CN109884700B - Multi-information fusion seismic velocity modeling method - Google Patents
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Abstract
The invention provides a multi-information fusion seismic velocity modeling method, which comprises the following steps: inputting information required by modeling; smoothing the acoustic logging speed; setting the number of virtual wells, and constructing the virtual wells; filling a speed model with the logging data along the construction trend, and weighting and summing filling results to obtain a logging speed interpolation model; fusing the logging interpolation speed with a priori speed, and adjusting based on the priori speed to serve as a middle-deep layer speed; determining a fusion bottom surface and a fusion area based on the ray coverage condition of the near-surface model, wherein the near-surface speed is adopted above the fusion bottom surface; and obtaining a uniform full velocity field and counting the well seismic velocity error by the weighted summation of the near-surface velocity and the middle-deep layer velocity in the fusion region. The velocity distribution obtained by the method contains high-frequency velocity details which are not available in the conventional result, lays a foundation for the later seismic chromatography inversion, and has wide application prospect.
Description
Technical Field
The invention relates to the technical field of seismic data processing of oil and gas exploration, in particular to a multi-information fusion seismic velocity modeling method.
Background
The seismic wave velocity plays an essential role in acquisition, processing, interpretation and evaluation of seismic data. With the continuous improvement of oil and gas exploration degree, the geological conditions of an exploration area become more and more complex, and the problems of strong heterogeneity of underground media, large longitudinal and transverse change of speed, high and steep structural development and the like provide challenges for seismic imaging and speed modeling methods.
In seismic exploration, static correction processing is difficult to adapt to complex conditions because undulating terrain and near-surface velocity changes have important influence on migration imaging, and a velocity model from shallow to deep needs to be established based on prestack depth migration of the undulating terrain. Under the limitation of technical conditions and practical data, it is also difficult to directly acquire a velocity model from the earth surface to the deep part through inversion. The common method is to invert a near-surface velocity model by using first-arrival wave tomography, obtain a middle-deep velocity model by using velocity analysis, and then splice or fuse the two to obtain a shallow-to-deep initial velocity model. For the exploration area with double complex geological conditions, the deep model obtained by applying conventional processing such as velocity analysis and the like has low precision, so that the later iterative inversion is not facilitated, and the obtained result is easy to have the condition of non-convergence. More information (construction, well logging and the like) is applied, a speed modeling method is researched, and the accuracy of the undulating surface speed model is necessary to be improved, so that the iterative convergence of chromatographic inversion is promoted, and the accuracy of the undulating surface prestack depth migration is facilitated. Therefore, a novel multi-information fusion seismic velocity modeling method is invented, and the technical problems are solved.
Disclosure of Invention
The invention aims to provide a multi-information fusion seismic velocity modeling method based on an interpolation algorithm.
The object of the invention can be achieved by the following technical measures: the multi-information fusion seismic velocity modeling method comprises the following steps: step 1, inputting information required by modeling; step 2, smoothing the acoustic logging speed; step 3, setting the number of virtual wells, and constructing the virtual wells; step 4, filling a velocity model with each logging data along a constructed dip angle, then solving a weighting coefficient of each logging data based on a Gaussian basis function, and carrying out weighted summation to obtain a logging velocity interpolation model; step 5, fusing the logging speed interpolation model with the prior offset speed, counting the ratio of the result to the prior offset speed, and adjusting the result based on the ratio to obtain a middle-deep-layer speed model; step 6, determining a fusion top surface and a fusion area of the near-surface model based on the ray coverage condition of the near-surface model; and 7, obtaining a uniform full velocity field through fusion of the near-surface velocity and the middle-deep layer velocity in the fusion region, outputting the uniform full velocity field as a final result, and deriving a relative error between the model and the logging speed.
The object of the invention can also be achieved by the following technical measures:
in step 1, the input information includes a near-surface velocity, ray coverage of a near-surface model, a constructed dip angle, a sonic logging velocity, and a priori offset velocity obtained by time-depth conversion, wherein the constructed dip angle is obtained by performing dip scanning analysis on a depth imaging profile.
In step 2, the sonic logging speed is locally smoothed based on a gaussian window.
In step 3, the number of virtual wells is set according to the logging distribution situation, and the speed information of the virtual wells is constructed on the basis of the prior offset speed so as to control the underground complex construction.
In step 3, dividing the work area into bins along the transverse direction and the longitudinal direction, marking the bin containing wells and the boundary of the work area as initial positions, then marking the adjacent bin, further marking the adjacent bin by taking the marked bin as a center, and repeating the steps until the whole work area is reached step by step, searching the bin marked at the latest, taking the bin and the prior offset speed thereof as the position and the speed of the virtual well, constructing the next virtual well on the basis, and repeating the steps to finish the set number so as to control the underground complex structure.
In step 4, the logging speed interpolation method is as follows: knowing the velocity v of n wellsiAnd (i is 1, …, n), extrapolating the speed measured by the ith well to the position of the jth well along the construction dip angle direction, and obtaining n multiplied by n extrapolation results by the speed of the n wells as the extrapolation result, and so on, so as to obtain the n multiplied by n extrapolation results
As an objective function, where vjVelocity, λ, measured for jth welliIs the weight coefficient of the ith well, v'i,jFor the results of extrapolation of the ith well to the jth well, | | xi-xj||2The distance between the ith well and the jth well is shown,
is a Gaussian function varying with distance, and has a specific form of
r is a distance; based on the least square idea, the weights of all wells are obtained through solving, weighted summation is carried out on all extrapolated data to obtain speed values at interpolation points, and point-by-point weighted summation is carried out to obtain a logging speed interpolation model.
In step 5, the grids without the speed values in the logging speed interpolation model are filled with the prior offset speed, and the speed near the filling boundary is subjected to linear weighting to obtain a fusion speed model.
In step 5, for the fusion velocity model, based on a large-scale sliding window, the ratios of the prior offset velocity to different regions of the fusion velocity model are counted step by step, and the fusion velocity is multiplied by the corresponding ratio, and the result is used as a middle-deep velocity model.
In step 6, based on the ray coverage of the near-surface model, the region boundary with the ray coverage larger than the set value is used as the fusion top surface of the near-surface model, and the near-surface speed is adopted completely above the fusion top surface.
In step 6, the interval of a given thickness below the fused top surface is used as a fusion area of the near-surface and middle-deep layer models.
In step 7, based on the distance from the top-bottom interface of the fusion region, inverse distance linear weighted summation is performed on the near-surface velocity and the middle-deep layer velocity, and a uniform full velocity field is obtained.
In step 7, the relative error between the model and the logging speed is used
As a quality control reference, where vmodelAs a result of the model at the well location, vsmooth_logThe smoothed logging speed.
According to the multi-information fusion seismic velocity modeling method, the precision of the undulating surface initial velocity model can be improved through the multi-information fusion seismic velocity modeling method, the obtained velocity distribution contains more high-frequency velocity details which are not available in conventional results, the seismic depth domain imaging precision is ensured, a foundation is laid for the later seismic tomography inversion, and the method has a wide application prospect.
Drawings
FIG. 1 is a flow diagram of one embodiment of a multi-information fusion seismic velocity modeling method of the present invention;
FIG. 2 is a schematic diagram of a work area in accordance with an embodiment of the present invention;
FIG. 3 is a two well velocity profile in an embodiment of the present invention;
FIG. 4 is a diagram of a virtual well application in accordance with an embodiment of the present invention;
FIG. 5 is a graphical illustration of the result of fusing the log velocity of FIG. 3 with a prior offset velocity in an embodiment of the present invention;
FIG. 6 is a graph showing the final result in accordance with an embodiment of the present invention;
FIG. 7 is an offset cross-sectional view of different velocities in an embodiment of the present invention.
Detailed Description
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
FIG. 1 is a flow chart of the multi-information fusion seismic velocity modeling method of the invention.
Inputting near-surface speed, ray coverage, a constructed dip angle, an acoustic logging speed and a priori deviation speed obtained by time-depth conversion, wherein the constructed dip angle is obtained by performing dip angle scanning analysis on a depth imaging section.
And step two, locally smoothing the acoustic logging speed based on a Gaussian window.
And step three, setting the number of virtual wells according to the well logging distribution situation, dividing the surface element of the work area along the transverse direction and the longitudinal direction, projecting the position of the well onto the corresponding surface element, marking the adjacent surface element by taking the surface element containing the well and the boundary of the work area as initial positions, further marking the adjacent surface element by taking the marked surface element as a center, repeating the steps until the whole work area is reached, searching the latest marked surface element, taking the surface element and the prior offset speed of the surface element as the position and the speed of the virtual well, constructing the next virtual well on the basis, repeating the steps to complete the set number so as to control the underground complex structure.
