CN116559957A - Amplitude-preserving processing method for ultra-deep weak reflection seismic data - Google Patents
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Abstract
The invention discloses a method for processing amplitude-preserving ultra-deep weak reflection seismic data, which is used for processing oil-gas seismic exploration and development data of ultra-deep weak reflection exploration areas and similar exploration areas, and comprises the following steps: step 1, preprocessing input data; step 2, a first-arrival high-frequency static correction processing step; 3. step progressive wave field separation processing, co-channel domain background noise consistency processing and 2.5DRNA denoising processing; step 4, speed analysis, residual static correction and amplitude compensation processing; step 5, tandem multiple pressing treatment; step 6, high-precision speed modeling processing; step 7, a prestack depth migration imaging processing step; 8. outputting the data. The invention has the advantages that: the imaging quality and the fidelity of the ultra-deep weak reflection seismic data processing are improved, reliable basic data are provided for lithology prediction and interpretation, and a favorable technical support is provided for efficient exploration and development.
Description
Technical Field
The invention relates to the field of geophysical exploration seismic data processing, in particular to an ultra-deep weak reflection seismic data amplitude-preserving processing method.
Background
With the continuous deep development work of seismic exploration, the quality requirements on the ultra-deep weak reflection seismic exploration data processing results are higher and higher. Because the ultra-deep wave field of the seismic data is complex, the energy is weak, the signal to noise ratio is low, and especially under the conditions that a strong wave impedance interface exists in the layer of the detection zone and multiple waves develop, the problem of low processing quality of the ultra-deep layer is more remarkable. The existing processing technology and processing flow are adopted to process ultra-deep weak reflection seismic data, so that the problems of incomplete signal-to-noise separation, incomplete multiple suppression, low weak reflection signal-to-noise ratio, low speed modeling precision, low imaging quality (namely 'two incomplete layers and three low layers') and the like are difficult to properly solve, and the fidelity of the quality and amplitude of the processed imaging is difficult to meet the interpretation requirement.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention provides an ultra-deep weak reflection seismic data amplitude-preserving processing method which is used for processing oil-gas seismic exploration and development data of an ultra-deep weak reflection detection area and similar detection areas, mainly solves the problem of processing the ultra-deep weak reflection seismic data of 'two incomplete and three low', and achieves the purpose of improving the imaging quality and the fidelity of the ultra-deep weak reflection seismic data processingAims of (a)Reliable basic data is provided for lithology prediction interpretation, and favorable technical support is provided for realizing efficient exploration and development.
In order to solve the technical problems, the invention adopts the following technical scheme:
a method for processing the amplitude-preserving of ultra-deep weak reflection seismic data is used for processing the oil-gas seismic exploration and development data of ultra-deep weak reflection exploration areas and similar exploration areas, and comprises the following steps:
step 1, preprocessing input data;
step 2, performing first-arrival high-frequency static correction processing on the data preprocessed in the step 1 to recover a seismic wave field;
step 3, carrying out step-by-step progressive wave field separation processing, co-channel domain background noise consistency processing and 2.5DRNA denoising processing on the data subjected to the first-arrival high-frequency static correction processing in step 2 so as to improve the signal-to-noise ratio of the weak reflection wave;
step 4, performing multiple iteration processing of speed analysis and residual static correction on the seismic data processed in the step 3, and then adopting surface consistency signal amplitude compensation processing;
step 5, performing surface multiple suppression, back scattering interlayer multiple suppression and high-precision radon multiple suppression on the seismic data processed in the step 4 to improve quality and amplitude fidelity of ultra-deep imaging;
step 6, building a prestack depth migration velocity model of the seismic data processed in the step 5;
step 7,By means ofStep 6, establishing a prestack depth migration velocity modelAndthe prestack depth migration method carries out migration operation imaging processing to realize deep weak reflectionHigh heightImaging the quality;
and 8, outputting the data processed in the previous steps 1-7 to obtain processed result data.
Further, the first arrival high-frequency static correction processing comprises the steps of performing tomographic inversion to calculate a static correction value, and performing interactive calculation on the static correction value required for leveling the first arrival wave in a common shot point domain, a common receiving point domain and a common offset distance domain respectively on the basis of small smooth imaging plane estimation.
