CN107884827A - Earthquake overlap speed turns the method and device of interval velocity - Google Patents
Earthquake overlap speed turns the method and device of interval velocity Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000012549 training Methods 0.000 claims abstract description 19
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- 238000013528 artificial neural network Methods 0.000 claims abstract description 10
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- 230000006870 function Effects 0.000 claims description 14
- 230000004913 activation Effects 0.000 claims description 9
- 238000012886 linear function Methods 0.000 claims description 8
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- 230000008717 functional decline Effects 0.000 claims description 3
- 210000004218 nerve net Anatomy 0.000 claims description 2
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- 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
- G01V1/32—Transforming one recording into another or one representation into another
- G01V1/325—Transforming one representation into another
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/40—Transforming data representation
- G01V2210/48—Other transforms
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Abstract
The invention provides a kind of method that earthquake overlap speed turns interval velocity.This method may comprise steps of：To earthquake stack velocity carry out dip correction, using by the earthquake overlap rate conversion as earthquake root mean sequare velocity；Well logging velocity of longitudinal wave is smoothed, to be matched with the earthquake root mean sequare velocity yardstick；The smooth velocity of longitudinal wave obtained after smoothing processing is normalized with corresponding earthquake root mean sequare velocity；Neutral net is trained using normalized smooth velocity of longitudinal wave and corresponding earthquake root mean sequare velocity as training data；And using the neural network prediction interval velocity trained, and carry out the renormalization of interval velocity.The method and device that the stack velocity of the present invention turns interval velocity is realized based on neural network algorithm, makes full use of the prior information of well-log information, improves the precision of interval velocity conversion.
Description
Technical field
The present invention relates to field of petroleum geophysical exploration, in particular it relates to which a kind of earthquake overlap speed turns interval velocity
Method and device.
Background technology
At seismic prospecting initial stage, generally by the way that earthquake overlap speed is turned into interval velocity to carry out seismic interpretation, construction is implemented,
Drilling well is and guided to dispose.The method that conventional stack velocity turns interval velocity includes classical Dix formula, and based on Dix formula
The methods of constraint or unconfinement inverting.
Classical Dix formula are assumed based on HORIZONTAL LAYERED MEDIUM WITH HIGH ACCURACY and first approximation is derived by.Inventor has found, complicated
Underground medium situation is generally unsatisfactory for the basic assumption of Dix formula, thus the interval velocity being converted to obtains with practical logging
Velocity of longitudinal wave trend difference is larger, is unfavorable for further seismic interpretation and instructs well site deployment.Therefore, it is necessary to develop one kind
The method and apparatus that high-precision earthquake overlap speed turns interval velocity.
The information for being disclosed in background of invention part is merely intended to deepen the reason of the general background technology to the present invention
Solution, and be not construed as recognizing or imply known to those skilled in the art existing of the information structure in any form
Technology.
The content of the invention
The present invention is in view of the shortcomings of the prior art and insufficient, it is proposed that a kind of stack velocity based on neural network algorithm turns
The method and device of interval velocity, the prior information of well-log information is made full use of, to improve the precision of interval velocity conversion, so as to improve
The precision and the success rate of drilling well deployment that seismotectonics is explained.
According to an aspect of the invention, it is proposed that a kind of stack velocity turns the method for interval velocity.This method can include：Over the ground
Shake stack velocity VRDip correction is carried out, by the earthquake overlap speed VRBe converted to earthquake root mean sequare velocityIt is vertical to logging well
Wave velocity is smoothed, with the earthquake root mean sequare velocityYardstick matches；It is flat to what is obtained after smoothing processing
Sliding velocity of longitudinal wave is normalized with corresponding earthquake root mean sequare velocity；By normalized smooth velocity of longitudinal wave and corresponding
Earthquake root mean sequare velocity trains neutral net as training data；And the neural network prediction interval velocity trained is utilized,
And carry out the renormalization of interval velocity.
Preferably, by the earthquake overlap speed VRBe converted to earthquake root mean sequare velocityFormula be：
Wherein,For stratigraphic dip.
Preferably, well logging velocity of longitudinal wave is smoothed using sliding translation method.
Preferably, the smooth velocity of longitudinal wave obtained after smoothing processing is returned with corresponding earthquake root mean sequare velocity
One, which changes the formula handled, is：
Wherein, XnormNormalized output data, its codomain scope are [- 1,1], and X is input data, XminAnd XmaxTo be defeated
Enter the minimum value and maximum of data.
Preferably, the neutral net chooses logarithm S types transfer function and linear function as activation primitive, using gradient
Decline adaptive learning training function to be trained.
