Rock-physics models are constructed for hydrate-bearing sediments in the Qilian Mountains permafrost region using the K–T equation model, and modes I and II of the effective medium model. The K–T equation models the...Rock-physics models are constructed for hydrate-bearing sediments in the Qilian Mountains permafrost region using the K–T equation model, and modes I and II of the effective medium model. The K–T equation models the seismic wave propagation in a two-phase medium to determine the elastic moduli of the composite medium. In the effective medium model, mode I, the hydrate is a component of the pore inclusions in mode I and in mode II it is a component of the matrix. First, the P-wave velocity, S-wave velocity, density, bulk modulus, and shear modulus of the sediment matrix are extracted from logging data.. Second, based on the physical properties of the main components of the sediments, rock-physics model is established using the K–T equation, and two additional rock-physics models are established assuming different hydrate-filling modes for the effective medium. The model and actual velocity data for the hydrate-bearing sediments are compared and it is found that the rock-physics model for the hydrate-filling mode II well reproduces the actual data.展开更多
Carbonate reservoirs exhibit strong heterogeneity in the distribution of pore types that can be quantitatively characterized by applying Xu–Payne multi-porosity model.However,there are some prerequisites to this mode...Carbonate reservoirs exhibit strong heterogeneity in the distribution of pore types that can be quantitatively characterized by applying Xu–Payne multi-porosity model.However,there are some prerequisites to this model the porosity and saturation need to be provided.In general,these application conditions are difficult to satisfy for seismic data.In order to overcome this problem,we present a two-step method to estimate the porosity and saturation and pore type of carbonate reservoirs from seismic data.In step one,the pore space of the carbonate reservoir is equivalent to a single-porosity system with an effective pore aspect ratio;then,a 3D rock-physics template(RPT)is established through the Gassmann’s equations and effective medium models;and then,the effective aspect ratio of pore,porosity and fluid saturation are simultaneously estimated from the seismic data based on 3D RPT.In step two,the pore space of the carbonate reservoir is equivalent to a triple-porosity system.Combined with the inverted porosity and saturation in the first step,the porosities of three pore types can be inverted from the seismic elastic properties.The application results indicate that our method can obtain accurate physical properties consistent with logging data and ensure the reliability of characterization of pore type.展开更多
Accurate shear wave velocity is very important for seismic inversion.However,few researches in the shear wave velocity in organic shale have been carried out so far.In order to analyze the structure of organic shale a...Accurate shear wave velocity is very important for seismic inversion.However,few researches in the shear wave velocity in organic shale have been carried out so far.In order to analyze the structure of organic shale and predict the shear wave velocity,the authors propose two methods based on petrophysical model and BP neural network respectively,to calculate shear wave velocity.For the method based on petrophysics model,the authors discuss the pore structure and the space taken by kerogen to construct a petrophysical model of the shale,and establish the quantitative relationship between the P-wave and S-wave velocities of shale and physical parameters such as pore aspect ratio,porosity and density.The best estimation of pore aspect ratio can be obtained by minimizing the error between the predictions and the actual measurements of the P-wave velocity.The optimal porosity aspect ratio and the shear wave velocity are predicted.For the BP neural network method that applying BP neural network to the shear wave prediction,the relationship between the physical properties of the shale and the elastic parameters is obtained by training the BP neural network,and the P-wave and S-wave velocities are predicted from the reservoir parameters based on the trained relationship.The above two methods were tested by using actual logging data of the shale reservoirs in the Jiaoshiba area of Sichuan Province.The predicted shear wave velocities of the two methods match well with the actual shear wave velocities,indicating that these two methods are effective in predicting shear wave velocity.展开更多
Existing seismic prediction methods struggle to effectively discriminate between fluids in tight gas reservoirs,such as those in the Sulige gas field in the Ordos Basin,where porosity and permeability are extremely lo...Existing seismic prediction methods struggle to effectively discriminate between fluids in tight gas reservoirs,such as those in the Sulige gas field in the Ordos Basin,where porosity and permeability are extremely low and the relationship between gas and water is complicated.In this paper,we have proposed a comprehensive seismic fluid identification method that combines ray-path elastic impedance(REI)inversion with fluid substitution for tight reservoirs.This approach is grounded in geophysical theory,forward modeling,and real data applications.We used geophysics experiments in tight gas reservoirs to determine that Brie's model is better suited to calculate the elastic parameters of mixed fluids than the conventional Wood’s model.This yielded a more reasonable and accurate fluid substitution model for tight gas reservoirs.We developed a forward model and carried out inversion of REI.which reduced the non-uniqueness problem that has plagued elastic impedance inversion in the angle domain.Our well logging forward model in the ray-path domain with different fluid saturations based on a fluid substitution model proved that REI identifies fluids more accurately when the ray parameters are large.The distribution of gas saturation can be distinguished from the crossplot of REI(p=0.10)and porosity.The inverted ray-path elastic impedance profile was further used to predict the porosity and gas saturation profile.Our new method achieved good results in the application of 2D seismic data in the western Sulige gas field.展开更多
Carbonate reservoirs have complex pore structures,which not only significantly affect the elastic properties and seismic responses of the reservoirs but also affect the accuracy of the prediction of the physical param...Carbonate reservoirs have complex pore structures,which not only significantly affect the elastic properties and seismic responses of the reservoirs but also affect the accuracy of the prediction of the physical parameters.The existing rockphysics inversion methods are mainly designed for clastic rocks,and the inversion objects are generally porosity and water saturation.The data used are primarily based on the elastic parameters,and the inversion methods are mainly linear approximations.To date,there has been a lack of a simultaneous pore structure and physical parameter inversion method for carbonate reservoirs.To solve these problems,a new Bayesian nonlinear simultaneous inversion method based on elastic impedance is proposed.This method integrates the differential effective medium model of multiple-porosity rocks,Gassmann equation,Amplitude Versus Offset(AVO)theory,Bayesian theory,and a nonlinear inversion algorithm to achieve the simultaneous quantitative prediction of the pore structure and physical parameters of complex porous reservoirs.The forward modeling indicates that the contribution of the pore structure,i.e.,the pore aspect ratio,to the AVO response and elastic impedance is second only to that of porosity and is far greater than that of water saturation.The application to real data shows that the new inversion method for determining the pore structure and physical parameters directly from pre-stack data can accurately predict a reservoir's porosity and water saturation and can evaluate the pore structure of the effective reservoir.展开更多
基金supported by the Institute of Geophysical and Geochemical Exploration(IGGE)CAGS of China(No.WH201207)
文摘Rock-physics models are constructed for hydrate-bearing sediments in the Qilian Mountains permafrost region using the K–T equation model, and modes I and II of the effective medium model. The K–T equation models the seismic wave propagation in a two-phase medium to determine the elastic moduli of the composite medium. In the effective medium model, mode I, the hydrate is a component of the pore inclusions in mode I and in mode II it is a component of the matrix. First, the P-wave velocity, S-wave velocity, density, bulk modulus, and shear modulus of the sediment matrix are extracted from logging data.. Second, based on the physical properties of the main components of the sediments, rock-physics model is established using the K–T equation, and two additional rock-physics models are established assuming different hydrate-filling modes for the effective medium. The model and actual velocity data for the hydrate-bearing sediments are compared and it is found that the rock-physics model for the hydrate-filling mode II well reproduces the actual data.
基金supported by the China National Key R D plan(2019YFC0605504)Scientific Research&Technology Development Project of China National Petroleum Corporation(Grant Nos.2017D-3504 and 2018D-4305)
文摘Carbonate reservoirs exhibit strong heterogeneity in the distribution of pore types that can be quantitatively characterized by applying Xu–Payne multi-porosity model.However,there are some prerequisites to this model the porosity and saturation need to be provided.In general,these application conditions are difficult to satisfy for seismic data.In order to overcome this problem,we present a two-step method to estimate the porosity and saturation and pore type of carbonate reservoirs from seismic data.In step one,the pore space of the carbonate reservoir is equivalent to a single-porosity system with an effective pore aspect ratio;then,a 3D rock-physics template(RPT)is established through the Gassmann’s equations and effective medium models;and then,the effective aspect ratio of pore,porosity and fluid saturation are simultaneously estimated from the seismic data based on 3D RPT.In step two,the pore space of the carbonate reservoir is equivalent to a triple-porosity system.Combined with the inverted porosity and saturation in the first step,the porosities of three pore types can be inverted from the seismic elastic properties.The application results indicate that our method can obtain accurate physical properties consistent with logging data and ensure the reliability of characterization of pore type.
基金National Natural Science Foundation of China(No.41874125,No.41430322).
文摘Accurate shear wave velocity is very important for seismic inversion.However,few researches in the shear wave velocity in organic shale have been carried out so far.In order to analyze the structure of organic shale and predict the shear wave velocity,the authors propose two methods based on petrophysical model and BP neural network respectively,to calculate shear wave velocity.For the method based on petrophysics model,the authors discuss the pore structure and the space taken by kerogen to construct a petrophysical model of the shale,and establish the quantitative relationship between the P-wave and S-wave velocities of shale and physical parameters such as pore aspect ratio,porosity and density.The best estimation of pore aspect ratio can be obtained by minimizing the error between the predictions and the actual measurements of the P-wave velocity.The optimal porosity aspect ratio and the shear wave velocity are predicted.For the BP neural network method that applying BP neural network to the shear wave prediction,the relationship between the physical properties of the shale and the elastic parameters is obtained by training the BP neural network,and the P-wave and S-wave velocities are predicted from the reservoir parameters based on the trained relationship.The above two methods were tested by using actual logging data of the shale reservoirs in the Jiaoshiba area of Sichuan Province.The predicted shear wave velocities of the two methods match well with the actual shear wave velocities,indicating that these two methods are effective in predicting shear wave velocity.
基金supported by the National Science and Technology Major Project(No.2016ZX05050 and 2017ZX05069)CNPC Major Technology Special Project(No.2016E-0503)
文摘Existing seismic prediction methods struggle to effectively discriminate between fluids in tight gas reservoirs,such as those in the Sulige gas field in the Ordos Basin,where porosity and permeability are extremely low and the relationship between gas and water is complicated.In this paper,we have proposed a comprehensive seismic fluid identification method that combines ray-path elastic impedance(REI)inversion with fluid substitution for tight reservoirs.This approach is grounded in geophysical theory,forward modeling,and real data applications.We used geophysics experiments in tight gas reservoirs to determine that Brie's model is better suited to calculate the elastic parameters of mixed fluids than the conventional Wood’s model.This yielded a more reasonable and accurate fluid substitution model for tight gas reservoirs.We developed a forward model and carried out inversion of REI.which reduced the non-uniqueness problem that has plagued elastic impedance inversion in the angle domain.Our well logging forward model in the ray-path domain with different fluid saturations based on a fluid substitution model proved that REI identifies fluids more accurately when the ray parameters are large.The distribution of gas saturation can be distinguished from the crossplot of REI(p=0.10)and porosity.The inverted ray-path elastic impedance profile was further used to predict the porosity and gas saturation profile.Our new method achieved good results in the application of 2D seismic data in the western Sulige gas field.
基金supported by the National Key Research and Development Program of China(Grant No.2019YFC0605504)the Scientific Research&Technology Development Project of China National Petroleum Corporation(Grant No.2017D-3504)。
文摘Carbonate reservoirs have complex pore structures,which not only significantly affect the elastic properties and seismic responses of the reservoirs but also affect the accuracy of the prediction of the physical parameters.The existing rockphysics inversion methods are mainly designed for clastic rocks,and the inversion objects are generally porosity and water saturation.The data used are primarily based on the elastic parameters,and the inversion methods are mainly linear approximations.To date,there has been a lack of a simultaneous pore structure and physical parameter inversion method for carbonate reservoirs.To solve these problems,a new Bayesian nonlinear simultaneous inversion method based on elastic impedance is proposed.This method integrates the differential effective medium model of multiple-porosity rocks,Gassmann equation,Amplitude Versus Offset(AVO)theory,Bayesian theory,and a nonlinear inversion algorithm to achieve the simultaneous quantitative prediction of the pore structure and physical parameters of complex porous reservoirs.The forward modeling indicates that the contribution of the pore structure,i.e.,the pore aspect ratio,to the AVO response and elastic impedance is second only to that of porosity and is far greater than that of water saturation.The application to real data shows that the new inversion method for determining the pore structure and physical parameters directly from pre-stack data can accurately predict a reservoir's porosity and water saturation and can evaluate the pore structure of the effective reservoir.