Further, the step-by-step progressive wave field separation processing comprises the following steps:
B1. coarse separation: inputting the seismic data which are processed in the step 1 and the step 2 and have wave field separation processing conditions, and selecting a proper denoising processing technology according to noise characteristics so as to realize rough separation of effective waves and noise;
B2. fine separation: aiming at the problem that part of effective wave remains in the noise data processed and output by the first wave field B1, the effective wave and the noise are finely separated by adopting a frequency division noise suppression method;
B3. subtracting and extracting: and (3) subtracting the pure noise data processed by the B1 and the B2 from the data before denoising by adopting a subtraction method so as to realize high-precision wave field separation.
Furthermore, the common-channel domain background noise consistency processing is to utilize the noise amplitude compensation data after the step-by-step progressive wave field separation processing to carry out frequency division amplitude suppression in the common-channel domain, so that the background noise amplitude and the frequency are relatively consistent, and then the background noise amplitude and the frequency are combined with the signal to obtain a prestack gather.
Further, the 2.5DRNA denoising process is to sort the seismic data according to the station number of the shot, wherein the station number direction is used as a main line, the detection station number direction of each shot is used as a connecting line to form a three-dimensional data body, and a three-dimensional FX prediction method is applied to separate coherent wave fields from incoherent noise so as to realize the denoising of the dataNoise (S)And (5) processing.
Further, the surface multiples are multiples associated with free surfaces in the predicted compressional seismic data.
Further, the back scattering interbed multiples suppression is predictive suppression of interbed multiples of the same level in the seismic data that are related to all formation interfaces.
Further, the high-precision radon multiple suppression is used for suppressing the residual multiple in the seismic data, and comprises the following steps:
C1：the corrected data is moved with the velocity of the primary or multiple,transforming the seismic data in the (t, x) domain to the (τ, p) domain by radon transform;
c2: by utilizing the dynamic correction time difference existing between the primary wave and the multiple wave,they are in different regions in the Tau-P domain, the area of the primary wave is resected,multiple separation in the (τ, p) domainOut of the way；
And C3: the radon inverse transform is back into the (t, x) domain and the multiples are subtracted from the seismic record using an adaptive subtraction method.
Further, the establishing of the prestack depth migration velocity model comprises the following steps:
d1: establishing an initial velocity model by using geological structure constraint, namely comprehensively utilizing regional geological structure, drilling logging information, shallow surface velocity and time migration velocity, and establishing the initial velocity model;
d2: optimizing the speed model of the D1 by horizon constrained grid tomography inversion, namely establishing and optimizing a shallow-middle layer speed model by utilizing CIP gather automatic residual delay pickup and a tomography equation;
d3: establishing a deep stratum velocity model by multi-information constraint, namely jointly utilizing heavy-magnetic-electric investigation data and seismic imaging data, sketching a stratum outline through processing and interpretation integration, establishing a deep stratum frame model by heavy-magnetic-electric joint inversion interpretation results, researching a deep background velocity spreading rule, and constructing a deep stratum velocity model;
d4: the shallow-medium-deep speed model is integrated, namely, the shallow-medium-speed model of D2 and the deep-layer speed model of D3 are organically spliced;
d5: and performing grid chromatography inversion iterative optimization on the integrated speed model to obtain a final prestack depth migration speed model.
The beneficial effects of the invention are as follows:
the processing method can better solve the processing problems of incomplete signal-to-noise separation, incomplete multiple suppression, low signal-to-noise ratio of the weak reflected waves, low speed modeling precision, low imaging quality and the like in the ultra-deep weak reflected wave seismic data processing, can remarkably improve the quality and amplitude fidelity of ultra-deep weak reflected wave imaging, can provide reliable basic data for lithology prediction and interpretation, and has good application prospect.