According to another aspect of the invention, it is proposed that a kind of earthquake overlap speed turns the device of interval velocity.The device can wrap
Include：For to earthquake stack velocity VRThe unit of dip correction is carried out, by the earthquake overlap speed VRIt is square to be converted to earthquake
Root speedFor to the unit that is smoothed of well logging velocity of longitudinal wave, with the earthquake root mean sequare velocityYardstick
Match somebody with somebody；It is normalized for the smooth velocity of longitudinal wave to being obtained after smoothing processing with corresponding earthquake root mean sequare velocity
Unit；For training nerve using normalized smooth velocity of longitudinal wave and corresponding earthquake root mean sequare velocity as training data
The unit of network；And for utilizing the neural network prediction interval velocity trained, and carry out the list of the renormalization of interval velocity
Member.
Preferably, by the earthquake overlap speed VRBe converted to earthquake root mean sequare velocityFormula be：
Wherein,For stratigraphic dip.
Preferably, well logging velocity of longitudinal wave is smoothed using sliding translation method.
Preferably, the smooth velocity of longitudinal wave obtained after smoothing processing is returned with corresponding earthquake root mean sequare velocity
One, which changes the formula handled, is：
Wherein, XnormNormalized output data, its codomain scope are [- 1,1], and X is input data, XminAnd XmaxTo be defeated
Enter the minimum value and maximum of data.
Preferably, the neutral net chooses logarithm S types transfer function and linear function as activation primitive, using gradient
Decline adaptive learning training function to be trained.
The present invention turns interval velocity method by the earthquake overlap speed based on neutral net, overcomes conventional Dix formula meter
Calculate interval velocity " blocky effect " and with well logging velocity of longitudinal wave trend it is not consistent the problem of, effectively improve earthquake overlap speed
Degree turns the precision and reliability of interval velocity, is explained for follow-up seismotectonics, entrapment implementation and well site deployment provide reliably
Foundation.
Methods and apparatus of the present invention has other characteristics and advantage, and these characteristics and advantage are attached from what is be incorporated herein
It will be apparent in figure and subsequent specific embodiment, or by the accompanying drawing and subsequent specific implementation being incorporated herein
Stated in detail in example, these the drawings and specific embodiments are provided commonly for explaining the certain principles of the present invention.
Brief description of the drawings
Disclosure illustrative embodiments are described in more detail in conjunction with the accompanying drawings, the disclosure above-mentioned and its
Its purpose, feature and advantage will be apparent, wherein, in disclosure illustrative embodiments, identical reference number
Typically represent same parts.
Fig. 1 shows that earthquake overlap speed according to an embodiment of the invention turns the flow chart of the method for interval velocity.
Fig. 2 is two layers of neural metwork training schematic diagram.
Fig. 3 a and Fig. 3 b are neural metwork training activation primitive.
Fig. 4 is based on the calculating of Dix formula conventional layer speed and inversion result.
Fig. 5 is that the earthquake overlap speed based on the present invention turns the result that the method for interval velocity is predicted.
Embodiment
The preferred embodiment of the disclosure is more fully described below with reference to accompanying drawings.Although the disclosure is shown in accompanying drawing
Preferred embodiment, however, it is to be appreciated that may be realized in various forms the disclosure without the embodiment party that should be illustrated here
Formula is limited.On the contrary, these embodiments are provided so that the disclosure is more thorough and complete, and can be by the disclosure
Scope is intactly communicated to those skilled in the art.
The present invention pre-processes to geological data and log data first, and utilizes corresponding well logging and geological data
Neutral net is trained, and further carries out interval velocity prediction using the neutral net trained, to obtain the layer of degree of precision
Speed.
Fig. 1 shows that earthquake overlap speed according to an embodiment of the invention turns the flow chart of the method for interval velocity.
In the present embodiment, this method specifically includes following steps：
1) to earthquake stack velocity VRDip correction is carried out, by the earthquake overlap speed VRBe converted to earthquake root mean square
Speed
For horizontal interface uniform dielectric, earthquake overlap speed is equal to earthquake root mean sequare velocity.When bed boundary has inclination angle
When, earthquake root mean sequare velocity is equal to the cosine that earthquake overlap speed is multiplied by stratigraphic dip.
Specifically, by earthquake overlap speed VRBe converted to earthquake root mean sequare velocityFormula be：
Wherein,For stratigraphic dip.
2) to well logging velocity of longitudinal wave be smoothed, with the earthquake root mean sequare velocityYardstick matches.