Filling a velocity model with each logging data along a constructed dip angle, then solving the weighting coefficient of each logging data based on a Gaussian basis function, and carrying out weighted summation to obtain a logging velocity interpolation model, wherein the solving method of the interpolation weight comprises the following steps: knowing the velocity v of n wellsiAnd (i is 1, …, n), extrapolating the measured speed of the ith well to the position of the jth well along the construction dip angle direction as an extrapolation result, and the like to obtain an n × n extrapolation result so as to obtain the n × n extrapolation result
As an objective function, where vjVelocity, λ, measured for jth welliIs the weight coefficient of the ith well, v'i,jFor the results of extrapolation of the ith well to the jth well, | | xi-xj||2The distance between the ith well and the jth well is shown,
as a function of distanceA Gaussian base function of the specific form
r is the distance. Based on the objective function and by combining the least square idea, the weight of each well can be obtained through solving.
And step five, filling the prior offset speed in the mesh without the speed value in the logging speed interpolation model, carrying out linear weighting on the speed near the filling boundary to obtain a fusion speed model, gradually counting the ratio of the prior offset speed to different areas of the fusion speed model on the basis, multiplying the fusion speed by the corresponding ratio, and taking the result as a middle-deep layer speed model.
And step six, based on the ray coverage condition of the near-surface model, taking the boundary of the area with the ray coverage larger than the set value as the fusion top surface of the near-surface model, and completely adopting the near-surface speed above the fusion top surface. And taking the set interval below the fusion top surface as a fusion area of the near-surface model and the middle-deep layer model.
And step seven, performing inverse distance linear weighted summation on the near-surface velocity and the middle-deep layer velocity based on the distance from the fusion region top-bottom interface to obtain a uniform full velocity field, and obtaining the relative error between the model and the logging velocity
As a quality control reference, where vmodelAs a result of the model at the well location, vsmooth_logThe smoothed logging speed.
In one embodiment of the present invention, FIG. 2 is a schematic of a work area, where the gray areas are the work area areas and the small squares represent the logging locations. The processing flow is as follows:
(1) inputting the constructed inclination angle information, an acoustic logging speed curve and a priori offset speed obtained by time-depth conversion;
(2) smoothing the logging speed curve, wherein the result is shown in fig. 3, and fig. 3(a) and 3(b) are comparison before and after smoothing of two wells, wherein a gray line is an acoustic logging curve, and a black line is a smoothing result;
(3) according to the logging distribution situation, adding 2 virtual wells, determining the positions of the virtual wells by a set algorithm as shown by a triangle in a figure 4(a), wherein the figure 4(b) is a section at a vertical line in the figure 4(a), the figure 4(c) is a result of not adding the virtual wells, and the figure 4(d) is a result of adding the virtual wells, and comparing the two results, the inaccurate result caused by the missing logging information of a complicated structural area is improved by adding the virtual wells;
(4) filling a velocity model with each logging data along a constructed dip angle, then solving an inverse distance weighting coefficient of each logging data based on a Gaussian basis function, and carrying out weighted summation to obtain a logging velocity interpolation model;
(5) fusing the logging speed interpolation model with the prior offset speed to obtain a fused speed model, wherein fig. 5(a) and 5(b) are the result of fusing the logging speed and the prior offset speed of fig. 3, a gray line is the prior offset speed, a black line is the fused speed, then counting the ratio of the result to the prior offset speed, and adjusting the result based on the ratio;
(6) determining a fusion bottom surface of the near-surface model based on the ray coverage condition of the near-surface model, and determining a fusion area, wherein the near-surface speed is completely adopted above the fusion bottom surface;
(7) and obtaining a uniform full-speed field by weighting and summing the reverse distances of the near-surface speed and the middle-deep layer speed in the fusion region, outputting the uniform full-speed field as a final result, and deriving the relative error between the model and the logging speed. FIG. 6(a) is the prior offset velocity, FIG. 6(b) is the fusion modeling result, and FIG. 6(c) is the relative error of the model and the logging velocity used, which can be used to evaluate the final result. Fig. 7(a) is a section using the prior offset velocity, fig. 7(b) is a section using the result of the method, and compared with the result, the section based on the method can image a large section more clearly.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, but rather the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention as defined by the appended claims.
