CN110941010A - Method for predicting drilling loss by using seismic data - Google Patents

Method for predicting drilling loss by using seismic data Download PDF

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CN110941010A
CN110941010A CN201811117109.8A CN201811117109A CN110941010A CN 110941010 A CN110941010 A CN 110941010A CN 201811117109 A CN201811117109 A CN 201811117109A CN 110941010 A CN110941010 A CN 110941010A
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seismic data
development zone
predicting
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filtering
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齐晴
孙振涛
田建华
陈勇
杨勤林
曹少蕾
董清源
朱博华
梁硕博
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China Petroleum and Chemical Corp
Sinopec Geophysical Research Institute
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Sinopec Geophysical Research Institute
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Abstract

The invention provides a method for predicting drilling loss by using seismic data, which comprises the following steps: s1: carrying out noise reduction and filtering processing on the original seismic data volume to obtain a filtering data volume; s2: carrying out coherent processing on the filtering data volume to obtain a coherent volume; s3: fusing the coherent body and the original seismic data body to obtain a fused data body; s4: determining the distribution area of the coherent body according to the section of the fusion data volume, and predicting the position of a crack development zone and/or a karst cave development zone according to the distribution area of the coherent body; s5: and predicting the drilling lost circulation interval according to the position of the fracture development zone and/or the karst cave development zone. The method can predict the drilling lost interval, and further provides guidance for drilling so as to select the optimal well trajectory, avoid the drilling lost interval and improve the drilling success rate.

Description

Method for predicting drilling loss by using seismic data
Technical Field
The invention belongs to the field of seismic exploration and development, and particularly relates to a method for predicting drilling loss by using seismic data.
Background
Lost circulation often occurs during well production when the drill encounters fracture development zones and/or cavern development zones. If the well is drilled in the stratum where the well leakage condition is easy to happen, the drilling is complex, and the well drilling in the stratum is easy to induce serious underground accidents such as blowout, drill sticking and the like. More importantly, drilling engineering accidents directly affect the discovery and protection of hydrocarbon reservoirs, and cause the reduction of exploration and development effects. The current common technical means is mainly to test and analyze the lost circulation through well logging, find the position of the lost circulation and determine the channel property of the lost circulation, i.e. the well drilling work is firstly carried out, and the well drilling stratum can be known as the lost circulation interval of the well drilling when the lost circulation is met. The method cannot predict the leakage of the stratum before drilling, so that guidance cannot be provided for drilling and the drilling loss interval is avoided. The method has a low drilling success rate.
Disclosure of Invention
In view of the above technical problems, the present invention provides a method for predicting drilling loss by using seismic data, so as to solve the problem in the prior art that the drilling success rate is low because the drilling loss cannot be predicted on the stratum before drilling, guidance cannot be provided for drilling, and drilling loss intervals are avoided.
The purpose of the invention is realized by the following technical scheme:
a method for predicting borehole loss using seismic data, comprising the steps of:
s1: carrying out noise reduction and filtering processing on the original seismic data volume to obtain a filtering data volume;
s2: carrying out coherent processing on the filtering data volume to obtain a coherent volume;
s3: fusing the coherent body and the original seismic data body to obtain a fused data body;
s4: determining the distribution area of the coherent body according to the section of the fusion data volume, and predicting the position of a crack development zone and/or a karst cave development zone according to the distribution area of the coherent body;
s5: and predicting the drilling lost circulation interval according to the position of the fracture development zone and/or the karst cave development zone.
Preferably, the noise reduction filtering process includes the steps of:
and performing Gaussian smoothing processing on the original seismic data body.
Preferably, the gaussian smoothing of the original seismic data volume specifically includes:
data u of current sampling point in original seismic data volumeσAnd performing partial derivative calculation in the x and y directions, wherein the formula is as follows:
Figure BDA0001810884360000021
respectively carrying out data u on current sampling points in the original seismic data volume according to a Gaussian smoothing formulaσPerforming Gaussian smoothing with the scale of rho on partial derivatives in the x and y directions to obtain a structure tensor J of the structure matrixρThe Gaussian smoothing formula is uσ 0＝uσ*Gρ(ii) a Wherein G isρThe parameters of the gaussian kernel are used as the parameters,
Figure BDA0001810884360000022
is represented by GρA gaussian kernel as a parameter;
structure tensor J of the structural matrixρComprises the following steps:
Figure BDA0001810884360000023
wherein, ▽ uσGradient of data representing current sampling point in original seismic data volume j11Represents uσThe partial derivatives in the x-direction are subjected to a Gaussian smoothing with a scale ρ, j12Represents uσThe partial derivatives in the x and y directions are subjected to a Gaussian smoothing with a scale rho, j22Represents uσThe partial derivatives in the y-direction are the result of gaussian smoothing with a scale ρ.
