CN115577616A - Carbonatite fracture-cave earthquake depicting method and device based on deep learning - Google Patents
Carbonatite fracture-cave earthquake depicting method and device based on deep learning Download PDFInfo
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Abstract
The invention discloses a carbonatite fracture hole seismic characterization method and device based on deep learning, and belongs to the technical field of oil and gas exploration and development. The method comprises the following steps: acquiring seismic data; horizontally stacking the seismic data to obtain a post-stack migration seismic data volume; extracting a root-mean-square amplitude data volume and a seismic sweet-spot attribute data volume based on the post-stack migration seismic data volume; picking up learning points on a seismic section obtained by horizontally stacking seismic data; inputting training samples and learning targets into a learning vector machine for training to obtain a deep learning network, wherein the training samples are a post-stack migration seismic data body, square root amplitude data and seismic dessert attributes, and the learning targets are learning points; and obtaining a fracture-cave characteristic attribute data volume by utilizing a deep learning network based on the post-stack migration seismic data volume, the square root amplitude data and the seismic dessert attribute. The method of the invention not only can well predict the fracture and the hole, but also has good prediction effect on the underground corrosion hole.
Description
Technical Field
The invention belongs to the technical field of oil-gas exploration and development, and particularly relates to a carbonatite fracture-cave seismic carving method and device based on deep learning.
Background
The carbonate oil-gas reservoir is one of the main oil-gas reservoir types in China, the formation of a carbonate reservoir is mainly influenced by the later karst transformation and fracture action to form secondary erosion holes and cracks, and a storage space is a fracture-cave system formed by the secondary erosion holes and cracks. The reservoir space mainly comprises semi-filled or unfilled residual large-scale karst caves and eroded cave seams formed by karst action, and the high-quality reservoir type mainly comprises cracks, eroded caves and large-scale caves and is the most important reservoir and main power production layer for high yield and stable yield of each large oil and gas field; the secondary pores formed by the buried organic corrosion are also important effective pores, and the development of the secondary pores is matched with the formation, evolution and transport and aggregation of hydrocarbons; the multi-stage superposition and transformation of the dissolution effects of the epigenetic karst and the buried organic are the best combination mode formed by the paleo-karst reservoir and the oil-gas reservoir.
At present, the carbonate fracture-cave prediction by utilizing seismic data is mainly predicted and identified by adopting the following technologies: firstly, an anisotropic crack prediction method is adopted to predict a crack hole development zone, the prediction technology needs to acquire seismic data in a wide azimuth to overcome the adverse effect of noise on an inversion result, and conventional seismic data are often not detailed enough, so that the prediction of the crack hole development zone is difficult to meet the requirements of development and production, the prediction is inaccurate, the time is long, and more crack hole system prediction and actual drilling production are inconsistent; secondly, carbonate fracture-vug structure earthquake characterization method, the method is characterized in that the transverse discontinuous characteristic of earthquake data is mainly utilized to predict carbonate development area and scale on the basis of fracture and crack prediction, the method has the advantages of good prediction effect on fracture-vug related to fracture and crack development, and the defect of poor prediction effect on carbonate karst cave formed by the erosion action of underground runoff and underground water; and thirdly, a method for estimating the density of the crack around the well, wherein the method is mainly characterized in that a training set and a testing set are established by utilizing an imaging logging technology (imaging logging is mainly used for crack prediction) and seismic attributes, so that a crack development area is predicted. The technology has an obvious crack prediction effect, but has a poor corrosion hole prediction effect.
In general, the current carbonate salt fracture hole prediction technology has a good effect on predicting fracture holes, but has a poor effect on predicting erosion type holes. However, the erosion vugs in the carbonate oil-gas exploration in China are also one of the fields which are widely developed and have good exploration effects, so that the carbonate erosion vugs prediction technology can well solve the problem of the carbonate erosion vugs in the oil-gas exploration.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a carbonatite fracture hole earthquake description method and device based on deep learning.
