CN107144878B - Fault identification method and device - Google Patents
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Abstract
The embodiment of the application discloses a fault identification method and device. The method comprises the following steps: acquiring seismic data of a target interval; performing frequency spectrum decomposition processing on the seismic data based on a preset frequency interval and the frequency range of the seismic data to obtain a plurality of seismic frequency division data of the target interval; preprocessing the seismic frequency division data to obtain preprocessed seismic frequency division data; performing color fusion processing on the seismic data of the target interval based on the preprocessed seismic frequency division data; and identifying the position of the fault in the target interval based on the seismic data after the color fusion processing. The fault recognition accuracy can be improved.
Description
Technical Field
The application relates to the technical field of seismic data interpretation of oil field exploration, in particular to a fault identification method and device.
Background
The strong pressure and tension generated during the movement of the earth crust exceed the strength of the rock stratum, so that the rock stratum is fractured, and rock blocks on two sides of the rock stratum relatively move along the fracture surface to form a structural form, which can be called a fault. The fault forms an early diagenetic fluid which flows along the fault to erode to form a seam-type hole reservoir layer to become an oil and gas storage space, and oil and gas are vertically moved along the fault in a later period to finally form a reservoir. The fault and the area nearby the fault are usually the main oil and gas distribution areas, which are favorable target areas for oil and gas exploration and well location deployment. Therefore, the formation of fault co-fracture-cavity reservoirs, the migration and accumulation of oil and gas, the distribution of the oil and gas and the like are all dense and inseparable. Therefore, the study of fractured layers in a fracture-cavity reservoir is very important.
In the prior art, the fault identification method mainly identifies the fault through the seismic attributes of the seismic data corresponding to the target interval. For example, faults may be identified by coherence properties of the seismic data. Specifically, along a certain horizon in the target interval, a coherence coefficient, that is, a waveform similarity, between adjacent seismic trace data in a preset time window of the horizon is calculated. When the correlation coefficient between the data of some two adjacent seismic channels is small, a fault may exist between the two adjacent seismic channels, and therefore the fault is identified. The method is mainly used for identifying the fault through the vertical dislocation of rock masses on two sides of the fault.
The inventor finds that at least the following problems exist in the prior art: at present, for fracture-cavity carbonate oil and gas reservoirs, internal faults are mainly walk-slip faults, and because the vertical dislocation of the walk-slip faults is not obvious, the existing fault identification method can cause difficulty in effectively identifying the walk-slip faults through the coherence attributes of seismic data.
Disclosure of Invention
The embodiment of the application aims to provide a fault identification method and a fault identification device so as to improve fault identification accuracy.
In order to solve the above technical problem, an embodiment of the present application provides a fault identification method and apparatus, which are implemented as follows:
a fault identification method, comprising:
acquiring seismic data of a target interval; performing frequency spectrum decomposition processing on the seismic data based on a preset frequency interval and the frequency range of the seismic data to obtain a plurality of seismic frequency division data of the target interval;
preprocessing the plurality of seismic frequency division data to obtain preprocessed seismic frequency division data;
performing color fusion processing on the seismic data of the target interval based on the preprocessed seismic frequency division data;
and identifying the position of the fault in the target interval based on the seismic data after the color fusion processing.
In a preferred embodiment, the preprocessing the plurality of seismic frequency division data to obtain the preprocessed seismic frequency division data includes: and carrying out layer leveling processing on the plurality of seismic frequency division data to obtain the seismic frequency division data after the layer leveling processing.
In a preferred embodiment, the performing layer leveling processing on the seismic frequency division data to obtain the seismic frequency division data after the layer leveling processing includes:
for first seismic frequency division data in the plurality of seismic frequency division data, correcting the sampling time of each sampling point on a first layer in the first seismic frequency division data to the sampling time of the first sampling point on the first layer to obtain the corrected time difference amount of each sampling point on the first layer; the first layer level represents a layer top surface position of a certain layer of the target layer segment;
and correcting the sampling time of the sampling points on the layer positions except the first layer position in the target layer section based on the correction time difference amount of each sampling point on the first layer position to obtain the first seismic frequency division data after the layer leveling processing.
