, can also be popularized and applied to boundary recognition in a plurality of fields such as reservoir physical property boundaries, underground fluid phase change boundaries, fault boundaries and the like, can be applied to auxiliary determination of geological dessert regions, crack development regions and the like, and has wide application prospect and practical significance.
The problem of boundary identification has become an irrevocable problem in future seismic description work. Therefore, a new geological boundary identification method based on plane data density estimation is invented, and the technical problems are solved.
Disclosure of Invention
The invention aims to provide a geological boundary identification method based on plane data density estimation, so as to improve the precision of identifying and describing geological boundaries by using seismic data.
The object of the invention can be achieved by the following technical measures: the geological boundary identification method based on the plane data density estimation comprises the following steps: step 1, preprocessing seismic attribute data output by a seismic interpretation system according to a data sampling interval and a plane distribution range to generate an input data matrix; step 2, estimating the planar data density of the attribute data by using an eight-neighbor Sobel deformation operator for the input data matrix obtained in the step 1; step 3, solving a first derivative of the planar data density obtained in the step 2, and calculating a data segmentation boundary by using the first derivative as input data and adopting a watershed algorithm; and step 4, taking the knowledge of geologists as guidance, and extracting the boundary of the target geologic body by adopting a supervision mode.
The object of the invention can also be achieved by the following technical measures:
in step 1, converting seismic attribute text data composed of three columns of x, y and z output by an interpretation system into a data table arranged in rows and columns, and establishing a mapping relation between row and column numbers and logical coordinates of the data table.
In step 1, the generated data table is stored in a two-dimensional array form and used as input data for the next operation.
In step 1, establishing a mapping relation between the row and column numbers of the data table and the logical coordinates, wherein each original data point coordinate is in one-to-one correspondence with the row and column numbers of the data table; and the data points corresponding to no data in the work area are correspondingly null values in the data table.
In step 2, for the data density at any point, an eight-neighbor distance weighting algorithm is adopted, and the obtained data density is a new direction-independent new parameter by taking an absolute value weighted average method.
In step 2, the data points that are spatially uniformly distributed at a fixed sampling interval are converted into data points that are numerically uniformly distributed, and the number of data points included in a unit area is defined as the data density.
In step 2, the eight-neighbor Sobel deformation operator longitudinal and transverse neighbor weight for data density estimation is set as √ 2, and the diagonal neighbor weight is set as 1; data density values are positive in all directions.
In step 3, the data density matrix obtained in step 2 is used as input data, and the change characteristics of the data density on a plane are highlighted by calculating a first derivative; and taking the first derivative of the data density as new input data, obtaining a segmentation boundary of the data density by adopting a watershed algorithm, and extracting a target geological boundary by taking geological knowledge as guidance.
In step 3, the first derivative of the data density obtained in step 2 is used for data boundary identification, and the data boundary identification is based on the fact that the seismic attribute numerical value has a mutation characteristic at the geological boundary; and determining the data density mutation boundary by judging whether the data points are positioned on the watershed point by point.
In step 4, the data density boundary is obtained by numerical calculation, the geological significance is given to the data density boundary in a supervision mode of experts, and required geological boundary data are extracted.
The geological boundary identification method based on plane data density estimation comprises the steps of defining and estimating the plane seismic attribute data density; and an algorithm for performing plane identification and description on the geologic body boundary by using the density data. According to the geological boundary identification method based on the plane data density estimation, the plane geological boundary data is obtained through quantitative calculation, uncertainty existing in the process of qualitatively describing the geological boundary by using seismic attributes is avoided, and the influence of an imaging color code and other human factors on the description of the geological boundary is eliminated. By applying the geological boundary identification method based on plane data density estimation, quantitative plane identification can be performed on the geological boundary which possibly exists by using the data density estimated from the plane seismic attribute data, so that the influence of human factors is eliminated, and the geological boundary identification precision is improved. The purpose of accurately and efficiently identifying various geological boundaries is achieved, the seismic description capacity is further improved, and the oil field exploration and development are assisted.