The purpose of the invention is realized by the following technical scheme:
according to the first aspect of the invention, the carbonate fracture cave earthquake depicting method based on deep learning comprises the following steps:
acquiring seismic data;
horizontally stacking the seismic data to obtain a post-stack migration seismic data volume;
extracting a root mean square amplitude data volume and a seismic dessert attribute data volume based on the post-stack migration seismic data volume;
picking up learning points on a seismic section obtained by horizontally stacking the seismic data;
inputting training samples and a learning target into a learning vector machine for training to obtain a deep learning network, wherein the training samples are a post-stack migration seismic data volume, square root amplitude data and seismic dessert attributes, and the learning target is the learning point;
and obtaining a fracture-cave characteristic attribute data volume by utilizing the deep learning network based on the post-stack migration seismic data volume, the square root amplitude data and the seismic dessert attribute.
Further, the carbonate fracture-cave seismic carving method comprises the following steps:
and displaying the space distribution characteristics of the carbonatite fracture-cave in a display mode of a section, a plane or a three-dimensional space based on the fracture-cave characteristic attribute data body.
Further, the method for extracting the root-mean-square amplitude data volume comprises the following steps:
and based on the post-stack migration seismic data volume, extracting a root mean square amplitude data volume of the post-stack migration seismic data volume by using an attribute extraction tool in the paramigm software.
Further, the extraction method of the seismic dessert attribute data volume comprises the following steps:
and extracting the dessert attribute data body of the post-stack migration seismic number by using an attribute extraction tool in the paramigm software based on the post-stack migration seismic data body.
Further, the learning point is specified by:
and picking up the learning point on the seismic section by adopting a mouse picking-up mode.
According to a second aspect of the invention, a carbonate fracture cave seismic delineation device based on deep learning comprises:
the data acquisition module is used for acquiring seismic data;
the data stacking module is used for horizontally stacking the seismic data to obtain a post-stack migration seismic data volume;
a data extraction module for extracting a root mean square amplitude data volume and a seismic dessert attribute data volume based on the post-stack migration seismic data volume;
the learning point picking module is used for picking learning points on the seismic section;
the learning network construction module is used for inputting training samples and learning targets into a learning vector machine for training to obtain a deep learning network, the training samples are post-stack migration seismic data bodies, square root amplitude data and seismic dessert attributes, and the learning targets are the learning points;
and the fracture-cave carving module is used for obtaining a fracture-cave characteristic attribute data body by utilizing the deep learning network based on the post-stack migration seismic data body, the square root amplitude data and the seismic dessert attribute.
Further, the carbonatite fracture-cave seismic depicting device further comprises:
and the display module is used for displaying the spatial distribution characteristics of the carbonatite fracture-cavity in a display mode of a section, a plane or a three-dimensional space based on the fracture-cavity characteristic attribute data volume.
Further, the data extraction module is specifically configured to extract a root mean square amplitude data volume and a sweet spot attribute data volume of the post-stack migration seismic data volume by using an attribute extraction tool in the paramigm software based on the post-stack migration seismic data volume.
Further, the learning point picking module is specifically used for picking the learning point on the seismic section in a mouse picking mode.
The beneficial effects of the invention are: the method organically combines the post-stack migration seismic data body, the root mean square amplitude data body and the dessert attribute data body to establish the deep learning network, and then intuitively describes the carbonatite fracture tunnel by using a machine learning method, thereby effectively predicting the spatial distribution characteristics and the plane distribution rule of the carbonatite fracture tunnel. Compared with the disclosed prediction technology, the method disclosed by the invention not only can be used for well predicting the fractured joint-vugs, but also has a good prediction effect on underground erosive vugs, and the distribution of the carbonate erosive vugs predicted by the method is higher in goodness of fit with the borehole.
Drawings
FIG. 1 is a flow chart of one embodiment of a carbonate fracture-cave seismic delineation method of the present invention;
FIG. 2 is a schematic illustration of a post-stack migration seismic data volume in one embodiment;
FIG. 3 is a schematic representation of a root mean square amplitude data volume in one embodiment;
FIG. 4 is a schematic diagram of a cookie property data body in one embodiment;
FIG. 5 is a schematic illustration of learning points in one embodiment;
FIG. 6 is a profile of a carbonate crevice obtained from deep learning in one embodiment;
FIG. 7 is a representation of carbonate crevice plane features obtained from deep learning in one embodiment;
FIG. 8 is a representation of the three-dimensional spatial features of carbonate crevices obtained by deep learning in one embodiment;
fig. 9 is a block diagram showing the components of an embodiment of the carbonatite fracture-cave seismic characterization device according to the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive effort based on the embodiments of the present invention, are within the scope of protection of the present invention.
Referring to fig. 1 to 9, the present invention provides a carbonate fracture-cave seismic characterization method and apparatus based on deep learning:
the invention provides a carbonatite fracture-cave seismic carving method based on deep learning in a first aspect, and as shown in fig. 1, the carbonatite fracture-cave seismic carving method comprises steps S100-S600, which are described in detail below.
S100, acquiring seismic data.
And S200, horizontally stacking the seismic data to obtain a post-stack migration seismic data volume.
As shown in fig. 2, from the post-stack migration seismic data volume, it can be seen that: carbonate fracture-cave systems typically exhibit "beading" characteristics on seismic sections. The seismic amplitude of a fracture-cave development area on the seismic section is enhanced, and the seismic amplitude is randomly reflected, strongly reflected and beaded reflected.
Generally, the output result of the seismic data after horizontal stacking is a time section. In some embodiments, on the basis of a finite difference method and a kirchhoff integral method migration principle, performing post-stack migration processing and analysis on geological model data by means of Vista software; analysis results show that for seismic records with relatively simple geological conditions and low signal-to-noise ratio, the effect of the post-stack migration processing is superior to that of the pre-stack migration processing, so that the post-stack migration still has advantages within a certain range.
And S300, extracting a root-mean-square amplitude data body and a seismic dessert attribute data body based on the post-stack migration seismic data body.
In some embodiments, a root mean square amplitude data volume of post-stack offset seismic counts is extracted using an attribute extraction tool in the paradigm software based on the post-stack offset seismic data volume.
In some embodiments, based on the post-stack migration seismic data volumes, a dessert attribute data volume for the post-stack migration seismic data is extracted using an attribute extraction tool in the paramigm software.
That is, in some embodiments, a root mean square amplitude data volume and a seismic dessert attribute data volume are extracted from the post-stack offset seismic data volume using attribute extraction tools in the paradigm software. In this embodiment, the number of learning samples is increased by extracting the root-mean-square amplitude data volume and the seismic dessert attribute data volume from the post-stack migration seismic data volume, which is beneficial to improving the accuracy of the deep learning network.
S400, learning points are picked up on a seismic section obtained by horizontally stacking the seismic data.
In some embodiments, a mouse picking mode is adopted to pick learning points on the seismic section, and the learning points have the characteristics of amplitude enhancement, strong reflection, beaded reflection and low speed in the earthquake.
S500, inputting training samples and learning targets into a learning vector machine for training to obtain a deep learning network, wherein the training samples are post-stack migration seismic data bodies, square root amplitude data and seismic dessert attributes, and the learning targets are the learning points.
Specifically, a post-stack migration seismic data volume, square root amplitude data and seismic dessert attributes are used as training samples, learning points are used as learning targets, the training samples and the learning targets are input into a learning vector machine, and a deep learning network is determined in an iterative updating mode; and if the correlation relationship is poor after iteration, returning to the iteration again until a preset effect is achieved.
S600, based on the post-stack migration seismic data volume, the square root amplitude data and the seismic dessert attribute, obtaining a fracture-cave characteristic attribute data volume by using the deep learning network.
Specifically, the post-stack migration seismic data volume, the square root amplitude data and the seismic dessert attribute are input into a deep learning network, and are carved in an iterative updating mode under the constraint of a training model to obtain a fracture-cave characteristic attribute data volume.
In some embodiments, the spatial distribution characteristics of the carbonate fracture holes are displayed in a sectional, planar or three-dimensional display mode based on the fracture hole characteristic attribute data body.
The method of this embodiment will be described below by way of example. Acquiring seismic data; a post-stack migration seismic data volume obtained by horizontally stacking the acquired seismic data is shown in fig. 2; a root mean square amplitude data volume extracted from the post-stack migration seismic data volume is shown in fig. 3, and a seismic dessert attribute data volume extracted from the post-stack migration seismic data volume is shown in fig. 4; the learning points picked up on the seismic section are shown in FIG. 5; inputting training samples and a learning target into a learning vector machine for training to obtain a deep learning network, wherein the training samples are a post-stack migration seismic data volume, square root amplitude data and seismic dessert attributes, and the learning target is the learning point; based on the post-stack migration seismic data volume, the square root amplitude data and the seismic dessert attribute, obtaining a fracture-cave characteristic attribute data volume by using the deep learning network; displaying the spatial distribution characteristics of the carbonatite fracture cavity in a section display mode based on the fracture cavity characteristic attribute data volume, wherein the section display mode is as shown in figure 6; displaying the spatial distribution characteristics of the carbonate fissure cavern in a planar display mode based on the fissure cavern characteristic attribute data volume, as shown in fig. 7; and displaying the spatial distribution characteristics of the carbonatite fracture cavity in a three-dimensional display mode based on the fracture cavity characteristic attribute data volume, as shown in fig. 8.
A second aspect of the present invention provides a carbonatite fracture-cave seismic mapping device based on deep learning, and as shown in fig. 9, the carbonatite fracture-cave seismic mapping device includes a data acquisition module, a data superposition module, a data extraction module, a learning point pickup module, a learning network construction module, and a fracture-cave mapping module.
And the data acquisition module is used for acquiring seismic data. In this embodiment, the data obtaining module may be configured to execute step S100 shown in fig. 1, and reference may be made to the description of step S100 for a detailed description of the data obtaining module.
And the data stacking module is used for horizontally stacking the seismic data to obtain a post-stack migration seismic data volume. In this embodiment, the data overlaying module may be configured to perform step S200 shown in fig. 1, and reference may be made to the description of step S200 for a detailed description of the data overlaying module.
And the data extraction module is used for extracting a root-mean-square amplitude data volume and a seismic dessert attribute data volume based on the post-stack migration seismic data volume. In this embodiment, the data extraction module may be configured to perform step S300 shown in fig. 1, and the detailed description about the data extraction module may refer to the description about step S300.
And the learning point picking module is used for picking up learning points on the seismic section. In this embodiment, the learning point picking module can be used to execute step S400 shown in fig. 1, and the detailed description about the learning point picking module can refer to the description of step S400.
The learning network construction module is used for inputting training samples and learning targets into a learning vector machine for training to obtain a deep learning network, the training samples are post-stack migration seismic data volumes, square root amplitude data and seismic dessert attributes, and the learning targets are the learning points. In this embodiment, the learning network building module can be used to execute step S500 shown in fig. 1, and the detailed description about the learning network building module can refer to the description about step S500.
And the fracture-cave carving module is used for obtaining a fracture-cave characteristic attribute data body by utilizing the deep learning network based on the post-stack migration seismic data body, the square root amplitude data and the seismic dessert attribute. In this embodiment, the hole carving module may be configured to execute step S600 shown in fig. 1, and the detailed description of the hole carving module may refer to the description of step S600.
