CN109884700A - Multi-information fusion seismic velocity modeling method - Google Patents
Multi-information fusion seismic velocity modeling method Download PDFInfo
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Abstract
The present invention provides a kind of multi-information fusion seismic velocity modeling method, which includes: information needed for input modeling；Acoustic logging speed is carried out smooth；Virtual log quantity is set, virtual log is constructed；Each log data is obtained into logging speed interpolation model along construction trend filling speed model and to filling result weighted sum respectively；Well logging interpolation speed is merged into priori speed, and is adjusted based on priori speed, as mid-deep strata speed；Ray coverage condition based on near-surface model determines fusion bottom surface and integration region, merges bottom surface or more and uses near-surface velocity；By, in the weighted sum of integration region, obtaining unified full velocity field to near-surface velocity and mid-deep strata speed and counting well shake velocity error.VELOCITY DISTRIBUTION obtained by this method includes the unexistent high frequency speed details of conventional result, and the seismic tomography inverting after being lays a good foundation, has broad application prospects.
Description
Technical field
The present invention relates to oil-gas exploration Seismic Data Processing Technique fields, especially relate to a kind of multi-information fusion earthquake
Velocity modeling method.
Background technique
Seimic wave velocity plays essential effect in the acquisition of seismic data, processing, explanation and evaluation.With oil
The continuous improvement of gas exploration degree, survey area geological conditions become to become increasingly complex, and underground medium heterogeneity is strong, speed is vertical
The problems such as cross directional variations are big, high-dip structure is developed proposes challenge to seismic imaging and velocity modeling method.
In seismic prospecting, because rolling topography and near-surface velocity variation have a major impact migration imaging, static correction
Processing is difficult to be suitable for complex situations, and the pre-stack depth migration based on relief surface needs to establish speed mould from shallow to deep
Type.It is limited by technical conditions and real data, is also difficult to directly acquire the speed mould by earth's surface to deep by inverting now
Type.Common way is using preliminary wave tomographic inversion near-surface velocity model, and application speed is analyzed to obtain mid-deep strata speed
Model is spent, the two is spliced or merged later, obtains initial velocity model from shallow to deep.Spy for double complex geological conditions
Area, the Deep model precision that application speed analysis etc. is conventionally treated is low, the iterative inversion after being unfavorable for, and acquired results are easy
The case where appearance does not restrain.Using more information (construction, well logging etc.), velocity modeling method is studied, relief surface speed is improved
The precision of model be it is very necessary, this will promote the iteration convergence of tomographic inversion, and then be conducive to relief surface prestack depth
The accuracy of offset.Thus we have invented a kind of new multi-information fusion seismic velocity modeling method, solves the above technology
Problem.
Summary of the invention
The multi-information fusion seismic velocity modeling method based on interpolation algorithm that the object of the present invention is to provide a kind of.
The purpose of the present invention can be achieved by the following technical measures: multi-information fusion seismic velocity modeling method, this is more
It includes: step 1 that information, which merges seismic velocity modeling method, information needed for input modeling；Step 2, to acoustic logging speed into
Row is smooth；Step 3, virtual log quantity is set, virtual log is constructed；Step 4, each log data is filled into speed along structure dip respectively
Model is spent, Gaussian bases is based on later, seeks the weighting coefficient of each log data, weighted sum obtains logging speed interpolation
Model；Step 5, logging speed interpolation model is merged into priori migration velocity, and the ratio of statistical result and priori migration velocity,
Result is adjusted based on ratio, obtains mid-deep strata rate pattern；Step 6, the ray coverage condition based on near-surface model,
Determine fusion top surface and the integration region of near-surface model；Step 7, by being merged to near-surface velocity and mid-deep strata speed
The fusion in region obtains unified full velocity field, exports as final result, and reduced model and the opposite of logging speed are missed
Difference.
The purpose of the present invention can be also achieved by the following technical measures:
In step 1, the information of input includes near-surface velocity, the ray covering of near-surface model, structure dip, sound wave
The priori migration velocity that logging speed, time and depth transfer obtain, wherein structure dip is to carry out dip scanning to Depth Imaging section
What analysis obtained.
In step 2, local smoothing method is carried out to acoustic logging speed based on Gauss window.
In step 3, according to well logging distribution situation, the quantity of virtual log is set, is based on priori migration velocity, construction is virtual
Well velocity information, to control the complicated structure of underground.