Because the longitudinal frame of well-log information is higher than seismic data, thus using well-log information synthetic seismogram it
It is preceding, it is necessary to matched with the yardstick of seismic data.Log is transformed into time-domain from Depth Domain first, then pair when
Between domain well logging information carry out resampling.
In the exemplary embodiment, sliding translation method can be used smoothly to be located to well logging velocity of longitudinal wave in time-domain
Reason, the part radio-frequency component of well-log information is eliminated, to be matched with earthquake root mean sequare velocity yardstick.After velocity of longitudinal wave of logging well is smooth
Speed is the interval velocity thought in geology.
3) the smooth velocity of longitudinal wave obtained after smoothing processing is normalized with corresponding earthquake root mean sequare velocity
Processing.
Specifically, used normalization formula can be：
Wherein, XnormNormalized output data, its codomain scope are [- 1,1], and X is input data, XminAnd XmaxTo be defeated
Enter the minimum value and maximum of data.
4) nerve is trained using normalized smooth velocity of longitudinal wave and corresponding earthquake root mean sequare velocity as training data
Network.
Smooth velocity of longitudinal wave is easy for neutral net with the purpose that corresponding earthquake root mean sequare velocity is normalized
It is trained.
Neutral net can be multilayer neural network.Preferably, neutral net is two layers of neutral net, chooses logarithm S types
Transfer function and linear function decline adaptive learning training function using gradient and are trained as activation primitive.
Specifically, the formula of logarithm S types transfer function can be：The formula of linear function can be：
F (x)=x.
Fig. 2 shows two layers of neural metwork training schematic diagram.It is as shown in Fig. 2 the smooth compressional wave through normalized is fast
Degree is inputted to neutral net with corresponding earthquake root mean sequare velocity as training data.In hidden layer, using number S type transfer functions
As activation primitive, in output layer, using linear function as activation primitive.Whole neutral net is instructed using adaptive learning
Practice function to be trained.
5) using the neural network prediction interval velocity trained, and the renormalization of interval velocity is carried out.
Interval velocity generally is converted to by Dix equations to earthquake root mean sequare velocity in the prior art.In the present embodiment, it is first
Neural metwork training is carried out with corresponding earthquake root mean sequare velocity first with existing well logging velocity of longitudinal wave, establishes one relatively
More accurately non-linear relation, then using corresponding earthquake root mean sequare velocity as input data, utilize the nerve net trained
Network, carry out the prediction of interval velocity.Obtained based on neural network algorithm using velocity of longitudinal wave with corresponding earthquake root mean sequare velocity
Interval velocity.And it is incorporated in the Normalized Relation established in step 3), the interval velocity renormalization being predicted.
Using example
Illustrate that the earthquake overlap speed of the present invention turns interval velocity below by way of the actual seismic data in Xinjiang work area
The validity of method.It will be understood by those skilled in the art that the example, only for the purposes of understanding the present invention, its any detail is simultaneously
It is not intended to limit the invention in any way.
Dip correction is done to the earthquake overlap speed asked for from actual seismic data first, by earthquake overlap rate conversion
For earthquake root mean sequare velocity；Afterwards to it is existing well logging velocity of longitudinal wave be smoothed, and to smooth velocity of longitudinal wave with it is corresponding
Earthquake root mean sequare velocity be normalized；Further choose the normalized smoothly vertical speed of the well and corresponding earthquake is equal
Root speed trains neutral net as shown in Figure 2 as training data, wherein, the logarithm S types chosen as shown in Figure 3 a turn
Function and linear function shown in Fig. 3 b are moved as the activation primitive trained；Finally, layer is carried out using the neutral net trained
Prediction of speed, and the renormalization of interval velocity is carried out, even if having showed conversion of the earthquake overlap speed to interval velocity.
Fig. 4 is the interval velocity for being calculated using conventional Dix formula and being obtained based on Dix formula invertings, and Fig. 5 is to utilize the present invention
Earthquake overlap speed turn the interval velocity that the method for interval velocity obtains.By contrast as can be seen that conventional calculated based on Dix formula
Obtained interval velocity curve generates " blocky effect ", and interval velocity and the trend for velocity of longitudinal wave of logging well differ greatly, and sharp
It is then higher with the interval velocity goodness of fit obtained by the present invention.It can be seen that superposition speed is effectively improved by method proposed by the present invention
Degree turns the precision and reliability of interval velocity, is explained for follow-up seismotectonics, entrapment implementation and well site deployment provide reliably
Foundation.