Preferably, the noise reduction filtering process further includes: the structure tensor J is subjected to matrix eigen decomposition theoremρDecomposing to obtain
Figure BDA0001810884360000024
Figure BDA0001810884360000025
Wherein λ is1、λ2Is JρCharacteristic value of (a) ("lambda1、λ2The corresponding orthonormal eigenvectors are:
Figure BDA0001810884360000026
wherein the content of the first and second substances,
Figure BDA0001810884360000031
v1representing a parallel to gradient ▽ uσDirection of (2) at a rate of change of
Figure BDA0001810884360000032
v2Indicating a perpendicular to the gradient ▽ uσDirection of (2) at a rate of change of
Figure BDA0001810884360000033
Theta is the gradient ▽ uσThe angle of the direction;
according to
Figure BDA0001810884360000034
And
Figure BDA0001810884360000035
judging the local characteristics of the current sampling point of the original seismic data volume extracted according to the structure tensor, wherein the diffusion coefficient mu of the local characteristics1And mu2The expression of (a) is:
Figure BDA0001810884360000036
wherein α is a constant;
and obtaining a constructed diffusion tensor according to the matrix characteristic decomposition theorem and the diffusion coefficient, wherein the expression is as follows:
Figure BDA0001810884360000037
wherein, a, b, c are components for constructing the diffusion tensor D, and are specifically expressed as:
Figure BDA0001810884360000038
preferably, the noise reduction filtering process further includes: obtaining a tensor diffusion equation representing a diffusion filtering result according to the structure diffusion tensor D:
Figure BDA0001810884360000039
t represents diffusion time, div (D ▽ u)σ) Representing the divergence of the current sampling point;
discretizing a tensor diffusion equation in an orthogonal coordinate system (x, y) to obtain a filtering iteration formula:
uσ k+1＝uσ k+Δt·div(D▽uσ k)
ukand uk+1Respectively representing the filtering results of the original seismic data body at k delta t and (k +1) delta t, wherein delta t is the diffusion time of one iteration, and k represents the number of sampling points;
and obtaining a filtering data volume through a filtering iteration formula.
Preferably, the step S2 specifically includes:
let two seismic traces X (N) and Y (N) have a cross-correlation function of:
Figure BDA0001810884360000041
wherein w is a correlation time window, N is a sampling point number, l is the time delay of seismic traces X (N) and Y (N), and N is the total number of sampling points;
the autocorrelation function for X (N) and Y (N) is:
Figure BDA0001810884360000042
Figure BDA0001810884360000043
calculating the coherence value of the nth sampling point in the seismic trace X (N) according to the cross-correlation function and the autocorrelation function of the seismic trace X (N) and the seismic trace Y (N), wherein the specific expression is as follows:
Figure BDA0001810884360000044
selecting the maximum value of the coherence values of the nth sampling point in the seismic trace X (N) as the final coherence value of the sampling point according to the following formula:
C1(n)＝MaxC1(n,l)；
the final coherence values of all sampling points in all seismic channels in the filtering data volume are obtained through calculation in the step, and a coherence volume is formed.
Preferably, the step S3 is specifically:
and fusing the coherent body and the original seismic data body to obtain a fused data body, and intercepting and displaying a section view of the fused data body.
Preferably, the step S4 is specifically:
and obtaining a coherent body distribution area according to the profile map of the fusion data volume, and predicting the distribution area as the position of a crack development zone and/or a karst cave development zone, wherein the coherence value of the position of the crack development zone and/or the karst cave development zone is greater than that of the position of a non-crack development zone and/or the karst cave development zone.
Preferably, the step S5 is specifically: predicting the position of the fracture development zone and/or the karst cave development zone as a drilling lost interval.
Preferably, the methods are all implemented by thin layer seismic inversion software SMI.
