CN106772586B - A kind of concealment fracture detection method based on seismic signal singularity - Google Patents
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Abstract
The invention belongs to the technical field of detection is broken in seismic prospecting, provide a kind of concealment fracture detection method based on seismic signal singularity, after the detection method passes through wavelet transform process, extract the singular value of seismic signal, establish the mapping relations of seismic signal singular value and breakpoint, the breakpoint location for accurately solving the fracture of section concealment may be implemented in a lineups, lineups distort, turn-off is small and the fracture of the concealment of weak signal.
Description
Technical field
The present invention relates to the technical fields that detection is broken in seismic prospecting, and in particular to one kind is based on seismic signal singularity
Concealment be broken detection method.
Background technique
Seismic Fracture detection is a kind of a kind of not only economic but also convenient crack detection method, Odling et al. (Odling N
E,Gillespie P,Bourgine B,et al.Variations in fracture system geometry and
their implications for fluid flow in fractured hydrocarbon reservoirs[J]
.Petroleum Geoscience, 1999,5 (4): 373-384.) it has been investigated that crack is important reservoir space and fluid
Percolating channels.It has been gradually formed in long-term scientific research and a variety of has carried out fracture detection method, Marfurt etc. using seismic data
People (Marfurt K J., Farmer S l., Bahorich M S., et al.3-D seismic attributes using
A semblance-based coherency algorithm.Geophysics, 1998,63 (4): 1150-1165.) propose
Multiple tracks cross-correlation, Gersztenkorn and Marfurt (Gersztenkorn A, Marfurt K in multi-channel analysis window
J.Eigenstructure-based coherence computations as an aid to 3-D structural and
Stratigraphic mapping.Geophysics, 1999,64 (5): 1468-1479.) based on multiple tracks eigen decomposition,
Marfurt(Blumentritt C H,Marfurt K J,Sullivan E C.Volume-based curvature
computations illuminate fracture orientations-Early to mid-Paleozoic,Central
Basin Platform, west Texas.Geophysics, 2006,71 (5): B159-B166.) inclination angle and orientation angular estimation,
(Blumentritt C H, Marfurt K J, the Sullivan E C.Volume-based curvature such as Blumentritt
computations illuminate fracture orientations-Early to mid-Paleozoic,Central
Basin Platform, west Texas.Geophysics, 2006,71 (5): B159-B166.), Al-Dossary and
Marfurt(Al-Dossary S,Marfurt K J.3D volumetric multispectral estimates of
Reflector curvature and rotation.Geophysics, 2006,71 (5): P41-P51.), Chopra and
Marfurt(Chopra S,Marfurt K J.Integration of coherence and volumetric
Curvature images.Leading Edge, 2010,29 (1): 1092-1107.) curvature attributes, (Chopra S,
Marfurt K J.Integration of coherence and volumetric curvature images.Leading
Edge, 2010,29 (1): 1092-1107.) fracture detection technique based on coherent body and curvature attributes, above-mentioned poststack fracture inspection
Survey method is differentiated in shallow-layer and noise is relatively high, affected by noise big.In addition, Chopra and Marfurt (Chopra S,
Marfurt K J.Integration of coherence and volumetric curvature images.Leading
Edge, 2010,29 (1): 1092-1107.) seismic properties are combined, joint is relevant, curvature carries out more attribute synthesis fracture detections.
Single poststack fracture detection is difficult to obtain good application effect, and seismic multi-attribute is it is possible to prevente effectively from because deep layer rock is opposite
Finer and close, speed is high, thus causes seismic reflection signals weaker, and lineups are mixed and disorderly, and seismic profile differentiates low fracture detection.