It is described above the presently disclosed embodiments, described above is exemplary, and non-exclusive, and
It is not limited to disclosed each embodiment.In the case of without departing from the scope and spirit of illustrated each embodiment, for this skill
Many modifications and changes will be apparent from for the those of ordinary skill in art field.The selection of term used herein, purport
The principle of each embodiment, practical application or improvement to the technology in market are best being explained, or is making the art
Other those of ordinary skill are understood that each embodiment disclosed herein.
Claims (10)
1. a kind of method that earthquake overlap speed turns interval velocity, it is characterised in that the described method comprises the following steps：
To earthquake stack velocity VRDip correction is carried out, by the earthquake overlap speed VRBe converted to earthquake root mean sequare velocity
To well logging velocity of longitudinal wave be smoothed, with the earthquake root mean sequare velocityYardstick matches；
The smooth velocity of longitudinal wave obtained after smoothing processing is normalized with corresponding earthquake root mean sequare velocity；
Neutral net is trained using normalized smooth velocity of longitudinal wave and corresponding earthquake root mean sequare velocity as training data；With
And
Using the neural network prediction interval velocity trained, and carry out the renormalization of interval velocity.
2. the method that earthquake overlap speed according to claim 1 turns interval velocity, wherein, by the earthquake overlap speed VR
Be converted to earthquake root mean sequare velocityFormula be：
Wherein,For stratigraphic dip.
3. the method that earthquake overlap speed according to claim 1 turns interval velocity, wherein, using sliding translation method to well logging
Velocity of longitudinal wave is smoothed.
4. the method that earthquake overlap speed according to claim 1 turns interval velocity, wherein, to being obtained after smoothing processing
Smooth velocity of longitudinal wave be with the formula that corresponding earthquake root mean sequare velocity is normalized：
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Wherein, XnormNormalized output data, its codomain scope are [- 1,1], and X is input data, XminAnd XmaxTo input number
According to minimum value and maximum.
5. the method that earthquake overlap speed according to claim 1 turns interval velocity, wherein, the neutral net chooses logarithm
S types transfer function and linear function decline adaptive learning training function using gradient and are trained as activation primitive.
6. a kind of earthquake overlap speed turns the device of interval velocity, it is characterised in that described device includes：
For to earthquake stack velocity VRThe unit of dip correction is carried out, by the earthquake overlap speed VRIt is equal to be converted to earthquake
Root speed
For to the unit that is smoothed of well logging velocity of longitudinal wave, with the earthquake root mean sequare velocityYardstick matches；
Place is normalized with corresponding earthquake root mean sequare velocity for the smooth velocity of longitudinal wave to being obtained after smoothing processing
The unit of reason；
For training nerve net using normalized smooth velocity of longitudinal wave and corresponding earthquake root mean sequare velocity as training data
The unit of network；And
For utilizing the neural network prediction interval velocity trained, and carry out the unit of the renormalization of interval velocity.
7. earthquake overlap speed according to claim 6 turns the device of interval velocity, wherein, by the earthquake overlap speed VR
Be converted to earthquake root mean sequare velocityFormula be：
Wherein,For stratigraphic dip.
8. earthquake overlap speed according to claim 6 turns the device of interval velocity, wherein, using sliding translation method to well logging
Velocity of longitudinal wave is smoothed.
9. earthquake overlap speed according to claim 6 turns the device of interval velocity, wherein, to being obtained after smoothing processing
Smooth velocity of longitudinal wave be with the formula that corresponding earthquake root mean sequare velocity is normalized：
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Wherein, XnormNormalized output data, its codomain scope are [- 1,1], and X is input data, XminAnd XmaxTo input number
According to minimum value and maximum.
10. earthquake overlap speed according to claim 6 turns the device of interval velocity, wherein, the neutral net selection pair
Number S types transfer function and linear function decline adaptive learning training function using gradient and are trained as activation primitive.
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| CN109884693A (en) * | 2019-01-18 | 2019-06-14 | 潜能恒信能源技术股份有限公司 | Adaptively move towards normal-moveout spectrum acquiring method and system |
| CN113900146A (en) * | 2020-07-06 | 2022-01-07 | 中国石油天然气股份有限公司 | Surface wave pressing method and system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109884693A (en) * | 2019-01-18 | 2019-06-14 | 潜能恒信能源技术股份有限公司 | Adaptively move towards normal-moveout spectrum acquiring method and system |
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| CN113900146A (en) * | 2020-07-06 | 2022-01-07 | 中国石油天然气股份有限公司 | Surface wave pressing method and system |
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