The foregoing is illustrative of the preferred embodiments of the present invention, and it is to be understood that the invention is not limited to the precise form disclosed herein and is not to be construed as limited to the exclusion of other embodiments, and that various other combinations, modifications, and environments may be used and modifications may be made within the scope of the concepts described herein, either by the above teachings or the skill or knowledge of the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (9)
1. The carbonatite fracture-cave seismic carving method based on deep learning is characterized by comprising the following steps of:
acquiring seismic data;
horizontally stacking the seismic data to obtain a post-stack migration seismic data volume;
extracting a root mean square amplitude data volume and a seismic dessert attribute data volume based on the post-stack migration seismic data volume;
picking up learning points on a seismic section obtained by horizontally stacking the seismic data;
inputting training samples and a learning target into a learning vector machine for training to obtain a deep learning network, wherein the training samples are a post-stack migration seismic data volume, square root amplitude data and seismic dessert attributes, and the learning target is the learning point;
and obtaining a fracture-cave characteristic attribute data volume by utilizing the deep learning network based on the post-stack migration seismic data volume, the square root amplitude data and the seismic dessert attribute.
2. The carbonate fracture cave seismic delineation method based on deep learning of claim 1, wherein the carbonate fracture cave seismic delineation method comprises:
and displaying the space distribution characteristics of the carbonatite fracture-cave in a display mode of a section, a plane or a three-dimensional space based on the fracture-cave characteristic attribute data body.
3. The carbonate fracture-cave seismic delineation method based on deep learning of claim 1, wherein the extraction method of the root-mean-square amplitude data volume is as follows:
and based on the post-stack migration seismic data volume, extracting a root mean square amplitude data volume of the post-stack migration seismic data volume by using an attribute extraction tool in the paramigm software.
4. The carbonate fracture-cave seismic delineation method based on deep learning of claim 1, wherein the extraction method of the seismic dessert attribute data volume is as follows:
and extracting the dessert attribute data body of the post-stack migration seismic number by using an attribute extraction tool in the paramigm software based on the post-stack migration seismic data body.
5. The carbonate fracture-cave seismic delineation method based on deep learning of claim 1, wherein the method for specifying the learning points is as follows:
and picking up the learning points on the seismic section by adopting a mouse picking-up mode.
6. Carbonatite fracture tunnel earthquake depicting device based on degree of depth study, its characterized in that includes:
the data acquisition module is used for acquiring seismic data;
the data stacking module is used for horizontally stacking the seismic data to obtain a post-stack migration seismic data volume;
a data extraction module for extracting a root mean square amplitude data volume and a seismic dessert attribute data volume based on the post-stack migration seismic data volume;
the learning point picking module is used for picking learning points on the seismic section;
the learning network construction module is used for inputting training samples and learning targets into a learning vector machine for training to obtain a deep learning network, wherein the training samples are post-stack migration seismic data volumes, square root amplitude data and seismic dessert attributes, and the learning targets are the learning points;
and the fracture-cave carving module is used for obtaining a fracture-cave characteristic attribute data body by utilizing the deep learning network based on the post-stack migration seismic data body, the square root amplitude data and the seismic dessert attribute.
7. The carbonatite fracture-cave seismic delineation apparatus based on deep learning of claim 6, wherein the carbonatite fracture-cave seismic delineation apparatus further comprises:
and the display module is used for displaying the spatial distribution characteristics of the carbonatite fracture-cavity in a display mode of a section, a plane or a three-dimensional space based on the fracture-cavity characteristic attribute data volume.
8. The carbonate fracture-cave seismic delineation device based on deep learning of claim 6, wherein the data extraction module is specifically configured to extract a root mean square amplitude data volume and a sweet spot attribute data volume of the post-stack migration seismic number based on the post-stack migration seismic data volume by using an attribute extraction tool in paradigm software.
9. The carbonate rock fracture-cave seismic delineation device based on deep learning of claim 6, wherein the learning point picking module is specifically configured to pick learning points on the seismic profile by means of mouse picking.
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