Drawings
FIG. 1 is a flow chart of an ultra-deep weak reflection seismic data processing;
FIG. 2 is a schematic view of a small smooth imaging surface;
FIG. 3 is a diagram showing the effect of the first-arrival high-frequency static correction process;
FIG. 4 is a diagram of the effect of a step-wise progressive seismic wavefield separation process;
FIG. 5 is a graph of the effect of co-channel domain background noise consistency processing;
FIG. 6 is a graph of 2.5DRNA treatment effect;
FIG. 7 is a diagram ofSurface of the bodyA graph of multiple pressing treatment effects;
FIG. 8 is a diagram ofSurface of the bodyTwo graphs of the multiple pressing treatment effect;
FIG. 9 is a graph showing the effect of multiple pressing treatment between backscatter layers;
FIG. 10 is a graph of the effect of high-precision radon multiple pressing;
FIG. 11 is a graph of a conventional velocity modeling (shallow middle layer) and multi-information constrained velocity modeling (deep layer) splice effect;
FIG. 12 is a graph comparing the effects of the prior art process and the process of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and the detailed description below, in order to make the objects, technical solutions and advantages of the present invention more clear and distinct. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the matters related to the present invention are shown in the accompanying drawings.
The invention provides a method for processing the amplitude-preserving of ultra-deep weak reflection seismic data, which is used for processing the oil-gas seismic exploration and development data of ultra-deep weak reflection exploration areas and similar exploration areas, as shown in figures 1 to 12, and comprises the following steps:
step 1, preprocessing input data: inputting seismic data, and carrying out pretreatment such as observation system definition, automatic first arrival pickup, spherical diffusion amplitude compensation, deconvolution and the like;
step 2, performing first-arrival high-frequency static correction processing on the data preprocessed in the step 1 to recover the seismic wave field, improving the coherence of the effective wave and laying a good foundation for implementing fine signal-to-noise separation and multiple suppression processing; as in figure 2Feeding of Out ofSmall smooth imaging surfaceSchematic representationA figure; FIG. 3 is a diagram showing the effect of the first-arrival high-frequency static correction process; after the first-arrival high-frequency static correction treatment, the method can better solve the problem of medium-short wavelength static correction, eliminate the high-frequency jitter phenomenon of the first-arrival jump time, and approximately consider that the seismic data are excited and received on a small smooth surface of the earth surface, and shallow refraction and refraction multiple at the moment recover better linear relation and better hyperbolic relation of the same phase axis of the seismic reflection, thus being a subsequent step-by-step transferThe method lays a good foundation for the separation treatment of the wave field;
step 3, carrying out step-by-step progressive wave field separation processing, co-channel domain background noise consistency processing and 2.5DRNA denoising processing on the data subjected to the first-arrival high-frequency static correction processing in step 2 so as to improve the signal-to-noise ratio of the weak reflection wave;
after the signal space energy is consistent due to the surface consistency amplitude compensation, single shot background noise energy is often inconsistent, prestack migration noise is generated, and migration imaging effect and amplitude fidelity are affected; therefore, by the serial application of the co-channel domain background noise consistency processing and the 2.5DRNA denoising processing, the signal to noise ratio of the weak reflection wave can be better improved, and the problem of low signal to noise ratio of the weak reflection wave can be better solved, as shown in fig. 5, which is a co-channel domain background noise consistency processing effect diagram, and fig. 6, which is a 2.5DRNA processing effect diagram;
step 4, performing multiple iterative processes of speed analysis and residual static correction on the seismic data processed in the step 3, so that the accuracy of the speed analysis and residual static correction can be improved; then, the amplitude compensation processing of the earth surface consistency signal is adopted, so that the amplitude fidelity of the seismic data imaging is improved;
step 5, performing surface multiple suppression, back scattering interlayer multiple suppression and high-precision radon multiple suppression on the seismic data processed in the step 4 to improve quality and amplitude fidelity of ultra-deep imaging;
the characteristics of the surface multiple and the interlayer multiple are different, the corresponding multiple prediction and pressing method is adopted, and the method adoptsSurface multipleThe method has the advantages that the problems of incomplete suppression of multiple waves can be well solved by organically combining suppression treatment, multiple wave suppression treatment between backscatter layers and high-precision radon multiple wave suppression treatment technology, the accurate suppression of multiple waves is realized, the signal-to-noise ratio of weak reflection is improved, and the method is suitable for amplitude preservation treatment of ultra-deep weak reflection seismic data; as shown in FIG. 7 and FIG. 8Surface multipleA pressing treatment effect diagram is shown in fig. 9, and is shown in fig. 10;
step 6, building a prestack depth migration velocity model of the seismic data processed in the step 5; the method adopts a five-step speed modeling technology combining heavy magnetic-electric vibration combined speed modeling with other speed modeling technologies, and better solves the problem of low accuracy of ultra-deep speed modeling;
step 7,By means ofStep 6, establishing a prestack depth migration velocity modelAndthe prestack depth migration method performs migration operation imaging processing, well solves the problem of low imaging quality, and realizes ultra-deep weak reflection seismic data processingHigh height Quality ofImaging, as shown in FIG. 12, is a graph comparing the effects of the prior art process with the inventive process;
and 8, outputting the data processed in the previous steps 1-7 to obtain processed result data.
