CN113031069A - Multi-information constraint intelligent chromatography static correction method for karst area - Google Patents
Multi-information constraint intelligent chromatography static correction method for karst area Download PDFInfo
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Abstract
The application discloses a multi-information constraint intelligent chromatography static correction method for a karst area, which comprises the steps of firstly, reading in multi-element data and prior information of actual seismic data, satellite surface elevation data, near-surface survey data, small refraction data, micro-logging data and the like of the karst area; secondly, constructing an initial speed model of the karst region in a multi-scale mode by adopting multivariate data fusion partitions; thirdly, calculating the ray length of the grid unit and the theoretical travel time value of each receiving point; and finally, calculating a final static correction value and outputting a static correction result. The method solves the problem of difficult static correction in seismic data processing in karst areas for a long time. The multi-solution property of inversion can be reduced to the greatest extent, and the inversion process is carried out under the control of definite geological significance; the inaccuracy of travel time chromatography caused by inaccurate first arrival picking is reduced to the maximum extent, so that a more accurate near-surface velocity model can be obtained, and a more accurate static correction value is finally obtained. The adaptability of seismic exploration in karst areas is improved.
Description
Technical Field
The invention relates to a complex region geophysical oil-gas exploration method, in particular to a method for solving the problem of difficult static correction in complex region seismic data processing by comprehensively utilizing multivariate data, and particularly relates to a multivariate information constraint intelligent chromatography static correction method in a karst region.
Background
Karst areas have certain potential in the aspects of oil and gas, unconventional resources and the like, and seismic exploration technology is an important technical support for exploring the resources. However, seismic data processing in this area typically faces many challenges due to the effects of near-surface complex seismic geological conditions. The statics correction problem is one of the very difficult problems to solve in the first place, because it has a significant effect on the effectiveness of other seismic data processing in the area.
A great deal of literature research shows that no static correction technology specially aiming at karst areas exists at present. In fact, the problem of statics correction in the karst area is most importantly the problem of adaptability to near-surface complex seismic geological conditions. Regarding static correction under the condition of a complex region near the surface, petroleum geophysical exploration 2005 discloses a three-dimensional refraction static correction technology and an application effect of a complex region of Yanghai Shen and the like, and the text provides a three-dimensional refraction static correction method through a series of steps of refraction first arrival pickup, refraction layer section division, refraction speed analysis and the like, and the method is suitable for the condition of the complex surface; the application of the first-arrival wave ray tomography in the static correction of a complex area is disclosed in the 2006 of "oil geophysical prospecting", the old and the Zhang-Jian, and the static correction value is calculated by a conjugate gradient inversion method of the first-arrival ray tomography; the petroleum geophysical exploration 2006 discloses 'complex region first arrival chromatography inversion static correction' of the Zhang-Guo and Liu Ji, and the text improves the precision of near-surface velocity modeling through first arrival chromatography and achieves a good effect in actual production; in 2014, the complex near-surface area comprehensive long-wavelength static correction method of Wangzhigang and the like is disclosed, and the problem of long-wavelength static correction is specially solved by comprehensively utilizing nonlinear problems obtained by linear near-surface speed, a model parameter substitution method, a first arrival travel time fitting technology and the like; the geophysical progress discloses a study on a complex near-surface first-motion wave chromatography inversion static correction technology of Zhanglin and the like in 2017, the study is based on a ray theory, the near-surface speed is inverted by using travel time, and model trial calculation shows that the static correction is effective in a complex mountain front zone; petroleum geophysical prospecting 2019 discloses a land surface consistency fusion technology of static correction values of a complex near-surface exploration area of a Wanglian and a Linberxiang, and provides a subarea sub-band static correction value fusion technology with the characteristic of land surface consistency in order to realize advantage complementation of different static correction values. The research work fully explains the severity of the static correction problem under the complex near-surface condition, and a certain static correction effect is achieved in a complex area. However, none of these research works has been directed specifically to the problems facing karst area statics correction, such as: the method comprises the following steps of fine description and mesh generation of a near-surface complex structure, ray tracing adaptive to complex meshes, accuracy and reliability of an inversion method constructed by a velocity model and the like. In view of this, the invention provides a multivariate information constraint intelligent chromatography static correction method for a karst region from multivariate data and prior information, and aims to establish a chromatography static correction method specially oriented to the karst region so as to well solve the problem that static correction of the region is difficult.
Disclosure of Invention
The invention aims to well solve the problem of difficult static correction in the karst region based on the multivariate data and lay a foundation for improving the processing effect of other seismic data in the karst region.