Compared with the prior art, the method for predicting the drilling loss by using the seismic data has the following advantages that
Has the advantages that:
according to the method, filtering processing is carried out on a three-dimensional original seismic data volume, coherent body calculation is carried out on the filtered data volume after filtering processing to obtain a coherent body, and the three-dimensional original seismic data volume and the coherent body are fused to obtain a fused data volume. And analyzing the profile of the fusion data volume to predict a fracture development zone and/or a karst cave development zone, wherein the position of the fracture development zone and/or the karst cave development zone is the drilling lost interval. The method can predict the drilling lost interval, and further provides guidance for drilling so as to select the optimal well trajectory, avoid the drilling lost interval and improve the drilling success rate.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
FIG. 1 is a schematic flow chart of a method according to an embodiment of the present invention;
FIG. 2 is a cross-sectional view of a fused data volume in an embodiment of the present invention.
Detailed Description
The following detailed description of the embodiments of the present invention will be provided with reference to the drawings and examples, so that how to apply the technical means to solve the technical problems and achieve the technical effects can be fully understood and implemented. It should be noted that, as long as there is no conflict, the embodiments and the features of the embodiments of the present invention may be combined with each other, and the technical solutions formed are within the scope of the present invention.
In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced without some of these specific details or with other methods described herein.
The description is further described with reference to the accompanying drawings.
As shown in fig. 1, which is a schematic flow chart of the method according to the embodiment of the present invention, the method specifically includes the following steps:
s1: and carrying out noise reduction and filtering processing on the original seismic data volume to obtain a filtering data volume.
S2: and carrying out coherent processing on the filtering data volume to obtain a coherent volume.
S3: and fusing the coherent body and the original seismic data body to obtain a fused data body.
S4: and determining the distribution area of the coherent body according to the section of the fusion data volume, and predicting the position of a crack development zone and/or a karst cave development zone according to the distribution area of the coherent body.
S5: and predicting the drilling lost circulation interval according to the position of the fracture development zone and/or the karst cave development zone.
Wherein:
before step S1, an original seismic data volume is input into thin-layer seismic inversion software SMI, where the original seismic data volume is a three-dimensional original seismic data volume and belongs to basic data. And the original seismic data volume can be displayed in three dimensions through thin-layer seismic inversion software SMI. Steps S1, S2, S3, S4, and S5 are all performed in thin-layer seismic inversion software SMI, and are implemented by the thin-layer seismic inversion software SMI, or may be implemented in other software that can implement the above steps.
Step S1: and carrying out noise reduction and filtering processing on the original seismic data volume to obtain a filtering data volume, and specifically carrying out Gaussian smoothing processing on the original seismic data volume to obtain the filtering data volume. Wherein:
the gaussian smoothing of the original seismic data volume specifically comprises:
setting the data of the current sampling point in the original seismic data body as uσFirstly, the data u of the current sampling point in the original seismic data volume is processedσAnd performing partial derivative calculation in the x and y directions, wherein the formula is as follows:
Figure BDA0001810884360000061
respectively carrying out data u on current sampling points in the original seismic data volume according to a Gaussian smoothing formulaσCarrying out Gauss smoothing with rho as scale on three partial derivatives in the x and y directions to obtain a structure tensor J of the structure matrixρ. The Gaussian smoothing formula is uσ 0＝uσ*GρWherein G isρThe parameters of the gaussian kernel are used as the parameters,
Figure BDA0001810884360000062
is represented by GρIs the gaussian kernel of the parameter.
Structure tensor J of the structural matrixρComprises the following steps:
Figure BDA0001810884360000063
wherein, ▽ uσGradient of data representing current sampling point in original seismic data volume j11、j12、j22Is an intermediate amount, j11Represents uσThe partial derivatives in the x-direction are subjected to a Gaussian smoothing with a scale ρ, j12Represents uσThe partial derivatives in the x and y directions are subjected to a Gaussian smoothing with a scale rho, j22Represents uσThe partial derivatives in the y-direction are the result of gaussian smoothing with a scale ρ.
This is done to avoid the influence of noise on the gradient estimation. Through with GρConvolution takes into account the surrounding information and avoids mutual cancellation when edges are oriented with the same direction and opposite sign.