The method realizes the processing of 'two incomplete and three low' of the ultra-deep weak reflection seismic data, thereby improving the imaging quality and the fidelity of the ultra-deep weak reflection seismic data processing, providing reliable basic data for lithology prediction and interpretation, and providing favorable technical support for realizing efficient exploration and development.
Further, the first arrival high-frequency static correction processing comprises the steps of performing tomographic inversion to calculate a static correction value, and performing interactive calculation on the static correction value required for leveling the first arrival wave in a common shot point domain, a common receiving point domain and a common offset distance domain respectively on the basis of small smooth imaging plane estimation.
Further, the step-by-step progressive wave field separation process comprises the following steps:
B1. coarse separation: inputting the seismic data which are processed in the step 1 and the step 2 and have wave field separation processing conditions, and selecting a proper denoising processing technology according to noise characteristics so as to realize rough separation of effective waves and noise;
B2. fine separation: aiming at the problem that part of effective wave remains in the noise data processed and output by the first wave field B1, the effective wave and the noise are finely separated by adopting a frequency division noise suppression method;
B3. subtracting and extracting: adopting a subtraction method to process, and subtracting the pure noise data processed by the B1 and the B2 from the data before denoising so as to realize high-precision wave field separation;
the step progressive wave field separation treatment is adopted to achieve the purpose of not losing weak effective reflection signals in the noise suppression treatment process, the influence of static correction and separation algorithm on the signal-to-noise separation effect is fully considered, the problem that the signal-to-noise separation is not thorough is solved well, and a step progressive seismic wave field separation treatment effect diagram is shown in fig. 4.
Further, the common-channel domain background noise consistency processing is to utilize the noise amplitude compensation data after the step-by-step progressive wave field separation processing to carry out frequency division amplitude suppression in the common-channel domain, so that the background noise amplitude and the frequency are relatively consistent, and then the background noise amplitude and the frequency are combined with the signal to obtain a prestack gather.
Further, 2.5DRNA denoising treatment is to sort seismic data according to shot station numbers, take the shot station number direction as a main line and the detection station number direction of each shot as a tie line to form a three-dimensional data body, apply a three-dimensional FX prediction method to separate coherent wave fields from incoherent noise so as to realize data denoisingNoise (S)And (5) processing.
Further, surface multiples are multiples in the predicted compressional seismic data that are related to free surfaces.
Further, the back-scattered interbed multiples suppression predicts the same-level interbed multiples in the suppressed seismic data associated with all formation interfaces.
Further, the high-precision radon multiples suppression is used for suppressing the residual multiples in the seismic data, and comprises the following steps:
C1：the corrected data is moved with the velocity of the primary or multiple,transforming the seismic data in the (t, x) domain to the (τ, p) domain by radon transform;
c2: by utilizing the dynamic correction time difference existing between the primary wave and the multiple wave,they are in different regions in the Tau-P domain, the area of the primary wave is resected,multiple separation in the (τ, p) domainOut of the way；
And C3: the Latin inverse transformation is returned to the (t, x) domain, and the multiple wave is subtracted from the seismic record by adopting an adaptive subtraction method;
the high-precision Ladong multiple pressing treatment is to perform Ladong transformation to a Tau-P domain after velocity motion correction of the primary wave or the multiple wave according to the difference of motion correction time difference of the multiple wave and the primary wave, wherein the primary wave and the multiple wave are in different areas in the Tau-P domain, and the method comprises the following steps ofOnce-throughThe wave region is ablated and inversely transformed back into the time domain,using adaptive subtraction to multiply from seismic The subtraction is done in the recording and,thereby realizing the suppression of multiple waves.