In step 3, work area is divided into face element along transverse and longitudinal, using the face element containing well and work area boundary as start bit
It sets, marks its adjacent face element, later centered on labeled face element, further mark its adjacent face element, and so on, gradually
To entire work area, and find face element labeled the latest, and using the face element and its priori migration velocity as virtual log position with
Speed carries out the building of lower a bite virtual log on this basis, and so on, the quantity of setting is completed, to control answering for underground
Miscellaneous construction.
In step 4, logging speed interpolation method is as follows: the speed v of known n mouthfuls of welli, (i=1 ..., n), by i-th mouthful
Well velocity measured is along the position that structure dip direction is extrapolated to jth mouth well, as extrapolating results, and so on, n mouthfuls of well speed
N × n extrapolating results are obtained altogether, withAs objective function, v in formulajIt is surveyed by jth mouth well
Speed, λiFor the weight coefficient of i-th mouthful of well, v 'i,jFor i-th mouthful of well be extrapolated to jth mouth well as a result, | | xi-xj||2Indicate i-th
Mouthful well at a distance from jth mouth well,For with the Gaussian bases of distance change, concrete form isR is distance；Base
In the least square thought, solution obtains the weight of each mouth well, each extrapolated data is weighted summation, is obtained at interpolation point
Velocity amplitude, point-by-point weighted sum obtain logging speed interpolation model.
In steps of 5, the grid in logging speed interpolation model without velocity amplitude is filled into priori migration velocity, and to filling
The speed of near border carries out linear weighted function, obtains fusion rate pattern.
In steps of 5, for merging rate pattern, it is based on a large scale sliding window, gradually counts priori migration velocity
With the ratio for merging rate pattern different zones, and fusion speed is multiplied with corresponding ratio, as a result as mid-deep strata speed
Model.
In step 6, the ray coverage condition based on near-surface model covers the zone boundary being greater than the set value with ray
As the fusion top surface of near-surface model, merges top surface or more and use near-surface velocity completely.
In step 6, to merge the section of the given thickness under top surface as the corresponding circle of sensation of near surface and mid-deep strata model
Domain.
In step 7, based at a distance from the bottom interface of integration region top, near-surface velocity and mid-deep strata speed are carried out anti-
Apart from linear weighted sum, unified full velocity field is obtained.
In step 7, with the relative error of model and logging speedAs quality
Control refers to, wherein vmodelFor the model result that well location is set, vsmooth_logFor smoothed out logging speed.
Multi-information fusion seismic velocity modeling method in the present invention, can by multi-information fusion seismic velocity modeling method
The precision of relief surface initial velocity model is improved, gained VELOCITY DISTRIBUTION contains the unexistent high frequency speed of more conventional results
Details, it is ensured that the precision of earthquake depth domain imaging, the seismic tomography inverting after being are laid a good foundation, and have wide application
Prospect.
Detailed description of the invention
Fig. 1 is the flow chart of a specific embodiment of multi-information fusion seismic velocity modeling method of the invention；
Fig. 2 is work area sketch map in a specific embodiment of the invention；
Fig. 3 is two mouthfuls of well speed curve diagrams in a specific embodiment of the invention；
Fig. 4 is the application drawing of virtual log in a specific embodiment of the invention；
Fig. 5 is showing for the result that the logging speed of Fig. 3 in a specific embodiment of the invention is merged with priori migration velocity
It is intended to；
Fig. 6 is final result display figure in a specific embodiment of the invention；
Fig. 7 is the migrated section figure of friction speed in a specific embodiment of the invention.
Specific embodiment
To enable above and other objects, features and advantages of the invention to be clearer and more comprehensible, preferably implementation is cited below particularly out
Example, and cooperate shown in attached drawing, it is described in detail below.
Fig. 1 is the flow chart of multi-information fusion seismic velocity modeling method of the present invention.
Step 1, input near-surface velocity and ray covering, structure dip, acoustic logging speed, time and depth transfer obtain
Priori migration velocity, wherein structure dip is to carry out dip scanning to Depth Imaging section to analyze.
Step 2 carries out local smoothing method to acoustic logging speed based on Gauss window.
Step 3 sets the quantity of virtual log according to well logging distribution situation, and work area is divided face element along transverse and longitudinal, will
The position of well projects on corresponding face element, using the face element containing well and work area boundary as initial position, marks its adjacent face element,
Later centered on labeled face element, its adjacent face element is further marked, and so on, gradually to entire work area, and find
Face element labeled the latest, and using the face element and its priori migration velocity as virtual log position and speed, on this basis into
The building of the lower a bite virtual log of row, and so on, the quantity of setting is completed, to control the complicated structure of underground.
Step 4 is based on Gaussian bases later, seeks respectively by each log data along structure dip filling speed model
The weighting coefficient of each log data, weighted sum obtain logging speed interpolation model, and wherein the acquiring method of interpolation weights is such as
Under: the speed v of known n mouthfuls of welli, i-th mouthful of well velocity measured is extrapolated to jth mouth well along structure dip direction by (i=1 ..., n)
Position at, as extrapolating results, and so on, obtain n × n extrapolating results, withMake
For objective function, v in formulajThe speed surveyed by jth mouth well, λiFor the weight coefficient of i-th mouthful of well, v 'i,jIt is extrapolated to for i-th mouthful of well
Jth mouth well as a result, | | xi-xj||2Indicate i-th mouthful of well at a distance from jth mouth well,For with the Gaussian bases of distance change,
Concrete form isR is distance.Based on the above objective function, in conjunction with the least square thought, can solve to obtain each mouth
The weight of well.
Grid in logging speed interpolation model without velocity amplitude is filled priori migration velocity by step 5, and to filling side
Speed near boundary carries out linear weighted function, obtains fusion rate pattern, herein on basis, gradually count priori migration velocity with
The ratio of rate pattern different zones is merged, and fusion speed is multiplied with corresponding ratio, is as a result used as mid-deep strata speed mould
Type.
Step 6, the ray coverage condition based on near-surface model, with the boundary in the region that ray covering is greater than the set value
As the fusion top surface of near-surface model, merges top surface or more and use near-surface velocity completely.To merge the setting area under top surface
Between integration region as near surface and mid-deep strata model.
Step 7, based at a distance from the bottom interface of integration region top, to near-surface velocity and mid-deep strata speed carry out instead away from
Offline property weighted sum, the full velocity field unified, and with the relative error of model and logging speedIt controls and refers to as quality, wherein vmodelFor the model result that well location is set,
vsmooth_logFor smoothed out logging speed.
In an application specific embodiment of the invention, Fig. 2 is work area sketch map, and figure grey area is work area range,
Small square indicates well logging position.Process flow is as follows:
(1) structure dip information is inputted, the priori migration velocity that acoustic logging rate curve and time and depth transfer obtain；
(2) logging speed curve is smoothed, as a result as shown in figure 3, Fig. 3 (a) and 3 (b) is smooth for two mouthfuls of wells
The comparison of front and back, wherein gray line is acoustic logging, and black line is sharpening result；
(3) according to well logging distribution situation, 2 mouthfuls of virtual logs are added, by the algorithm of setting, determining virtual log position is as schemed
Shown in triangle in 4 (a), Fig. 4 (b) be Fig. 4 (a) in vertical line at section, Fig. 4 (c) be not added with virtual log as a result,
Fig. 4 (d) is addition virtual log as a result, comparing the two it is found that the addition of virtual log is improved because of complex structural area well logging information
Missing and caused by result inaccuracy；
(4) respectively by each log data along structure dip filling speed model, it is based on Gaussian bases later, seeks each
The inverse distance-weighting coefficient of log data, weighted sum obtain logging speed interpolation model；
(5) logging speed interpolation model is merged into priori migration velocity, obtains fusion rate pattern, Fig. 5 (a) and Fig. 5 (b)
For Fig. 3 logging speed merged with priori migration velocity as a result, wherein gray line is priori migration velocity, black line is fusion speed
Degree, the ratio of statistical result and priori migration velocity, is adjusted result based on ratio later；
(6) the ray coverage condition based on near-surface model determines the fusion bottom surface of near-surface model, and determines corresponding circle of sensation
Domain merges bottom surface or more and uses near-surface velocity completely；
(7) by summing to near-surface velocity and mid-deep strata speed in the inverse distance-weighting of integration region, unification is obtained
Full velocity field is exported as final result, and the relative error of reduced model and logging speed.Fig. 6 (a) is priori offset speed
Degree, Fig. 6 (b) be integration modeling as a result, Fig. 6 (c) is the relative error of model Yu logging speed used, can be used for evaluation and most terminates
Fruit.Fig. 7 (a) is the section using priori migration velocity, and Fig. 7 (b) is the section using this method result, and comparing result is based on
The section of this method is relatively sharp to the imaging of big cross section.
The foregoing is merely presently preferred embodiments of the present invention and oneself, not with the present invention for limitation, it is all in essence of the invention
Made impartial modifications, equivalent substitutions and improvements etc., should be included in patent covering scope of the invention within mind and principle.