Claims (12)
1. The multi-information fusion seismic velocity modeling method is characterized by comprising the following steps:
step 1, inputting information required by modeling;
step 2, smoothing the acoustic logging speed;
step 3, setting the number of virtual wells, and constructing the virtual wells;
step 4, filling a velocity model with each logging data along a constructed dip angle, then solving a weighting coefficient of each logging data based on a Gaussian basis function, and carrying out weighted summation to obtain a logging velocity interpolation model;
step 5, fusing the logging speed interpolation model with the prior offset speed, counting the ratio of the result to the prior offset speed, and adjusting the result based on the ratio to obtain a middle-deep-layer speed model;
step 6, determining a fusion top surface and a fusion area of the near-surface model based on the ray coverage condition of the near-surface model;
and 7, obtaining a uniform full velocity field through fusion of the near-surface velocity and the middle-deep layer velocity in the fusion region, outputting the uniform full velocity field as a final result, and deriving a relative error between the model and the logging speed.
2. The multi-information fusion seismic velocity modeling method according to claim 1, wherein in step 1, the input information comprises a near-surface velocity, ray coverage of a near-surface model, a formation dip angle, a sonic logging velocity and a priori migration velocity obtained by time-depth conversion, wherein the formation dip angle is obtained by dip angle scanning analysis of a depth imaging section.
3. The multi-information fusion seismic velocity modeling method of claim 1, wherein in step 2, sonic logging velocities are locally smoothed based on a gaussian window.
4. The multi-information fusion seismic velocity modeling method of claim 1, wherein in step 3, the number of virtual wells is set according to well logging distribution, and virtual well velocity information is constructed based on a priori migration velocity to control complex subsurface formations.
5. The multi-information fusion seismic velocity modeling method according to claim 4, wherein in step 3, the work area is divided into bins along the transverse direction and the longitudinal direction, the bin containing the wells and the work area boundary are used as starting positions, the adjacent bins are marked, then the marked bin is used as a center, the adjacent bins are further marked, and the like, the whole work area is gradually reached, the latest marked bin is searched, the bin and the prior offset velocity thereof are used as the position and the velocity of the virtual well, the construction of the next virtual well is carried out on the basis, and the like, the set number is completed, and the complex underground structure is controlled.
6. The multi-information fusion seismic velocity modeling method of claim 1, wherein in step 4, the log velocity interpolation method is as follows: knowing the velocity v of n wellsiI is 1, …, n, extrapolating the speed of the ith well to the position of the jth well along the construction dip angle direction, and the like, wherein n well speeds obtain n multiplied by n extrapolation results in total so as to obtain the extrapolation result
As an objective function, where vjVelocity, λ, measured for jth welliIs the weight coefficient of the ith well, v'i,jFor the results of extrapolation of the ith well to the jth well, | | xi-xj||2The distance between the ith well and the jth well is shown,
is a Gaussian function varying with distance, and has a specific form of
r is a distance; based on least square thought, solving to obtain the weight of each well, and carrying out weighted summation on each extrapolation data to obtain interpolationAnd (4) carrying out point-by-point weighted summation on the velocity values at the value points to obtain a logging velocity interpolation model.
7. The multi-information fusion seismic velocity modeling method according to claim 1, characterized in that in step 5, a grid of no-velocity values in the logging velocity interpolation model is filled with prior migration velocities, and velocities near the filled boundary are linearly weighted to obtain a fusion velocity model.
8. The multi-information fusion seismic velocity modeling method according to claim 7, characterized in that in step 5, for the fusion velocity model, the ratios of the prior migration velocity to different regions of the fusion velocity model are counted step by step based on a large-scale sliding window, and the fusion velocity is multiplied by the corresponding ratio, and the result is used as the middle-deep velocity model.
9. The multi-information fusion seismic velocity modeling method according to claim 1, wherein in step 6, based on the ray coverage of the near-surface model, the region boundary with the ray coverage larger than the set value is used as the fusion top surface of the near-surface model, and the near-surface velocity is adopted completely above the fusion top surface.
10. The multi-information fusion seismic velocity modeling method of claim 9, wherein in step 6, an interval of a given thickness below the fusion top surface is used as a fusion region of the near-surface and the mid-depth models.
11. The multi-information fusion seismic velocity modeling method of claim 1, wherein in step 7, inverse distance linear weighted summation is performed on near-surface and mid-depth velocities based on the distance to the top-bottom interface of the fusion region to obtain a uniform full velocity field.
12. The method of modeling multiple information fusion seismic velocities according to claim 11, characterized in that in step 7, model and log velocities are usedRelative error of
As a quality control reference, where vmodelAs a result of the model at the well location, vsmooth_logThe smoothed logging speed.
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