Further, the establishment of the prestack depth migration velocity model comprises the following steps:
d1: establishing an initial velocity model by using geological structure constraint, namely comprehensively utilizing regional geological structure, drilling logging information, shallow surface velocity and time migration velocity, and establishing the initial velocity model;
d2: optimizing the speed model of the D1 by horizon constrained grid tomography inversion, namely establishing and optimizing a shallow-middle layer speed model by utilizing CIP gather automatic residual delay pickup and a tomography equation;
d3: establishing a deep stratum velocity model by multi-information constraint, namely jointly utilizing heavy-magnetic-electric investigation data and seismic imaging data, sketching a stratum outline through processing and interpretation integration, establishing a deep stratum frame model by heavy-magnetic-electric joint inversion interpretation results, researching a deep background velocity spreading rule, and constructing a deep stratum velocity model;
d4: the shallow-medium-deep speed model is integrated, namely, the shallow-medium-speed model of D2 and the deep-layer speed model of D3 are organically spliced; the two parts of speed modeling are organically spliced in a processing and interpretation integrated speed modeling mode, so that the combined speed modeling of heavy magnetic and electric shock is realized, and the accuracy of ultra-deep speed modeling is improved; FIG. 11 is a graph showing the splicing effect of conventional velocity modeling (shallow middle layer) and multi-information constraint velocity modeling (deep layer);
d5: and performing grid chromatography inversion iterative optimization on the integrated speed model to obtain a final prestack depth migration speed model.
As shown in fig. 3 to 12, this embodiment is a process item of imaging three-dimensional seismic data of some ultra-deep weak reflection. The surface topography of the detection area can be divided into three zones, namely a small sand hill, a large sand hill, a populus forest and a ground surface height difference of 100 meters, the large sand hill is taken as a main part, the single shot of the large sand hill topography has low relative signal to noise ratio, the same-phase axis torsion is obvious, the interference wave takes the strong surface wave as a main part, the multiple wave is very developed, the middle layer has a strong wave impedance interface, the shielding effect is strong, the ultra-deep reflected wave energy is weak, the signal to noise ratio is low, the high-quality imaging difficulty is high, and the ultra-deep weak effective signal imaging processing technology and the processing flow are researched, so that the ultra-deep weak reflected seismic data processing method is discovered, and the ideal processing effect is obtained by adopting the processing method.
The above embodiments are only for illustrating the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement the same, and are not intended to limit the scope of the present invention. All equivalent changes or modifications made in accordance with the essence of the present invention are intended to be included within the scope of the present invention.
Claims (9)
1. The amplitude-preserving processing method for the ultra-deep weak reflection seismic data is characterized by comprising the following steps of:
step 1, preprocessing input data;
step 2, performing first-arrival high-frequency static correction processing on the data preprocessed in the step 1 to recover a seismic wave field;
step 3, carrying out step-by-step progressive wave field separation processing, co-channel domain background noise consistency processing and 2.5DRNA denoising processing on the data subjected to the first-arrival high-frequency static correction processing in step 2 so as to improve the signal-to-noise ratio of the weak reflection wave;
step 4, performing multiple iteration processing of speed analysis and residual static correction on the seismic data processed in the step 3, and then adopting surface consistency signal amplitude compensation processing;
step 5, performing surface multiple suppression, back scattering interlayer multiple suppression and high-precision radon multiple suppression on the seismic data processed in the step 4 to improve quality and amplitude fidelity of ultra-deep imaging;
step 6, building a prestack depth migration velocity model of the seismic data processed in the step 5;
step 7, performing migration operation imaging processing by using the pre-stack depth migration velocity model and the pre-stack depth migration method established in the step 6 so as to realize high-quality imaging of deep weak reflection;
and 8, outputting the data processed in the previous steps 1-7 to obtain processed result data.
2. The ultra-deep weak reflection seismic data amplitude-preserving processing method of claim 1, wherein the method comprises the following steps: the first arrival high-frequency static correction processing comprises the steps of performing tomographic inversion to calculate a static correction value, and performing interactive calculation on the static correction value required for leveling the first arrival wave in a common shot point domain, a common receiving point domain and a common offset distance domain respectively on the basis of small smooth imaging plane estimation.
