CN114488306B - A method and apparatus for optimizing and processing pre-stack gather data of fluvial reservoirs. - Google Patents
A method and apparatus for optimizing and processing pre-stack gather data of fluvial reservoirs.Info
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- CN114488306B CN114488306B CN202011148879.6A CN202011148879A CN114488306B CN 114488306 B CN114488306 B CN 114488306B CN 202011148879 A CN202011148879 A CN 202011148879A CN 114488306 B CN114488306 B CN 114488306B
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- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/36—Effecting static or dynamic corrections on records, e.g. correcting spread; Correlating seismic signals; Eliminating effects of unwanted energy
- G01V1/362—Effecting static or dynamic corrections; Stacking
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- G01V2210/00—Details of seismic processing or analysis
- G01V2210/50—Corrections or adjustments related to wave propagation
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- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/50—Corrections or adjustments related to wave propagation
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Abstract
The embodiment of the invention discloses a river phase reservoir along-river pre-stack gather data optimization processing method and device, which are used for providing pre-stack gather data, describing a river space spreading form, applying the pre-stack gather data to obtain along-river pre-stack gather data, denoising the along-river pre-stack gather data to obtain denoised along-river pre-stack gather data, correcting the amplitude of the denoised along-river pre-stack gather data to obtain corrected gather data, obtaining angle gather data by changing the angle of the corrected gather data, and preprocessing the angle gather data to obtain leveling corrected gather data. According to the river storage layer pre-stack seismic inversion method, the river space distribution form is described, and the river space distribution form is applied to the pre-stack gather data, so that the pre-stack gather data along the river are obtained, and the steps of denoising, leveling correction and the like are further carried out, so that the accuracy and the efficiency of the river storage layer pre-stack seismic inversion can be improved.
Description
Technical Field
The invention belongs to the field of oil and gas geophysical exploration, and particularly relates to a river phase reservoir data optimization processing method and device along a pre-stack channel set.
Background
According to data statistics, the petroleum reserves which are assigned to river-phase reservoirs account for more than 50% of the reserves which are used for developing oil fields in China at present, wherein the curvelet-dam-phase reservoirs account for a large proportion, and the braided river alluvial reservoirs form some world-class large oil and gas fields, which means that most large reservoirs belong to river phases or river-triangular intercontinental phases. Therefore, the research on rivers and river-delta, especially the research on the curtreta and the braided river, is a fundamental key work related to the reasonable development and utilization of oil and gas resources of various oil fields.
Pre-stack seismic inversion is a main means of river-phase reservoir prediction and fluid detection, and at present, development of pre-stack inversion technology is still mainly based on correction and improvement of various inversion algorithms, and quality of inversion data is often ignored. If seismic data with poor processing is adopted, even if an advanced complex pre-stack inversion algorithm is adopted, the final inversion effect is affected, and the seismic gathers used for pre-stack inversion must have high quality, so that the pre-stack gathers need to be processed more finely, the common reflection point gathers (CRP) are required to be flatter and have smaller dynamic correction stretching effect and higher signal to noise ratio while the relative strong and weak relation of the reflected wave amplitude is ensured.
In recent years, scholars at home and abroad sequentially put forward a plurality of optimization processing technologies and methods, such as Wu Changyu and the like put forward a prestack seismic data regularization processing technology and a prestack random noise attenuation technology, so that the problem of prestack migration imaging of different work areas is solved, cheng Yukun and the like adopt a high-density velocity analysis technology, no time difference superposition of CRP gathers is realized, liu Suxu adopts a successive approximation method to process the prestack gathers aiming at complex structures, carries out depth migration imaging, zhang Zheng and the like put forward anisotropic dynamic correction to process residual time differences existing in the gathers, xu Zilong put forward a partial waveform residual time difference correction and a time-space variable wavelet threshold fidelity denoising method based on transverse sliding, improves the precision of residual time difference correction, well ensures the fidelity in the denoising process, xu and the like put forward a structural filtering-based absolute value gather flattening method, and Zhou Peng and the like put forward a residual time difference-independent absolute value gather flattening method, and can better level the prestack seismic gathers and remove far waveform distortion.
However, these methods can improve the quality of the gathers to a certain extent aiming at a certain characteristic problem, and most of combinations of various gather optimization methods are ignored, and reasonable combinations can better optimize the gathers, in addition, river-phase reservoirs have the heterogeneity in space, the oil field developer only pays attention to lithology, physical properties and oil-gas content in the river phase zone, but does not pay attention to the geological condition of the sedimentary background outside the phase zone, and the method cannot be well used for improving the seismic inversion precision and efficiency before the river phase reservoir is stacked.