And for concealment fracture detection detection, in construction level, successively there is Steen et al. (Steen.,
Sverdrup E,Hanssen T H.Predicting the distribution of small faults in a
hydrocarbon reservoir by combining outcrop,seismic and well data[J]
.Geological Society London Special Publications, 1998,147 (1): 27-50.) just according to stratum
Inclination angle, azimuth and change of pitch angle rate, the result that this method obtains realize that oil gas stores up in combination with well log interpretation and model data
Concealment fracture detection in layer, but be difficult to detect for the weak stratum of seismic reflection；On Discussion of Earthquake Attribute Technology, Lawrence
(Lawrence P.Seismic attributes in the characterization of small‐scale
Reservoir faults in Abqaiq Field.Leading Edge, 1998,17 (4): 521-525.) according to earthquake category
Property and Neves et al. (Neves F A, Zahrani M S, Bremkamp S W.Detection of potential
fractures and small faults using seismic attributes.Leading Edge,2004,23(9):
903-906.) combine Spectral Decomposition Technique and Discussion of Earthquake Attribute Technology detection concealment fracture.Although this method can provide plane
Concealment fracture detection attributed graph, still cannot accurately solve the breakpoint location of section concealment fracture；In present information subject
On, Duchesne et al. (Duchesne M J, Halliday E J, Barrie J V.Analyzing seismic
imagery in the time–amplitude and time–frequency domains to determine fluid
nature and migration pathways:A case study from the Queen Charlotte Basin,
offshore British Columbia.Journal of Applied Geophysics,2011,73(2):111–120.)
Pass through time-frequency analysis technology and Basir et al. (Basir H M, Javaherian A, Yaraki M T.Multi-attribute
ant-tracking and neural network for fault detection:a case study of an
Iranian oilfield.Journal of Geophysics&Engineering, 2013,10 (1): 15009-15018.) it mentions
More attribute ant bodies and nerual network technique identify craven fault out.Although this method can provide the concealment fracture inspection of plane
Attributed graph is surveyed, the breakpoint location of section concealment fracture still cannot be accurately solved.
Summary of the invention
It is an object of the invention to overcoming the shortcomings of above-mentioned background technique, and provide a kind of based on seismic signal singularity
Concealment is broken detection method and extracts the singular value of seismic signal after the detection method passes through wavelet transform process, establish earthquake
The mapping relations of signal singular values and breakpoint accurately solve the breakpoint location of section concealment fracture, may be implemented one together
In phase axis, lineups distort, turn-off is small and the fracture of the concealment of weak signal.
To achieve the above object, a kind of concealment based on seismic signal singularity provided by the invention is broken detection side
Method, comprising the following steps:
S1: original earthquake data is read in；
S2: the original earthquake data read in step S1 is subjected to seismotectonics analysis and area deposition signature analysis is distinguished
It determines fracture size and seismic horizon, is broken size with normal throw (Fi+1-Fi) indicate, wherein FiIndicate i-th layer of upper disk earthquake
Reflection interval, Fi+1Indicate i+1 layer lower wall seismic reflection time, FiUnit is ms；Seismic horizon is with the continuous of seismic event
Property determines, with TiIndicate i-th layer of seismic horizon, TiUnit is ms；
S3: fracture size obtained in step S2 and seismic horizon are subjected to difference operation, seek fracture size respectively
(Fi+1-Fi) and seismic horizon thickness (Ti+1-Ti), and size (F will be brokeni+1-Fi) and seismic horizon thickness (Ti+1-Ti) conduct
Constrained parameters；
S4: by the fracture size (F in step S3i+1-Fi) and seismic horizon thickness (Ti+1-Ti) substituted into as constrained parameters
The formula of wavelet transformationWherein, scale factor a is by seismic horizon thickness
(Ti+1-Ti) determine, shift factor b is by fracture size (Fi+1-Fi) determine, then the random noise of seismic data is filtered,
Complete SNR estimation and compensation, seismic data after being filtered；
S5: seismic data and original earthquake data after the filtering that step S4 is obtained first carry out Fourier transformation, really respectively
The frequency bandwidth and dominant frequency of seismic data and original earthquake data after fixed filtering；Then time frequency analysis is carried out using wavelet transformation,
Determine the energy of seismic data and original earthquake data after filtering；If seismic data and original earthquake data meet quality after filtering
Control standard thens follow the steps S6, and step S2 is back to if being unsatisfactory for quality control standard and reanalyses TiAnd Fi, when minimally
Shake layer position thickness (Ti+1-Ti) in fracture size meet Fi+1-FiAs the final argument of filtering processing when ≠ 0；
S6: wavelet transformation: root is carried out to the seismic data for meeting step S5 quality control standard according to theory of wavelet transformation
Meet five orthogonality, supportive, symmetry, vanishing moment and regularity aspects according to wavelet function to determine wavelet functionIt is right
Scale factor a and shift factor b carries out discretization according to binary mode, that is, passes through a pair of of conjugate filter { h (n) } and { g
(n) } binary wavelet decomposition is carried out to signal f:
Wherein,hjAnd gjRespectively by h and g in each pair of adjacent sample
Originally it interleaves 2j-1 null element to obtain, to obtain the wavelet coefficient of 1~JWhenWhen, J layers of Coefficients of Approximation is 0；
S7: the seismic data after step S6 wavelet transformation is subjected to singularity processing: determines that each layer wavelet coefficient is odd
Anisotropic position and its corresponding value are for jth layer singular valueIfAndThenValue at point t is singular value, remembers that all singular value point time is tj 1,
tj 2,…,tj Nj, then corresponding singular value are as follows:
In above-mentioned technical proposal, in the step S5, detailed process is as follows for Fourier transformation: setting x (n) having as N point
Limit for length's sequence, then its Fourier transformation are as follows:
Wherein WN=e-j2*π/N, the frequency of seismic data and original earthquake data after filtering can be determined using Fourier transform
Bandwidth and dominant frequency.
