This paper extends the previous work[1]for the three-temperature gray radiative transfer equations to the frequency-dependent case.Since the additional frequency variable is considered,the equations are more complicat...This paper extends the previous work[1]for the three-temperature gray radiative transfer equations to the frequency-dependent case.Since the additional frequency variable is considered,the equations are more complicated than those in the gray case.Moreover,opacity may be typically a decreasing function of the frequency variable in applications.At the same spatial location,the equations can be in the optically thick case for low frequency photons,while in the optically thin case for high frequency ones.Thus,the resulting discrete equations can significantly increase the computational cost for opacity having the multi-scale property in multiple frequency radiation.Due to the presence of the radiation-electron coupling,electronion coupling,and electron-ion diffusion terms,the model under consideration exhibits strong nonlinearity and strong coupling properties.In this paper,the multigroup method is used to discretize the frequency variable and the H_(N)^(T)method to discretize the angular variable first.Then,within the framework of a unified gas kinetic scheme(UGKS),a multigroup H_(N)^(T)-UGKS method is constructed to solve this complex model iteratively.Furthermore,it can be shown that as the Knudsen number tends to zero,with variations in the electron-ion coupling,absorption,and scattering coefficients,the multigroup H_(N)^(T)-UGKS scheme can converge to numerical schemes for the single-temperature,two-temperature,and the frequency-dependent three-temperature,two-temperature diffusion limit equations,respectively.Finally,several numerical examples are provided to validate the effectiveness and stability of the proposed scheme.展开更多
The phase velocity of seismic waves varies with the propagation frequency, and thus frequency-dependent phenomena appear when CO2 gas is injected into a reservoir. By dynamically considering these phenomena with reser...The phase velocity of seismic waves varies with the propagation frequency, and thus frequency-dependent phenomena appear when CO2 gas is injected into a reservoir. By dynamically considering these phenomena with reservoir conditions it is thus feasible to extract the frequency-dependent velocity factor with the aim of monitoring changes in the reservoir both before and after CO2 injection. In the paper, we derive a quantitative expression for the frequency-dependent factor based on the Robinson seismic convolution model. In addition, an inversion equation with a frequency-dependent velocity factor is constructed, and a procedure is implemented using the following four processing steps： decomposition of the spectrum by generalized S transform, wavelet extraction of cross-well seismic traces, spectrum equalization processing, and an extraction method for frequency-dependent velocity factor based on the damped least-square algorithm. An attenuation layered model is then established based on changes in the Q value of the viscoelastic medium, and spectra of migration profiles from forward modeling are obtained and analyzed. Frequency-dependent factors are extracted and compared, and the effectiveness of the method is then verified using a synthetic data. The frequency-dependent velocity factor is finally applied to target processing and oil displacement monitoring based on real seismic data obtained before and after CO2 injection in the G89 well block within Shengli oilfield. Profiles and slices of the frequency-dependent factor determine its ability to indicate differences in CO2 flooding, and the predicting results are highly consistent with those of practical investigations within the well block.展开更多
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
A key problem in seismic inversion is the identification of the reservoir fluids. Elastic parameters, such as seismic wave velocity and formation density, do not have sufficient sensitivity, thus, the conventional amp...A key problem in seismic inversion is the identification of the reservoir fluids. Elastic parameters, such as seismic wave velocity and formation density, do not have sufficient sensitivity, thus, the conventional amplitude-versus-offset（AVO） method is not applicable. The frequency-dependent AVO method considers the dependency of the seismic amplitude to frequency and uses this dependency to obtain information regarding the fluids in the reservoir fractures. We propose an improved Bayesian inversion method based on the parameterization of the Chapman model. The proposed method is based on 1） inelastic attribute inversion by the FDAVO method and 2） Bayesian statistics for fluid identification. First, we invert the inelastic fracture parameters by formulating an error function, which is used to match observations and model data. Second, we identify fluid types by using a Markov random field a priori model considering data from various sources, such as prestack inversion and well logs. We consider the inelastic parameters to take advantage of the viscosity differences among the different fluids possible. Finally, we use the maximum posteriori probability for obtaining the best lithology/fluid identification results.展开更多
基金supported by the Beijing Natural Science Foundation(Z230003)for Sunby the National Key R&D Program(2020YFA0712200)+1 种基金the National Key Project(GJXM92579)the Sino-German Science Center(GZ 1465)for Jiang。
文摘This paper extends the previous work[1]for the three-temperature gray radiative transfer equations to the frequency-dependent case.Since the additional frequency variable is considered,the equations are more complicated than those in the gray case.Moreover,opacity may be typically a decreasing function of the frequency variable in applications.At the same spatial location,the equations can be in the optically thick case for low frequency photons,while in the optically thin case for high frequency ones.Thus,the resulting discrete equations can significantly increase the computational cost for opacity having the multi-scale property in multiple frequency radiation.Due to the presence of the radiation-electron coupling,electronion coupling,and electron-ion diffusion terms,the model under consideration exhibits strong nonlinearity and strong coupling properties.In this paper,the multigroup method is used to discretize the frequency variable and the H_(N)^(T)method to discretize the angular variable first.Then,within the framework of a unified gas kinetic scheme(UGKS),a multigroup H_(N)^(T)-UGKS method is constructed to solve this complex model iteratively.Furthermore,it can be shown that as the Knudsen number tends to zero,with variations in the electron-ion coupling,absorption,and scattering coefficients,the multigroup H_(N)^(T)-UGKS scheme can converge to numerical schemes for the single-temperature,two-temperature,and the frequency-dependent three-temperature,two-temperature diffusion limit equations,respectively.Finally,several numerical examples are provided to validate the effectiveness and stability of the proposed scheme.
基金supported by the Pilot Project of Sinopec(P14085)
文摘The phase velocity of seismic waves varies with the propagation frequency, and thus frequency-dependent phenomena appear when CO2 gas is injected into a reservoir. By dynamically considering these phenomena with reservoir conditions it is thus feasible to extract the frequency-dependent velocity factor with the aim of monitoring changes in the reservoir both before and after CO2 injection. In the paper, we derive a quantitative expression for the frequency-dependent factor based on the Robinson seismic convolution model. In addition, an inversion equation with a frequency-dependent velocity factor is constructed, and a procedure is implemented using the following four processing steps： decomposition of the spectrum by generalized S transform, wavelet extraction of cross-well seismic traces, spectrum equalization processing, and an extraction method for frequency-dependent velocity factor based on the damped least-square algorithm. An attenuation layered model is then established based on changes in the Q value of the viscoelastic medium, and spectra of migration profiles from forward modeling are obtained and analyzed. Frequency-dependent factors are extracted and compared, and the effectiveness of the method is then verified using a synthetic data. The frequency-dependent velocity factor is finally applied to target processing and oil displacement monitoring based on real seismic data obtained before and after CO2 injection in the G89 well block within Shengli oilfield. Profiles and slices of the frequency-dependent factor determine its ability to indicate differences in CO2 flooding, and the predicting results are highly consistent with those of practical investigations within the well block.
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supported by the 973 Program of China(No.2013CB429805)the National Natural Science Foundation of China(No.41174080)
文摘A key problem in seismic inversion is the identification of the reservoir fluids. Elastic parameters, such as seismic wave velocity and formation density, do not have sufficient sensitivity, thus, the conventional amplitude-versus-offset（AVO） method is not applicable. The frequency-dependent AVO method considers the dependency of the seismic amplitude to frequency and uses this dependency to obtain information regarding the fluids in the reservoir fractures. We propose an improved Bayesian inversion method based on the parameterization of the Chapman model. The proposed method is based on 1） inelastic attribute inversion by the FDAVO method and 2） Bayesian statistics for fluid identification. First, we invert the inelastic fracture parameters by formulating an error function, which is used to match observations and model data. Second, we identify fluid types by using a Markov random field a priori model considering data from various sources, such as prestack inversion and well logs. We consider the inelastic parameters to take advantage of the viscosity differences among the different fluids possible. Finally, we use the maximum posteriori probability for obtaining the best lithology/fluid identification results.