Claims (12)
1. multi-information fusion seismic velocity modeling method, which is characterized in that the multi-information fusion seismic velocity modeling method includes:
Step 1, information needed for input modeling；
Step 2, acoustic logging speed is carried out smooth；
Step 3, virtual log quantity is set, virtual log is constructed；
Step 4, respectively by each log data along structure dip filling speed model, it is based on Gaussian bases later, seeks each survey
The weighting coefficient of well data, weighted sum obtain logging speed interpolation model；
Step 5, logging speed interpolation model is merged into priori migration velocity, and the ratio of statistical result and priori migration velocity,
Result is adjusted based on ratio, obtains mid-deep strata rate pattern；
Step 6, the ray coverage condition based on near-surface model determines fusion top surface and the integration region of near-surface model；
Step 7, by the way that near-surface velocity and mid-deep strata speed in the fusion of integration region, are obtained unified full velocity field, made
It is exported for final result, and the relative error of reduced model and logging speed.
2. multi-information fusion seismic velocity modeling method according to claim 1, which is characterized in that in step 1, input
Information include that near-surface velocity, the ray covering of near-surface model, structure dip, acoustic logging speed, time and depth transfer obtain
Priori migration velocity, wherein structure dip be to Depth Imaging section carry out dip scanning analyze.
3. multi-information fusion seismic velocity modeling method according to claim 1, which is characterized in that in step 2, be based on
Gauss window carries out local smoothing method to acoustic logging speed.
4. multi-information fusion seismic velocity modeling method according to claim 1, which is characterized in that in step 3, according to
Well logging distribution situation, sets the quantity of virtual log, is based on priori migration velocity, virtual log velocity information is constructed, to control underground
Complicated structure.
5. multi-information fusion seismic velocity modeling method according to claim 4, which is characterized in that in step 3, by work
Area is divided into face element along transverse and longitudinal, using the face element containing well and work area boundary as initial position, marks its adjacent face element, later with
Centered on labeled face element, further mark its adjacent face element, and so on, gradually to entire work area, and find the latest by
The face element of label, and using the face element and its priori migration velocity as virtual log position and speed, it carries out on this basis next
The building of mouth virtual log, and so on, the quantity of setting is completed, to control the complicated structure of underground.
6. multi-information fusion seismic velocity modeling method according to claim 1, which is characterized in that in step 4, well logging
Speed interpolation method is as follows: the speed v of known n mouthfuls of welli, (i=1 ..., n), by the speed of i-th mouthful of well along structure dip direction
It is extrapolated at the position of jth mouth well, as extrapolating results, and so on, n mouthfuls of well speed obtain n × n extrapolating results altogether, withAs objective function, v in formulajThe speed surveyed by jth mouth well, λiFor the power of i-th mouthful of well
Weight coefficient, v 'i,jFor i-th mouthful of well be extrapolated to jth mouth well as a result, | | xi-xj||2Indicate i-th mouthful of well at a distance from jth mouth well,For with the Gaussian bases of distance change, concrete form isR is distance；Based on the least square thought, solve
The weight of each mouth well is obtained, each extrapolated data is weighted summation, obtains the velocity amplitude at interpolation point, point-by-point weighting is asked
With obtain logging speed interpolation model.
7. multi-information fusion seismic velocity modeling method according to claim 1, which is characterized in that in steps of 5, will survey
Grid in well speed interpolation model without velocity amplitude fills priori migration velocity, and carries out to the speed of filling near border linear
Weighting obtains fusion rate pattern.
8. multi-information fusion seismic velocity modeling method according to claim 7, which is characterized in that in steps of 5, for
Merge rate pattern, be based on a large scale sliding window, gradually count priori migration velocity with merge rate pattern different zones
Ratio, and fusion speed is multiplied with corresponding ratio, as a result as mid-deep strata rate pattern.
9. multi-information fusion seismic velocity modeling method according to claim 1, which is characterized in that in step 6, be based on
The ray coverage condition of near-surface model, using the zone boundary that ray covering is greater than the set value as the fusion top of near-surface model
Face merges top surface or more and uses near-surface velocity completely.
10. multi-information fusion seismic velocity modeling method according to claim 9, which is characterized in that in step 6, with
Merge integration region of the section of the given thickness under top surface as near surface and mid-deep strata model.
11. multi-information fusion seismic velocity modeling method according to claim 1, which is characterized in that in step 7, base
In at a distance from the bottom interface of integration region top, near-surface velocity and mid-deep strata speed obtain instead apart from linear weighted sum
The full velocity field that must unify.
12. multi-information fusion seismic velocity modeling method according to claim 11, which is characterized in that in step 7, with
The relative error of model and logging speedIt controls and refers to as quality, wherein vmodel
For the model result that well location is set, vsmooth_logFor smoothed out logging speed.
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