In a preferred embodiment, the preprocessing the plurality of seismic frequency division data to obtain the preprocessed seismic frequency division data includes: and converting the first seismic frequency division data in the plurality of seismic frequency division data into an equal geological time data body.
In a preferred embodiment, the color fusion processing of the seismic data of the target interval based on the preprocessed seismic frequency division data includes:
acquiring second seismic frequency division data, third seismic frequency division data and fourth seismic frequency division data in the preprocessed seismic frequency division data; the second seismic frequency division data, the third seismic frequency division data and the fourth seismic frequency division data are respectively three different seismic frequency division data in the preprocessed seismic frequency division data;
and performing color fusion processing on the seismic data of the target interval based on the second seismic frequency division data, the third seismic frequency division data and the fourth seismic frequency division data.
In a preferred embodiment, the performing color fusion processing on the seismic data of the target interval based on the second seismic frequency division data, the third seismic frequency division data, and the fourth seismic frequency division data includes:
setting a first color component value for the second seismic frequency division data; setting a second color component value for the third seismic frequency division data and a third color component value for the fourth seismic frequency division data;
and performing color fusion processing on the seismic data of the target interval based on the second seismic frequency division data with the set first color component value, the third seismic frequency division data with the set second color component value and the fourth seismic frequency division data with the set third color component value.
In a preferred embodiment, the setting a first color component value for the second seismic frequency division data includes:
splitting a numerical value interval between the maximum amplitude value in the second seismic frequency division data and the minimum amplitude value in the second seismic frequency division data according to the type of the first color in an equal proportion;
and mapping the amplitude value of the second seismic frequency division data and the first color component value of the first color type one by one according to the split numerical value interval.
In a preferred embodiment, the performing color fusion processing on the seismic data of the target interval based on the second seismic frequency division data with the first color component value, the third seismic frequency division data with the second color component value, and the fourth seismic frequency division data with the third color component value includes:
reserving second seismic frequency division data, third seismic frequency division data and fourth seismic frequency division data in the seismic data of the target interval;
generating new seismic data by using the reserved seismic frequency division data;
performing RGB primary color fusion processing on the first color component value, the second color component value and the third color component value to obtain a comprehensive color component value;
and taking the new seismic data with the comprehensive color component values as the seismic data after the color fusion processing.
In a preferred embodiment, the identifying the position of the fault in the interval of interest based on the seismic data after the color fusion processing includes:
performing the edge slice processing on the seismic data after the color fusion processing to obtain first edge slice seismic data; the first strategical slice seismic data represent seismic data of a horizon tangent plane after the strategical leveling processing in the seismic data after the color fusion processing;
identifying a location of an interruption in the interval of interest based on the first bedding slice seismic data.
In a preferred embodiment, the identifying the location of the fault in the interval of interest based on the first slice-along-layer seismic data includes: and when the color brightness value at the position of a second sampling point in the first edge slice seismic data is greater than or equal to a preset brightness threshold value, taking the position of the second sampling point as the position of the fault.
A fault identification device, the device comprising: the system comprises a frequency spectrum decomposition processing module, a preprocessing module, a color fusion processing module and a fault identification module; wherein,
the frequency spectrum decomposition processing module is used for acquiring seismic data of a target interval; performing frequency spectrum decomposition processing on the seismic data based on a preset frequency interval and the frequency range of the seismic data to obtain a plurality of seismic frequency division data of the target interval;
the preprocessing module is used for preprocessing the plurality of seismic frequency division data to obtain preprocessed seismic frequency division data;
the color fusion processing module is used for performing color fusion processing on the seismic data of the target interval based on the preprocessed seismic frequency division data;
and the fault identification module is used for identifying the position of the fault in the target interval based on the seismic data after the color fusion processing.
In a preferred embodiment, the fault identification module includes: the device comprises an edge slice processing module and a fault position identification module; wherein,
the system comprises a color fusion processing module, a color fusion processing module and a boundary slice processing module, wherein the boundary slice processing module is used for performing boundary slice processing on the seismic data after the color fusion processing to obtain first boundary slice seismic data; the first strategical slice seismic data represent seismic data of a horizon tangent plane after the strategical leveling processing in the seismic data after the color fusion processing;
and the fault position identification module is used for identifying the position of the fault in the target interval based on the first bedding slice seismic data.