Then, the structure tensor J obtained by the equation (1) is subjected to the matrix eigen decomposition theoremρDecomposing to obtain JρDecomposed formula (2):
Figure BDA0001810884360000071
further, formula (3) is obtained by formula (2),
Figure BDA0001810884360000072
wherein, λ in the formula (3)1、λ2Is JρCharacteristic value of (a) ("lambda1、λ2The corresponding orthonormal eigenvectors are:
Figure BDA0001810884360000073
wherein the content of the first and second substances,
Figure BDA0001810884360000074
v1representing a parallel to gradient ▽ uσDirection of (i.e. rate of change is greatest)Large direction, rate of change of
Figure BDA0001810884360000075
v2Indicating a perpendicular to the gradient ▽ uσI.e. the direction with the smallest rate of change, of
Figure BDA0001810884360000076
Theta is the gradient ▽ uσThe angle of the direction.
According to
Figure BDA0001810884360000077
And
Figure BDA0001810884360000078
judging the local features of the current sampling point in the original seismic data extracted according to the structure tensor, and the diffusion coefficient mu of the local features1、μ2The expression of (a) is:
Figure BDA0001810884360000079
wherein α is a constant μ1、μ2Independently controlling local features at v1And v2Directional diffusion behavior, thereby enhancing edge information while attenuating noise.
Decomposition theorem and diffusion coefficient mu according to matrix characteristics1、μ2A structural diffusion tensor D is obtained, whose expression is:
Figure BDA0001810884360000081
wherein, a, b, c are components for constructing the diffusion tensor D, and are specifically expressed as:
Figure BDA0001810884360000082
and finally, obtaining a tensor diffusion equation representing a diffusion filtering result according to the constructed diffusion tensor D:
Figure BDA0001810884360000083
wherein t represents diffusion time, div (D ▽ u)σ) Representing the divergence of the current sample point, i.e., the final diffusion filter result of the data at the current sample point.
Discretizing a tensor diffusion equation in an orthogonal coordinate system (x, y) to obtain a filtering iteration formula:
uσ k+1＝uσ k+Δt·div(D▽uσ k) (8)
wherein u iskAnd uk+1Respectively representing the filtering results of the original seismic data body at k delta t and (k +1) delta t, wherein delta t is the diffusion time of one iteration, and k represents the number of sampling points.
And (3) obtaining a data result of the data filtering of each sampling point in the original seismic data volume through a filtering iteration formula (8), and forming a filtering data volume by the data results of the data filtering of all the sampling points.
The diffusion filtering technology starts from a diffusion equation of physics, takes an original seismic data body as an initial condition, and obtains diffused data by solving a partial differential equation related to time. In the diffusion process, local structure information is obtained by introducing a structure tensor, the expansion tensor is designed according to the structure information, different diffusion systems are adopted in different directions, and edge information is protected while denoising is carried out.
The step S2 specifically includes:
let two seismic traces in the filtered data volume be X (N) and Y (N), and their cross-correlation function be:
Figure BDA0001810884360000084
(n＝0,1,2…N-1)，
wherein w is the correlation time window, N is the sampling point number, l is the time delay of seismic traces X (N) and Y (N), and N is the total number of sampling points.
The autocorrelation function for X (N) and Y (N) is:
Figure BDA0001810884360000091
Figure BDA0001810884360000092
calculating the coherence value of the nth sampling point in the seismic trace X (N) according to the cross-correlation function and the autocorrelation function of the seismic trace X (N) and the seismic trace Y (N), namely expressions (9), (10) and (11), wherein the specific expression is as follows:
Figure BDA0001810884360000093
all the coherence values of the nth sample point are calculated according to expression (12).
Selecting the maximum value of the coherence values of the nth sampling point in the seismic trace X (N) as the final coherence value of the sampling point according to the following formula:
C1(n)＝MaxC1(n,l)； (13)
the value obtained by the calculation of the formula (13) is the final coherence value of the nth sampling point.
And calculating to obtain the final coherence value of all sampling points in all seismic channels in the filtering data volume through the steps, and forming a coherence volume.
The step S3 specifically includes:
and (4) fusing the coherent body obtained by the final calculation in the step (S2) with the original seismic data body by using SMI software to obtain a fused data body, and intercepting and displaying a section view of the fused data body. The SMI software is provided with corresponding buttons and options, and simultaneously selects the original seismic data body and the coherent data body under the folder, so that fusion display can be realized. After completion, step S4 is executed.
