CN109100794B - Time window weighted coherent velocity inversion method and system - Google Patents
Time window weighted coherent velocity inversion method and system Download PDFInfo
- Publication number
- CN109100794B CN109100794B CN201710470477.XA CN201710470477A CN109100794B CN 109100794 B CN109100794 B CN 109100794B CN 201710470477 A CN201710470477 A CN 201710470477A CN 109100794 B CN109100794 B CN 109100794B
- Authority
- CN
- China
- Prior art keywords
- time
- layer
- velocity
- coherence
- window
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 28
- 230000001427 coherent effect Effects 0.000 title claims abstract description 18
- 238000012360 testing method Methods 0.000 claims abstract description 17
- 230000015572 biosynthetic process Effects 0.000 claims abstract description 4
- 238000012887 quadratic function Methods 0.000 claims description 7
- 238000009499 grossing Methods 0.000 claims description 4
- 238000005070 sampling Methods 0.000 claims description 4
- 230000000694 effects Effects 0.000 abstract description 4
- 238000005755 formation reaction Methods 0.000 abstract description 3
- 230000008859 change Effects 0.000 abstract description 2
- 238000012545 processing Methods 0.000 abstract description 2
- 238000004364 calculation method Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 3
- 230000005012 migration Effects 0.000 description 3
- 238000013508 migration Methods 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005498 polishing Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/30—Analysis
- G01V1/303—Analysis for determining velocity profiles or travel times
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
- G01V2210/62—Physical property of subsurface
- G01V2210/622—Velocity, density or impedance
- G01V2210/6222—Velocity; travel time
Landscapes
- Engineering & Computer Science (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Acoustics & Sound (AREA)
- Environmental & Geological Engineering (AREA)
- Geology (AREA)
- General Life Sciences & Earth Sciences (AREA)
- General Physics & Mathematics (AREA)
- Geophysics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a time window weighted coherent velocity inversion method and a system, wherein the method comprises the following steps: picking up main time layer data on the superposition section; calculating the travel time of the CMP gather; setting a weighting factor in the time window, wherein the length of the weighting factor is the same as the length of the time window; the coherence coefficient is calculated and the layer velocity of the layer is picked up. The method of the invention obtains the depth domain layer velocity, is suitable for seismic data with low signal-to-noise ratio and has higher precision; the same applies to formations where there is a lateral change in velocity. The theoretical model test and the actual data processing both achieve good effects.
Description
Technical Field
The invention belongs to the field of prestack depth migration velocity modeling, provides a depth domain layer velocity model for depth domain migration velocity modeling, and particularly relates to a time window weighted coherent velocity inversion method and a time window weighted coherent velocity inversion system.
Background
The method comprises the steps of conducting coherent velocity inversion, utilizing original CMP (chemical mechanical polishing) gather data, picking up main structural positions on a superposition section, selecting a series of test velocities for each structural position, generating a CMP gather travel time curve through ray tracing, then calculating a coherent coefficient between a theoretical curve and an actual curve in a certain time window, and directly obtaining the depth domain layer velocity of the layer according to the size of the coherent coefficient.
The traditional method for calculating the coherence coefficient is that the weight of each value in a time window is equal, the method is suitable for data with higher signal-to-noise ratio, if the signal-to-noise ratio is low, especially when abnormal interference values exist, the value of the coherence coefficient is affected, and thus the speed precision is affected.
Disclosure of Invention
In order to overcome the interference of abnormal values in low signal-to-noise ratio data to coherent coefficient calculation, a weighting factor is added in a coherent coefficient calculation formula, the length of the weighting factor is set to be the same as the length of a time window, namely, each actual value in the time window is multiplied by a weighting value, the closer to a theoretical curve, the larger the weighting value is (the maximum value is 1), namely, the larger the weighting value is, and the smaller the weighting value is otherwise. Therefore, the effect of suppressing interference is achieved, and the speed inversion precision is improved.
According to an aspect of the present invention, there is provided a time window weighted coherence velocity inversion method, including:
picking up main time layer data on the superposition section;
calculating the travel time of the CMP gather;
setting a weighting factor in the time window, wherein the length of the weighting factor is the same as the length of the time window;
the coherence coefficient is calculated and the layer velocity of the layer is picked up.
Further, a certain CMP gather interval is selected to calculate the layer velocity, and the global layer velocity distribution is obtained through interpolation and smoothing.
Further, by calculating the velocity of each layer, the depth domain layer velocities of all the layers of the CMP gather are obtained.
Further, calculating the CMP gather trip time includes: selecting a test speed, converting the time layer bit data picked up on the section into a depth domain, and performing ray tracing by using the depth layer to obtain the travel time of the CMP gather.
Further, a series of test speeds are selected for each structural horizon from shallow to deep.
Further, the weighting factor within the time window is set by means of a quadratic function.