Disclosure of Invention
In view of the above, the embodiment of the invention provides a method and a device for optimizing data of a river facies reservoir along a pre-stack gather, which at least solve the technical problems of low accuracy and poor efficiency of inversion of a river facies reservoir pre-stack earthquake in the prior art.
In a first aspect, an embodiment of the present invention provides a method for optimizing data of a pre-stack gather of a river-phase reservoir along a river, including:
providing pre-stack gather data;
Describing the river channel space distribution form, and applying the river channel space distribution form to the prestack gather data to obtain the prestack gather data along the river channel;
denoising the pre-stack channel set data along the river channel to obtain denoised pre-stack channel set data along the river channel;
correcting the amplitude of the denoised channel set data before the river channel stack to obtain corrected channel set data;
acquiring angle gather data by changing the angle of the correction gather data;
and preprocessing the angle gather data to obtain leveling correction gather data.
Optionally, before obtaining the pre-stack gather data along the river channel, including:
and extracting the seismic data volume, wherein the extracted seismic data volume comprises a data volume scan and a data volume index.
Optionally, the step of correcting the amplitude of the denoised channel set data before the river channel stack to obtain corrected channel set data includes:
And overlapping all the denoised pre-stack gather data along the river channel according to different offset distances to form gather data, and then carrying out least square fitting on each pre-stack gather data and the gather data to obtain corrected gather data.
Optionally, before obtaining the correction gather data, the method includes:
and carrying out least square fitting on each piece of prestack gather data and the gather data to obtain a correction coefficient.
Optionally, the expression of the correction coefficient is:
Wherein t represents time, h represents offset, seis _o (x, y, h, t) represents de-noised pre-stack gather data along the river, seis (h, t) represents gather data, and q (h, t) represents a correction coefficient.
Optionally, the expression of the correction gather data seis _c (x, y, h, t) is:
wherein seis _o (x, y, h, t) represents de-noised pre-stack gather data along the river, q (h, t) represents a correction coefficient, seis _c (x, y, h, t) represents correction gather data.
Optionally, in the step of preprocessing the angle gather data, the preprocessing refers to leveling processing by adopting a gather leveling technology.
Optionally, after acquiring the angle gather data, the method further includes:
and performing super-gather processing on the angle gather data to improve the transverse continuity of the angle gather data.
In a second aspect, an embodiment of the present invention further provides an apparatus for optimizing data of a pre-stack gather of a river-phase reservoir along a river, including:
A pre-stack gather data module for providing pre-stack gather data;
the river channel along-the-river channel pre-stack gather data module is used for describing the river channel space distribution form and applying the river channel space distribution form to the pre-stack gather data to obtain river channel along-the-river channel pre-stack gather data;
The denoising module is used for denoising the pre-stack channel set data along the river channel to obtain denoised pre-stack channel set data along the river channel;
the correction module corrects the amplitude of the denoised channel set data before the river channel stack to obtain corrected channel set data;
the angle gather data module is used for obtaining angle gather data by changing the angle of the correction gather data;
and the preprocessing module is used for preprocessing the angle gather data to obtain leveling correction gather data.
Optionally, before describing the river space distribution form, the method includes:
the river channel along pre-stack gather data module is also used for selecting river channel sensitive attributes, extracting plane attributes and determining through well position calibration and threshold values.
According to the river storage layer pre-stack seismic inversion method, the river space distribution form is described, and the river space distribution form is applied to the pre-stack gather data, so that the pre-stack gather data along the river are obtained, and the steps of denoising, leveling correction and the like are further carried out, so that the accuracy and the efficiency of the river storage layer pre-stack seismic inversion can be improved.
Additional features and advantages of the invention will be set forth in the detailed description which follows.
Drawings
The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular descriptions of exemplary embodiments of the invention as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts throughout the exemplary embodiments of the invention.
FIG. 1 shows a flow chart of a method for optimizing data of a river facies reservoir along a pre-stack gather according to a first embodiment of the present invention;
FIG. 2 shows a flow chart of a technique for pre-stack gather data along a river channel in accordance with a first embodiment of the present invention;
FIGS. 3a-3b are schematic diagrams showing the comparison of the front-to-back plane attributes of the data extracted along the river course gather according to the second embodiment of the present invention;
FIGS. 4a-4b are schematic diagrams showing trace collection presentation in a vertical river direction after extracting trace collection data along a river in accordance with a second embodiment of the present invention;
FIG. 5 is a schematic diagram showing the optimization efficiency of the subsequent steps after extracting the pre-stack gather data along the river course in the first embodiment of the present invention;
FIG. 6 is a schematic diagram showing the comparison of the explanation effect of the river top interface before and after the data of the river course prestack gather is extracted in the second embodiment of the invention;
FIG. 7 is a schematic diagram showing a comparison of denoising along a river course prestack gather data in accordance with an embodiment of the present invention;
FIGS. 8a-8b are schematic diagrams showing a comparison of post-denoising correction along a pre-stack trace set of a river in accordance with an embodiment of the present invention;
FIG. 9 is a schematic diagram showing the conversion of a data offset gather along a river pre-stack gather into an angle gather after denoising according to an embodiment of the present invention;
FIG. 10 is a diagram showing the effect of the super gather processing according to the first embodiment of the present invention;
FIG. 11 is a schematic diagram of leveling correction gather data according to a first embodiment of the present invention;
FIG. 12 is a schematic diagram of partial angle overlay gather data according to a second embodiment of the present invention.