Drawings
FIG. 1 is a flow chart of one embodiment of a method for geologic boundary identification based on planar data density estimation in accordance with the present invention;
FIG. 2 is a diagram of raw attributes extracted from a seismic interpretation system in an embodiment of the invention;
FIG. 3 is a graph of the derivative of the data density from the planar seismic attributes in an embodiment of the present invention;
FIG. 4 is a graph showing data boundaries picked by the watershed algorithm in accordance with an embodiment of the present invention;
fig. 5 is a schematic diagram of a geological boundary of a target geologic body obtained by expert supervision according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
As shown in fig. 1, fig. 1 is a flow chart of a geological boundary identification method based on planar data density estimation according to the present invention.
At step 101, planar seismic attribute data is input. The flow proceeds to step 102.
In step 102, input planar seismic data is preprocessed to generate a data table arranged in rows and columns, and a mapping relation between logical coordinates and row and column numbers is established. And preprocessing the seismic attribute data output by the seismic interpretation system according to the data sampling interval and the plane distribution range to generate an input data matrix. Converting seismic attribute text data which is output by an interpretation system and consists of three columns of x, y and z into a data table which is arranged according to rows and columns, and establishing a mapping relation between row and column numbers and logical coordinates of the data table. The generated data table is stored in a two-dimensional array form and is used as input data of the next operation. Establishing a mapping relation between the row and column numbers of the data table and the logical coordinates, wherein each original data point coordinate is in one-to-one correspondence with the row and column numbers of the data table; and the data points corresponding to no data in the work area are correspondingly null values in the data table. The flow proceeds to step 103.
In step 103, the plane data density of the attribute data is estimated using the eight-neighbor sobel deformation operator for the data table generated in step 102. For the data density at any point, the method adopts an eight-neighbor distance weighting algorithm, and makes the obtained data density a new direction-independent new parameter by taking an absolute value weighted average. The meaning of the planar data density estimation is to convert data points which are uniformly distributed in space at fixed sampling intervals into data points which are uniformly distributed in numerical value, and the number of the data points contained in a unit area is defined as the data density. The eight-neighbor Sobel deformation operator longitudinal and transverse neighbor weight for data density estimation is set to be V2, and the diagonal neighbor weight is set to be 1; data density values are positive in all directions. The flow proceeds to step 104.
In step 104, for the plane data density estimated in step 103, first a first derivative is obtained, and then a watershed algorithm is used to obtain a segmentation boundary of the data density. The data density matrix obtained in the step 103 is used as input data, and the change characteristics of the data density on the plane are highlighted by calculating a first derivative; and taking the first derivative of the data density as new input data, obtaining a segmentation boundary of the data density by adopting a watershed algorithm, and extracting a target geological boundary by taking geological knowledge as guidance. Performing data boundary identification by using the first derivative of the data density obtained in the step 2 according to the fact that the seismic attribute has mutation at the geological boundary; and determining the data density mutation boundary by judging whether the data points are positioned on the watershed point by point. The flow proceeds to step 105.
In step 105, the boundary of the target geologic body is extracted by a supervising method with the knowledge of the geologist as guidance. The data density boundary is calculated numerically and needs to be assigned geological significance in a manner supervised by experts in step 105, and required geological boundary data is extracted.
In an embodiment of the present invention, fig. 2 is a graph of seismic attributes extracted by the GF2.0 interpretation system.
Step 1, outputting attribute data in a text format as input data, and entering step 2.
And 2, preprocessing input data, storing the input data in a data table form and establishing a relation between a data row number and a data column number and a logic coordinate.
In step 3, estimating the data density by adopting an eight-neighbor Sobel deformation operator; the first derivative was made to the data density and the results are shown in figure 3.
In step 4, the first derivative formed in step 3 is subjected to image segmentation by using a watershed algorithm, and the obtained result shown in fig. 4 is used as the result for carrying out geology expert supervision and identification in step 5, and the input condition of the target geological boundary is extracted.
Fig. 5 shows the boundary of the target geologic body obtained through the above process, and it can be seen that the effect of human factors has been eliminated by the result of boundary identification.