The step S4 specifically includes:
and analyzing the displayed section map of the fusion data volume to obtain a distribution region with a larger coherent value in the coherent volume, and predicting the distribution region as the position of a crack development zone and/or a karst cave development zone. And the coherence value of the position of the crack development zone and/or the karst cave development zone is larger than that of the position of the non-crack development zone and/or the karst cave development zone.
Specifically, as shown in fig. 2, which is a cross-sectional view of a fused data volume in an embodiment of the present invention, a longitudinally distributed strip-shaped region in the fused data volume is a distribution region with a larger coherence value in the coherent volume, and the larger the coherence value is, the more likely the region is a crack development zone and/or a karst cave development zone. Conversely, the position coherence of the coherent body without the longitudinally distributed banded region is smaller, and the less likely it is to be a crack development zone and/or a karst cave development zone. The horizontal stripe regions in the figure are the original seismic data volumes.
The step S5 specifically includes: and after the positions of the fracture development zones and/or the karst cave development zones are predicted, predicting the positions of the fracture development zones and/or the karst cave development zones as the drilling lost interval. The positions of the fracture development zones and/or the karst cave development zones are easy to cause well leakage accidents, so the fracture development zones and/or the karst cave development zones are predicted to be drilling lost intervals. And (4) avoiding the drilling lost interval during drilling.
According to the method, filtering processing is carried out on a three-dimensional original seismic data volume, coherent body calculation is carried out on the filtered data volume after filtering processing to obtain a coherent body, and the three-dimensional original seismic data volume and the coherent body are fused to obtain a fused data volume. And analyzing the profile of the fusion data volume to predict a fracture development zone and/or a karst cave development zone, wherein the position of the fracture development zone and/or the karst cave development zone is the drilling lost interval. The method can predict the drilling lost interval, and further provides guidance for drilling so as to select the optimal well trajectory and avoid the drilling lost interval, thereby improving the drilling success rate.
It should be noted that, although the embodiments of the present invention are described above, the descriptions are only for the convenience of understanding the present invention and are not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (10)

1. A method for predicting borehole loss using seismic data, comprising the steps of:
s1: carrying out noise reduction and filtering processing on the original seismic data volume to obtain a filtering data volume;
s2: carrying out coherent processing on the filtering data volume to obtain a coherent volume;
s3: fusing the coherent body and the original seismic data body to obtain a fused data body;
s4: determining the distribution area of the coherent body according to the section of the fusion data volume, and predicting the position of a crack development zone and/or a karst cave development zone according to the distribution area of the coherent body;
s5: and predicting the drilling lost circulation interval according to the position of the fracture development zone and/or the karst cave development zone.
2. The method of predicting borehole loss using seismic data as claimed in claim 1, wherein said noise reduction filtering process comprises the steps of:
and performing Gaussian smoothing processing on the original seismic data body.
3. The method of predicting borehole loss using seismic data as recited in claim 2, wherein gaussian smoothing the original seismic data volume comprises:
data u of current sampling point in original seismic data volumeσAnd performing partial derivative calculation in the x and y directions, wherein the formula is as follows:
Figure FDA0001810884350000011
respectively carrying out data u on current sampling points in the original seismic data volume according to a Gaussian smoothing formulaσPartial derivatives in x, y directionsPerforming Gaussian smoothing with the scale of rho to obtain a structure tensor J of the structure matrixρThe Gaussian smoothing formula is uσ 0＝uσ*Gρ(ii) a Wherein G isρThe parameters of the gaussian kernel are used as the parameters,
Figure FDA0001810884350000013
is represented by GρA gaussian kernel as a parameter;
structure tensor J of the structural matrixρComprises the following steps:
Figure FDA0001810884350000012
wherein the content of the first and second substances,
Figure FDA0001810884350000014
gradient of data representing current sampling point in original seismic data volume j11Represents uσThe partial derivatives in the x-direction are subjected to a Gaussian smoothing with a scale ρ, j12Represents uσThe partial derivatives in the x and y directions are subjected to a Gaussian smoothing with a scale rho, j22Represents uσThe partial derivatives in the y-direction are the result of gaussian smoothing with a scale ρ.
4. The method of predicting borehole loss using seismic data as recited in claim 3, wherein said noise reduction filtering process further comprises: the structure tensor J is subjected to matrix eigen decomposition theoremρDecomposing to obtain
Figure FDA0001810884350000021
Figure FDA0001810884350000022
Wherein λ is1、λ2Is JρCharacteristic value of (a) ("lambda1、λ2Corresponding orthogonalityThe normalized feature vector is:
Figure FDA0001810884350000023
wherein the content of the first and second substances,
Figure FDA0001810884350000024
v1representing parallel to the gradient
Figure FDA0001810884350000025
Direction of (2) at a rate of change of
Figure FDA0001810884350000026
v2Representing a perpendicular to the gradient
Figure FDA00018108843500000213
Direction of (2) at a rate of change of
Figure FDA0001810884350000027
Theta is a gradient
Figure FDA00018108843500000214
The angle of the direction;
according to
Figure FDA0001810884350000028
And
Figure FDA0001810884350000029
judging the local characteristics of the current sampling point of the original seismic data volume extracted according to the structure tensor, wherein the diffusion coefficient mu of the local characteristics1And mu2The expression of (a) is:
Figure FDA00018108843500000210
wherein α is a constant;
and obtaining a constructed diffusion tensor according to the matrix characteristic decomposition theorem and the diffusion coefficient, wherein the expression is as follows:
Figure FDA00018108843500000211
wherein, a, b, c are components for constructing the diffusion tensor D, and are specifically expressed as:
Figure FDA00018108843500000212
5. the method of predicting borehole loss using seismic data as recited in claim 4, wherein said noise reduction filtering process further comprises:
obtaining a tensor diffusion equation representing a diffusion filtering result according to the structure diffusion tensor D:
Figure FDA0001810884350000031
t represents the diffusion time and the diffusion time,
Figure FDA0001810884350000032
divergence of data representing a current sample point;
discretizing a tensor diffusion equation in an orthogonal coordinate system (x, y) to obtain a filtering iteration formula:
Figure FDA0001810884350000033
ukand uk+1Respectively representing the filtering results of the original seismic data body at k delta t and (k +1) delta t, wherein delta t is the diffusion time of one iteration, and k represents the number of sampling points;
and obtaining a filtering data volume through a filtering iteration formula.
6. The method for predicting borehole loss using seismic data as claimed in claim 5, wherein said step S2 specifically comprises:
let two seismic traces X (N) and Y (N) have a cross-correlation function of:
Figure FDA0001810884350000034
wherein w is a correlation time window, N is a sampling point number, l is the time delay of seismic traces X (N) and Y (N), and N is the total number of sampling points;
the autocorrelation function for X (N) and Y (N) is:
Figure FDA0001810884350000035
Figure FDA0001810884350000036
calculating the coherence value of the nth sampling point in the seismic trace X (N) according to the cross-correlation function and the autocorrelation function of the seismic trace X (N) and the seismic trace Y (N), wherein the specific expression is as follows:
Figure FDA0001810884350000041
selecting the maximum value of the coherence values of the nth sampling point in the seismic trace X (N) as the final coherence value of the sampling point according to the following formula:
C1(n)＝MaxC1(n,l)；
the final coherence values of all sampling points in all seismic channels in the filtering data volume are obtained through calculation in the step, and a coherence volume is formed.
7. The method for predicting borehole loss using seismic data as claimed in claim 6, wherein said step S3 is embodied as:
and fusing the coherent body and the original seismic data body to obtain a fused data body, and intercepting and displaying a section view of the fused data body.
8. The method for predicting borehole loss using seismic data as claimed in claim 7, wherein said step S4 is embodied as:
and obtaining a coherent body distribution area according to the profile map of the fusion data volume, and predicting the distribution area as the position of a crack development zone and/or a karst cave development zone, wherein the coherence value of the position of the crack development zone and/or the karst cave development zone is greater than that of the position of a non-crack development zone and/or the karst cave development zone.
9. The method for predicting borehole loss using seismic data as claimed in claim 8, wherein said step S5 is embodied as: predicting the position of the fracture development zone and/or the karst cave development zone as a drilling lost interval.
10. The method for predicting borehole loss using seismic data according to any of claims 1-9, wherein the method is implemented by thin layer seismic inversion software SMI.
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