Detailed Description
Preferred embodiments of the present invention will be described in more detail below. While the preferred embodiments of the present invention are described below, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein.
A river facies reservoir along the river pre-stack gather data optimization processing method comprises the following steps:
providing pre-stack gather data;
specifically, the pre-stack gather data is a result data, generally CRP gather data, in the seismic data processing process, which is a technical term in the art and will not be described in detail herein.
Describing the river channel space distribution form, and applying the river channel space distribution form to the prestack gather data to obtain the prestack gather data along the river channel;
Specifically, the method includes the steps of describing the river channel, namely determining the space position of the river channel, namely XY coordinates and depth (t) of the boundary of the river channel through seismic attributes capable of reflecting the space distribution characteristics of the river channel, and extracting data located under the coordinates from mass seismic data according to the determined XY coordinates of the boundary of the river channel.
Denoising the pre-stack channel set data along the river channel to obtain denoised pre-stack channel set data along the river channel;
correcting the amplitude of the denoised channel set data before the river channel stack to obtain corrected channel set data;
acquiring angle gather data by changing the angle of the correction gather data;
and preprocessing the angle gather data to obtain leveling correction gather data.
Optionally, before obtaining the pre-stack gather data along the river channel, including:
and extracting the seismic data volume, wherein the extracted seismic data volume comprises a data volume scan and a data volume index.
Optionally, the step of correcting the amplitude of the denoised channel set data before the river channel stack to obtain corrected channel set data includes:
And overlapping all the denoised pre-stack gather data along the river channel according to different offset distances to form gather data, and then carrying out least square fitting on each pre-stack gather data and the gather data to obtain corrected gather data.
Optionally, before obtaining the correction gather data, the method includes:
and carrying out least square fitting on each piece of prestack gather data and the gather data to obtain a correction coefficient.
Optionally, the expression of the correction coefficient is:
Wherein t represents time, h represents offset, seis _o (x, y, h, t) represents de-noised pre-stack gather data along the river, seis (h, t) represents gather data, and q (h, t) represents a correction coefficient.
Optionally, the expression of the correction gather data seis _c (x, y, h, t) is:
wherein seis _o (x, y, h, t) represents de-noised pre-stack gather data along the river, q (h, t) represents a correction coefficient, seis _c (x, y, h, t) represents correction gather data.
Optionally, in the step of preprocessing the angle gather data, the preprocessing refers to leveling processing by adopting a gather leveling technology.
Optionally, after acquiring the angle gather data, the method further includes:
and performing super-gather processing on the angle gather data to improve the transverse continuity of the angle gather data.
Embodiment one:
this embodiment will be described in detail with reference to shallow dwarf river sandstone reservoirs in Sichuan basin.
Fig. 1 shows a flow chart of a method for optimizing data of a pre-stack gather of a river-phase reservoir according to the present embodiment.
S1, acquiring pre-stack gather data along a river channel
The technical process for acquiring the pre-stack gather data along the river channel specifically comprises the steps of starting from river channel sensitive attribute optimization, extracting plane attribute, marking out river channel space distribution form through well position calibration and threshold value determination, and applying the river channel space distribution form to the pre-stack gather data to obtain the pre-stack gather data along the river channel, which can be used for subsequent processing and interpretation, as shown in figure 2.
Specifically, the method includes the steps of describing the river channel, namely determining the space position of the river channel, namely XY coordinates and depth (t) of the boundary of the river channel through seismic attributes capable of reflecting the space distribution characteristics of the river channel, and extracting data located under the coordinates from mass seismic data according to the determined XY coordinates of the boundary of the river channel.
The seismic data volume belongs to mass data, the extraction algorithm of the seismic data volume is many, in order to obtain high extraction speed, the data retrieval speed needs to be improved, and the required CDP point is rapidly positioned, and specifically comprises two parts of data volume scanning and data volume indexing.
Data volume scanning:
Before the data is loaded into the database, the whole scanning is carried out on the data of the rolling head and the track head of the data body, the attribute information related to the data body is scanned out and stored in the database in the form of structured data, so that the basic information inquiry of the data body is facilitated.
