According to the Chapman multi-scale rock physical model, the seismic response characteristics vary for different fluid-saturated reservoirs. For class I AVO reservoirs and gas-saturation, the seismic response is a hi...According to the Chapman multi-scale rock physical model, the seismic response characteristics vary for different fluid-saturated reservoirs. For class I AVO reservoirs and gas-saturation, the seismic response is a high-frequency bright spot as the amplitude energy shifts. However, it is a low-frequency shadow for the Class III AVO reservoirs saturated with hydrocarbons. In this paper, we verified the high-frequency bright spot results of Chapman for the Class I AVO response using the frequency-dependent analysis of a physical model dataset. The physical model is designed as inter-bedded thin sand and shale based on real field geology parameters. We observed two datasets using fixed offset and 2D geometry with different fluid- saturated conditions. Spectral and time-frequency analyses methods are applied to the seismic datasets to describe the response characteristics for gas-, water-, and oil-saturation. The results of physical model dataset processing and analysis indicate that reflection wave tuning and fluid-related dispersion are the main seismic response characteristic mechanisms. Additionally, the gas saturation model can be distinguished from water and oil saturation for Class I AVO utilizing the frequency-dependent abnormal characteristic. The frequency-dependent characteristic analysis of the physical model dataset verified the different spectral response characteristics corresponding to the different fluid-saturated models. Therefore, by careful analysis of real field seismic data, we can obtain the abnormal spectral characteristics induced by the fluid variation and implement fluid detection using seismic data directly.展开更多
Seismic deconvolution plays an important role in the seismic characterization of thin-layer structures and seismic resolution enhancement.However,the trace-by-trace processing strategy is applied and ignores the spati...Seismic deconvolution plays an important role in the seismic characterization of thin-layer structures and seismic resolution enhancement.However,the trace-by-trace processing strategy is applied and ignores the spatial connection along seismic traces,which gives the deconvolved result strong ambiguity and poor spatial continuity.To alleviate this issue,we developed a structurally constrained deconvolution algorithm.The proposed method extracts the refl ection structure characterization from the raw seismic data and introduces it to the multichannel deconvolution algorithm as a spatial refl ection regularization.Benefi ting from the introduction of the reflection regularization,the proposed method enhances the stability and spatial continuity of conventional deconvolution methods.Synthetic and field data examples confi rm the correctness and feasibility of the proposed method.展开更多
Conventional time-space domain and frequency-space domain prediction filtering methods assume that seismic data consists of two parts, signal and random noise. That is, the so-called additive noise model. However, whe...Conventional time-space domain and frequency-space domain prediction filtering methods assume that seismic data consists of two parts, signal and random noise. That is, the so-called additive noise model. However, when estimating random noise, it is assumed that random noise can be predicted from the seismic data by convolving with a prediction error filter. That is, the source-noise model. Model inconsistencies, before and after denoising, compromise the noise attenuation and signal-preservation performances of prediction filtering methods. Therefore, this study presents an inversion-based time-space domain random noise attenuation method to overcome the model inconsistencies. In this method, a prediction error filter （PEF）, is first estimated from seismic data; the filter characterizes the predictability of the seismic data and adaptively describes the seismic data＇s space structure. After calculating PEF, it can be applied as a regularized constraint in the inversion process for seismic signal from noisy data. Unlike conventional random noise attenuation methods, the proposed method solves a seismic data inversion problem using regularization constraint; this overcomes the model inconsistency of the prediction filtering method. The proposed method was tested on both synthetic and real seismic data, and results from the prediction filtering method and the proposed method are compared. The testing demonstrated that the proposed method suppresses noise effectively and provides better signal-preservation performance.展开更多
Edge reflections are inevitable in numerical modeling of seismic wavefields, and they are usually attenuated by absorbing boundary conditions. However, the commonly used perfectly matched layer （PML） boundary condit...Edge reflections are inevitable in numerical modeling of seismic wavefields, and they are usually attenuated by absorbing boundary conditions. However, the commonly used perfectly matched layer （PML） boundary condition requires special treatment for the absorbing zone, and in three-dimensional （3D） modeling, it has to split each variable into three corresponding variables, which increases the computing time and memory storage. In contrast, the hybrid absorbing boundary condition （HABC） has the advantages such as ease of implementation, less computation time, and near-perfect absorption; it is thus able to enhance the computational efficiency of 3D elastic wave modeling. In this study, a HABC is developed from two-dimensional （2D） modeling into 3D modeling based on the I st Higdon one way wave equations, and a HABC is proposed that is suitable for a 3D elastic wave numerical simulation. Numerical simulation results for a homogenous model and a complex model indicate that the proposed HABC method is more effective and has better absorption than the traditional PML method.展开更多
There are complex heterogeneous entities in the underground medium,and the heterogeneous scale has a substantial impact on wave propagation.In this study,we used a set of 11 samples of glass beads as high-velocity het...There are complex heterogeneous entities in the underground medium,and the heterogeneous scale has a substantial impact on wave propagation.In this study,we used a set of 11 samples of glass beads as high-velocity heterogeneous bodies to evaluate the impact of such heterogeneous bodies on the propagation of P-wave.We vary the heterogeneous scale by changing the diameter of the glass beads from 0.18 to 11 mm while keeping the same volume proportion(10%)of the beads for the set of 11 samples.The pulse transmission method was used to record measurements at the ultrasonic frequencies of 0.34,0.61,and 0.84 MHz in the homogeneous matrix.The relationship between P-wave fi eld features and heterogeneity scale,P-wave velocity,and the multiple of the wave number and heterogeneous scale(ka)was observed in the laboratory,which has sparked widespread interest and research.Heterogeneous scale affects P-wave propagation,and its wave field changes are complex.The waveform,amplitude,and velocity of the recorded P-waves correlate with the heterogeneous scale.For the forward scattering while large-scale heterogeneities,noticeable direct and diff racted waves are observed in the laboratory,which indicates that the infl uence of direct and diff racted waves cannot be ignored for large-scale heterogeneities.The relationship between velocity and ka shows frequency dependence;the reason is that the magnitude of change in velocity caused by wave number is diff erent from that caused by heterogeneous scale.According to the change in the recorded waveform,amplitude variation,or the relationship between the velocity measured at diff erent frequencies and the heterogeneous scale,the identifi ed turning points of the ray approximation are all around ka=10.When ka is less than 1,the velocity changes slowly and gradually approaches the eff ective medium velocity.The ray velocity measured for heterogeneous media with large velocity perturbations in the laboratory is signifi cantly smaller than the velocity predicted by the perturbation theory.展开更多
The model-driven inversion method and data-driven prediction method are eff ective to obtain velocity and density from seismic data.The former necessitates initial models and cannot provide high-resolution inverted pa...The model-driven inversion method and data-driven prediction method are eff ective to obtain velocity and density from seismic data.The former necessitates initial models and cannot provide high-resolution inverted parameters because it primarily employs medium-frequency information from seismic data.The latter can predict parameters with high resolution,but it require a signifi cant number of accurate training samples,which are typically in limited supply.To solve the problems mentioned for these two methods,we propose a model-data-driven AVO inversion method based on multiple objective functions.The proposed method implements network training,network optimization,and network inversion by using three independent objective functions.Tests on synthetic and fi eld data show that the proposed method can invert high-accuracy and high-resolution velocity and density with a few training samples.展开更多
The conventional fast converted-wave imaging method directly uses backward Pand converted S-wavefield to produce joint images. However, this image is accompanied by strong background noises, because the wavefi elds in...The conventional fast converted-wave imaging method directly uses backward Pand converted S-wavefield to produce joint images. However, this image is accompanied by strong background noises, because the wavefi elds in all propagation directions contribute to it. Given this issue, we improve the conventional imaging method in the two aspects. First, the amplitude-preserved P-and S-wavef ield are obtained by using an improved space-domain wavef ield separation scheme to decouple the original elastic wavef ield. Second, a convertedwave imaging condition is constructed based on the directional-wavefield separation and only the wavefields propagating in the same directions used for cross-correlation imaging, resulting in effectively eliminating the imaging artifacts of the wavefields with different directions;Complex-wavefi eld extrapolation is adopted to decompose the decoupled P-and S-wavefield into directional-wavefields during backward propagation, this improves the eff iciency of the directional-wavef ield separation. Experiments on synthetic data show that the improved method generates more accurate converted-wave images than the conventional one. Moreover, the improved method has application potential in micro-seismic and passive-source exploration due to its source-independent characteristic.展开更多
基金supported by the National Science and Technology Major Project (No. 2011ZX05019-008)the National Natural Science Foundation of China (No. 41074080)+1 种基金the Science Foundation of China University of Petroleum, Beijing (No. KYJJ2012-05-11)supported by the CNPC international collaboration program through the Edinburgh Anisotropy Project (EAP) of the British Geological Survey (BGS) and the CNPC Key Geophysical Laboratory at the China University of Petroleum and CNPC geophysical prospecting projects for new method and technique research
文摘According to the Chapman multi-scale rock physical model, the seismic response characteristics vary for different fluid-saturated reservoirs. For class I AVO reservoirs and gas-saturation, the seismic response is a high-frequency bright spot as the amplitude energy shifts. However, it is a low-frequency shadow for the Class III AVO reservoirs saturated with hydrocarbons. In this paper, we verified the high-frequency bright spot results of Chapman for the Class I AVO response using the frequency-dependent analysis of a physical model dataset. The physical model is designed as inter-bedded thin sand and shale based on real field geology parameters. We observed two datasets using fixed offset and 2D geometry with different fluid- saturated conditions. Spectral and time-frequency analyses methods are applied to the seismic datasets to describe the response characteristics for gas-, water-, and oil-saturation. The results of physical model dataset processing and analysis indicate that reflection wave tuning and fluid-related dispersion are the main seismic response characteristic mechanisms. Additionally, the gas saturation model can be distinguished from water and oil saturation for Class I AVO utilizing the frequency-dependent abnormal characteristic. The frequency-dependent characteristic analysis of the physical model dataset verified the different spectral response characteristics corresponding to the different fluid-saturated models. Therefore, by careful analysis of real field seismic data, we can obtain the abnormal spectral characteristics induced by the fluid variation and implement fluid detection using seismic data directly.
基金National Key R&D Program of China(No.2018YFA0702504)the National Natural Science Foundation of China(Nos.42074141,41874141)the Strategic Cooperation Technology Projects of CNPC and CUP(ZLZX2020-03).
文摘Seismic deconvolution plays an important role in the seismic characterization of thin-layer structures and seismic resolution enhancement.However,the trace-by-trace processing strategy is applied and ignores the spatial connection along seismic traces,which gives the deconvolved result strong ambiguity and poor spatial continuity.To alleviate this issue,we developed a structurally constrained deconvolution algorithm.The proposed method extracts the refl ection structure characterization from the raw seismic data and introduces it to the multichannel deconvolution algorithm as a spatial refl ection regularization.Benefi ting from the introduction of the reflection regularization,the proposed method enhances the stability and spatial continuity of conventional deconvolution methods.Synthetic and field data examples confi rm the correctness and feasibility of the proposed method.
基金supported by the National Natural Science Foundation of China(No.41474109)the China National Petroleum Corporation under grant number 2016A-33
文摘Conventional time-space domain and frequency-space domain prediction filtering methods assume that seismic data consists of two parts, signal and random noise. That is, the so-called additive noise model. However, when estimating random noise, it is assumed that random noise can be predicted from the seismic data by convolving with a prediction error filter. That is, the source-noise model. Model inconsistencies, before and after denoising, compromise the noise attenuation and signal-preservation performances of prediction filtering methods. Therefore, this study presents an inversion-based time-space domain random noise attenuation method to overcome the model inconsistencies. In this method, a prediction error filter （PEF）, is first estimated from seismic data; the filter characterizes the predictability of the seismic data and adaptively describes the seismic data＇s space structure. After calculating PEF, it can be applied as a regularized constraint in the inversion process for seismic signal from noisy data. Unlike conventional random noise attenuation methods, the proposed method solves a seismic data inversion problem using regularization constraint; this overcomes the model inconsistency of the prediction filtering method. The proposed method was tested on both synthetic and real seismic data, and results from the prediction filtering method and the proposed method are compared. The testing demonstrated that the proposed method suppresses noise effectively and provides better signal-preservation performance.
基金supported by the National Natural Science Foundation of China(No.41474110)
文摘Edge reflections are inevitable in numerical modeling of seismic wavefields, and they are usually attenuated by absorbing boundary conditions. However, the commonly used perfectly matched layer （PML） boundary condition requires special treatment for the absorbing zone, and in three-dimensional （3D） modeling, it has to split each variable into three corresponding variables, which increases the computing time and memory storage. In contrast, the hybrid absorbing boundary condition （HABC） has the advantages such as ease of implementation, less computation time, and near-perfect absorption; it is thus able to enhance the computational efficiency of 3D elastic wave modeling. In this study, a HABC is developed from two-dimensional （2D） modeling into 3D modeling based on the I st Higdon one way wave equations, and a HABC is proposed that is suitable for a 3D elastic wave numerical simulation. Numerical simulation results for a homogenous model and a complex model indicate that the proposed HABC method is more effective and has better absorption than the traditional PML method.
基金supported by the National Science and Technology Major Project of China(No.2017ZX05005-004).
文摘There are complex heterogeneous entities in the underground medium,and the heterogeneous scale has a substantial impact on wave propagation.In this study,we used a set of 11 samples of glass beads as high-velocity heterogeneous bodies to evaluate the impact of such heterogeneous bodies on the propagation of P-wave.We vary the heterogeneous scale by changing the diameter of the glass beads from 0.18 to 11 mm while keeping the same volume proportion(10%)of the beads for the set of 11 samples.The pulse transmission method was used to record measurements at the ultrasonic frequencies of 0.34,0.61,and 0.84 MHz in the homogeneous matrix.The relationship between P-wave fi eld features and heterogeneity scale,P-wave velocity,and the multiple of the wave number and heterogeneous scale(ka)was observed in the laboratory,which has sparked widespread interest and research.Heterogeneous scale affects P-wave propagation,and its wave field changes are complex.The waveform,amplitude,and velocity of the recorded P-waves correlate with the heterogeneous scale.For the forward scattering while large-scale heterogeneities,noticeable direct and diff racted waves are observed in the laboratory,which indicates that the infl uence of direct and diff racted waves cannot be ignored for large-scale heterogeneities.The relationship between velocity and ka shows frequency dependence;the reason is that the magnitude of change in velocity caused by wave number is diff erent from that caused by heterogeneous scale.According to the change in the recorded waveform,amplitude variation,or the relationship between the velocity measured at diff erent frequencies and the heterogeneous scale,the identifi ed turning points of the ray approximation are all around ka=10.When ka is less than 1,the velocity changes slowly and gradually approaches the eff ective medium velocity.The ray velocity measured for heterogeneous media with large velocity perturbations in the laboratory is signifi cantly smaller than the velocity predicted by the perturbation theory.
基金financially supported by the Important National Science and Technology Specific Project of China (Grant No. 2016ZX05047-002)
文摘The model-driven inversion method and data-driven prediction method are eff ective to obtain velocity and density from seismic data.The former necessitates initial models and cannot provide high-resolution inverted parameters because it primarily employs medium-frequency information from seismic data.The latter can predict parameters with high resolution,but it require a signifi cant number of accurate training samples,which are typically in limited supply.To solve the problems mentioned for these two methods,we propose a model-data-driven AVO inversion method based on multiple objective functions.The proposed method implements network training,network optimization,and network inversion by using three independent objective functions.Tests on synthetic and fi eld data show that the proposed method can invert high-accuracy and high-resolution velocity and density with a few training samples.
基金supported by the National Science and Technology Major Project of China(No.2017ZX05018-005)National Natural Science Foundation of China(No.41474110)
文摘The conventional fast converted-wave imaging method directly uses backward Pand converted S-wavefield to produce joint images. However, this image is accompanied by strong background noises, because the wavefi elds in all propagation directions contribute to it. Given this issue, we improve the conventional imaging method in the two aspects. First, the amplitude-preserved P-and S-wavef ield are obtained by using an improved space-domain wavef ield separation scheme to decouple the original elastic wavef ield. Second, a convertedwave imaging condition is constructed based on the directional-wavefield separation and only the wavefields propagating in the same directions used for cross-correlation imaging, resulting in effectively eliminating the imaging artifacts of the wavefields with different directions;Complex-wavefi eld extrapolation is adopted to decompose the decoupled P-and S-wavefield into directional-wavefields during backward propagation, this improves the eff iciency of the directional-wavef ield separation. Experiments on synthetic data show that the improved method generates more accurate converted-wave images than the conventional one. Moreover, the improved method has application potential in micro-seismic and passive-source exploration due to its source-independent characteristic.