The purpose of the invention is realized by the following technical scheme:
a multi-information constraint intelligent chromatography static correction method for karst areas comprises the following steps:
a. reading actual seismic data, satellite surface elevation data, near-surface survey data, small refraction data, micro-logging data multi-element data and prior information in a karst area;
b. constructing a complex near-surface initial velocity model of the karst region by adopting a multivariate data fusion partition multi-scale random modeling method, and subdividing the velocity model by adopting a near-surface variable grid;
c. calculating the ray length of each grid unit in the inverse model space and the theoretical travel time value of each receiving point in the variable grid by a ray tracing method;
d. c, adopting a method of combining intelligent and automatic manual quality inspection, picking up the first arrival travel time value of the actual seismic data in the karst area, and subtracting the travel time value theoretically calculated in the step c to obtain a travel time residual error;
e. b, continuously iteratively updating an inversion velocity model by using the travel time residual as a target function and using other data except the seismic data in the step a as constraint conditions through a multivariate information constraint chromatography method;
f. calculating a static correction value according to the velocity model, performing primary static correction on the seismic data, jumping back to the step d on the seismic data after the primary correction, and performing secondary primary arrival pickup and chromatography to obtain a final velocity model;
g. and calculating a final static correction value according to the speed model, and outputting a static correction result.
Has the advantages that: the invention provides a multi-information constraint intelligent chromatography static correction method for a karst area, aiming at the problem of difficult static correction of the karst area, and the method has the following advantages: the method fully utilizes actual seismic data, satellite surface elevation data, near-surface survey data, small refraction data and micro-logging data multi-element data, and can ensure the reliability of calculation of static correction values to the maximum extent; constructing a complex near-surface initial velocity model in a karst region by adopting a multivariate data fusion partition multi-scale random modeling method, and describing not only a large-scale construction background of a medium but also small-scale random disturbance; the variable grid can finely depict a near-surface velocity model, and ray tracing in the grid can fully ensure the accuracy of forward calculation; fourthly, the first arrival travel time can be efficiently, quickly and stably picked up by intelligently and automatically combining a manual inspection method; the chromatographic inversion method under the constraint of multivariate information can reduce the inversion multiple solution to the maximum extent, so that the inversion process is implemented under the control of definite geological significance, and a high-resolution speed model can be obtained; and sixthly, the secondary first arrival picking and chromatography can reduce the inaccuracy of travel time chromatography caused by the inaccuracy of the first arrival picking to the maximum extent, so that a more accurate near-surface velocity model can be obtained, and a more accurate static correction value can be finally obtained. In conclusion, the method can fully utilize as much basic data as possible, firstly obtain the speed model as accurate as possible under the steady progressive flow, then calculate the accurate static correction value, and finally well solve the problem of difficult static correction in the karst area. The method of the invention provides powerful technical support for improving the processing quality of the seismic data in the karst area, thereby improving the adaptability and application effect of the seismic exploration technology in the resource exploration aspect of the karst area.
Drawings
FIG. 1 is a flow chart of a multivariate information constraint intelligent chromatography static correction method for a karst region;
FIG. 2 is a diagram of a tomographic inversion near-surface velocity model obtained by the method of the present invention;
FIG. 3 is a comparison graph of single shot seismic records before and after static correction by the method of the present invention;
FIG. 4 is a cross-sectional comparison graph of the cross-section before and after the static correction of the method of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
A multi-information constraint intelligent chromatography static correction method for karst areas comprises the following steps:
a. reading actual seismic data, satellite surface elevation data, near-surface survey data, small refraction data, micro-logging data multi-element data and prior information in a karst area;
b. constructing a complex near-surface initial velocity model of the karst region by adopting a multivariate data fusion partition multi-scale random modeling method, and subdividing the velocity model by adopting a near-surface variable grid;
c. calculating the ray length of each grid unit in the inverse model space and the theoretical travel time value of each receiving point in the variable grid by a ray tracing method;
d. c, adopting a method of combining intelligent and automatic manual quality inspection, picking up the first arrival travel time value of the actual seismic data in the karst area, and subtracting the travel time value theoretically calculated in the step c to obtain a travel time residual error;
e. b, continuously iteratively updating an inversion velocity model by using the travel time residual as a target function and using other data except the seismic data in the step a as constraint conditions through a multivariate information constraint chromatography method;
f. calculating a static correction value according to the velocity model, performing primary static correction on the seismic data, jumping back to the step d on the seismic data after the primary correction, and performing secondary primary arrival pickup and chromatography to obtain a final velocity model;
g. and calculating a final static correction amount according to the speed model, and outputting a final result.