Claims (10)
1. The geological boundary identification method based on the plane data density estimation is characterized by comprising the following steps of:
step 1, preprocessing seismic attribute data output by a seismic interpretation system according to a data sampling interval and a plane distribution range to generate an input data matrix;
step 2, estimating the planar data density of the attribute data by using an eight-neighbor Sobel deformation operator for the input data matrix obtained in the step 1;
step 3, solving a first derivative of the planar data density obtained in the step 2, and calculating a data segmentation boundary by using the first derivative as input data and adopting a watershed algorithm; and
and 4, taking the knowledge of geologists as guidance, and extracting the boundary of the target geologic body by adopting a supervision mode.
2. The method for identifying geological boundaries based on planar data density estimation according to claim 1, characterized in that in step 1, the seismic attribute text data composed of three columns x, y and z outputted by the interpretation system is converted into a data table arranged by rows and columns, and the mapping relationship between the row number and the logical coordinates is established in the data table.
3. The method of claim 2, wherein in step 1, the generated data table is stored in a two-dimensional array as input data for the next operation.
4. The geological boundary identification method based on planar data density estimation according to claim 2, characterized in that in step 1, a mapping relation between data table row and column numbers and logical coordinates is established, and each original data point coordinate forms a one-to-one correspondence with the data table row and column numbers; and the data points corresponding to no data in the work area are correspondingly null values in the data table.
5. The method for identifying geological boundaries based on planar data density estimation of claim 1, wherein in step 2, for the data density at any point, an eight-neighbor sobel deformation operator is adopted, and the obtained data density is a new direction-independent new parameter by taking the weighted average of absolute values.
6. The method of claim 5, wherein in step 2, the spatially uniform data points at a fixed sampling interval are converted into numerically uniform data points, and the number of data points contained in a unit area is defined as the data density.
7. The geological boundary identification method based on plane data density estimation according to claim 5, characterized in that in step 2, the eight-neighbor Sobel deformation operator longitudinal and transverse neighbor weight for data density estimation is set as √ 2, and the diagonal neighbor weight is set as 1; data density values are positive in all directions.
8. The geological boundary identification method based on plane data density estimation according to claim 1, characterized in that in step 3, the data density matrix obtained in step 2 is used as input data, and the change characteristics of the data density on the plane are highlighted by calculating the first derivative; and taking the first derivative of the data density as new input data, obtaining a segmentation boundary of the data density by adopting a watershed algorithm, and extracting a target geological boundary by taking geological knowledge as guidance.
9. The method of claim 8, wherein in step 3, data boundary identification is performed using the first derivative of the data density obtained in step 2, based on the presence of abrupt features in the seismic attribute values at the geological boundary; and determining the data density mutation boundary by judging whether the data points are positioned on the watershed point by point.
10. The method of claim 1, wherein in step 4, the data density boundary is obtained by numerical calculation, and is assigned with geological significance in a manner supervised by experts, and the required geological boundary data is extracted.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810141072.6A CN108072898B (en) | 2018-02-11 | 2018-02-11 | Geological boundary identification method based on planar data density estimation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810141072.6A CN108072898B (en) | 2018-02-11 | 2018-02-11 | Geological boundary identification method based on planar data density estimation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108072898A CN108072898A (en) | 2018-05-25 |
| CN108072898B true CN108072898B (en) | 2021-02-26 |
Family
ID=62155103
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810141072.6A Active CN108072898B (en) | 2018-02-11 | 2018-02-11 | Geological boundary identification method based on planar data density estimation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108072898B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112031740B (en) * | 2019-05-15 | 2024-02-02 | 中国石油化工股份有限公司 | Shale gas geological dessert evaluation method and device based on stratum elements |