3. The ultra-deep weak reflection seismic data amplitude-preserving processing method of claim 1, wherein the method comprises the following steps: the step-by-step progressive wave field separation processing comprises the following steps:
B1. coarse separation: inputting the seismic data which are processed in the step 1 and the step 2 and have wave field separation processing conditions, and selecting a proper denoising processing technology according to noise characteristics so as to realize rough separation of effective waves and noise;
B2. fine separation: aiming at the problem that part of effective wave remains in the noise data processed and output by the first wave field B1, the effective wave and the noise are finely separated by adopting a frequency division noise suppression method;
B3. subtracting and extracting: and (3) subtracting the pure noise data processed by the B1 and the B2 from the data before denoising by adopting a subtraction method so as to realize high-precision wave field separation.
4. The ultra-deep weak reflection seismic data amplitude-preserving processing method of claim 1, wherein the method comprises the following steps: the common-channel domain background noise consistency processing is to utilize noise amplitude compensation data after the step-by-step progressive wave field separation processing to carry out frequency division amplitude suppression in the common-channel domain, so that the background noise amplitude and the frequency are relatively consistent, and then the background noise amplitude and the frequency are combined with signals to obtain a prestack gather.
5. The ultra-deep weak reflection seismic data amplitude-preserving processing method of claim 1, wherein the method comprises the following steps: the 2.5DRNA denoising processing is to sort seismic data according to shot station numbers, take the shot station number direction as a main line and the detection station number direction of each shot as a tie line to form a three-dimensional data body, and apply a three-dimensional FX prediction method to separate coherent wave fields from incoherent noise so as to realize denoising processing of the data.
6. The ultra-deep weak reflection seismic data amplitude-preserving processing method of claim 1, wherein the method comprises the following steps: the surface multiples suppression is predictive suppression of multiples in seismic data that are related to free surfaces.
7. The ultra-deep weak reflection seismic data amplitude-preserving processing method of claim 1, wherein the method comprises the following steps: the back scattering interbed multiples suppression is predictive suppression of interbed multiples of the same level in the seismic data that are related to all stratum interfaces.
8. The ultra-deep weak reflection seismic data amplitude-preserving processing method of claim 1, wherein the method comprises the following steps: the high-precision radon multiple suppression is used for suppressing the residual multiple in the seismic data, and comprises the following steps:
c1: transforming the seismic data in the (t, x) domain into the (τ, p) domain by radon transform using the velocity-motion corrected data of the primary or multiple;
c2: utilizing the dynamic correction time difference between the primary wave and the multiple wave, wherein the dynamic correction time difference is positioned in different areas in a Tau-P domain, the area of the primary wave is cut off, and the multiple wave is separated in a (Tau, P) domain;
and C3: the radon inverse transform is back into the (t, x) domain and the multiples are subtracted from the seismic record using an adaptive subtraction method.
9. The ultra-deep weak reflection seismic data amplitude-preserving processing method of claim 1, wherein the method comprises the following steps: the establishment of the prestack depth migration velocity model comprises the following steps:
d1: establishing an initial velocity model by using geological structure constraint, namely comprehensively utilizing regional geological structure, drilling logging information, shallow surface velocity and time migration velocity, and establishing the initial velocity model;
d2: optimizing the speed model of the D1 by horizon constrained grid tomography inversion, namely establishing and optimizing a shallow-middle layer speed model by utilizing CIP gather automatic residual delay pickup and a tomography equation;
d3: establishing a deep stratum velocity model by multi-information constraint, namely jointly utilizing heavy-magnetic-electric investigation data and seismic imaging data, sketching a stratum outline through processing and interpretation integration, establishing a deep stratum frame model by heavy-magnetic-electric joint inversion interpretation results, researching a deep background velocity spreading rule, and constructing a deep stratum velocity model;
d4: the shallow-medium-deep speed model is integrated, namely, the shallow-medium-speed model of D2 and the deep-layer speed model of D3 are organically spliced;
d5: and performing grid chromatography inversion iterative optimization on the integrated speed model to obtain a final prestack depth migration speed model.
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