The embodiment of the application provides a fault identification method and a fault identification device, and the fault identification method comprises the following steps of firstly, carrying out frequency spectrum decomposition processing on seismic data based on a preset frequency interval and the frequency range of the seismic data to obtain a plurality of seismic frequency division data of a target interval; then preprocessing is carried out on the basis of the plurality of seismic frequency division data, and color fusion processing is carried out on the seismic data according to the processed seismic frequency division data; and finally, identifying the position of the fault in the target interval according to the seismic data after the color fusion processing. Therefore, the position of the fault layer in the target fault layer can be effectively identified through the difference of the colors of the fault layer area and the non-fault layer area on the bedding slice of the seismic data after color fusion processing, and the fault layer is not influenced insignificantly by the vertical dislocation of the walk-slip fault layer. Therefore, the identification precision of the fault layer in the target layer segment can be improved.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort.
FIG. 1 is a flow chart of an embodiment of a fault identification method of the present application;
FIG. 2 is a schematic illustration of a layer leveling process in an embodiment of the present application;
FIG. 3 is a schematic diagram of fault identification using the method of the present invention in an embodiment of the present application;
FIG. 4 is a block diagram of an embodiment of a fault identification device according to the present application;
fig. 5 is a structural diagram of a fault identification module in an embodiment of the fault identification device of the present application.
Detailed Description
The embodiment of the application provides a fault identification method and device.
In order to make those skilled in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Fig. 1 is a flowchart of an embodiment of a fault identification method according to the present application. As shown in fig. 1, the fault identification method includes the following steps.
Step S101: acquiring seismic data of a target interval; and carrying out frequency spectrum decomposition processing on the seismic data based on a preset frequency interval and the frequency range of the seismic data to obtain a plurality of seismic frequency division data of the target interval.
The seismic data may be the result of stacking seismic frequency division data at a plurality of different frequencies.
Seismic data may be acquired for the interval of interest. The seismic data may be a collection of seismic data for a plurality of sample points. The seismic data may be a three-dimensional data volume comprised of a inline dimension, a crossline dimension, and a sample time dimension. The three dimensions are orthogonal two by two. The seismic data may include: amplitude at any sample point location, and spectral information corresponding to the seismic data. The spectral information may include a frequency range of the seismic data. The spectral information may also include a dominant frequency of the seismic data. The seismic data may be decomposed into seismic frequency division data of a plurality of frequencies of a preset interval frequency based on a frequency range of the seismic data. For example, the frequency range of the seismic data may be 15-60 Hz. The preset interval frequency may be 5 hz. The seismic data may be decomposed into seismic sub-frequency data at frequencies of 25, 30, 35, 40, 45, 50 hertz. Each of the seismic spread data may include a single frequency component. The seismic frequency division data may be a three-dimensional data volume of a certain frequency.
Step S102: and preprocessing the plurality of seismic frequency division data to obtain the preprocessed seismic frequency division data.
In one embodiment, the preprocessing the plurality of seismic frequency division data to obtain the preprocessed seismic frequency division data may be: and carrying out layer leveling processing on the plurality of seismic frequency division data to obtain the seismic frequency division data after the layer leveling processing.
The layer leveling processing is performed on the plurality of seismic frequency division data, specifically, for a first seismic frequency division data in the plurality of seismic frequency division data, the sampling time of each sampling point on a first layer in the first seismic frequency division data may be corrected to the sampling time of a first sampling point on the first layer, and the corrected time difference amount of each sampling point on the first layer may be obtained. The first seismic cross-over data may be any seismic cross-over data of the plurality of seismic cross-over data. The first sample point may be any sample point on the first horizon. The first layer bit may represent a layer top surface position of a layer of the destination layer segment. And based on the correction time difference amount of each sampling point on the first layer, correcting the sampling time of the sampling points on the layers except the first layer in the target layer to obtain the first seismic frequency division data after the layer leveling. Specifically, for sampling points with the same main measurement line dimension value, the same cross measurement line dimension value, and different sampling time dimension values, the difference amount during correction may be the same. Therefore, when the fault layer in the target layer section is identified by the subsequent bedding slice, the interference caused by the lithological change between layers can be eliminated. For example, fig. 2 is a schematic diagram of a layer leveling process in an embodiment of the present application. Fig. 2 (a) is a schematic view before layer leveling processing, and fig. 2 (b) is a schematic view after layer leveling processing.