| CN112464143A (en) * | 2020-10-23 | 2021-03-09 | 中国石油天然气集团有限公司 | Method and device for identifying underground coal in-situ gasification boundary |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012141799A3 (en) * | 2011-02-25 | 2013-03-14 | University Of Florida Research Foundation, Inc. | Detection of sinkholes or anomalies |
| CN104730596A (en) * | 2015-01-25 | 2015-06-24 | 中国石油大学(华东) | Discrete fracture modeling method based on multiscale factor restraint |
| CN105467461A (en) * | 2015-12-01 | 2016-04-06 | 长江地球物理探测（武汉）有限公司 | Method for recognizing geological anomalous body through employing two-dimensional apparent resistivity data |
| CN107220964A (en) * | 2017-05-03 | 2017-09-29 | 长安大学 | A kind of linear feature extraction is used for geology Taking stability appraisal procedure |
| CN107229068A (en) * | 2016-03-24 | 2017-10-03 | 中国石油化工股份有限公司 | Method and apparatus for recognizing exploration geophysics signal boundary |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB2511744B (en) * | 2013-03-11 | 2020-05-20 | Reeves Wireline Tech Ltd | Methods of and apparatuses for identifying geological characteristics in boreholes |
-
2018
- 2018-02-11 CN CN201810141072.6A patent/CN108072898B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012141799A3 (en) * | 2011-02-25 | 2013-03-14 | University Of Florida Research Foundation, Inc. | Detection of sinkholes or anomalies |
| CN104730596A (en) * | 2015-01-25 | 2015-06-24 | 中国石油大学(华东) | Discrete fracture modeling method based on multiscale factor restraint |
| CN105467461A (en) * | 2015-12-01 | 2016-04-06 | 长江地球物理探测（武汉）有限公司 | Method for recognizing geological anomalous body through employing two-dimensional apparent resistivity data |
| CN107229068A (en) * | 2016-03-24 | 2017-10-03 | 中国石油化工股份有限公司 | Method and apparatus for recognizing exploration geophysics signal boundary |
| CN107220964A (en) * | 2017-05-03 | 2017-09-29 | 长安大学 | A kind of linear feature extraction is used for geology Taking stability appraisal procedure |
Non-Patent Citations (2)
| Title |
|---|
| A new approach for salt dome detection using a 3D multidirectional edge detector;Asjad Amin 等;《APPLIED GEOPHYSICS》;20150930;第12卷(第3期);第334-342页 * |
| 基于分水岭算法的地震属性异常体边缘检测技术;白瑜 等;《中州煤炭》;20151231(第9期);第97-100、128页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108072898A (en) | 2018-05-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Carneiro et al. | Semiautomated geologic mapping using self-organizing maps and airborne geophysics in the Brazilian Amazon | |
| US9618639B2 (en) | Method and system for image-guided fault extraction from a fault-enhanced seismic image | |
| CN111596978A (en) | Web page display method, module and system for lithofacies classification by artificial intelligence | |
| CN107545577A (en) | Sedimentary facies image partition method based on neutral net | |
| CN116665067B (en) | Ore finding target area optimization system and method based on graph neural network | |
| CN108072898B (en) | Geological boundary identification method based on planar data density estimation | |
| Masroor et al. | A multiple-input deep residual convolutional neural network for reservoir permeability prediction | |
| US9285502B2 (en) | System and method for lacunarity analysis | |
| WO2020231379A1 (en) | System and method for identifying subsurface structures | |
| CN115659848B (en) | Method for rapidly predicting two-dimensional basin-based substrate interface based on deep learning network | |
| CA3177867A1 (en) | Structured representations of subsurface features for hydrocarbon system and geological reasoning | |
| Wang et al. | A method for well log data generation based on a spatio-temporal neural network | |
| Yu-Feng et al. | Three-dimensional gravity inversion based on 3D U-Net++ | |
| CN114548299A (en) | Earthquake phase identification method and device, computer equipment and storage medium | |
| CN114299330A (en) | Seismic facies classification method | |
| Spichak | Modern methods for joint analysis and inversion of geophysical data | |
| CN115880455A (en) | Three-dimensional intelligent interpolation method based on deep learning | |
| CN116934603A (en) | Logging curve missing segment completion method and device, storage medium and electronic equipment | |
| CN112612935A (en) | Complete logging data acquisition method based on self-reasoning model | |
| Wang et al. | Intelligent seismic stratigraphic modeling using temporal convolutional network | |
| CN111597752A (en) | Cross-hole resistivity CT deep learning inversion method and system for balancing inter-hole sensitivity | |
| CN116990865B (en) | Microseism event detection method and system based on deep migration learning | |
| Ebuna et al. | Statistical approach to neural network imaging of karst systems in 3D seismic reflection data | |
| CN114152979B (en) | Method and device for classifying seismic waveforms under time-varying window frame | |
| Datskiv et al. | Framework for automatic globally optimal well log correlation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |