CN113568049A - Method and device for identifying coal seam and computer readable storage medium - Google Patents
Method and device for identifying coal seam and computer readable storage medium Download PDFInfo
- Publication number
- CN113568049A CN113568049A CN202110429527.6A CN202110429527A CN113568049A CN 113568049 A CN113568049 A CN 113568049A CN 202110429527 A CN202110429527 A CN 202110429527A CN 113568049 A CN113568049 A CN 113568049A
- Authority
- CN
- China
- Prior art keywords
- coal seam
- seismic
- coal
- determining
- frequency
- 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.)
- Pending
Links
- 239000003245 coal Substances 0.000 title claims abstract description 270
- 238000000034 method Methods 0.000 title claims abstract description 77
- 239000011435 rock Substances 0.000 claims abstract description 35
- 238000005070 sampling Methods 0.000 claims abstract description 18
- 230000006870 function Effects 0.000 claims description 16
- 238000012897 Levenberg–Marquardt algorithm Methods 0.000 claims description 7
- 238000004590 computer program Methods 0.000 claims description 5
- 238000007493 shaping process Methods 0.000 claims description 4
- 125000004429 atom Chemical group 0.000 description 53
- 238000000926 separation method Methods 0.000 description 14
- 238000004422 calculation algorithm Methods 0.000 description 12
- 238000010586 diagram Methods 0.000 description 12
- 238000000354 decomposition reaction Methods 0.000 description 10
- 239000004576 sand Substances 0.000 description 10
- 230000000694 effects Effects 0.000 description 9
- 230000008569 process Effects 0.000 description 9
- 238000012545 processing Methods 0.000 description 9
- 238000013016 damping Methods 0.000 description 8
- 238000004458 analytical method Methods 0.000 description 6
- 230000015572 biosynthetic process Effects 0.000 description 6
- 238000004364 calculation method Methods 0.000 description 6
- 239000011159 matrix material Substances 0.000 description 5
- 230000004044 response Effects 0.000 description 5
- 238000011160 research Methods 0.000 description 4
- 238000012360 testing method Methods 0.000 description 4
- 238000011161 development Methods 0.000 description 3
- 239000012530 fluid Substances 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 239000002131 composite material Substances 0.000 description 2
- 238000009795 derivation Methods 0.000 description 2
- 238000009499 grossing Methods 0.000 description 2
- 230000036039 immunity Effects 0.000 description 2
- ONUFESLQCSAYKA-UHFFFAOYSA-N iprodione Chemical compound O=C1N(C(=O)NC(C)C)CC(=O)N1C1=CC(Cl)=CC(Cl)=C1 ONUFESLQCSAYKA-UHFFFAOYSA-N 0.000 description 2
- 230000007480 spreading Effects 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 238000003786 synthesis reaction Methods 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 125000004432 carbon atom Chemical group C* 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 239000004927 clay Substances 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 230000010339 dilation Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 229930195733 hydrocarbon Natural products 0.000 description 1
- 150000002430 hydrocarbons Chemical class 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
- 230000004083 survival effect Effects 0.000 description 1
- 230000002123 temporal effect Effects 0.000 description 1
- 238000012795 verification 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
- G01V1/40—Seismology; Seismic or acoustic prospecting or detecting specially adapted for well-logging
- G01V1/44—Seismology; Seismic or acoustic prospecting or detecting specially adapted for well-logging using generators and receivers in the same well
- G01V1/48—Processing data
- G01V1/50—Analysing data
-
- 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
- G01V2210/61—Analysis by combining or comparing a seismic data set with other data
- G01V2210/616—Data from specific type of measurement
- G01V2210/6169—Data from specific type of measurement using well-logging
-
- 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
- G01V2210/624—Reservoir parameters
Landscapes
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Acoustics & Sound (AREA)
- Environmental & Geological Engineering (AREA)
- Geology (AREA)
- Remote Sensing (AREA)
- General Life Sciences & Earth Sciences (AREA)
- General Physics & Mathematics (AREA)
- Geophysics (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
The application relates to a method, a device and a computer readable storage medium for identifying coal seams, wherein the method comprises the following steps: acquiring seismic reflection signals; obtaining a preset rock elastic parameter by using the seismic reflection signal for inversion, wherein the preset rock elastic parameter is a rock elastic parameter sensitive to a coal bed; determining the position of the coal bed according to a preset coal bed threshold value and a preset rock elastic parameter, wherein the position of the coal bed is a sampling time point of a seismic reflection signal; determining the time range of the seismic reflection signal corresponding to the coal seam according to the position of the coal seam; and matching and tracking the seismic reflection signals in the time range to obtain coal seam reflection signals. According to the method, the seismic data are used for inverting rock elastic parameters sensitive to the coal bed, the position of the coal bed is determined according to the rock elastic parameters, the seismic data of the position of the coal bed are matched and tracked, coal bed signals are identified, and identification of the widely distributed complex thin coal bed is achieved.
Description
Technical Field
The present application relates to the field of oil and gas seismic exploration, and in particular, to a method, an apparatus, and a computer-readable storage medium for identifying a coal seam.
Background
In oil and gas seismic exploration of an oil-gas-containing basin, the existence of a coal-based stratum brings great resistance to exploration and development, multiple layers of coal layers in the coal-based stratum are overlapped in the longitudinal direction, multiple sets of coal layers are directly contacted with sandstone to form a sand-coal thin interbed, coal-containing reservoir seismic response information is mixed with coal layer reflection information, the coal layer reflection interferes with amplitude and frequency information of the reservoir, and uncertainty of seismic interpretation is increased. On the one hand, bright spots, which are usually seen on seismic sections, are erroneously interpreted as an indication of the presence of hydrocarbons, and on the other hand, the strong amplitude caused by the very low longitudinal wave impedance may mask the reflections from reservoirs near the coal seam.
The problem to be solved urgently is how to establish an effective seismic data target processing method on the basis of weak seismic response of the coal seam, and to lay a data foundation for high-quality reservoir evaluation and fluid identification.
Disclosure of Invention
To solve the above technical problem or at least partially solve the above technical problem, the present application provides a method, an apparatus and a computer-readable storage medium for identifying a coal seam.
In a first aspect, the present application provides a method of identifying a coal seam, comprising: acquiring seismic reflection signals; obtaining a preset rock elastic parameter by using the seismic reflection signal for inversion, wherein the preset rock elastic parameter is a rock elastic parameter sensitive to a coal bed; determining the position of the coal bed according to a preset coal bed threshold value and a preset rock elastic parameter, wherein the position of the coal bed is a sampling time point of a seismic reflection signal; determining the time range of the seismic reflection signal corresponding to the coal seam according to the position of the coal seam; and matching and tracking the seismic reflection signals in the time range to obtain coal seam reflection signals.
In some embodiments, determining the time range of the seismic reflection signal corresponding to the coal seam according to the coal seam position comprises: determining a coal seam roof and a coal seam floor according to preset rock elastic parameters, wherein the coal seam roof and the coal seam floor correspond to sampling time points of seismic reflection signals; determining an expansion value according to a coal seam floor and a coal seam roof; and determining the time range of the seismic reflection signal corresponding to the coal seam according to the expansion value by taking the position of the coal seam as the center.
In some embodiments, the above mentioned dilations are determined from the floor and roof of the coal seam as followsAnd (3) spreading value: n × Δ t, where n × Δ t denotes an expansion value, Δ t denotes a sampling interval of the seismic reflection signal, and n ═ t (C)location)roof-t(Clocation)floor) Wherein t (C)location)roofAnd t (C)location)floorRespectively representing a coal seam roof and a coal seam floor; and determining the time range of the seismic reflection signal corresponding to the coal seam according to the following mode by taking the position of the coal seam as the center and according to the expansion value: deltat＝[t(Clocation)-n*Δt,t(Clocation)+n*Δt]Wherein, deltatRepresents the time range, t (C)location) The coal seam position is shown, and n x delta t represents an expansion value.
In some embodiments, in performing matching pursuit on the seismic reflection signals in the time range, scanning is performed in an overcomplete dictionary atom library with a time at an envelope maximum point of complex seismic signals of the seismic reflection signals in the time range as an initial value of a central time of a mother wavelet, an instantaneous phase at the envelope maximum point as an initial value of a phase of the mother wavelet, and an instantaneous frequency at the envelope maximum point as an initial value of a dominant frequency of the mother wavelet, wherein the overcomplete dictionary atom library is defined by an EMD eigenmode function.
In some embodiments, the instantaneous frequency at the envelope maximum point is determined as follows: determining the continuous phase of said complex seismic signal; and determining the instantaneous frequency at the envelope maximum point according to the continuous phase of the complex seismic signal.
In some embodiments, the EMD eigenmode function is:
wherein a (t) represents the amplitude,
which is indicative of the instantaneous phase of the phase,
which is indicative of the instantaneous frequency of the frequency,
to represent
The defined harmonic column vector constitutes a subspace, and λ represents a parameter controlling the smoothness of the function.
In some embodiments, the overcomplete dictionary atom library is represented as:
wherein D represents an overcomplete dictionary atom library, ωγ(t) represents the mother wavelet of the overcomplete dictionary atom library,
in order to be the atoms after the modulation,
to control the parameter set, tcRepresents time, fcWhich is indicative of the instantaneous frequency of the frequency,
representing the instantaneous phase, and Γ representing the time range and its corresponding frequency and phase.
In some embodiments, determining the instantaneous frequency at the envelope maxima from the continuous phase of the complex seismic signals comprises: and determining the instantaneous frequency at the envelope maximum point by using a damped least squares method and a reshaping regularization operator according to the continuous phase of the complex seismic signal.
In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor; the computer program, when executed by a processor, performs the steps of a method of identifying a coal seam.
In a third aspect, the present application provides a computer readable storage medium having stored thereon a program for identifying a coal seam, the program for identifying a coal seam when executed by a processor implementing the steps of the method for identifying a coal seam.
Compared with the prior art, the technical scheme provided by the embodiment of the application has the following advantages: according to the method provided by the embodiment of the application, rock elasticity parameters sensitive to the coal bed are inverted through the seismic data, the position of the coal bed is determined according to the rock elasticity parameters, the seismic data of the position of the coal bed are matched and tracked, coal bed signals are identified, and identification of the widely distributed complex thin coal bed is achieved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without any inventive exercise.
FIG. 1 is a flow chart of one embodiment of a method for identifying a coal seam provided in an example of the present application;
FIG. 2 is a schematic diagram illustrating the construction of a coal seam indicator according to petrophysical analysis in an embodiment of the present application;
FIG. 3a is a schematic diagram of seismic data of a coal-bearing reservoir according to an embodiment of the present application;
FIG. 3b is a schematic diagram illustrating inversion of coal seam indicators according to an embodiment of the present application;
FIG. 4a is a schematic diagram of a frequency solution for a continuous phase seismic signal according to an improvement in the frequency of the continuous phase seismic signal in the absence of noise according to an embodiment of the present application;
FIG. 4b is a schematic diagram of a seismic signal frequency solution under the condition that the signal-to-noise ratio is equal to 1 in the embodiment of the present application;
FIG. 5 is a schematic illustration of coal seam identification and separation according to an embodiment of the present application;
FIG. 6a is a theoretical model of the reflection interface of the coal seam under the consistent background of the upper and lower sandstones according to the embodiment of the present application;
FIG. 6b is a theoretical model of a reflection interface of a coal seam under the background of overlying sandstone and underlying mudstone according to the embodiment of the present application;
FIG. 6c is a theoretical model of the reflection interface of the coal-containing seam of the 4 sets of coal-bed complexes according to the embodiment of the present application;
FIG. 7a is a schematic diagram of a two-dimensional coal seam reflection background sand shale thin interbed theoretical geologic model according to an embodiment of the present application;
FIG. 7b is a schematic illustration of forward seismic recordings of a theoretical model without noise interference according to an embodiment of the present application;
FIG. 7c is a seismic record of an embodiment of the present application after stripping the reflection from the coal seam;
FIG. 7d is a representation of a coal seam reflection seismic record identified in accordance with an embodiment of the present application;
FIG. 7e is a coal seam reflection seismic record forward modeling a theoretical coal seam model without noise interference according to an embodiment of the present application;
FIG. 8a is a schematic diagram of seismic data of a coal-bearing reservoir of actual data according to an embodiment of the present application;
FIG. 8b is a schematic diagram of seismic data after reflection separation of a real data coal seam according to an embodiment of the present application;
FIG. 8c is a schematic diagram of coal bed seismic data for actual data identification in accordance with an embodiment of the present application;
FIGS. 8e and 8d are respectively a time-frequency analysis of matching pursuits before and after removal of a single coal seam according to an embodiment of the present disclosure;
fig. 9 is a hardware schematic diagram of an implementation manner of a computer device according to an embodiment of the present application.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In the following description, suffixes such as "module", "component", or "unit" used to denote elements are used only for the convenience of description of the present application, and have no specific meaning by themselves. Thus, "module", "component" or "unit" may be used mixedly.
The embodiment of the present application provides a method for identifying a coal seam, as shown in fig. 1, the method includes steps S102 to S110.
Step 102, seismic reflection signals are obtained.
And S104, obtaining a preset rock elastic parameter by using the seismic reflection signal for inversion, wherein the preset rock elastic parameter is a rock elastic parameter sensitive to a coal bed.
And S106, determining the coal seam position according to a preset coal seam threshold value and the preset rock elastic parameter, wherein the coal seam position is a sampling time point of the seismic reflection signal.
And S108, determining the time range of the seismic reflection signal corresponding to the coal seam according to the position of the coal seam.
And step S110, matching and tracking the seismic reflection signals in the time range to obtain coal seam reflection signals.
According to the method and the device, the rock elastic parameters sensitive to the coal bed are inverted by using the seismic data, the position of the coal bed is determined according to the rock elastic parameters, the seismic data of the position of the coal bed are matched and tracked, coal bed signals are identified, and the identification of the widely distributed complex thin coal bed is realized.
As an example, the preset rock elasticity parameter is longitudinal wave impedance, in an embodiment of the present application, a longitudinal wave impedance threshold is set as the preset coal seam threshold in step S106, a longitudinal wave impedance value is smaller than the preset longitudinal wave impedance threshold, the position is determined as a coal seam position, and the coal seam position is a sampling time point of the seismic reflection signal. A coal seam location may include a plurality of sampling time points, typically a plurality of sampling time points in series. Since the sampling time points correspond to the longitudinal depth of the formation, a plurality of sampling time points correspond to a longitudinal depth range of the formation. One or more coal seams distributed at different longitudinal depths may be included in a seismic reflection.
In the embodiment of the present application, the time range corresponding to the coal seam may be determined by various methods. In one embodiment, in step S108, determining a coal seam roof and a coal seam floor according to preset rock elasticity parameters, where the coal seam roof and the coal seam floor correspond to sampling time points of the seismic reflection signals; determining an expansion value according to a coal seam floor and a coal seam roof; and determining the time range of the seismic reflection signal corresponding to the coal seam according to the expansion value by taking the coal seam as the center.
In some embodiments, the above-mentioned spreading value is determined in the following manner: n × Δ t, where n × Δ t denotes an expansion value, Δ t denotes a sampling interval of the seismic reflection signal, and n ═ t (C)location)roof-t(Clocation)floor) Wherein t (C)location)roofAnd t (C)location)floorRespectively representing a coal seam roof and a coal seam floor; determining the time range of the seismic reflection signal corresponding to the coal seam according to the following modes: deltat＝[t(Clocation)-n*Δt,t(Clocation)+n*Δt]Wherein, deltatRepresents the above time range, t (C)location) Indicating the position of the coal seam, and n x Δ t indicates the spread value.
In the embodiment of the present application, in the step S104, the predicted rock elasticity parameter may be obtained by performing an inversion by using a known technique before the filing date of the present application, which is not limited in the embodiment of the present application.
In the embodiment of the present application, in the step S110, a matching pursuit method before the filing date of the present application may be adopted. An improved match tracking method is also provided and will be described later herein.
Determining coal bed sensitive rock elasticity parameters
As an example, first, rock elasticity parameters are estimated based on a theoretical seismic petrophysical model; then, analyzing the quantitative relation between the rock elastic parameters and the coal quality content of the stratum; and secondly, analyzing an intersection graph of the coal bed indicating factors by using the logging data, calculating indicating coefficients of different elastic parameters to the coal bed, and constructing the most sensitive coal bed indicating factors.
In the formula, gamma represents a coal bed indicating coefficient of an elastic parameter obtained through calculation based on a statistical theory, L represents actual logging data of a coal bed development section, M represents actual logging data of a non-coal bed development position, mean (-) represents a mean operator, and std (-) represents a standard deviation operator.
Petrophysical analysis provides a link between coal seam properties and seismic attributes, and it is important to study sensitive coal seam petrophysical parameters to simulate the seismic response of each thin coal seam before large-scale coal seam data identification is performed.
As an illustrative example, known data from an oil field is used to identify and rank coal bed sensitive petrophysical parameters. As shown in fig. 2, the coal seam (coal) corresponds to low compressional wave impedance, the sandstone (clay) corresponds to high compressional wave impedance, and the mudstone (sand) compressional wave impedance is relatively lower than the coal seam and higher than the sandstone.
Rock elastic parameter inversion
As an example, L is utilized0The norm regularization method is characterized in that a stratum reflection coefficient is assumed to have sparsity, an inversion target functional based on matching pursuit is constructed, a low-frequency prior information constraint item of a model parameter is added to enhance inversion stability, and the sparse target functional is as follows:
in the formula (2), d is seismic reflection data, G is a forward action matrix, and min represents a variable value when the target function takes the minimum value;
is the L2 norm of seismic reflection data, representing the degree of fit of actual seismic data and forward seismic data;
is the L2 norm of the low frequency prior model, prevents the over-fitting phenomenon,
for the integral operator, r is the formation reflection coefficient, ξpIs a low-frequency a-priori model,
λ is the constraint coefficient of the low frequency model, representing the reflection result and the xi of the low frequency prior modelpThe degree of fit between. The higher the signal-to-noise ratio of the seismic data is, the smaller the value of lambda is, and conversely, the larger the value of lambda is; r isitThe number of iterations is preset for the invention; k is the upper limit of the preset iteration times of the matching tracking algorithm; ε represents a threshold for iterative error between the actual seismic data and the synthetic data and is the termination condition for iteration of the control matching pursuit inversion algorithm.
Obtaining the formation reflection coefficient by solving the formula (2), and further obtaining the absolute longitudinal wave impedance I of the formationp(t) can be obtained from the following integral equation:
wherein exp (·) denotes a natural exponent operator,
for integral operator, Ip(t) and Ip(t0) Respectively, the actual longitudinal wave impedance and the reference longitudinal wave impedance, t is the time position of the actual longitudinal wave impedance, t0Is the time position at the longitudinal wave impedance.
Based on the research result of the rock physics, the stratum longitudinal wave impedance under the constraint of the coal bed hard threshold is proposed as the coal bed indicator CcoalAnd predicting the coal seam space distribution, namely:
Ccoal＝Ip(t),{Ip(t)＜e} (4)
and e is a threshold value of the longitudinal wave impedance at the position of the coal seam obtained based on rock physical analysis aiming at the lithology characteristics of the coal seam in the research area. Fig. 3a shows the seismic data of the coal-bearing reservoir for the actual data. And as shown in fig. 3b, a schematic diagram of the coal seam space identified by the coal seam indicator factor is shown.
Improved matching pursuit
In some embodiments, in performing matching pursuit on the seismic reflection signals within the time range, scanning is performed in an overcomplete dictionary atom library with a time at an envelope maximum point of complex seismic signals of the seismic reflection signals within the time range as an initial value of a central time of a mother wavelet, an instantaneous phase at the envelope maximum point as an initial value of a phase of the mother wavelet, and an instantaneous frequency at the envelope maximum point as an initial value of a dominant frequency of the mother wavelet, wherein the overcomplete dictionary atom library is defined by an EMD eigenmode function (component). Compared with global search, the dynamic search greatly improves the calculation efficiency and provides possibility for applying the matching pursuit algorithm to coal bed data processing.
In some embodiments, the EMD is defined by a set of all possible eigenmode functions (IMFs) obtained by EMD decomposition, where the EMD eigenmode function is:
wherein D isimf(t) represents an EMD eigenmode function (component), a represents amplitude;
which represents the instantaneous phase of the phase,
which is indicative of the instantaneous frequency of the frequency,
to represent
The defined harmonic column vector constitutes a subspace, and λ represents a parameter controlling the smoothness of the function.
Since the precondition assumption for the eigenmode function is stationary, the energy and instantaneous frequency of the signal do not change drastically over time and will, in general, vary greatly
A fourier basis constructed to be overcomplete is most efficient.
In the above formula, the first and second carbon atoms are,
representing a phase function, with parameter k controlling the phase smoothness and parameter λ controlling the IMF smoothness. span { } represents a spatial function,
wherein
The operator represents the largest integer less than x.
The overcomplete dictionary atom library is represented as:
wherein D represents an overcomplete dictionary atom library, ωγ(t) represents the mother wavelet of the overcomplete dictionary atom library,
in order to be the atoms after the modulation,
to control the parameter set, tcRepresents time, fcWhich is indicative of the instantaneous frequency of the frequency,
representing the instantaneous phase, Γ representing the time range and its corresponding set of frequencies and phases, a (t) representing the amplitude;
indicating the instantaneous phase.
Assuming the coal horizon position ClocationWith a time t (C)location) Distinguishing the top and bottom of the coal seam, and assuming that the top of the coal seam is (C)location)roofThe coal seam floor is (C)location)floorTherefore, the coal bed time-frequency atom matching neighborhood constraint (time range) is as follows:
δt＝[t(Clocation)-nΔt,t(Clocation)+nΔt]wherein n ═ Clocation)roof-(Clocation)floorAnd Δ t is the sampling interval of t). Therefore, the parameter searching method can be expressed as:
wherein a (t),
And f (t) is the instantaneous amplitude, instantaneous phase and instantaneous frequency of the signal; t is tcIndicating the central time corresponding to the instantaneous amplitude maximum a (t) near the location of the coal seam, f (t)c) And
respectively represent tcThe corresponding instantaneous frequency and instantaneous phase. Compared with global search, the dynamic search greatly improves the calculation efficiency and provides possibility for applying a tracking algorithm to three-dimensional coal seam data processing.
In some embodiments, the instantaneous frequency at the envelope maximum point is determined in the following manner: determining the continuous phase of said complex seismic signal; and determining the instantaneous frequency at the envelope maximum point according to the continuous phase of the complex seismic signal.
In some embodiments, determining the instantaneous frequency at the envelope maxima from the continuous phase of the complex seismic signals comprises: and determining the instantaneous frequency at the envelope maximum point by using a damped least squares method and a reshaping regularization operator according to the continuous phase of the complex seismic signal.
By means of a damped least square method, a shaping regularization operator is added to carry out smoothing processing on data, the method for solving the instantaneous frequency of the matching atoms is improved, the stable instantaneous frequency is obtained, and the main frequency of the matching atoms is obtained from the stable instantaneous frequency.
Determining instantaneous frequency based on continuous phase
The complex seismic traces of successive signals x (t) are:
in the formula: h (t) Hilbert transform of x (t); a (t) is the seismic trace envelope;
is the instantaneous phase of the signal.
Let ψ, φ be the continuous phase and the dominant phase, P, Q be the operators to calculate ψ, φ, respectively, the continuous phase being defined as:
ψ＝P(φ)＝φ+kπ (10)
wherein k is a positive integer. From formula (9):
Δψ(j)＝ψ(j+1)-ψ(j)∈(0，π] (11)
q is an operator for solving the phase of the main value, namely P is the inverse operation of Q.
φ＝f(ψ)＝x-1(ψ) (12)
P (phi) is located in the interval (-pi, pi),
Δψ(j)＝x{P[Δφ(j)]} (13)
the calculation formula of the continuous phase obtained by substituting the equation (10) is as follows:
the conventional instantaneous frequency f (t) is the instantaneous phase
Is rate of change, i.e.
Replacing the instantaneous phase with a continuous phase
Introducing a damping least square method, and adding a shaping regularization operator to obtain the instantaneous frequency:
f(t)＝[λ2I+(SV)T(SV)]-1(SV)Tl (16)
in the formula: lambda is a weight coefficient, and is generally 1 to 5 percent of the maximum value of the element in V; i is a unit vector; s is a shaping regularization operator; l and V are vector matrixes formed by l (t) and V (t) respectively.
The approximation accuracy of equation (16) will be explained below. Temporal frequency analysis of seismic signals is performed. Fig. 4a and 4b show the instantaneous frequency results obtained in the case of no noise and with a signal-to-noise ratio equal to 1, respectively. In fig. 4a and 4b, (a) to (f) are synthetic seismic signal, conventional instantaneous frequency, instantaneous frequency based on continuous phase derivation, instantaneous frequency based on continuous phase conventional damped least squares method, local frequency, and instantaneous frequency based on continuous phase modified damped least squares method, respectively. It can be seen that in fig. 4a and 4b, comparing (b) with (c), since the conventional instantaneous frequency is the derivative of the phase with respect to time, many "negative frequency" phenomena appear in the calculation result. Although the instantaneous frequency based on continuous phase derivation avoids the phenomenon of negative value, the instantaneous frequency is abnormal at 0.4-0.5 seconds and the like. In fig. 4a and 4b, (d), (f) are instantaneous frequencies found based on continuous phase direct inversion and improved damping, it can be seen that the damping least square method solves the instantaneous frequency anomaly phenomenon, and the instantaneous frequencies found by improved damping are smoother than the conventional damping, and the frequency anomaly points are more prominent; in fig. 4a and 4b, (e) is the local frequency, and as can be seen from the comparison of (e) and (f) in fig. 4a and 4b, the instantaneous frequency can better conform to the real frequency value based on the continuous phase, and the high frequency component is more prominent. From the results of fig. 4a and 4b, it can be seen that the instantaneous frequency solution based on the present disclosure can process noisy seismic data well, and is smoother relative to the local frequency curve, and highlights frequency anomaly points more, so that the present disclosure has good noise immunity.
Improved matching pursuit principle
The improved matching pursuit mathematical principle proposed herein is as follows:
wherein S is a seismic reflection gather, ωiI is 1. ltoreq. M for matching decomposed atoms (EMD atoms), n is random noise contained in actual seismic data, aiAnd D is an overcomplete dictionary atom library constructed under the constraint of seismic inversion information, and gamma represents a set of atom time, frequency and phase parameters.
Herein, the matching algorithm adopts a strategy of screening coal seam atoms in batches, firstly, a hard iteration threshold value delta needs to be set for each matching process, and atoms meeting coal seam indicator factor constraints and iteration threshold values are listed as candidate matching sets, which is specifically expressed as follows:
|Si|≥δ|kmax|,Si＝<ωi,Rs> (18)
in the formula, SiThe inner product value of the residual error of the previous iteration and the ith EMD atom is obtained; rsIs an overlapGeneration of residual vector, kmaxFor the maximum absolute value in the inner product vector for each iteration, assume that the seismic signal is based on M (M) of the match after N iterations>N) atoms forming an atom matrix JN＝[ω1,ω2,…ωM]And then, successively correcting the matched coal seam atom amplitude by using a damping least square algorithm, namely:
aN＝[(JN)T(JN)+σ2I]-1(JN)TS (19)
in the formula, aNFor the corrected amplitude after the Nth iteration, I is the identity matrix, σ2Is the damping factor. Suppose SNIs the survival signal of the Nth iteration, and the best matching atom obtained by searching is omegan+1Then S isNCan be expressed as:
SN＝S-JNan (20)
wherein ω isn+1Satisfies the following conditions:
|<SN,ωn+1>|＝supi∈(1,2,…N)|SN,ωi| (21)
and if the times of matching wavelets reach the preset times after the iterative decomposition of the N steps or the residual energy of the signals after the iterative decomposition is far lower than the energy threshold, finishing the signal decomposition. The coal seam signal can be expressed as a linear combination of N best matching atoms and a residual SN+1And (3) the sum:
for a seismic signal S containing a coal seam determined by a priori positions, effective matching decomposition is carried out as follows:
firstly, a complex seismic signal S (t) is constructed by using Hilbert transform on signals near a prior position, and a time position t at a signal envelope maximum value is determinediK (k is a maximum number), and the instantaneous frequency ω at that time position is obtainediI 1,2.. k and phase
tiAs an initial value of the central time of the mother wavelet,
as a phase of the mother wavelet
Initial value of, ωiThe corresponding frequency at the maximum value is taken as the main frequency f of the mother waveletiThe initial values of the control parameters are taken as the center, scanning is carried out on the overcomplete dictionary atom library, the accurate value of each atom can be finally determined, the atom most relevant to the original signal is searched in the determined atoms by utilizing dynamic scanning, and the dynamic dictionary atom library D' of iterative search is obtainedγi(t)}(D'∈D)，gγi(t) is an atom and D is an overcomplete dictionary atom library.
Suppose that iteration 1 requires scanning m time-frequency atoms in the dynamic dictionary atom library:
in the formula, t0Time at the search center location for each best matching wavelet; omega (t)0) And
instantaneous frequency and phase information of the signal at the search center position, respectively; u [ omega (t)0)]、 
Search paths for angular frequency and phase, respectively. Here, in the constructed dynamic dictionary atom library, k atoms may be generated in the front, but in practice only m atoms are needed for representation, k>And m, searching the least atoms capable of representing the coal seam signals as far as possible.
Time-frequency atom most relevant to signal in first iteration
Satisfy the requirement of
The signal S is represented as an edge gγ1(t) components in the direction and residual components. Namely, it is
R1And S is a residual signal after the first iteration. And sequentially iterating in the above manner until the preset iteration times and energy threshold (the energy of the n best matching atoms reaches the proportion of the total energy) are reached, and stopping iteration. And if the iteration is performed for n times, the decomposition process of the signal is completed. And if the final condition is not met, separating the 1 atom and starting the next iteration, and continuously constructing a dynamic dictionary atom library.
Based on M (M) of matching after N iterations>N) (here greater than because a coal seam signal may match multiple atoms in a constructed dynamic wavelet base) atom matrix J is formed by atomsN＝[ω1,ω2,…ωM]Then, the matched coal bed atom amplitude is corrected gradually by using a damped least square algorithm, namely aN＝[(JN)T(JN)+σ2I]-1(JN)TS，aNFor the corrected amplitude after the Nth iteration, I is the identity matrix, σ2Finally, the coal seam signal is expressed as a linear combination of n optimal matching atoms for the damping factor
In the formula: ri-1S is a residual signal after i-1 iterations; rnS is a residual signal after n iterations, and R is equal to 10S is the original signal. Finally, amplitude correction is carried out on all coal seam atoms obtained through matching, and the combination of the optimal atoms is selected to represent coal seam signals.
In the text, an over-complete dictionary is established, point-by-point iteration is realized, and a maximum value is preferentially selected in the iteration, so that the search is convenient. Firstly, establishing an over-complete atom dictionary, comparing the similarity between residual signals and atoms, and then finding out a target atom representing the minimum residual; removing the projection of the old residual signal to obtain a new residual signal; and repeating the process until the error of the decomposition result is small enough or other convergence conditions are met, stopping iteration, and completing the whole sparse decomposition process, wherein the iteration times are set according to the requirements of signal decomposition.
The improved matching pursuit coal seam reflection identification implementation flow of the application is explained below, and as shown in fig. 5, the coal seam indication factor of seismic inversion is used for accurately picking the position of a main coal seam to provide prior information for an improved matching pursuit algorithm, the search range based on the matching pursuit algorithm of an EMD dictionary is constrained, then the coal seam reflection information is matched in the EMD dictionary, quantitative characterization and identification are carried out on the coal seam reflection, the shielding effect of the coal seam reflection on effective reflection information of a target layer can be effectively eliminated, and the calculation efficiency is greatly improved. An improved matching pursuit coal seam reflection identification schematic (fig. 5) under the constraint of coal seam indication parameters is shown. In fig. 5 (a), black is prior information of coal seam index, and gray is a designed corresponding gaussian window; in fig. 5 (b), the black color is the coal seam seismic data indicated by the coal seam indicator, the light color is the corresponding gaussian window processed data, and the gray color is the coal seam seismic data matched by using the improved matching pursuit algorithm; in fig. 5 (c), the dark color is the actual seismic record, and the light color is the seismic record after the separated coal seam corresponding to the actual seismic record. The data continuity provided based on the coal seam index prior information is poor, so that a corresponding Gaussian window is designed to carry out smoothing processing on the data, the coal seam seismic records extracted by the method and the comparison between the original coal seam records and the seismic records after coal seam separation can find that the original seismic data have a good separation effect at the coal seam prior position, the coal seam reflection information is well suppressed, the processed coal seam data have good transverse continuity, and the phenomena of non-continuity of a same phase axis, waveform inversion and the like do not occur.
Authentication
The feasibility of the inversion method is further verified, and the feasibility verification comprises two parts:
the first step is as follows: a numerical experiment testing method for feasibility is characterized in that a one-dimensional thin coal seam model and a two-dimensional thin coal seam model which are complex in theory are arranged, a synthetic seismic record is built based on a conventional coal seam numerical model, firstly, under the condition of the one-dimensional thin coal seam model, coal seam identification and separation are carried out by adopting an improved matching tracking method driven by seismic inversion, the accuracy of coal seam identification is tested, then, identification is carried out on the two-dimensional thin coal seam model, and the stability of the improved method is tested.
The second step is that: the effectiveness of the method is tested through actual seismic data processing, two-dimensional seismic sections with complex geological structures and coal beds widely distributed are selected, coal bed identification and separation are carried out through the improved matching tracking method driven by seismic inversion, and stability and reliability of coal bed identification can be improved through comparison with actual logging data.
To further illustrate the feasibility and effectiveness of the present disclosure, two examples are presented below.
Example 1: theoretical model testing, see fig. 6a to 6c, and fig. 7a to 7e in detail.
To more clearly illustrate the noise immunity and reliability of the present matching pursuit algorithm, theoretical model tests were developed herein using well log data after the re-sampling of the well side-channel. In FIGS. 6a to 6c, the sandstone speed is about 4160m/s, the coal seam speed is 2850m/s, and the mudstone speed is 3885 m/s. 6a to 6c, (a) is a theoretical longitudinal wave impedance model, and (b) is a theoretical reflection coefficient sequence, wherein the reflection coefficient generated by the coal seam appears around 160 ms; (c) forward modeling of 25Hz zero phase Rake wavelets, (d) synthesis of seismic records; (e) the coal bed reflection records matched by the method are utilized; (f) seismic gathers after coal seam reflection separation are obtained; (g) for comparison of single-channel seismic records before and after processing, it is noted that (g) the thick line is the coal seam reflection identification result, and the thin line is the corresponding forward performance record of coal-bearing seismic. (d) The composite record in (f) and the matching pursuit record in (e) can show that the waveforms of the two seismic records are basically consistent, and the comparison between the front and the back of the processing gathers in (f) and (g) shows that the matching pursuit technology based on the EMD dictionary has a good separation effect on the coal seam reflection under different geological backgrounds, and the reflection information of the weak and small reflection layers is well recovered.
In the case of considering complex coal seam composite effects, in order to further verify the feasibility and effectiveness of matching tracking and identification methods based on the EMD dictionary in the context of coal seam reflection, reference is made to fig. 7a to 7 e. In FIG. 7a, a sand shale coal thin interbed longitudinal wave impedance model under the coal bed reflection background is constructed according to the actual geological conditions of a working area, the model background (light color) is large set of mudstone (average speed is 3650m/s and density is 2.6kg/m3), the middle (black color) is a widely developed thin coal bed (average speed is 2800m/s and density is 1.65kg/m3), thin sandstone (gray color) (average speed is 4500m/s and density is 2.45kg/m3), the specific parameters are shown in the table, the model size is 1200CDP in the transverse direction, the sampling time in the longitudinal direction is 800ms, figure 7b is a forward seismic record using a theoretical model of Morlet wavelet synthesis at 25Hz, therefore, the effective reflection information of the thin sand bodies is submerged by the coal seam reflection due to the influence of the widely developed thin coal seam, and the real form and position of the sand bodies are difficult to identify. Fig. 7c is a seismic record after stripping the coal seam reflection by the present method, fig. 7d is a coal seam reflection seismic record identified by the present method, and fig. 7e is a coal seam seismic record forward of a theoretical model. The comparison shows that the coal seam reflection record identified by the method is basically consistent with the theoretical model forward modeling record, the influence of the coal seam interference is obviously weakened, the seismic response comparison of the thin sandstone is well restored, the coal seam reflection seismic record identified by the method is basically consistent with the theoretical model forward modeling record, the influence of the coal seam interference is obviously weakened, and the seismic response of the thin sandstone can be restored. Fig. 7a to 7e verify that matching pursuit based on the EMD dictionary has a certain coal seam identification effect for the complex sand-mud-coal-rock thin interbed longitudinal wave impedance two-dimensional model, and comparison between the removed coal seam record and the theoretical coal seam synthetic record indicates that the method can achieve basic identification of each coal seam for the widely distributed complex sand-mud-coal model, and has a better separation effect, indicating that the method has better practicability for the matching pursuit of the complex sand-mud-coal coupled model. The comparison of the front and rear records of the coal bed can discover that the weak and small reflection information is effectively recovered on the premise of not changing the surrounding rock reflection information by matching, tracking and reflecting separation, the coal bed record matched and identified is consistent with the theoretical coal bed synthetic record, and the separated coal bed seismic record is basically consistent with the coal bed seismic record without coal bed.
Example 2: the actual data testing is shown in detail in FIGS. 8a to 8 d.
On the basis of verifying the feasibility of the method and the stability of the algorithm, the method is applied to seismic exploration examples of Z oil fields so as to verify the practicability of the method to land data. The research area has widely developed thin coal layers, the sand-coal-mud coupling mode is complex, the interference of seismic data reflected by the thin coal layers is serious, and false bright spots of lithological identification are easily caused, particularly in a mud-coal dense reflection section; the reservoir sensitive parameter prediction error based on the conventional seismic inversion is large, the reservoir sensitive parameter prediction error is easily influenced by coal bed reflection, the spatial distribution of the reservoir is difficult to implement, and in order to accurately extract the reflection information of sand bodies, the reflection of a thin coal bed needs to be identified and separated. The number of the coal seams of the work area is about 18-25, the thickness range of a single coal seam is 0.5-2m, the number of the sandstone is about 16-17, the thickness range of a single sandstone is 1-30m, and a plurality of thin coal seams are coupled with the thin sandstone. As can be seen from fig. 8a, a thin coal seam which is widely developed exists in the work area, the transverse continuity of the coal seam is poor, sandstone reflection information is submerged in coal seam reflection, and real sand bodies are difficult to identify. The coal reflection record obtained by identification is shown in fig. 8c, and analysis shows that the coal seam reflection can be effectively matched and separated, and weak reflection information near the coal seam reflection is displayed. It can be seen from fig. 8c that after the coal seam reflection separation, the weak reflection information of the sand body is relatively obvious, but there is no persuasion only from the comparison of the seismic profiles before and after the separation, in order to quantitatively describe the characteristic features of the sand body on the attributes after the coal seam reflection separation, the text compares the spectrum decomposition when the single-channel seismic records before and after the coal seam are removed, so as to further analyze the effects of the coal seam reflection layer before and after the removal, as shown in fig. 8d, after the coal seam reflection separation, the reflection information of the coal seam is suppressed, the weak information of the sand body is highlighted (at the line frame), and the feasibility of the method is further verified. The model and actual data show that the method has a good effect of stripping coal layer reflection in the earthquake records of multiple developed thin coal layers, so that the earthquake section without coal layer reflection can be used for predicting reservoir and identifying fluid, and powerful guarantee is provided for obtaining target layer information.
The researches of the example 1 and the example 2 show that the interference of the seismic effect of the widely distributed complex thin coal seam on the fluid identification result is large, and the interference cannot be ignored in the quantitative explanation of the oil-gas content of the earthquake, so that the effectiveness and the practical application prospect of the method are further verified.
The embodiment of the application also provides computer equipment. Fig. 9 is a schematic hardware configuration diagram of an implementation manner of a computer device provided in an embodiment of the present application, and as shown in fig. 9, a computer device 10 according to an embodiment of the present application includes: including at least but not limited to: a memory 11 and a processor 12 communicatively coupled to each other via a system bus. It is noted that fig. 9 only shows a computer device 10 with components 11-12, but it is to be understood that not all shown components are required to be implemented, and that more or fewer components may be implemented instead.
In this embodiment, the memory 11 (i.e., the readable storage medium) includes a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the memory 11 may be an internal storage unit of the computer device 10, such as a hard disk or a memory of the computer device 10. In other embodiments, the memory 11 may also be an external storage device of the computer device 10, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like, provided on the computer device 10. Of course, the memory 11 may also include both internal and external storage devices of the computer device 10. In this embodiment, the memory 11 is generally used for storing an operating system and various types of software installed in the computer device 10. Further, the memory 11 may also be used to temporarily store various types of data that have been output or are to be output.
The present embodiment also provides a computer-readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application store, etc., on which a computer program is stored, which when executed by a processor implements corresponding functions. The computer readable storage medium of the present embodiment is for storing program code for a method of identifying a coal seam, which when executed by a processor implements the method of identifying a coal seam.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on this understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk), and includes instructions for enabling a terminal (e.g., a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.
While the present embodiments have been described with reference to the accompanying drawings, it is not intended that the present application be limited to the specific embodiments described above, which are intended as illustrative rather than limiting, and that those of ordinary skill in the art, in light of the present disclosure, will be able to make various changes and modifications to the disclosed embodiments without departing from the spirit and scope of the present application as set forth in the appended claims.
Claims (10)
1. A method of identifying a coal seam, comprising:
acquiring seismic reflection signals;
obtaining a preset rock elasticity parameter by using the seismic reflection signal for inversion, wherein the preset rock elasticity parameter is a rock elasticity parameter sensitive to a coal bed;
determining the coal seam position according to a preset coal seam threshold value and the preset rock elastic parameter, wherein the coal seam position is a sampling time point of a seismic reflection signal;
determining the time range of seismic reflection signals corresponding to the coal seam according to the position of the coal seam;
and matching and tracking the seismic reflection signals in the time range to obtain coal seam reflection signals.
2. The method of claim 1, wherein determining a time range of seismic reflection signals corresponding to the coal seam from the coal seam location comprises:
determining a coal seam roof and a coal seam floor according to the preset rock elastic parameters, wherein the coal seam roof and the coal seam floor correspond to sampling time points of seismic reflection signals;
determining an expansion value according to the coal seam floor and the coal seam roof;
and determining the time range of the seismic reflection signal corresponding to the coal seam according to the expansion value by taking the position of the coal seam as a center.
3. The method of claim 2,
determining the expansion value according to the coal seam floor and the coal seam roof in the following mode: n × Δ t, where n × Δ t denotes the spread value, Δ t denotes a sampling interval of the seismic reflection signal, and n ═ t (C)location)roof-t(Clocation)floor) Wherein t (C)location)roofAnd t (C)location)floorRespectively representing a coal seam roof and a coal seam floor;
and determining the time range of the seismic reflection signal corresponding to the coal seam according to the expansion value by taking the coal seam position as the center and the following modes: deltat＝[t(Clocation)-n*Δt,t(Clocation)+n*Δt]Wherein, deltatRepresents said time range, t (C)location) And n x Δ t represents the spread value.
4. The method of any one of claims 1 to 3, wherein in performing match-pursuit on the seismic reflection signals within the time range, scanning is performed in an overcomplete dictionary atom library with time at an envelope maximum point of complex seismic signals of the seismic reflection signals within the time range as an initial value of a mother wavelet center time, an instantaneous phase at the envelope maximum point as an initial value of a mother wavelet phase, and an instantaneous frequency at the envelope maximum point as an initial value of a mother wavelet main frequency, wherein the overcomplete dictionary atom library is defined by an EMD eigenmode function.
5. The method of claim 4, wherein the instantaneous frequency at the envelope maximum is determined as follows:
determining a continuous phase of the complex seismic signal;
determining instantaneous frequencies at the envelope maxima from the continuous phase of the complex seismic signals.
6. The method of claim 4, wherein the EMD eigenmode function is:
wherein a (t) represents the amplitude,
which is indicative of the instantaneous phase of the phase,
which is indicative of the instantaneous frequency of the frequency,
to represent
The defined harmonic column vector constitutes a subspace, and λ represents a parameter controlling the smoothness of the function.
7. The method of claim 6, wherein the overcomplete dictionary atom library is represented as:
wherein D represents the overcomplete dictionary atom library, ωγ(t) represents the mother wavelet of the overcomplete dictionary atom library,
in order to be the atoms after the modulation,
to control the parameter set, tcRepresents time, fcWhich is indicative of the instantaneous frequency of the frequency,
representing the instantaneous phase, and Γ representing the time range and its corresponding frequency and phase.
8. The method of claim 5, wherein determining the instantaneous frequency at the envelope maxima from the continuous phase of the complex seismic signals comprises:
and determining the instantaneous frequency at the envelope maximum point by using a damped least squares method and a shaping regularization operator according to the continuous phase of the complex seismic signal.
9. A computer device, characterized in that the computer device comprises:
a memory, a processor, and a computer program stored on the memory and executable on the processor;
the computer program when executed by the processor performs the steps of the method of identifying a coal seam as claimed in any of claims 1 to 8.
10. A computer-readable storage medium, having stored thereon a program for identifying a coal seam, which program, when executed by a processor, carries out the steps of the method of identifying a coal seam according to any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110429527.6A CN113568049A (en) | 2021-04-21 | 2021-04-21 | Method and device for identifying coal seam and computer readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110429527.6A CN113568049A (en) | 2021-04-21 | 2021-04-21 | Method and device for identifying coal seam and computer readable storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113568049A true CN113568049A (en) | 2021-10-29 |
Family
ID=78161273
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110429527.6A Pending CN113568049A (en) | 2021-04-21 | 2021-04-21 | Method and device for identifying coal seam and computer readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113568049A (en) |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105044777A (en) * | 2015-07-01 | 2015-11-11 | 中国石油天然气股份有限公司 | Method of detecting elimination of seismic marked layer strong reflection amplitude based on empirical mode decomposition |
| CN105093315A (en) * | 2014-04-25 | 2015-11-25 | 中国石油化工股份有限公司 | Coal seam strong reflection signal removing method |
| WO2016041189A1 (en) * | 2014-09-19 | 2016-03-24 | 杨顺伟 | Method for evaluating shale gas reservoir and seeking desert area |
| CN106772579A (en) * | 2016-12-07 | 2017-05-31 | 中国矿业大学(北京) | Earthquake prestack inversion method and apparatus in a kind of girdle |
| CN106842298A (en) * | 2015-12-04 | 2017-06-13 | 中国石油化工股份有限公司 | A kind of unconformity strong reflection auto-adaptive separating method based on match tracing |
| CN107450100A (en) * | 2016-06-01 | 2017-12-08 | 中国石油化工股份有限公司 | Thin reservoir thickness prediction method and device |
| CN107643539A (en) * | 2016-07-21 | 2018-01-30 | 中国石油化工股份有限公司 | A kind of method that strong screen layer is peeled off based on the analysis of coal seam seismic response features |
| CN108051856A (en) * | 2017-12-14 | 2018-05-18 | 西南石油大学 | A kind of time-frequency domain Fluid Identification Method based on AMD-HHT |
| CN109709607A (en) * | 2018-12-07 | 2019-05-03 | 中国石油天然气股份有限公司 | A kind of prediction thin sandstone reservoirs thickness approach and device |
| CN111679317A (en) * | 2019-03-11 | 2020-09-18 | 中国石油天然气集团有限公司 | Seismic signal medium-frequency component extraction method, high-frequency component reconstruction method and system |
-
2021
- 2021-04-21 CN CN202110429527.6A patent/CN113568049A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105093315A (en) * | 2014-04-25 | 2015-11-25 | 中国石油化工股份有限公司 | Coal seam strong reflection signal removing method |
| WO2016041189A1 (en) * | 2014-09-19 | 2016-03-24 | 杨顺伟 | Method for evaluating shale gas reservoir and seeking desert area |
| CN105044777A (en) * | 2015-07-01 | 2015-11-11 | 中国石油天然气股份有限公司 | Method of detecting elimination of seismic marked layer strong reflection amplitude based on empirical mode decomposition |
| CN106842298A (en) * | 2015-12-04 | 2017-06-13 | 中国石油化工股份有限公司 | A kind of unconformity strong reflection auto-adaptive separating method based on match tracing |
| CN107450100A (en) * | 2016-06-01 | 2017-12-08 | 中国石油化工股份有限公司 | Thin reservoir thickness prediction method and device |
| CN107643539A (en) * | 2016-07-21 | 2018-01-30 | 中国石油化工股份有限公司 | A kind of method that strong screen layer is peeled off based on the analysis of coal seam seismic response features |
| CN106772579A (en) * | 2016-12-07 | 2017-05-31 | 中国矿业大学(北京) | Earthquake prestack inversion method and apparatus in a kind of girdle |
| CN108051856A (en) * | 2017-12-14 | 2018-05-18 | 西南石油大学 | A kind of time-frequency domain Fluid Identification Method based on AMD-HHT |
| CN109709607A (en) * | 2018-12-07 | 2019-05-03 | 中国石油天然气股份有限公司 | A kind of prediction thin sandstone reservoirs thickness approach and device |
| CN111679317A (en) * | 2019-03-11 | 2020-09-18 | 中国石油天然气集团有限公司 | Seismic signal medium-frequency component extraction method, high-frequency component reconstruction method and system |
Non-Patent Citations (6)
| Title |
|---|
| SERGEY FOMEL: "Shaping regularization in geophysical-estimation problems", vol. 72, no. 72, pages 29 * |
| THOMAS Y. HOU, ZUOQIANG SHI: "Data-driven time-frequency analysis", vol. 35, no. 35, pages 284 - 308 * |
| 刘占族;张雷;霍丽娜;曹孟起;丁清香;高广亮;: "地质统计学反演在煤层气薄储层识别中的应用", 石油地球物理勘探, no. 1, pages 30 - 36 * |
| 刘江 等: "煤相约束叠前地质统计学在东海A气田实践", 海洋石油, vol. 2020, no. 2, pages 33 - 35 * |
| 张兰 等: "东海西湖凹陷平湖组富煤环境相控储层预测技术", 现代地质, vol. 33, no. 2, pages 337 - 344 * |
| 许璐，吴笑荷，张明振，印兴耀，宗兆云: "基于局部频率约束的动态匹配追踪强反射识别与分离方法", vol. 54, no. 54, pages 587 - 593 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Wu et al. | Least-squares horizons with local slopes and multigrid correlations | |
| US5798982A (en) | Method for inverting reflection trace data from 3-D and 4-D seismic surveys and identifying subsurface fluid and pathways in and among hydrocarbon reservoirs based on impedance models | |
| US6944546B2 (en) | Method and apparatus for inversion processing of well logging data in a selected pattern space | |
| US6801473B2 (en) | Method for estimating and removing artifact noise from seismic data | |
| US8892388B2 (en) | Box counting enhanced modeling | |
| Wu et al. | Generating a relative geologic time volume by 3D graph-cut phase unwrapping method with horizon and unconformity constraints | |
| Hoyes et al. | A review of “global” interpretation methods for automated 3D horizon picking | |
| US9348047B2 (en) | Modeling of parallel seismic textures | |
| US8255165B2 (en) | Method for predicting differences in subsurface conditions | |
| CN106249299A (en) | The lower thin layer weak reflected seismic energy restoration methods of strong reflection shielding and device | |
| US20110247829A1 (en) | Tracking geologic object and detecting geologic anomalies in exploration seismic data volume | |
| US11531127B2 (en) | Methods and systems for reference-based inversion of seismic image volumes | |
| Katterbauer et al. | EMSE: Synergizing EM and seismic data attributes for enhanced forecasts of reservoirs | |
| Azevedo | Model reduction in geostatistical seismic inversion with functional data analysis | |
| Mahadik et al. | Fault detection and optimization in seismic dataset using multiscale fusion of a geometric attribute | |
| CN110954958A (en) | Crack and fault prediction method and system | |
| CN117607995A (en) | Quasi-impedance seismic lithology inversion method utilizing fusion of natural gamma and wave impedance | |
| CN113568049A (en) | Method and device for identifying coal seam and computer readable storage medium | |
| Wang et al. | Prestack inversion based on anisotropic Markov random field–maximum posterior probability inversion and its application to identify shale gas sweet spots | |
| George et al. | Bedding angle identification from BIF marker shales via modified dynamic time warping | |
| CN108983288B (en) | Oil-water identification method based on time-frequency spectrum image characteristic analysis | |
| CN113947559A (en) | Seismic attribute multi-scale fusion method and device, electronic equipment and medium | |
| Bhark | Multiscale spectral-domain parameterization for history matching in structured and unstructured grid geometries | |
| CN116774285B (en) | Thin interbed prediction method, device, equipment and medium based on characteristic curve reconstruction | |
| CN112558147B (en) | Polarization analysis method and system for microseism data in well |
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 |