Data volume index:
In order to obtain very high seismic data extraction speed, the data volume can be scanned channel by channel to establish an index file, and the index file records X, Y coordinates of each seismic channel, inline, crossline numbers and absolute positions of channel data storage. Therefore, if X, Y coordinates of each section of broken line of the section to be extracted are obtained, an index file corresponding to the data body of the work area can be found through the structured data information of the database, the absolute position of one data channel of the data body corresponding to the coordinates can be found through X, Y coordinates, the position of the data channel can be directly positioned in the data body to read the data, the whole data body is prevented from being scanned, and the index file is generally very small and can be scanned once quickly, so that the data extraction speed can be doubled through the index of the data body.
The acquired pre-stack gather data along the river channel has the following advantages in the aspects of subsequent data processing and interpretation:
Firstly, the efficiency of river phase reservoir prediction and fluid detection can be improved in the aspects of gather processing, pre-stack inversion, attribute extraction and the like, and fig. 5 shows an efficiency comparison diagram of a plurality of key processing interpretation modules before and after gather extraction in the embodiment, and secondly, the accuracy of river phase reservoir prediction and fluid detection can be improved, thereby being beneficial to river top and bottom interface interpretation and eliminating background surrounding rock influence.
S2, denoising process
The method is characterized in that the method is based on an improved median filtering method, the principle of the method is that sampling points of adjacent seismic traces at the same time are picked up inside the channel pre-stack data, noise influence can be reduced through cutting off the abnormal points, and the method comprises the specific steps of firstly carrying out numerical sorting on the seismic data at a certain moment in the channel pre-stack data, removing a part of maximum and minimum values, then averaging the residual sample points to obtain the average value as the current sample point, and carrying out numerical value denoising on the current sample point as the current sample point in the channel pre-stack data, wherein the method is used for carrying out numerical value denoising on the current sample point in the channel pre-stack graph 7.
The median filter calculation formula is:
Wherein S represents the seismic data, X, Y and t are three dimensions of the seismic data, and represent the X direction, the Y direction and the time direction t respectively. mn is the size of the median filter window function, m is the window size in the X direction, and n is the window size in the Y direction.
S3, acquiring correction gather data
The obtained denoised pre-stack trace set data along the river usually has a strong-weak-strong amplitude relationship, in theory, four types of AVO have no strong-weak-strong amplitude relationship along with the increase of offset distance except for phase conversion, the non-fidelity AVO relationship is very unfavorable for subsequent AVO analysis and AVO inversion, and the reason for the phenomenon is that the amplitude abnormality caused by the acquisition system is mostly concerned about the acquisition system, in the technical scheme, the amplitude abnormality is carried out on the co-offset distance trace set after offset, and the amplitude abnormality of the three-dimensional observation system which is not completely eliminated in the offset process is aimed at.
The method comprises the specific steps of superposing all denoised pre-stack gather data along a river channel into gather data according to different offset distances, wherein the gather data only comprises time t and offset distance h information, and is recorded as seis (h, t).
A least squares fit is performed on each pre-stack gather data seis _ o (x, y, h, t) to the gather data seis (h, t) to obtain a correction factor q (h, t).
The expression of the correction coefficient is:
f(q(h,t))=min||seis(h,t)*q(h,t)-seis_o(x,y,h,t)||2
The corrected gather is:
seis_c(x,y,h,t)=seis_o(x,y,h,t)*q(h,t)
wherein the general representation of the pre-stack gather data comprises 4 dimensions, an X-direction, a Y-direction, a time t and an offset h, seis _o (X, Y, h, t) represents the pre-stack gather data, seis (h, t) represents the gather data, q (h, t) represents the correction coefficient, seis _c (X, Y, h, t) represents the correction gather data.
Fig. 8 is a schematic diagram showing the comparison before and after correction in the present embodiment.
S4, acquiring angle trace set data
In the preprocessing of the seismic data, after correction, the corrected trace set data is still in an offset range, but because the AVO analysis is based on the incident angle as a variable, in order to facilitate the AVO analysis, the fixed offset record needs to be converted into a superimposed trace set record with a fixed incident angle or a certain angle range, when in angle processing, the corresponding parts of the fixed offset record in a certain reflection angle range are generally combined to obtain a reflected angle trace, the process is repeated, and different angles or angle ranges are changed to obtain different angle traces to form an angle trace set, so that the angle trace set with different angles can be obtained, and fig. 9 is a schematic diagram of the data offset distance converted into the angle trace set data along the river channel stack after denoising in the embodiment.