Further, a coherence coefficient with the theoretical curve is calculated by multiplying each value within the time window by a weighted value.
Further, calculating the coherence coefficient includes: a time window is opened on the CMP gather, and the coherence coefficient of the current test speed is calculated according to the following formula:
in the formula: k is half the length of the time window, wiN is the number of traces of the CMP gather as a weighting factor; u shapej[it+τ(V,Z)]Representing actual CMP gather data of a jth track with time of it + tau (V, Z), wherein i is a sample point serial number in a time window; t is a time sampling interval in milliseconds; τ represents the raytrace computed travel time; v is a velocity vector; z is a depth point vector.
Further, aiming at different test speeds, a series of coherence coefficients are obtained, and the speed corresponding to the maximum value of the coherence coefficients is the layer speed of the layer.
According to another aspect of the present invention, there is provided a time window weighted coherence velocity inversion system, comprising:
a memory storing computer-executable instructions;
a processor executing computer executable instructions in the memory to perform the steps of:
picking up main time layer data on the superposition section;
calculating the travel time of the CMP gather;
setting a weighting factor in the time window, wherein the length of the weighting factor is the same as the length of the time window;
the coherence coefficient is calculated and the layer velocity of the layer is picked up.
The method of the invention obtains the depth domain layer velocity, is suitable for seismic data with low signal-to-noise ratio and has higher precision; the same applies to formations where there is a lateral change in velocity. The theoretical model test and the actual data processing both achieve good effects.
Drawings
The above and other objects, features and advantages of the present disclosure will become more apparent by describing in greater detail exemplary embodiments thereof with reference to the attached drawings, in which like reference numerals generally represent like parts throughout.
Fig. 1 shows a schematic diagram of the calculation of the coherence coefficient by the time window weighting method.
Figure 2 shows a method flow diagram of an embodiment of the invention.
FIG. 3 illustrates Daqing data overlay profile horizon picking in an embodiment of the invention.
FIG. 4 shows the depth domain layer velocity coherent inversion results of Daqing data in an embodiment of the invention.
Detailed Description
Preferred embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While the preferred embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The principle of the coherent velocity inversion method is similar to NMO stacking velocity analysis, input data are an original CMP gather and main structural positions picked on a stacking section, a series of test velocities are selected for each structural position from shallow to deep, ray tracing is carried out layer by layer from the earth surface, and a new CMP gather travel time curve is generated.
Then, within a certain time window, a weighting factor (less than or equal to 1) is set, the length being the same as the time window length. Firstly, each actual curve value in the time window is multiplied by a weighted value, and then the coherence coefficient between the actual curve value and the theoretical curve is calculated. And aiming at different test speeds, obtaining a series of coherence coefficients, wherein the speed corresponding to the maximum value of the coherence coefficients is the layer speed of the layer, and calculating the speed of the next layer by analogy until the last layer is calculated, so that the depth domain layer speeds of all layers of the CMP gather are obtained.
The disclosure provides a time window weighted coherent velocity inversion method, which includes:
picking up main time layer data on the superposition section;
calculating the travel time of the CMP gather;
setting a weighting factor in the time window, wherein the length of the weighting factor is the same as the length of the time window;
the coherence coefficient is calculated and the layer velocity of the layer is picked up.
Specifically, the process of the time window weighted coherent velocity inversion method may be: inputting original CMP gather data and main structure position data picked on a superposition section, selecting a series of test speeds for each structure position from shallow to deep, and performing ray tracing layer by layer from the ground surface to generate a new CMP gather travel time curve; setting weighting factors in the time window, generally selecting a form of quadratic function, wherein the length of the quadratic function is the same as the length of the time window, multiplying each value in the time window by a weighted value, and calculating a coherence coefficient between the weighted value and a theoretical curve. And aiming at different test speeds, obtaining a series of coherence coefficients, wherein the speed corresponding to the maximum value of the coherence coefficients is the layer speed of the layer, and calculating the speed of the next layer by analogy until the last layer is calculated, so that the depth domain layer speeds of all layers of the CMP gather are obtained.
In order to reduce the amount of computation, it is not necessary to compute every CMP gather, and a certain CMP gather interval may be selected for computation. And finally, obtaining global layer velocity distribution through interpolation and smoothing, and establishing a velocity model for prestack depth migration.
As shown in fig. 2, the method according to the embodiment of the present invention mainly includes the following steps:
(1) picking up a main time horizon on the superimposed profile;
(2) selecting a test speed;
(3) calculating the travel time of the CMP gather: converting the time horizon picked up from the section into a depth domain, and performing ray tracing by using the depth horizon to obtain the travel time of the CMP gather;
(4) setting a weighting factor wiCalculating a coherence coefficient;
a time window is opened on the CMP gather, and the coherence coefficient of the current test speed is calculated according to the following formula:
in the formula: k is half the length of the time window, wiN is the number of traces of the CMP gather as a weighting factor; u shapej[it+τ(V,Z)]Represents the actual CMP gather of the jth trace at time it + τ (V, Z)Data, wherein i is a sample point serial number in a time window; t is a time sampling interval, generally in milliseconds; τ represents the raytrace computed travel time; v is a velocity vector; z is a depth point vector. FIG. 1 is a schematic diagram of coherence coefficient calculation.