In another embodiment, the preprocessing the plurality of seismic frequency division data to obtain the preprocessed seismic frequency division data may be: a first seismic cross-over data of the plurality of seismic cross-over data may be converted to an iso-geologic time data volume. And the sampling time of different sampling point positions on the slice along any layer in the target interval in the equal geological time data body is the same.
Step S103: and performing color fusion processing on the seismic data of the target interval based on the preprocessed seismic frequency division data.
Tuned thickness refers to the minimum thickness along the layer that can be resolved from seismic frequency division data for a certain frequency, typically a quarter of the wavelength of the seismic frequency division data for that frequency. When the thickness of the stratum of a geologic body in the interval of interest is close to the tuned thickness of the seismic frequency division data of the frequency, the incident seismic waves and the reflected seismic waves at the position of the geologic body interfere and have enhanced amplitude, so that the geologic body can be easily identified in the seismic data containing the seismic frequency division data of the frequency, for example, the seismic amplitude of the geologic body can be larger than the amplitude of other seismic data. Due to the fact that the tuning thicknesses of the seismic frequency division data of different frequencies are different, geologic bodies with different thicknesses can be distinguished according to the seismic frequency division data of different frequencies.
Second seismic frequency division data, third seismic frequency division data and fourth seismic frequency division data in the preprocessed seismic frequency division data can be obtained. The second seismic frequency division data, the third seismic frequency division data and the fourth seismic frequency division data may be three different seismic frequency division data in the preprocessed seismic frequency division data, respectively. Three different seismic frequency division data can be sequentially selected from small to large in the preprocessed seismic frequency division data in a frequency arithmetic progression mode. For example, the second seismic frequency division data is seismic frequency division data with a frequency of 25 hz in the preprocessed seismic frequency division data. The third seismic frequency division data is seismic frequency division data with the frequency of 35 Hz in the preprocessed seismic frequency division data. The fourth seismic frequency division data is seismic frequency division data with the frequency of 45 Hz in the preprocessed seismic frequency division data. And performing color fusion processing on the seismic data of the target interval based on the second seismic frequency division data, the third seismic frequency division data and the fourth seismic frequency division data.
Further, color fusion processing is carried out on the seismic data of the target interval based on the second seismic frequency division data, the third seismic frequency division data and the fourth seismic frequency division data. In particular, a first color component value may be set for the second seismic partitioning data. The first color may be red, blue or green. The first color component value may be determined according to a kind of the first color. For example, if the types of the first colors are 256, the values of the first color components may be 0 to 255. The setting a first color component value for the second seismic frequency division data may specifically include: the numerical value interval between the maximum amplitude value in the second seismic frequency division data and the minimum amplitude value in the second seismic frequency division data can be split according to the type of the first color in equal proportion; according to the split value interval, the amplitude value of the second seismic frequency division data and the first color component value of the first color type may be mapped one to one. Similarly, a second color component value may be set for the third seismic frequency division data and a third color component value may be set for the fourth seismic frequency division data. The second color may be red, blue or green; the third color may be red, blue or green; and the first color, the second color and the third color are different from each other. The setting of the second color component value and the third color component value may be the same as the setting of the first color component value of the second seismic frequency division data, and is not repeated here. Based on the second seismic frequency division data with the first color component value, the third seismic frequency division data with the second color component value, and the fourth seismic frequency division data with the third color component value, color fusion processing can be performed on the seismic data of the target interval.
Further, the color fusion processing is performed on the seismic data of the target interval based on the second seismic frequency division data with the first color component value, the third seismic frequency division data with the second color component value, and the fourth seismic frequency division data with the third color component value. Specifically, the second seismic frequency division data, the third seismic frequency division data, and the fourth seismic frequency division data in the seismic data of the target interval may be retained. And generating new seismic data by using the reserved seismic frequency division data. Performing RGB primary color fusion processing on the first color component value, the second color component value, and the third color component value to obtain a composite color component value. And taking the new seismic data with the comprehensive color component values as the seismic data after the color fusion processing. Thus, in the three seismic frequency division data, when the tuning thickness of at least one seismic frequency division data is close to the stratum thickness of the fault layer in the target interval, the non-fault area and the fault area in the seismic data after the color fusion processing can be displayed through different colors.