Example 1
a. Reading actual seismic data, satellite surface elevation data, near-surface survey data, small refraction data, micro-logging data multi-element data and prior information in a karst area; in specific implementation, the data form basic data of the invention, wherein seismic data are the most important, and other data exist as prior information of chromatographic inversion;
b. constructing a complex near-surface initial velocity model of the karst region by adopting a multivariate data fusion partition multi-scale random modeling method, and subdividing the velocity model by adopting a near-surface variable grid; in specific implementation, the initial model is important and determines whether the chromatographic inversion can be rapidly converged to a global optimal solution, and the grid division is the basis of subsequent forward and inversion;
c. calculating the ray length of each grid unit in the inverse model space and the theoretical travel time value of each receiving point in the variable grid by a ray tracing method; in specific implementation, the ray length of each grid unit in the inversion model space forms an inversion sensitivity matrix, and the accuracy of theoretical travel time determines the inversion convergence speed;
d. c, adopting a method of combining intelligent and automatic manual quality inspection, picking up the first arrival travel time value of the actual seismic data in the karst area, and subtracting the travel time value theoretically calculated in the step c to obtain a travel time residual error; in specific implementation, the accuracy of the first arrival travel time pickup determines the accuracy and reliability of the chromatographic inversion velocity model;
e. b, continuously iteratively updating an inversion velocity model by using the travel time residual as a target function and using other data except the seismic data in the step a as constraint conditions through a multivariate information constraint chromatography method; in specific implementation, the constraint of the multivariate information can ensure that the inversion process is implemented in a clear geological sense, so that the inversion reliability is ensured;
f. calculating a static correction value according to the iteratively updated inversion velocity model, performing primary static correction on the seismic data, jumping back to the step d on the seismic data after the primary correction, and performing secondary primary arrival picking and chromatography to obtain a final velocity model; in specific implementation, a more accurate and reliable speed model can be obtained through secondary first arrival picking and chromatography;
g. calculating a final static correction value according to the final speed model, and outputting a static correction result; in specific implementation, the static correction value is added into the original single-shot seismic data for correction, so that the quality of the seismic data can be improved to a great extent, and the static correction problem is well eliminated.
Fig. 2 shows a near-surface velocity model of the research area obtained after two rounds of first-arrival chromatographic inversion, and analysis of the near-surface velocity model shows that the near-surface velocity distribution of the research area is very complex, the topography fluctuation is severe, the heterogeneity of the velocity distribution is very strong, and the model has a good degree of coincidence with near-surface survey data. FIG. 3 shows the case of single shot seismic data before (FIG. 3a) and after (FIG. 3b) statics, comparing the two can find: through the improvement of the effective signal quality of the seismic data after the final static correction, the problems of no crisp first arrival jump, heavy far offset interference, refraction cross-layers and the like (particularly the circled area in the figure 3a) are eliminated. Meanwhile, fig. 4 also shows the case of the superimposed section before static correction (fig. 4a) and after static correction (fig. 4b), and comparing the two results shows that: when the static correction value is not added, the section imaging is poor, and the same phase axis is discontinuous (especially a circle defined area in fig. 4 a); after the static correction value is applied, the in-phase axis is more continuous, the signal-to-noise ratio is improved, and the section imaging effect is better (especially a circle defined area in fig. 4 b). Therefore, the static correction problem caused by the near-surface abnormality is basically eliminated based on the method. In summary, some core data in the specific implementation and embodiments shown in fig. 2-4 can verify that: the method fully utilizes the multivariate data, adopts a multivariate information constraint intelligent chromatography method and a steady progressive flow, firstly obtains an accurate near-surface velocity model through two rounds of first arrival picking and chromatography, and accurately calculates the final static correction value based on the accurate near-surface velocity model, thereby finally well eliminating the static correction problem caused by the near-surface of the karst region. The method lays a foundation for further improving the processing quality of the seismic data in the karst region, and further improves the application effect of the seismic exploration technology in resource exploration in the karst region.
Claims (1)
1. A multi-information constraint intelligent chromatography static correction method for karst areas comprises the following steps:
a. reading actual seismic data, satellite surface elevation data, near-surface survey data, small refraction data, micro-logging data multi-element data and prior information in a karst area;
b. constructing a complex near-surface initial velocity model of the karst region by adopting a multivariate data fusion partition multi-scale random modeling method, and subdividing the velocity model by adopting a near-surface variable grid;
c. calculating the ray length of each grid unit in the inverse model space and the theoretical travel time value of each receiving point in the variable grid by a ray tracing method;
d. c, adopting a method of combining intelligent and automatic manual quality inspection, picking up the first arrival travel time value of the actual seismic data in the karst area, and subtracting the travel time value theoretically calculated in the step c to obtain a travel time residual error;
e. b, continuously iteratively updating an inversion velocity model by using the travel time residual as a target function and using other data except the seismic data in the step a as constraint conditions through a multivariate information constraint chromatography method;
f. calculating a static correction value according to the iteratively updated inversion velocity model, performing primary static correction on the seismic data, jumping back to the step d on the seismic data after the primary correction, and performing secondary primary arrival picking and chromatography to obtain a final velocity model;
g. and calculating a final static correction value according to the final speed model, and outputting a static correction result.
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| CN113589369A (en) * | 2021-08-02 | 2021-11-02 | 华北水利水电大学 | Large cave depot group earthquake stability evaluation method |
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