In the method of the present invention, a weighting factor in the form of a quadratic function is generally selected. The general form of the quadratic function may be y ═ ax2+ bx + c, a ≠ 0, where a, b, c are constants. As weighting factors, the form of the quadratic function is chosen to be: w is ai＝a(ti-t0)2+c，a＜0,c＞0，t0Is the time at the center of the time window, wi0. For example: w is ai＝-(ti-t0)2And/kt + 1, k is half the time window length, and t is the time sampling interval.
(5) Pick-up speed: a series of coherence coefficients are calculated, the largest of which corresponds to the best velocity estimate.
(6) And carrying out interpolation and smoothing on the layer velocity values of all the calculation points to obtain global layer velocity distribution.
To facilitate understanding of the solution of the embodiments of the present invention and the effects thereof, a specific application example is given below. It will be understood by those skilled in the art that this example is merely for the purpose of facilitating an understanding of the present invention and that any specific details thereof are not intended to limit the invention in any way.
According to the principle, the inventor writes coherent velocity inversion software, and verifies Daqing data, and the attached figure 3 shows that six layers are picked up in the horizon picking of the Daqing data stacking section: t1-1, t2-1, t3-1, t4-1, t5-1 and t 6-1. FIG. 4 is the inverted depth domain interval velocity, shown along the horizon of the formation, with the height of the vertical line representing the magnitude of the velocity, and the scale bar below the graph. It can be seen that the superficial velocity is relatively smooth and the accuracy is relatively high, and the deep layer is influenced by error transfer, so that the velocity accuracy is reduced, but the velocity accuracy is relatively high in general.
Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terms used herein were chosen in order to best explain the principles of the embodiments, the practical application, or technical improvements to the techniques in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (9)
1. A time-window weighted coherence velocity inversion method, comprising:
picking up main time layer data on the superposition section;
calculating the travel time of the CMP gather;
setting a weighting factor in the time window, wherein the length of the weighting factor is the same as the length of the time window;
calculating a coherence coefficient and picking up the layer speed of the layer;
wherein the coherence factor with the theoretical curve is calculated by multiplying each value within the time window by a weighted value.
2. The time-window weighted coherent velocity inversion method of claim 1, wherein a certain CMP gather interval is selected to calculate a layer velocity, and a global layer velocity distribution is obtained by interpolation and smoothing.
3. The time-window weighted coherent velocity inversion method of claim 1, wherein the depth domain layer velocities of all layers of the CMP gather are obtained by calculating the velocity of each layer.
4. The time-window weighted coherence velocity inversion method of claim 1, wherein computing CMP gather travel times comprises: selecting a test speed, converting the time layer bit data picked up on the section into a depth domain, and performing ray tracing by using the depth layer to obtain the travel time of the CMP gather.
5. The time-window weighted coherent velocity inversion method of claim 4, wherein a series of test velocities are selected for each formation horizon from shallow to deep.
6. The method of time-window weighted coherence velocity inversion of claim 1, wherein the weighting factors within a time window are set by a quadratic function.
7. The time-window weighted coherence velocity inversion method of claim 1, wherein computing coherence coefficients comprises: a time window is opened on the CMP gather, and the coherence coefficient of the current test speed is calculated according to the following formula:
in the formula: k is half the length of the time window, wiN is the number of traces of the CMP gather as a weighting factor; u shapej[it+τ(V,Z)]Representing actual CMP gather data of a jth track with time of it + tau (V, Z), wherein i is a sample point serial number in a time window; t is a time sampling interval in milliseconds; τ represents the raytrace computed travel time; v is a velocity vector; z is a depth point vector.
8. The time-window weighted coherence velocity inversion method of claim 7, wherein a series of coherence coefficients are obtained for different test velocities, and the velocity corresponding to the maximum value of the coherence coefficients is the layer velocity of the layer.