Step S104: and identifying the position of the fault in the target interval based on the seismic data after the color fusion processing.
Specifically, the seismic data after the color fusion processing is sliced along a layer to obtain first sliced along a layer seismic data. The first sliced along-horizon seismic data may represent seismic data of a sliced along-horizon in the color-fused processed seismic data. Based on the first slice-along-layer seismic data, a location of an interruption in the interval of interest may be identified.
Further, based on the first slice-along-layer seismic data, a location of a discontinuity in the interval of interest is identified. Specifically, when a color brightness value at a second sampling point position in the first edge slice seismic data is greater than or equal to a preset brightness threshold value, the second sampling point position may be taken as the position of the fault. The preset brightness threshold value is 200-256. The second sampling point may be any sampling point in the first sliced along slice seismic data.
For example, fig. 3 is a schematic diagram of fault identification by using the method of the present invention in the embodiment of the present application. The thick black lines in fig. 3 indicate the positions of the main faults in the interval of interest, and the thin black lines in fig. 3 indicate the positions of the secondary faults in the interval of interest. As shown in FIG. 3, the method of the present invention can clearly identify the positions of the main fault and the secondary fault in the target interval.
According to the fault identification method, firstly, based on a preset frequency interval and the frequency range of the seismic data, frequency spectrum decomposition processing is carried out on the seismic data to obtain a plurality of seismic frequency division data of the target interval; then preprocessing is carried out on the basis of the plurality of seismic frequency division data, and color fusion processing is carried out on the seismic data according to the processed seismic frequency division data; and finally, identifying the position of the fault in the target interval according to the seismic data after the color fusion processing. Therefore, the position of the fault layer in the target fault layer can be effectively identified through the difference of the colors of the fault layer area and the non-fault layer area on the bedding slice of the seismic data after color fusion processing, and the fault layer is not influenced insignificantly by the vertical dislocation of the walk-slip fault layer. Therefore, the identification precision of the fault layer in the target layer segment can be improved.
Fig. 4 is a structural diagram of the fault identification device according to the embodiment of the present application. As shown in fig. 4, the fault recognition apparatus may include: a spectral decomposition processing module 100, a preprocessing module 200, a color fusion processing module 300, and a fault identification module 400.
The spectrum decomposition processing module 100 may be configured to acquire seismic data of a target interval; and carrying out frequency spectrum decomposition processing on the seismic data based on a preset frequency interval and the frequency range of the seismic data to obtain a plurality of seismic frequency division data of the target interval.
The preprocessing module 200 may be configured to preprocess the seismic frequency division data to obtain the preprocessed seismic frequency division data.
The color fusion processing module 300 may be configured to perform color fusion processing on the seismic data of the target interval based on the preprocessed seismic frequency division data.
The fault identification module 400 may be configured to identify a fault location in the target interval based on the seismic data after the color fusion processing.
Fig. 5 is a structural diagram of a fault identification module in an embodiment of the fault identification device of the present application. As shown in fig. 5, the break layer recognition model 400 in fig. 4 may include: a slice-along-layer processing module 410 and a fault location identification module 420.
The slab-boundary slice processing module 410 may be configured to perform slab-boundary slice processing on the seismic data after the color fusion processing, so as to obtain first slab-boundary slice seismic data; the first sliced along-horizon seismic data may represent seismic data of a sliced along-horizon in the color-fused processed seismic data.
The fault location identification module 420 may be configured to identify a location of a fault in the interval of interest based on the first sliced along-the-horizon seismic data.
The fault identification device embodiment corresponds to the fault identification method, the method embodiment of the application can be realized, and the technical effect of the method embodiment can be achieved.
In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate a dedicated integrated circuit chip 2. Furthermore, nowadays, instead of manually making an integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Language Description Language), traffic, pl (core unified Programming Language), HDCal, JHDL (Java Hardware Description Language), langue, Lola, HDL, laspam, hardsradware (Hardware Description Language), vhjhd (Hardware Description Language), and vhigh-Language, which are currently used in most popular applications. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory.
Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing the present application.
From the above description of the embodiments, it is clear to those skilled in the art that the present application can be implemented by software plus necessary general hardware platform. With this understanding in mind, the present solution, or portions thereof that contribute to the prior art, may be embodied in the form of a software product, which in a typical configuration includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory. The computer software product may include instructions for causing a computing device (which may be a personal computer, a server, or a network device, etc.) to perform the methods described in the various embodiments or portions of embodiments of the present application. The computer software product may be stored in a memory, which may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium. Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include transitory computer readable media (transient media), such as modulated data signals and carrier waves.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The application is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.
The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
While the present application has been described with examples, those of ordinary skill in the art will appreciate that there are numerous variations and permutations of the present application without departing from the spirit of the application, and it is intended that the appended claims encompass such variations and permutations without departing from the spirit of the application.
Claims (10)
1. A fault identification method, comprising:
acquiring seismic data of a target interval; performing frequency spectrum decomposition processing on the seismic data based on a preset frequency interval and the frequency range of the seismic data to obtain a plurality of seismic frequency division data of the target interval;
preprocessing the seismic frequency division data to obtain preprocessed seismic frequency division data, wherein the preprocessing comprises the following steps: for first seismic frequency division data in the plurality of seismic frequency division data, correcting the sampling time of each sampling point on a first layer in the first seismic frequency division data to the sampling time of the first sampling point on the first layer to obtain the corrected time difference amount of each sampling point on the first layer; the first layer level represents a layer top surface position of a certain layer of the target layer segment; correcting the sampling time of the sampling points on the layers except the first layer in the target interval based on the correction time difference amount of each sampling point on the first layer to obtain the preprocessed first seismic frequency division data;
performing color fusion processing on the seismic data of the target interval based on the preprocessed seismic frequency division data;
and identifying the position of the fault in the target interval based on the seismic data after the color fusion processing.
2. The fault identification method of claim 1, wherein the preprocessing the plurality of seismic frequency division data to obtain the preprocessed seismic frequency division data comprises: and converting the first seismic frequency division data in the plurality of seismic frequency division data into an equal geological time data body.
3. The fault identification method according to claim 1, wherein the performing color fusion processing on the seismic data of the target interval based on the preprocessed seismic frequency division data comprises:
acquiring second seismic frequency division data, third seismic frequency division data and fourth seismic frequency division data in the preprocessed seismic frequency division data; the second seismic frequency division data, the third seismic frequency division data and the fourth seismic frequency division data are respectively three different seismic frequency division data in the preprocessed seismic frequency division data;
and performing color fusion processing on the seismic data of the target interval based on the second seismic frequency division data, the third seismic frequency division data and the fourth seismic frequency division data.
4. The fault identification method according to claim 3, wherein the color fusion processing of the seismic data of the target interval based on the second seismic frequency division data, the third seismic frequency division data and the fourth seismic frequency division data comprises:
setting a first color component value for the second seismic frequency division data; setting a second color component value for the third seismic frequency division data and a third color component value for the fourth seismic frequency division data;
and performing color fusion processing on the seismic data of the target interval based on the second seismic frequency division data with the set first color component value, the third seismic frequency division data with the set second color component value and the fourth seismic frequency division data with the set third color component value.
5. A fault identification method according to claim 4, wherein said setting a first color component value for said second seismic frequency division data comprises:
splitting a numerical value interval between the maximum amplitude value in the second seismic frequency division data and the minimum amplitude value in the second seismic frequency division data according to the type of the first color in an equal proportion;
and mapping the amplitude value of the second seismic frequency division data and the first color component value of the first color type one by one according to the split numerical value interval.
6. The fault identification method according to claim 4, wherein the color fusing the seismic data of the target interval based on the second seismic frequency division data with the first color component value, the third seismic frequency division data with the second color component value, and the fourth seismic frequency division data with the third color component value comprises:
reserving second seismic frequency division data, third seismic frequency division data and fourth seismic frequency division data in the seismic data of the target interval;
generating new seismic data by using the reserved second seismic frequency division data, the reserved third seismic frequency division data and the reserved fourth seismic frequency division data;
performing RGB primary color fusion processing on the first color component value, the second color component value and the third color component value to obtain a comprehensive color component value;
and taking the new seismic data with the comprehensive color component values as the seismic data after the color fusion processing.