In above-mentioned technical proposal, in the step S5, quality control standard is while meeting following two condition: its
One, the frequency bandwidth and dominant frequency after seismic data is fourier transformed with original earthquake data after filtering are consistent；Second, after filtering
Seismic data and original earthquake data carry out the energy coincidence after wavelet transformation.
In above-mentioned technical proposal, in the step S6, by wavelet transformation is defined as: to arbitrary function f (x) ∈ L2(R),
Its continuous wavelet transform fundamental relation is expressed from the next:
For the wavelet basis function met certain condition, fundamental relation is expressed from the next:
In formula, scale factor a is by formation thickness parameter (Ti+1-Ti) determine, and a ≠ 0, if a > 1, basic function is equivalent to
Function is stretched, the time width of window is increased, frequency spectrum constriction is simultaneously mobile to low frequency direction；If a < 1, video stretching and to height
Frequency direction is mobile；Shift factor b is by fracture size (Fi+1-Fi) determine, the translation position corresponding to time shaft；T is round trip
When.
Compared with prior art, the present invention has the advantage that
First, the present invention is by extracting the singular value of seismic signal, establishing seismic signal singular value after wavelet transform process
With the mapping relations of breakpoint, the breakpoint location of section concealment fracture is accurately solved, may be implemented in a lineups, same to phase
Axis distorts, turn-off is small and the fracture of the concealment of weak signal.
Second, detection method of the invention can be sought at any time tRemember all singular value point time
For tj 1,tj 2,…,tj Nj, then corresponding singular value are as follows:And in the prior art not
The breakpoint for being able to achieve any time extracts, thereforeIt is high to be broken detection accuracy, may be implemented in a lineups, lineups
It distorts, turn-off is small and the fracture of the concealment of weak signal；
Meet third, the present invention is derived by a series of treated seismic datasAndThenValue at point t is singular value, therefore the detection method can be extracted directly
The irregularity information for reflecting fracture information, establishes the mapping relations of seismic signal singular value and breakpoint, and it is hidden accurately to solve section
The breakpoint location of covering property fracture eliminates other indirect means because of error caused by itself method and its applicability；
Detailed description of the invention
Fig. 1 is implementation process diagram of the invention；
Fig. 2 a is original seismic data spectrum analysis figure in the present embodiment；
Fig. 2 b is seismic data spectrum analysis figure after filtering in the present embodiment；
Fig. 3 a is the local time frequency characteristics figure of original seismic data in the present embodiment；
Fig. 3 b is the local time frequency characteristics figure of seismic data after filtering in the present embodiment
Fig. 4 a is the geological model for examining the present embodiment method；
Fig. 4 b is the Seismic forward record for examining the present embodiment method；
Fig. 4 c is the singular value breaking point detection result for examining the present embodiment method；
Fig. 5 a is actual seismic section；
Fig. 5 b is the breaking point detection result through the present embodiment method；
Fig. 6 a crosses the actual seismic section of WP1 well；
Fig. 6 b was WP1 well coherent body fracture detection section；
Fig. 6 c was singular value fracture detection section of the WP1 well through the method for the present invention.
Specific embodiment
Below with reference to the embodiment performance that the present invention will be described in detail, but they and do not constitute a limitation of the invention,
It is only for example.Simultaneously by illustrating that advantages of the present invention will become clearer and be readily appreciated that.
A kind of concealment based on seismic signal singularity of the present embodiment is broken detection method, comprising the following steps:
S1: original earthquake data is read in；
S2: carrying out structural analysis for the original earthquake data read in step S1, first progress fault interpretation, determines fracture
Size, with normal throw (Fi+1-Fi) indicate that (unit ms, Fi indicated i-th layer of upper disk seismic reflection time, Fi+1It indicates
Thei+1The layer lower wall seismic reflection time)；Then seismic horizon explanation is carried out, earthquake is determined according to the continuity of seismic event
Layer position Ti (Ti unit is ms, and Ti indicates i-th layer)；On the basis of fracture and layer position are explained, determined according to geology background knowledge
Seismic reflection character with important Sedimentology, determining has the seismic horizon thickness of the Seismic reflection character of Sedimentology
(Ti+1-Ti)。
S3: fracture size obtained in step S2 and seismic horizon are subjected to difference operation, seek fracture size respectively
(Fi+1-Fi) and seismic horizon thickness (Ti+1-Ti), and size (F will be brokeni+1-Fi) and seismic horizon thickness (Ti+1-Ti) conduct
Constrained parameters；
S4: the constrained parameters in S3 are substituted into the formula of wavelet transformation, by wavelet transformation is defined as: to arbitrary function f (x)
∈L2(R), continuous wavelet transform are as follows:
The basic function of wavelet transformationIt is window functionShift factor b and scale factor a result.
In formula: a ≠ 0 is scale factor；B is shift factor；For the wavelet basis function met certain condition.The variation of a
It can change the size of window, if scale factor a > 1, basic function, which is equivalent to, stretches function, increase the time width of window,
Frequency spectrum constriction is simultaneously mobile to low frequency direction；And a < 1, video stretching are simultaneously mobile to high frequency direction.Big scale factor corresponds to
Low frequency end, frequency resolution is high, temporal resolution is low；The parameter of small scale corresponds to front end, and frequency resolution is low, the time divides
Resolution is high, and here it is the multi-resolution characteristics of wavelet transformation.
In formulaWherein scale factor a is by seismic horizon thickness
(Ti+1-Ti) determine, shift factor b is by fracture size Fi+1-FiIt determines, the random noise of seismic data is filtered, complete
SNR estimation and compensation, seismic data after being filtered；
S5: seismic data and original earthquake data after the filtering that step S4 is obtained first carry out Fourier transformation, Fu respectively
In leaf transformation detailed process is as follows: set x (n) as the finite length sequence of N point, then its Fourier transformation are as follows:
Wherein WN=e-j2*π/N, the frequency of seismic data and original earthquake data after filtering can be determined using Fourier transform
Bandwidth and dominant frequency can determine the frequency bandwidth and dominant frequency information of seismic data using Fourier transform (formula 1), by right
The spectrum analysis of filtering front and back seismic data, can determine the quality of seism processing in the overall situation.Only because of Fourier transform
Can be in the spectrum information of entire period, but the minutia of each period of Earthquake occurrence control data filter process is wanted,
It needs to screen the local feature of signal, spectrum analysis then is carried out to local signal.Wavelet transformation can effectively from
Signal local feature is extracted in signal, this method carries out multiple dimensioned refinement to signal by operations such as flexible and translations, reaches
The local spectrum feature for analyzing signal carries out time frequency analysis using wavelet transformation, meets orthogonality, support according to wavelet function
Property, symmetry, vanishing moment and the aspect of regularity five determine wavelet functionTo scale factor a and shift factor b according to
Binary mode carries out discretization, i.e., carries out binary wavelet to signal f by a pair of of conjugate filter { h (n) } and { g (n) }
It decomposes:
Wherein,hjAnd gjRespectively by h and g in each pair of adjacent sample
Originally it interleaves 2j-1 null element to obtain, to obtain the wavelet coefficient of 1~JWhenWhen, J layers of Coefficients of Approximation is 0；Determine the energy (local time of seismic data and original earthquake data after filtering
Frequency feature), quality control standard is while meeting following two condition: first, seismic data and original earthquake data after filtering
Frequency bandwidth after being fourier transformed is consistent with dominant frequency；Second, seismic data and original earthquake data carry out small echo after filtering
Transformed energy coincidence；The quality of seismic data filter effect is mainly controlled in terms of frequency bandwidth, dominant frequency and energy three
System, the consistency of three illustrate that filtering is that effectively, 2a is original seismic data spectrum signature in figure, and Fig. 2 b is after filtering
Seismic data spectrum signature, comparison diagram 2a have found that frequency bandwidth and dominant frequency feature are consistent with Fig. 2 b；3a is original earthquake money in figure
Expect local time frequency characteristics, Fig. 3 b is seismic data local time frequency characteristics after filtering, both comparison diagram 3a and Fig. 3 b discovery energy one
It causes, therefore filter effect can be determined according to three frequency bandwidth, dominant frequency and energy aspects, then seismic data and original after filtering
Beginning seismic data meets quality control standard and thens follow the steps S6；
S6: wavelet transformation is carried out to the seismic data for meeting step S5 quality control standard according to theory of wavelet transformation:
Meet 5 orthogonality, supportive, symmetry, vanishing moment and regularity aspects according to wavelet function to select small echo
FunctionIn order to reduce redundancy, discretization is carried out according to binary mode to scale factor a and shift factor, i.e., it is a pair of total
Yoke filter { h (n) } and { g (n) } carry out binary wavelet decomposition to signal f:
WhereinhjAnd gjRespectively by h and g in each pair of adjacent sample
It interleaves 2j-1 null element to obtain, to obtain the wavelet coefficient of 1~JWhen
When, J layers of Coefficients of Approximation is 0.
S7: data singularity processing will be carried out after step S6 wavelet transformation, Fig. 4 a is that the difference of different breaking properties is disconnected
Away from theory position, Fig. 4 b is the forward record under Fig. 4 a Model Condition, and Fig. 4 c is breaking point detection through the invention, right
It finds than Fig. 4 a geological model and Fig. 4 b forward record, is difficult to by artificial fault interpretation, and pass through singular value
Can determine each layer wavelet coefficient singularity position and its corresponding value (see Fig. 4 c), i.e., can clearly be detected on Fig. 4 c
Breakpoint, and be consistent with Fig. 4 a model breakpoint location.In order to further confirm applicability of the invention, in actual seismic data
On, Fig. 5 a is actual seismic data, is also difficult to find out breakpoint from artificial earthquake interpretation horizon, can understand from Fig. 5 b and detect
Breakpoint out.
In order to further illustrate the difference with other prior arts, the present invention, which is unique in that, can directly extract earthquake
The singular value of data any time tIfAndThenValue at point t is singular value, remembers that all singular value point time is tj 1,tj 2,…,tj Nj.It is so corresponding odd
Different value are as follows:
Fig. 6 a was WP1 well seismic profile, the coherent body fracture detection section that Fig. 6 b was WP1 well different moments t, thus
It can be seen that the influence of TWT t, Fig. 6 c was WP1 well any time t singularity fracture detection section, therefore from Fig. 6 c and was schemed
6b comparison is as can be seen that Fig. 6 c is broken detection accuracy ratio Fig. 6 b high.
The content that this specification is not described in detail belongs to the prior art well known to professional and technical personnel in the field.
Claims (4)
1. a kind of concealment based on seismic signal singularity is broken detection method, which comprises the following steps:
S1: original earthquake data is read in；
S2: the original earthquake data read in step S1 is subjected to seismotectonics analysis and area deposition signature analysis determines respectively
It is broken size and seismic horizon, is broken size with normal throw (Fi+1-Fi) indicate, wherein FiIndicate i-th layer of upper disk seismic reflection
Time, Fi+1Indicate i+1 layer lower wall seismic reflection time, FiUnit is ms；Seismic horizon is come with the continuity of seismic event
It determines, with TiIndicate i-th layer of seismic horizon, TiUnit is ms；
S3: fracture size obtained in step S2 and seismic horizon are subjected to difference operation, seek fracture size (F respectivelyi+1-Fi)
And seismic horizon thickness (Ti+1-Ti), and size (F will be brokeni+1-Fi) and seismic horizon thickness (Ti+1-Ti) it is used as constrained parameters；
S4: by the fracture size (F in step S3i+1-Fi) and seismic horizon thickness (Ti+1-Ti) as constrained parameters substitution small echo
The formula of transformationWherein, scale factor a is by seismic horizon thickness (Ti+1-
Ti) determine, shift factor b is by fracture size (Fi+1-Fi) determine, then the random noise of seismic data is filtered, is completed
SNR estimation and compensation, seismic data after being filtered；
S5: seismic data and original earthquake data after the filtering that step S4 is obtained first carry out Fourier transformation respectively, determine filter
The frequency bandwidth and dominant frequency of seismic data and original earthquake data after wave；Then time frequency analysis is carried out using wavelet transformation, determined
The energy of seismic data and original earthquake data after filtering；If seismic data and original earthquake data meet quality control after filtering
Standard thens follow the steps S6, and step S2 is back to if being unsatisfactory for quality control standard and reanalyses TiAnd Fi, when minimum earthquake layer
Position thickness (Ti+1-Ti) in fracture size meet Fi+1-FiAs the final argument of filtering processing when ≠ 0；
S6: wavelet transformation is carried out to the seismic data for meeting step S5 quality control standard according to theory of wavelet transformation: according to small
Wave function meets five orthogonality, supportive, symmetry, vanishing moment and regularity aspects to determine wavelet functionTo scale
Factor a and shift factor b carries out discretization according to binary mode, i.e., right by a pair of of conjugate filter { h (n) } and { g (n) }
Signal f carries out binary wavelet decomposition:
Wherein,hjAnd gjIt is interleave respectively by h and g in each pair of adjacent sample
2j-1 null element obtains, to obtain the wavelet coefficient of 1~JWhenWhen, the
J layers of Coefficients of Approximation is 0；
S7: the seismic data after step S6 wavelet transformation is subjected to singularity processing: determining each layer wavelet coefficient singularity
Position and its corresponding value, be for jth layer singular valueIfAndThenValue at point t is singular value, remembers that all singular value point time is tj 1,
tj 2,…,tj Nj, then corresponding singular value are as follows:
2. the concealment according to claim 1 based on seismic signal singularity is broken detection method, which is characterized in that
In the step S5, detailed process is as follows for Fourier transformation: setting x (n) as the finite length sequence of N point, then its Fourier transformation are as follows:
WhereinThe frequency band of seismic data and original earthquake data after filtering can be determined using Fourier transform
Width and dominant frequency.
3. the concealment according to claim 1 or 2 based on seismic signal singularity is broken detection method, feature exists
In in the step S5, quality control standard is while meeting following two condition: first, seismic data and original after filtering
Frequency bandwidth after beginning seismic data is fourier transformed is consistent with dominant frequency；Second, seismic data and original earthquake number after filtering
According to the energy coincidence after progress wavelet transformation.
4. the concealment according to claim 3 based on seismic signal singularity is broken detection method, which is characterized in that
In the step S6, by wavelet transformation is defined as: to arbitrary function f (x) ∈ L2(R), continuous wavelet transform fundamental relation by
Following formula indicates:
For the wavelet basis function met certain condition, fundamental relation is expressed from the next:
In formula, scale factor a is by seismic horizon thickness (Ti+1-Ti) determine, and a ≠ 0, if a > 1, basic function is equivalent to function
It stretches, increases the time width of window, frequency spectrum constriction is simultaneously mobile to low frequency direction；If a < 1, video stretching and to high frequency direction
It is mobile；Shift factor b is by fracture size (Fi+1-Fi) determine, the translation position corresponding to time shaft；T is TWT.
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| CN107918153B (en) * | 2018-01-10 | 2019-08-06 | 成都理工大学 | A kind of seismic signal coherence high-precision detecting method |
| CN108508486B (en) * | 2018-03-09 | 2019-12-03 | 成都北方石油勘探开发技术有限公司 | A kind of FRACTURE PREDICTION analysis method based on structural evolution stratum strain |
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