However, in AVO analysis, we often do not need all angle gather data, and in angle gather data of one angle, we do not need all angle ranges, so we can superimpose angle gather data of a certain range to form angle partial superimposed gather data according to needs, which is the most basic angle superimposed gather data in AVO analysis, however in angle partial superimposed gather data, each angle partial superimposed gather data has a central angle gather, and in actual processing, in order to improve the signal-to-noise ratio of AVO gather records and improve certain transverse resolution, records with coverage of three times or more in each angle gather can be selected to be superimposed.
For angular trace ranges, not all of the trace sets within the angular range may be superimposed, as is known, the actual seismic data acquisition is covered multiple times. The purpose is to be able to effectively increase the signal-to-noise ratio and to increase the lateral resolution, so that we can determine the extent of the angular tracks from the definition of the width of the first fresnel zone.
S5, super gather processing
The signal-to-noise ratio analysis is performed on the angle gather data, if the signal-to-noise ratio is insufficient, further super gather processing can be performed to improve the signal-to-noise ratio, wherein the super gather processing is the stacking of the surface elements within a certain range to improve the transverse continuity of the gather, and fig. 10 is a schematic diagram of the effect of the super gather processing on the angle gather data in the embodiment.
S6, obtaining leveling correction gather data
The existing residual time difference correction technology is mostly realized by adopting anisotropic speed analysis, but the problem of leveling cannot be solved well due to difficult calculation of anisotropic parameters, in the technical scheme, preprocessing carries out leveling treatment on angle trace set data by adopting a trace set leveling technology based on automatic tracking of a same phase axis, the basic principle of the trace leveling technology is that significant layer sites are extracted at the similar position of the waveform of the pre-stack trace, automatic layer position tracking is carried out, leveling correction is carried out on the obtained trace time shift amount, residual dynamic correction processing under no speed is realized, and fig. 11 is a graph of the residual time difference correction effect of the angle trace in the embodiment.
Embodiment two:
The embodiment uses the clastic rock blocks of the Sichuan basin in China for detailed description.
Fig. 1 shows a flow chart of a river facies reservoir data optimization processing method along a pre-stack gather, which specifically comprises the following steps:
s1, acquiring pre-stack gather data along a river channel
The technical process for extracting the channel pre-stack gathers along the river channel comprises the steps of optimizing the sensitive attribute of the river channel, extracting the plane attribute, marking the space distribution form of the river channel through well position calibration and threshold determination, applying the space distribution form to the pre-stack gathers data to obtain the pre-stack gather data along the river channel which can be used for subsequent processing explanation, wherein the technical process for extracting the gather data along the river channel is shown in fig. 2, fig. 3a-3b are front-rear plane attribute comparison diagrams for extracting the gather data along the river channel, fig. 4a-4b are front-rear plane attribute comparison diagrams for extracting the gather data along the river channel, gather data along the river channel in the direction of the vertical river channel in the embodiment, and redundant information irrelevant to the explanation of the river channel can be seen to be removed from the processed gather, so that the basis is provided for improving the efficiency and the precision of the subsequent processing explanation.
The extracted river phase reservoir has the following advantages in the aspects of subsequent data processing and interpretation along the river channel prestack gather data, that ① can improve the river phase reservoir prediction and fluid detection efficiency in the aspects of gather processing, prestack inversion, attribute extraction and the like. FIG. 5 shows the comparison of the efficiency of several key processing interpretation modules before and after the gather extraction in the implementation, it can be seen that the single operation efficiency is improved by more than 90% in the aspects of the targeted processing of the gather before the gather is extracted, or the seismic inversion and the attribute extraction, ② improves the accuracy of river phase reservoir prediction and fluid detection, is beneficial to the interpretation of river channel top-bottom interfaces and the elimination of background surrounding rock influence, the problems that the river phase reservoir has shorter transverse continuity and floating up-down time difference due to the influence of sedimentation, the existence of the factors is not beneficial to the interpretation of river channel top-bottom interfaces, the extracted data of the gather along the river channel can be used for more accurately identifying the top-bottom interfaces of the river channel due to the elimination of the influence of peripheral redundant seismic traces, and FIG. 6 compares the continuous well section of the data of the gather along the river channel before the gather, and the river channel top-bottom interfaces along the river channel overlap data can be seen to be more accurate.
S2, denoising process
The time migration of the data along the river channel pre-stack gathers is usually oriented to the target post-stack seismic data, various noises, especially Gaussian noises, are usually existed in the pre-stack data before the stacking, and the denoising processing is carried out before the angle conversion and the partial stacking, so as to improve the signal-to-noise ratio of the gather and the signal-to-noise ratio of the final angle-division stacked data. The principle of the method is that sampling points of adjacent seismic channels with the same time are picked up in the data of the channel pre-stack gather along the channel, and noise influence can be reduced by cutting off the abnormal points. The specific steps are that firstly, the value ordering is carried out on the seismic data along a certain moment in the river channel prestack gather data, a part of maximum value and minimum value are removed, then the arithmetic average is obtained on the rest sampling points, the arithmetic average is used as the sampling point value of the current central channel at the moment, and fig. 7 is a comparison schematic diagram before and after denoising along the river channel prestack gather data in the embodiment.
The median filter calculation formula is:
Wherein S represents the seismic data, X, Y and t are three dimensions of the seismic data, and represent the X direction, the Y direction and the time direction t respectively. mn is the size of the median filter window function, m is the window size in the X direction, and n is the window size in the Y direction.
S3, acquiring correction gather data
The denoised data of the channel pre-stack gathers usually have an amplitude relation of 'strong-weak-strong', theoretically, besides the phase conversion, four types of AVO do not exist, which type of AVO has the amplitude relation of 'strong-weak-strong' along with the increase of the offset distance, the non-fidelity AVO relation is very unfavorable for the subsequent AVO analysis and AVO inversion, and the reason for the phenomenon is that the amplitude abnormality caused by the acquisition system is mostly concerned about the acquisition system, in the technical scheme, the amplitude abnormality of the three-dimensional observation system which is not completely eliminated in the offset process is carried out on the co-offset distance gathers after the offset.
The method comprises the specific steps of superposing all denoised pre-stack gather data along a river channel into gather data according to different offset distances, wherein the gather data only comprises time t and offset distance h information, and is recorded as seis (h, t).
A least squares fit is performed on each pre-stack gather data seis _ o (x, y, h, t) to the gather data seis (h, t) to obtain a correction factor q (h, t).
The expression of the correction coefficient is:
f(q(h,t))=min||seis(h,t)*q(h,t)-seis_o(x,y,h,t)||2
The corrected gather is:
seis_c(x,y,h,t)=seis_o(x,y,h,t)*q(h,t)
wherein the general representation of the pre-stack gather data comprises 4 dimensions, an X-direction, a Y-direction, a time t and an offset h, seis _o (X, Y, h, t) represents the pre-stack gather data, seis (h, t) represents the gather data, q (h, t) represents the correction coefficient, seis _c (X, Y, h, t) represents the correction gather data.
Fig. 8a-8b are schematic diagrams showing the comparison before and after correction in the present embodiment.
S4, acquiring angle trace set data
In the preprocessing of seismic data, after correction, the corrected trace data is still in the offset domain, but because AVO analysis is variable with respect to the angle of incidence, fixed offset records need to be converted into superimposed trace records at fixed angles of incidence or over a range of angles in order to facilitate AVO analysis. When processing angles, the corresponding parts of the fixed offset record within a certain reflection angle range are generally combined to obtain a reflected angle track, the process is repeated, different angles or angle ranges are changed to obtain different angle tracks, and an angle track set is formed, so that the angle track sets of different angles can be obtained, and fig. 9 shows that the data offset distance of the denoised channel pre-stack track set is converted into the angle track set data in the embodiment.
However, in AVO analysis, we often do not need all angle gather data, and in angle gather data of one angle, we do not need all angle ranges, so we can superimpose angle gather data of a certain range into angle partial superimposed gather data according to needs, which is the most basic angle superimposed gather data in AVO analysis. However, in the angle-section superimposed gather data, each angle-section superimposed gather data has a central angle-section, and in the actual processing, in order to improve the signal-to-noise ratio of the AVO gather records and to improve a certain lateral resolution, records up to three or more times of coverage in each angle-section gather data may be selected for superimposition.
For angular track ranges, not all of the angle range's gather data may be superimposed. The actual seismic data acquisition is known to be covered multiple times, so that the signal-to-noise ratio and the transverse resolution can be effectively improved, and therefore, the range of the angle channel can be determined according to the definition of the width of the first Fresnel zone. Fig. 12 is a comparison of a mid-angle stack (15 °) of a partial angle stack gather data section with a full stack section, which is the input data for a subsequent pre-stack seismic inversion in this embodiment.
S5, super gather processing
The signal-to-noise ratio analysis is performed on the angle gather data, if the signal-to-noise ratio is insufficient, further super gather processing can be performed to improve the signal-to-noise ratio, wherein the super gather processing is the stacking of the surface elements within a certain range, so that the transverse continuity of the gather data is improved, and fig. 10 is a schematic diagram of the effect of the super gather processing of the angle gather data in the embodiment.
S6, obtaining leveling correction gather data
The existing residual time difference correction technology is mostly realized by adopting anisotropic speed analysis, but the problem of leveling cannot be solved well due to difficult calculation of anisotropic parameters, in the technical process, the preprocessing adopts a gather leveling technology based on the automatic tracking of the same phase axis on angle gather data for leveling, the basic principle is that salient layer sites are extracted from the similar positions of trace waveforms, automatic layer position tracking is carried out, leveling correction is carried out on the obtained trace time shift amount, residual motion correction processing under no speed is realized, and fig. 11 is an angle trace residual time difference correction effect diagram in the embodiment.
Embodiment III:
the embodiment of the invention provides a device for optimizing data of a river-phase reservoir along a pre-stack gather, which comprises the following components:
A pre-stack gather data module for providing pre-stack gather data;
the river channel along-the-river channel pre-stack gather data module is used for describing the river channel space distribution form and applying the river channel space distribution form to the pre-stack gather data to obtain river channel along-the-river channel pre-stack gather data;
Specifically, from river channel sensitive attribute optimization, plane attribute extraction is performed, a river channel space distribution form is depicted through well position calibration and threshold value determination, and the river channel space distribution form is applied to pre-stack gather data to obtain the pre-stack gather data along the river channel, which can be used for subsequent processing and interpretation.
The seismic data volume belongs to mass data, the extraction algorithm of the seismic data volume is many, in order to obtain high extraction speed, the data retrieval speed needs to be improved, and the required CDP point is rapidly positioned, and specifically comprises two parts of data volume scanning and data volume indexing.
Data volume scanning:
Before the data is loaded into the database, the whole scanning is carried out on the data of the rolling head and the track head of the data body, the attribute information related to the data body is scanned out and stored in the database in the form of structured data, so that the basic information inquiry of the data body is facilitated.
Data volume index:
In order to obtain very high seismic data extraction speed, the data volume can be scanned channel by channel to establish an index file, and the index file records X, Y coordinates of each seismic channel, inline, crossline numbers and absolute positions of channel data storage. Therefore, if X, Y coordinates of each section of broken line of the section to be extracted are obtained, an index file corresponding to the data body of the work area can be found through the structured data information of the database, the absolute position of one data channel of the data body corresponding to the coordinates can be found through X, Y coordinates, the position of the data channel can be directly positioned in the data body to read the data, the whole data body is prevented from being scanned, and the index file is generally very small and can be scanned once quickly, so that the data extraction speed can be doubled through the index of the data body.
The acquired pre-stack gather data along the river channel has the following advantages in the aspects of subsequent data processing and interpretation:
Firstly, river phase reservoir prediction and fluid detection efficiency can be improved in the aspects of gather processing, pre-stack inversion, attribute extraction and the like, and secondly, river phase reservoir prediction and fluid detection precision can be improved, river top and bottom interface interpretation is facilitated, and background surrounding rock influence is eliminated.
The denoising module is used for denoising the pre-stack channel set data along the river channel to obtain denoised pre-stack channel set data along the river channel;
Specifically, the time migration of conventional channel set data along the river channel is usually oriented to target post-stack seismic data, various noises, especially Gaussian noises, usually exist before stacking, the denoising treatment is carried out before angle conversion and partial stacking, so as to improve the signal-to-noise ratio of the channel set, improve the signal-to-noise ratio of final angle stacked data, remove a plurality of random noise methods, such as median filtering, polynomial fitting, f-x domain prediction denoising, time-space wavelet threshold fidelity denoising based on transverse sliding, CRP channel set rolling stacking to form super channel set denoising, and the like:
Wherein S represents the seismic data, X, Y and t are three dimensions of the seismic data, and represent the X direction, the Y direction and the time direction t respectively. mn is the size of the median filter window function, m is the window size in the X direction, and n is the window size in the Y direction.
The correction module corrects the amplitude of the denoised channel set data before the river channel stack to obtain corrected channel set data;
specifically, all the denoised pre-stack gather data along the river are overlapped into gather data according to different offset distances, and the gather data only comprises time t and offset distance h information and is recorded as seis (h, t).
A least squares fit is performed on each pre-stack gather data seis _ o (x, y, h, t) to the gather data seis (h, t) to obtain a correction factor q (h, t).
The expression of the correction coefficient is:
f(q(h,t))=min||seis(h,t)*q(h,t)-seis_o(x,y,h,t)||2
The corrected gather is:
seis_c(x,y,h,t)=seis_o(x,y,h,t)*q(h,t)
where t represents time, h represents offset, seis _o (x, y, h, t) represents pre-stack gather data, seis (h, t) represents gather data, q (h, t) represents correction coefficients, seis _c (x, y, h, t) represents correction gather data.
The angle gather data module is used for obtaining angle gather data by changing the angle of the correction gather data;
Specifically, in the preprocessing of the seismic data, after correction, the correction gather data is still in an offset domain, but because the AVO analysis is based on the incident angle as a variable, in order to facilitate the AVO analysis, the fixed offset record needs to be converted into a superimposed gather record with a fixed incident angle or within a certain angle range, when in angle processing, the corresponding parts of the fixed offset record within a certain reflection angle range are generally combined to obtain a reflected angle gather, and the process is repeated, so that different angles or angle ranges can be changed to obtain different angle gathers to form an angle gather, and thus, the angle gathers with different angles can be obtained.
And the preprocessing module is used for preprocessing the angle gather data to obtain leveling correction gather data.
Specifically, the preprocessing carries out leveling treatment on the angle trace set by adopting a trace set leveling technology based on the same-phase axis automatic tracking, and the basic principle of the trace set leveling technology is that significant layer sites are extracted at the trace set waveform similarity, the horizon automatic tracking is carried out, the leveling correction is carried out on the obtained trace set time shift amount, and the remaining dynamic correction treatment under the condition of no speed is realized.
The foregoing description of embodiments of the invention has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described.
Claims (8)
1. The river facies reservoir along the river pre-stack gather data optimization processing method is characterized by comprising the following steps:
providing pre-stack gather data;
Describing the river channel space distribution form, and applying the river channel space distribution form to the prestack gather data to obtain the prestack gather data along the river channel;
denoising the pre-stack channel set data along the river channel to obtain denoised pre-stack channel set data along the river channel;
correcting the amplitude of the denoised channel set data before the river channel stack to obtain corrected channel set data;
acquiring angle gather data by changing the angle of the correction gather data;
Preprocessing the angle gather data to obtain leveling correction gather data;
the step of correcting the amplitude of the denoised channel set data before the river channel stack to obtain corrected channel set data comprises the following steps:
Overlapping all denoised channel set data before channel stack according to different offset distances to form channel set data, and then carrying out least square fitting on each channel set data before stack and the channel set data to obtain correction channel set data;
Before describing the river space distribution form, the method comprises the following steps:
and selecting river channel sensitive attributes, extracting plane attributes, and determining through well position calibration and threshold values.
2. The method for optimizing river phase reservoir set data along a river course according to claim 1, comprising, before obtaining the set data along the river course:
and extracting the seismic data volume, wherein the extracted seismic data volume comprises a data volume scan and a data volume index.
3. The method for optimizing data of a pre-stack gather of a river facies reservoir according to claim 1, comprising, before obtaining the corrected gather data:
and carrying out least square fitting on each piece of prestack gather data and the gather data to obtain a correction coefficient.
4. A method for optimizing data of a river facies reservoir along a pre-stack gather according to claim 3, wherein the expression of the correction factor is:
Wherein t represents time, h represents offset, seis _o (x, y, h, t) represents de-noised pre-stack gather data along the river, seis (h, t) represents gather data, and q (h, t) represents a correction coefficient.
5. The method for optimizing data of a pre-stack gather of a river facies reservoir according to claim 1, wherein the corrected gather data seis _c (x, y, h, t) has the expression:
wherein seis _o (x, y, h, t) represents de-noised pre-stack gather data along the river, q (h, t) represents a correction coefficient, seis _c (x, y, h, t) represents correction gather data.
6. The method for optimizing river facies reservoir along pre-stack gather data according to claim 1, wherein in the step of preprocessing the angle gather data, the preprocessing refers to leveling by adopting a gather leveling technology.
7. The method for optimizing river facies reservoir along pre-stack gather data according to claim 1, further comprising, after obtaining the angle gather data:
and performing super-gather processing on the angle gather data to improve the transverse continuity of the angle gather data.
8. A device for optimizing data of a river facies reservoir along a pre-stack gather, comprising:
A pre-stack gather data module for providing pre-stack gather data;
the river channel along-the-river channel pre-stack gather data module is used for describing the river channel space distribution form and applying the river channel space distribution form to the pre-stack gather data to obtain river channel along-the-river channel pre-stack gather data;
The denoising module is used for denoising the pre-stack channel set data along the river channel to obtain denoised pre-stack channel set data along the river channel;
the correction module corrects the amplitude of the denoised channel set data before the river channel stack to obtain corrected channel set data;
the angle gather data module is used for obtaining angle gather data by changing the angle of the correction gather data;
the preprocessing module is used for preprocessing the angle gather data to obtain leveling correction gather data;
the step of correcting the amplitude of the denoised channel set data before the river channel stack to obtain corrected channel set data comprises the following steps:
Overlapping all denoised channel set data before channel stack according to different offset distances to form channel set data, and then carrying out least square fitting on each channel set data before stack and the channel set data to obtain correction channel set data;
Before describing the river space distribution form, the method comprises the following steps:
the river channel along pre-stack gather data module is also used for selecting river channel sensitive attributes, extracting plane attributes and determining through well position calibration and threshold values.
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