7. The fault identification method according to claim 1, wherein identifying the position of the fault in the target interval based on the seismic data after the color fusion processing comprises:
performing the edge slice processing on the seismic data after the color fusion processing to obtain first edge slice seismic data; the first strategical slice seismic data represent seismic data of a horizon tangent plane after the strategical leveling processing in the seismic data after the color fusion processing;
identifying a location of an interruption in the interval of interest based on the first bedding slice seismic data.
8. The fault identification method of claim 7, wherein identifying the location of the fault in the interval of interest based on the first sliced seismic data comprises: and when the color brightness value at the position of a second sampling point in the first edge slice seismic data is greater than or equal to a preset brightness threshold value, taking the position of the second sampling point as the position of the fault.
9. A fault identification device, characterized in that the device comprises: the system comprises a frequency spectrum decomposition processing module, a preprocessing module, a color fusion processing module and a fault identification module; wherein,
the frequency spectrum decomposition processing module is used for acquiring seismic data of a target interval; performing frequency spectrum decomposition processing on the seismic data based on a preset frequency interval and the frequency range of the seismic data to obtain a plurality of seismic frequency division data of the target interval;
the preprocessing module is used for preprocessing the plurality of seismic frequency division data to obtain preprocessed seismic frequency division data; the preprocessing module is used for correcting the sampling time of each sampling point on a first layer in the first seismic frequency division data to the sampling time of a first sampling point on the first layer in the first seismic frequency division data to obtain the corrected time difference amount of each sampling point on the first layer, the first layer represents the layer top surface position of a certain layer of the target interval, and the sampling time of the sampling points on the layers except the first layer in the target interval is corrected based on the corrected time difference amount of each sampling point on the first layer to obtain the preprocessed first seismic frequency division data;
the color fusion processing module is used for performing color fusion processing on the seismic data of the target interval based on the preprocessed seismic frequency division data;
and the fault identification module is used for identifying the position of the fault in the target interval based on the seismic data after the color fusion processing.
10. A fault identification device according to claim 9 wherein the fault identification module comprises: the device comprises an edge slice processing module and a fault position identification module; wherein,
the system comprises a color fusion processing module, a color fusion processing module and a boundary slice processing module, wherein the boundary slice processing module is used for performing boundary slice processing on the seismic data after the color fusion processing to obtain first boundary slice seismic data; the first strategical slice seismic data represent seismic data of a horizon tangent plane after the strategical leveling processing in the seismic data after the color fusion processing;
and the fault position identification module is used for identifying the position of the fault in the target interval based on the first bedding slice seismic data.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103048685A (en) * | 2011-10-13 | 2013-04-17 | 陈红兵 | Seismic multi-attribute RGBA (red, green, blue, alpha) color fusion method |
| CN105182415A (en) * | 2015-09-02 | 2015-12-23 | 中国石油化工股份有限公司 | Tomography identification method based on frequency division processing |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7248539B2 (en) * | 2003-04-10 | 2007-07-24 | Schlumberger Technology Corporation | Extrema classification |
| US10393899B2 (en) * | 2013-10-31 | 2019-08-27 | Exxonmobil Upstream Research Company | Automatic tracking of faults by slope decomposition |
-
2017
- 2017-04-14 CN CN201710247321.5A patent/CN107144878B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103048685A (en) * | 2011-10-13 | 2013-04-17 | 陈红兵 | Seismic multi-attribute RGBA (red, green, blue, alpha) color fusion method |
| CN105182415A (en) * | 2015-09-02 | 2015-12-23 | 中国石油化工股份有限公司 | Tomography identification method based on frequency division processing |
Non-Patent Citations (2)
| Title |
|---|
| 准噶尔盆地玛湖地区地震裂缝预测方法研究;赵小辉;《中国优秀硕士学位论文全文数据库 基础科学辑》;20170315(第3期);第21-22页 |
| 断层识别技术在贝中油田的应用;李占东 等;《数学的实践与认识》;20150331;第45卷(第5期);第94-100页 |
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