9. A time-window weighted coherence velocity inversion system, comprising:
a memory storing computer-executable instructions;
a processor executing computer executable instructions in the memory to perform the steps of:
picking up main time layer data on the superposition section;
calculating the travel time of the CMP gather;
setting a weighting factor in the time window, wherein the length of the weighting factor is the same as the length of the time window;
calculating a coherence coefficient and picking up the layer speed of the layer;
wherein the coherence factor with the theoretical curve is calculated by multiplying each value within the time window by a weighted value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710470477.XA CN109100794B (en) | 2017-06-20 | 2017-06-20 | Time window weighted coherent velocity inversion method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710470477.XA CN109100794B (en) | 2017-06-20 | 2017-06-20 | Time window weighted coherent velocity inversion method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109100794A CN109100794A (en) | 2018-12-28 |
| CN109100794B true CN109100794B (en) | 2020-12-01 |
Family
ID=64795685
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710470477.XA Active CN109100794B (en) | 2017-06-20 | 2017-06-20 | Time window weighted coherent velocity inversion method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109100794B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117434592B (en) * | 2023-02-24 | 2024-05-07 | 中国石油化工股份有限公司 | Seismic data processing method and device and electronic equipment |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104111475A (en) * | 2014-07-29 | 2014-10-22 | 中国石油集团川庆钻探工程有限公司地球物理勘探公司 | Self-adaption high-precision and covariance regularizing type seismic data stacking velocity analysis method |
| CN106257309A (en) * | 2016-01-28 | 2016-12-28 | 中国石油天然气股份有限公司 | Post-stack seismic data volume processing method and device |
| WO2017044431A1 (en) * | 2015-09-07 | 2017-03-16 | Schlumberger Technology Corporation | Method and system for imaging dipping structures |
| CN106569269A (en) * | 2015-10-12 | 2017-04-19 | 中国石油化工股份有限公司 | Preferred trace weighting velocity spectrum calculating method |
-
2017
- 2017-06-20 CN CN201710470477.XA patent/CN109100794B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104111475A (en) * | 2014-07-29 | 2014-10-22 | 中国石油集团川庆钻探工程有限公司地球物理勘探公司 | Self-adaption high-precision and covariance regularizing type seismic data stacking velocity analysis method |
| WO2017044431A1 (en) * | 2015-09-07 | 2017-03-16 | Schlumberger Technology Corporation | Method and system for imaging dipping structures |
| CN106569269A (en) * | 2015-10-12 | 2017-04-19 | 中国石油化工股份有限公司 | Preferred trace weighting velocity spectrum calculating method |
| CN106257309A (en) * | 2016-01-28 | 2016-12-28 | 中国石油天然气股份有限公司 | Post-stack seismic data volume processing method and device |
Non-Patent Citations (1)
| Title |
|---|
| "相干速度反演方法研究";周巍 等;《中国地球物理2009》;20091231;第104页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109100794A (en) | 2018-12-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105353412B (en) | A kind of well shakes the computational methods and system of joint average velocity field | |
| CN105277978B (en) | A kind of method and device for determining near-surface velocity model | |
| CN105334542B (en) | Any Density Distribution complex geologic body gravitational field is quick, high accuracy forward modeling method | |
| CA2683618C (en) | Inverse-vector method for smoothing dips and azimuths | |
| CN107688554B (en) | Frequency domain identification method based on self-adaptive Fourier decomposition | |
| CN113552625B (en) | Multi-scale full waveform inversion method for conventional land-domain seismic data | |
| CN112698390B (en) | Pre-stack seismic inversion method and device | |
| CN106643715A (en) | Indoor inertial navigation method based on bp neural network improvement | |
| CN108731700B (en) | Weighted Euler pre-integration method in visual inertial odometer | |
| CN103973263B (en) | Approximation filter method | |
| CN109633762A (en) | The method of correlation constraint conditional joint inverting gravity and magnetic data based on SIN function | |
| CN105319589A (en) | Full-automatic three-dimensional tomography inversion method using a local event slope | |
| CN109799530A (en) | Rayleigh waves dispersion curve inversion method for seismic surface wave exploration | |
| CN111580163B (en) | Full waveform inversion method and system based on non-monotonic search technology | |
| CN102338887B (en) | Irregular-size space-variant grid tomography imaging statics correction method | |
| CN104316961B (en) | Method for obtaining geological parameters of weathered layer | |
| CN104749625B (en) | A kind of geological data inclination angle method of estimation based on Regularization Technique and device | |
| CN109100794B (en) | Time window weighted coherent velocity inversion method and system | |
| CN109711051B (en) | Pile top displacement nonlinear prediction method considering sliding bed rock mass structure characteristics | |
| CN108508481B (en) | A kind of method, apparatus and system of longitudinal wave converted wave seismic data time match | |
| CN105353409B (en) | A kind of method and system for full waveform inversion focus to be inhibited to encode cross-talk noise | |
| CN107479091B (en) | A method of extracting reverse-time migration angle gathers | |
| Shi et al. | A layer-stripping method for 3D near-surface velocity model building using seismic first-arrival times | |
| CN105550514B (en) | A kind of method for building up and system of the rate pattern based on dual path integration | |
| CN103217715B (en) | Multiple dimensioned regular grid Static Correction of Tomographic Inversion method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |