Quantitative prediction of reservoir properties(e.g., gas saturation, porosity, and shale content) of tight reservoirs is of great significance for resource evaluation and well placements. However, the complex pore st...Quantitative prediction of reservoir properties(e.g., gas saturation, porosity, and shale content) of tight reservoirs is of great significance for resource evaluation and well placements. However, the complex pore structures, poor pore connectivity, and uneven fluid distribution of tight sandstone reservoirs make the correlation between reservoir parameters and elastic properties more complicated and thus pose a major challenge in seismic reservoir characterization. We have developed a partially connected double porosity model to calculate elastic properties by considering the pore structure and connectivity, and to analyze these factors' influences on the elastic behaviors of tight sandstone reservoirs. The modeling results suggest that the bulk modulus is likely to be affected by the pore connectivity coefficient, while the shear modulus is sensitive to the volumetric fraction of stiff pores. By comparing the model predictions with the acoustic measurements of the dry and saturated quartz sandstone samples, the volumetric fraction of stiff pores and the pore connectivity coefficient can be determined. Based on the calibrated model, we have constructed a 3D rock physics template that accounts for the reservoir properties' impacts on the P-wave impedance, S-wave impedance, and density. The template combined with Bayesian inverse theory is used to quantify gas saturation, porosity, clay content, and their corresponding uncertainties from elastic parameters. The application of well-log and seismic data demonstrates that our 3D rock physics template-based probabilistic inversion approach performs well in predicting the spatial distribution of high-quality tight sandstone reservoirs in southwestern China.展开更多
A new estimation method was proposed by combining the corresponding state principle with the group contribution method through introducing the concept of assumed-critical properties. Combining it with the Reidel equat...A new estimation method was proposed by combining the corresponding state principle with the group contribution method through introducing the concept of assumed-critical properties. Combining it with the Reidel equation, a new acentric factor correlation equation (CSGC-Reidel) was developed. Contribution values of 70 groups were obtained by correlating acentric factor data of 228 organic compounds of 14 type substances including saturated hydrocarbons, unsaturated hydrocarbons, cyclanes, aromatics, oxygen compounds, nitrogen compounds,halohydrocarbons, etc. The average error of acentric factor is 3.52% between the literature data and the predicated with the new estimation method.展开更多
In most literature about joint direction of arrival（DOA） and polarization estimation, the case that sources possess different power levels is seldom discussed. However, this case exists widely in practical applicati...In most literature about joint direction of arrival（DOA） and polarization estimation, the case that sources possess different power levels is seldom discussed. However, this case exists widely in practical applications, especially in passive radar systems. In this paper, we propose a joint DOA and polarization estimation method for unequal power sources based on the reconstructed noise subspace. The invariance property of noise subspace（IPNS） to power of sources has been proved an effective method to estimate DOA of unequal power sources. We develop the IPNS method for joint DOA and polarization estimation based on a dual polarized array. Moreover, we propose an improved IPNS method based on the reconstructed noise subspace, which has higher resolution probability than the IPNS method. It is theoretically proved that the IPNS to power of sources is still valid when the eigenvalues of the noise subspace are changed artificially. Simulation results show that the resolution probability of the proposed method is enhanced compared with the methods based on the IPNS and the polarimetric multiple signal classification（MUSIC） method. Meanwhile, the proposed method has approximately the same estimation accuracy as the IPNS method for the weak source.展开更多
The flash points of organic compounds were estimated using a hybrid method that includes a simple group contribution method (GCM) implemented in an artificial neural network (ANN) with particle swarm optimization (PSO...The flash points of organic compounds were estimated using a hybrid method that includes a simple group contribution method (GCM) implemented in an artificial neural network (ANN) with particle swarm optimization (PSO). Different topologies of a multilayer neural network were studied and the optimum architecture was determined. Property data of 350 compounds were used for training the network. To discriminate different substances the molecular structures defined by the concept of the classical group contribution method were given as input variables. The capabilities of the network were tested with 155 substances not considered in the training step. The study shows that the proposed GCM+ANN+PSO method represent an excellent alternative for the estimation of flash points of organic compounds with acceptable accuracy (AARD = 1.8%; AAE = 6.2 K).展开更多
The group-contribution (GC) methods suffer from a limitation concerning to the prediction of process-related indexes, e.g., thermal efficiency. Recently developed analytical models for thermal efficiency of organic Ra...The group-contribution (GC) methods suffer from a limitation concerning to the prediction of process-related indexes, e.g., thermal efficiency. Recently developed analytical models for thermal efficiency of organic Rankine cycles (ORCs) provide a possibility of overcoming the limitation of the GC methods because these models formulate thermal efficiency as functions of key thermal properties. Using these analytical relations together with GC methods, more than 60 organic fluids are screened for medium-low temperature ORCs. The results indicate that the GC methods can estimate thermal properties with acceptable accuracy (mean relative errors are 4.45%-11.50%);the precision, however, is low because the relative errors can vary from less than 0.1% to 45.0%. By contrast, the GC-based estimation of thermal efficiency has better accuracy and precision. The relative errors in thermal efficiency have an arithmetic mean of about 2.9% and fall within the range of 0-24.0%. These findings suggest that the analytical equations provide not only a direct way of estimating thermal efficiency but an accurate and precise approach to evaluating working fluids and guiding computer-aided molecular design of new fluids for ORCs using GC methods.展开更多
A general version of the inverted exponential distribution is introduced, studied and analyzed. This generalization depends on the method of Marshall-Olkin to extend a family of distributions. Some statistical and rel...A general version of the inverted exponential distribution is introduced, studied and analyzed. This generalization depends on the method of Marshall-Olkin to extend a family of distributions. Some statistical and reliability properties of this family are studied. In addition, numerical estimation of the maximum likelihood estimate（MLE） parameters are discussed in details. As an application, some real data sets are analyzed and it is observed that the presented family provides a better fit than some other known distributions.展开更多
Ordinary differential equation(ODE) models are widely used to model dynamic processes in many scientific fields.Parameter estimation is usually a challenging problem,especially in nonlinear ODE models.The most popular...Ordinary differential equation(ODE) models are widely used to model dynamic processes in many scientific fields.Parameter estimation is usually a challenging problem,especially in nonlinear ODE models.The most popular method,nonlinear least square estimation,is shown to be strongly sensitive to outliers.In this paper,robust estimation of parameters using M-estimators is proposed,and their asymptotic properties are obtained under some regular conditions.The authors also provide a method to adjust Huber parameter automatically according to the observations.Moreover,a method is presented to estimate the initial values of parameters and state variables.The efficiency and robustness are well balanced in Huber estimators,which is demonstrated via numerical simulations and chlorides data analysis.展开更多
This paper is concerned with the statistical inference of partially linear varying coefficient dynamic panel data model with incidental parameter, including efficient estimation of the parametric and nonparametric com...This paper is concerned with the statistical inference of partially linear varying coefficient dynamic panel data model with incidental parameter, including efficient estimation of the parametric and nonparametric components and consistent determination of the lagged order. For the parametric component, we propose an efficient semiparametric generalized method-of-moments（GMM） estimator and establish its asymptotic normality. For the nonparametric component, B-spline series approximation is employed to estimate the unknown coefficient functions, which are shown to achieve the optimal nonparametric convergence rate. A consistent estimator of the variance of error component is also constructed. In addition, by using the smooth-threshold GMM estimating equations, we propose a variable selection method to identify the significant order of lagged terms automatically and remove the irrelevant regressors by setting their coefficient to zeros. As a result, it can consistently determine the true lagged order and specify the significant exogenous variables. Further studies show that the resulting estimator has the same asymptotic properties as if the true lagged order and significant regressors were known prior, i.e., achieving the oracle property. Numerical experiments are conducted to evaluate the finite sample performance of our procedures. An example of application is also illustrated.展开更多
Protein science is an interdisciplinary research field of understanding the structure,function,and interactions of proteins,as well as their functions in biological processes.Protein science provides particularly impo...Protein science is an interdisciplinary research field of understanding the structure,function,and interactions of proteins,as well as their functions in biological processes.Protein science provides particularly important tools for drug discovery,1 a process characterized by long cycling,high risk,and high investment.To accelerate drug discovery,several challenges remain in advancing protein science:(1)Multi-scale modeling:elements related to proteins have a variety of scales,from atoms to cells;(2)Property estimation:the property of proteins is determined by many factors,which challenges accurate measurement;(3)Structure understanding:the complex and dynamic biological structures of proteins are hard to estimate.While traditional methods have been developed based on biological experiments,they are expensive and time-consuming.With the availability of big data and high computing power,emerging data-driven technologies powered by high-performance computing have been revolutionizing scientific discovery due to their significant advantages in pattern recognition and predictive modeling.Fig.1 shows an overview of various protein science-related artificial intelligence(AI)models.A landmark model,AlphaFold2,scored 92.4 points on the CASP14 standard dataset in the protein folding prediction task,indicating that its predicted structures closely match the real structure.2 The high performance of AI models on complex conformation prediction shows the great potential of AI in biological modeling,demonstrating the possibility of AI technology enhancing the efficiency of biological research to accelerate the traditional pathway of drug discovery.展开更多
Seismic Rock physics plays a bridge role between the rock moduli and physical properties of the hydrocarbon reservoirs.Prestack seismic inversion is an important method for the quantitative characterization of elastic...Seismic Rock physics plays a bridge role between the rock moduli and physical properties of the hydrocarbon reservoirs.Prestack seismic inversion is an important method for the quantitative characterization of elasticity,physical properties,lithology and fluid properties of subsurface reservoirs.In this paper,a high order approximation of rock physics model for clastic rocks is established and one seismic AVO reflection equation characterized by the high order approximation(Jacobian and Hessian matrix)of rock moduli is derived.Besides,the contribution of porosity,shale content and fluid saturation to AVO reflectivity is analyzed.The feasibility of the proposed AVO equation is discussed in the direct estimation of rock physical properties.On the basis of this,one probabilistic AVO inversion based on differential evolution-Markov chain Monte Carlo stochastic model is proposed on the premise that the model parameters obey Gaussian mixture probability prior model.The stochastic model has both the global optimization characteristics of the differential evolution algorithm and the uncertainty analysis ability of Markov chain Monte Carlo model.Through the cross parallel of multiple Markov chains,multiple stochastic solutions of the model parameters can be obtained simultaneously,and the posterior probability density distribution of the model parameters can be simulated effectively.The posterior mean is treated as the optimal solution of the model to be inverted.Besides,the variance and confidence interval are utilized to evaluate the uncertainties of the estimated results,so as to realize the simultaneous estimation of reservoir elasticity,physical properties,discrete lithofacies and dry rock skeleton.The validity of the proposed approach is verified by theoretical tests and one real application case in eastern China.展开更多
Based on the scattering properties of nonspherical dust aerosol, a new method is developed for retrieving dust aerosol optical depths of dusty clouds. The dusty clouds are defined as the hybrid system of dust plume an...Based on the scattering properties of nonspherical dust aerosol, a new method is developed for retrieving dust aerosol optical depths of dusty clouds. The dusty clouds are defined as the hybrid system of dust plume and cloud. The new method is based on transmittance measurements from surface-based instruments multi-filter rotating shadowband radiometer （MFRSR） and cloud parameters from lidar measurements. It uses the difference of absorption between dust aerosols and water droplets for distinguishing and estimating the optical properties of dusts and clouds, respectively. This new retrieval method is not sensitive to the retrieval error of cloud properties and the maximum absolute deviations of dust aerosol and total optical depths for thin dusty cloud retrieval algorithm are only 0.056 and 0.1, respectively, for given possible uncertainties. The retrieval error for thick dusty cloud mainly depends on lidar-based total dusty cloud properties.展开更多
基金supported by the National Natural Science Foundation of China (42104121)the Scientific Research and Technology Development Project of the CNPC (2021DJ0606)。
文摘Quantitative prediction of reservoir properties(e.g., gas saturation, porosity, and shale content) of tight reservoirs is of great significance for resource evaluation and well placements. However, the complex pore structures, poor pore connectivity, and uneven fluid distribution of tight sandstone reservoirs make the correlation between reservoir parameters and elastic properties more complicated and thus pose a major challenge in seismic reservoir characterization. We have developed a partially connected double porosity model to calculate elastic properties by considering the pore structure and connectivity, and to analyze these factors' influences on the elastic behaviors of tight sandstone reservoirs. The modeling results suggest that the bulk modulus is likely to be affected by the pore connectivity coefficient, while the shear modulus is sensitive to the volumetric fraction of stiff pores. By comparing the model predictions with the acoustic measurements of the dry and saturated quartz sandstone samples, the volumetric fraction of stiff pores and the pore connectivity coefficient can be determined. Based on the calibrated model, we have constructed a 3D rock physics template that accounts for the reservoir properties' impacts on the P-wave impedance, S-wave impedance, and density. The template combined with Bayesian inverse theory is used to quantify gas saturation, porosity, clay content, and their corresponding uncertainties from elastic parameters. The application of well-log and seismic data demonstrates that our 3D rock physics template-based probabilistic inversion approach performs well in predicting the spatial distribution of high-quality tight sandstone reservoirs in southwestern China.
文摘A new estimation method was proposed by combining the corresponding state principle with the group contribution method through introducing the concept of assumed-critical properties. Combining it with the Reidel equation, a new acentric factor correlation equation (CSGC-Reidel) was developed. Contribution values of 70 groups were obtained by correlating acentric factor data of 228 organic compounds of 14 type substances including saturated hydrocarbons, unsaturated hydrocarbons, cyclanes, aromatics, oxygen compounds, nitrogen compounds,halohydrocarbons, etc. The average error of acentric factor is 3.52% between the literature data and the predicated with the new estimation method.
基金supported by the National Natural Science Foundation of China(61501142)the China Postdoctoral Science Foundation(2015M571414)+3 种基金the Fundamental Research Funds for the Central Universities(HIT.NSRIF.2016102)Shandong Provincial Natural Science Foundation(ZR2014FQ003)the Natural Scientific Research Innovation Foundation in Harbin Institute of Technology(HIT.NSRIF 2013130HIT(WH)XBQD 201022)
文摘In most literature about joint direction of arrival（DOA） and polarization estimation, the case that sources possess different power levels is seldom discussed. However, this case exists widely in practical applications, especially in passive radar systems. In this paper, we propose a joint DOA and polarization estimation method for unequal power sources based on the reconstructed noise subspace. The invariance property of noise subspace（IPNS） to power of sources has been proved an effective method to estimate DOA of unequal power sources. We develop the IPNS method for joint DOA and polarization estimation based on a dual polarized array. Moreover, we propose an improved IPNS method based on the reconstructed noise subspace, which has higher resolution probability than the IPNS method. It is theoretically proved that the IPNS to power of sources is still valid when the eigenvalues of the noise subspace are changed artificially. Simulation results show that the resolution probability of the proposed method is enhanced compared with the methods based on the IPNS and the polarimetric multiple signal classification（MUSIC） method. Meanwhile, the proposed method has approximately the same estimation accuracy as the IPNS method for the weak source.
文摘The flash points of organic compounds were estimated using a hybrid method that includes a simple group contribution method (GCM) implemented in an artificial neural network (ANN) with particle swarm optimization (PSO). Different topologies of a multilayer neural network were studied and the optimum architecture was determined. Property data of 350 compounds were used for training the network. To discriminate different substances the molecular structures defined by the concept of the classical group contribution method were given as input variables. The capabilities of the network were tested with 155 substances not considered in the training step. The study shows that the proposed GCM+ANN+PSO method represent an excellent alternative for the estimation of flash points of organic compounds with acceptable accuracy (AARD = 1.8%; AAE = 6.2 K).
基金Project(51778626) supported by the National Natural Science Foundation of China
文摘The group-contribution (GC) methods suffer from a limitation concerning to the prediction of process-related indexes, e.g., thermal efficiency. Recently developed analytical models for thermal efficiency of organic Rankine cycles (ORCs) provide a possibility of overcoming the limitation of the GC methods because these models formulate thermal efficiency as functions of key thermal properties. Using these analytical relations together with GC methods, more than 60 organic fluids are screened for medium-low temperature ORCs. The results indicate that the GC methods can estimate thermal properties with acceptable accuracy (mean relative errors are 4.45%-11.50%);the precision, however, is low because the relative errors can vary from less than 0.1% to 45.0%. By contrast, the GC-based estimation of thermal efficiency has better accuracy and precision. The relative errors in thermal efficiency have an arithmetic mean of about 2.9% and fall within the range of 0-24.0%. These findings suggest that the analytical equations provide not only a direct way of estimating thermal efficiency but an accurate and precise approach to evaluating working fluids and guiding computer-aided molecular design of new fluids for ORCs using GC methods.
基金supported by the Research Center of the Female Scientific and Medical Colleges,Deanship of Scientific Research,King Saud University
文摘A general version of the inverted exponential distribution is introduced, studied and analyzed. This generalization depends on the method of Marshall-Olkin to extend a family of distributions. Some statistical and reliability properties of this family are studied. In addition, numerical estimation of the maximum likelihood estimate（MLE） parameters are discussed in details. As an application, some real data sets are analyzed and it is observed that the presented family provides a better fit than some other known distributions.
基金supported by the Natural Science Foundation of China under Grant Nos.11201317,11028103,11231010,11471223Doctoral Fund of Ministry of Education of China under Grant No.20111108120002+1 种基金the Beijing Municipal Education Commission Foundation under Grant No.KM201210028005the Key project of Beijing Municipal Educational Commission
文摘Ordinary differential equation(ODE) models are widely used to model dynamic processes in many scientific fields.Parameter estimation is usually a challenging problem,especially in nonlinear ODE models.The most popular method,nonlinear least square estimation,is shown to be strongly sensitive to outliers.In this paper,robust estimation of parameters using M-estimators is proposed,and their asymptotic properties are obtained under some regular conditions.The authors also provide a method to adjust Huber parameter automatically according to the observations.Moreover,a method is presented to estimate the initial values of parameters and state variables.The efficiency and robustness are well balanced in Huber estimators,which is demonstrated via numerical simulations and chlorides data analysis.
基金supported by SHUFE Graduate Innovation and Creativity Funds(No.2011130151)supported by grants from the National Natural Science Foundation of China(NSFC)(No.11071154)+1 种基金partially supported by the Leading Academic Discipline Program211 Project for Shanghai University of Finance and Economics
文摘This paper is concerned with the statistical inference of partially linear varying coefficient dynamic panel data model with incidental parameter, including efficient estimation of the parametric and nonparametric components and consistent determination of the lagged order. For the parametric component, we propose an efficient semiparametric generalized method-of-moments（GMM） estimator and establish its asymptotic normality. For the nonparametric component, B-spline series approximation is employed to estimate the unknown coefficient functions, which are shown to achieve the optimal nonparametric convergence rate. A consistent estimator of the variance of error component is also constructed. In addition, by using the smooth-threshold GMM estimating equations, we propose a variable selection method to identify the significant order of lagged terms automatically and remove the irrelevant regressors by setting their coefficient to zeros. As a result, it can consistently determine the true lagged order and specify the significant exogenous variables. Further studies show that the resulting estimator has the same asymptotic properties as if the true lagged order and significant regressors were known prior, i.e., achieving the oracle property. Numerical experiments are conducted to evaluate the finite sample performance of our procedures. An example of application is also illustrated.
基金supported by the National Science and Technology Major Project(2023ZD0121401).
文摘Protein science is an interdisciplinary research field of understanding the structure,function,and interactions of proteins,as well as their functions in biological processes.Protein science provides particularly important tools for drug discovery,1 a process characterized by long cycling,high risk,and high investment.To accelerate drug discovery,several challenges remain in advancing protein science:(1)Multi-scale modeling:elements related to proteins have a variety of scales,from atoms to cells;(2)Property estimation:the property of proteins is determined by many factors,which challenges accurate measurement;(3)Structure understanding:the complex and dynamic biological structures of proteins are hard to estimate.While traditional methods have been developed based on biological experiments,they are expensive and time-consuming.With the availability of big data and high computing power,emerging data-driven technologies powered by high-performance computing have been revolutionizing scientific discovery due to their significant advantages in pattern recognition and predictive modeling.Fig.1 shows an overview of various protein science-related artificial intelligence(AI)models.A landmark model,AlphaFold2,scored 92.4 points on the CASP14 standard dataset in the protein folding prediction task,indicating that its predicted structures closely match the real structure.2 The high performance of AI models on complex conformation prediction shows the great potential of AI in biological modeling,demonstrating the possibility of AI technology enhancing the efficiency of biological research to accelerate the traditional pathway of drug discovery.
基金supported by the National Grand Project for Science and Technology(Grant Nos.2016ZX05024-004,2017ZX05009-001,2017ZX05036-005)the Science Foundation from SINOPEC Key Laboratory of Geophysics(Grant No.WTYJY-WX2019-0104)。
文摘Seismic Rock physics plays a bridge role between the rock moduli and physical properties of the hydrocarbon reservoirs.Prestack seismic inversion is an important method for the quantitative characterization of elasticity,physical properties,lithology and fluid properties of subsurface reservoirs.In this paper,a high order approximation of rock physics model for clastic rocks is established and one seismic AVO reflection equation characterized by the high order approximation(Jacobian and Hessian matrix)of rock moduli is derived.Besides,the contribution of porosity,shale content and fluid saturation to AVO reflectivity is analyzed.The feasibility of the proposed AVO equation is discussed in the direct estimation of rock physical properties.On the basis of this,one probabilistic AVO inversion based on differential evolution-Markov chain Monte Carlo stochastic model is proposed on the premise that the model parameters obey Gaussian mixture probability prior model.The stochastic model has both the global optimization characteristics of the differential evolution algorithm and the uncertainty analysis ability of Markov chain Monte Carlo model.Through the cross parallel of multiple Markov chains,multiple stochastic solutions of the model parameters can be obtained simultaneously,and the posterior probability density distribution of the model parameters can be simulated effectively.The posterior mean is treated as the optimal solution of the model to be inverted.Besides,the variance and confidence interval are utilized to evaluate the uncertainties of the estimated results,so as to realize the simultaneous estimation of reservoir elasticity,physical properties,discrete lithofacies and dry rock skeleton.The validity of the proposed approach is verified by theoretical tests and one real application case in eastern China.
基金supported by the Knowledge Innovation Program of the Chinese Academy of Sciences (No.IAP09311)the National Natural Science Foundation of China (Nos.40725015 and 40633017)
文摘Based on the scattering properties of nonspherical dust aerosol, a new method is developed for retrieving dust aerosol optical depths of dusty clouds. The dusty clouds are defined as the hybrid system of dust plume and cloud. The new method is based on transmittance measurements from surface-based instruments multi-filter rotating shadowband radiometer （MFRSR） and cloud parameters from lidar measurements. It uses the difference of absorption between dust aerosols and water droplets for distinguishing and estimating the optical properties of dusts and clouds, respectively. This new retrieval method is not sensitive to the retrieval error of cloud properties and the maximum absolute deviations of dust aerosol and total optical depths for thin dusty cloud retrieval algorithm are only 0.056 and 0.1, respectively, for given possible uncertainties. The retrieval error for thick dusty cloud mainly depends on lidar-based total dusty cloud properties.
