Permeability is affected by complex factors such as the subsurface geological structure and porosity-permeability correlation.For highly heterogeneous reservoirs with complex pore structures,it is extremely challengin...Permeability is affected by complex factors such as the subsurface geological structure and porosity-permeability correlation.For highly heterogeneous reservoirs with complex pore structures,it is extremely challenging to spatially characterize(predict)permeability using seismic data.The conventional way of permeability prediction intends to convert underground refl ection data into the elastic parameters sensitive to underground fluids,build a universal low-dimensional template via petrophysical modeling and ultimately deliver spatial prediction of permeability.However,this method is restrained by the actual subsurface condition,selected well-logging sensitive parameters and the accuracy of the computed elastic parameters and fails to simulate the petrophysical mechanisms of complex reservoir permeability,which reduces the permeability prediction accuracy.The method proposed in this paper combines petrophysics and artificial intelligence and integrates multiple types of information to build the high-dimensional petrophysical template for permeability,in an attempt to improve the spatial characterization and prediction accuracy of permeability.The field testing demonstrates the high application performance and effective improvement in prediction accuracy and fluvial channel characterization.展开更多
Pore scale variables(e.g.,porosity,grain size)are important indexes to predict the hydraulic properties of porous geomaterials.X-ray images from ten types of intact sandstones and another type of sandstone samples sub...Pore scale variables(e.g.,porosity,grain size)are important indexes to predict the hydraulic properties of porous geomaterials.X-ray images from ten types of intact sandstones and another type of sandstone samples subjected to triaxial compression are used to investigate the permeability and fracture characteristics.A novel double threshold segmentation algorithm is proposed to segment cracks,pores and grains,and pore scale variables are defined and extracted from these X-ray CT images to study the geometric characteristics of microstructures of porous geomaterials.Moreover,novel relations among these pore scale variables for permeability prediction are established,and the evolution process of cracks is investigated.The results indicate that the porescale permeability is prominently improved by cracks.In addition,excellent agreements are found between the measured and the estimated pore scale variables and permeability.The established correlations can be employed to effectively identify the hydraulic properties of porous geomaterials.展开更多
Accurately and efficiently predicting the permeability of porous media is essential for addressing a wide range of hydrogeological issues.However,the complexity of porous media often limits the effectiveness of indivi...Accurately and efficiently predicting the permeability of porous media is essential for addressing a wide range of hydrogeological issues.However,the complexity of porous media often limits the effectiveness of individual prediction methods.This study introduces a novel Particle Swarm Optimization-based Permeability Integrated Prediction model(PSO-PIP),which incorporates a particle swarm optimization algorithm enhanced with dy-namic clustering and adaptive parameter tuning(KGPSO).The model integrates multi-source data from the Lattice Boltzmann Method(LBM),Pore Network Modeling(PNM),and Finite Difference Method(FDM).By assigning optimal weight coefficients to the outputs of these methods,the model minimizes deviations from actual values and enhances permeability prediction performance.Initially,the computational performances of the LBM,PNM,and FDM are comparatively analyzed on datasets consisting of sphere packings and real rock samples.It is observed that these methods exhibit computational biases in certain permeability ranges.The PSOPIP model is proposed to combine the strengths of each computational approach and mitigate their limitations.The PSO-PIP model consistently produces predictions that are highly congruent with actual permeability values across all prediction intervals,significantly enhancing prediction accuracy.The outcomes of this study provide a new tool and perspective for the comprehensive,rapid,and accurate prediction of permeability in porous media.展开更多
To effectively predict the permeability index of smelting process in the imperial smelting furnace, an intelligent prediction model is proposed. It integrates the case-based reasoning （CBR） with adaptive par- ticle ...To effectively predict the permeability index of smelting process in the imperial smelting furnace, an intelligent prediction model is proposed. It integrates the case-based reasoning （CBR） with adaptive par- ticle swarm optimization （PSO）. The nmnber of nearest neighbors and the weighted features vector are optimized online using the adaptive PSO to improve the prediction accuracy of CBR. The adaptive inertia weight and mutation operation are used to overcome the premature convergence of the PSO. The proposed method is validated a compared with the basic weighted CBR. The results show that the proposed model has higher prediction accuracy and better performance than the basic CBR model.展开更多
The sealing performance of a bentonite barrier is highly dependent on its seepage characteristics, which are directly related to the characteristics of its pore structure. Based on scanning electron microscopy(SEM) an...The sealing performance of a bentonite barrier is highly dependent on its seepage characteristics, which are directly related to the characteristics of its pore structure. Based on scanning electron microscopy(SEM) and focused ion beam-SEM(FIB-SEM), the pore structure of bentonite was characterized at different scales. First, a reasonable gray threshold was determined through back analysis, and the image was binarized based on the threshold. In addition, binary images were used to analyze bentonite’s pore structure(porosity and pore size distribution). Furthermore, the effects of different algorithms on the pore structure characterization were evaluated. Then, permeability calculations were performed based on the previous pore structure characteristics and a modified permeability prediction model. For permeability prediction based on the three-dimensional model, the effect of pore tortuosity was also considered. Finally, the accuracy of numerical calculations was verified by conducting macroscopic gas and alcohol permeability experiments. This approach provides a better understanding of the microscale mechanism of gas transport in bentonite and the importance of pore structures at different scales in determining its seepage characteristics.展开更多
An in vitro blood-brain barrier(BBB) model is critical for enabling rapid screening of the BBB permeability of the drugs targeting on the central nervous system.Though many models have been developed, their reproducib...An in vitro blood-brain barrier(BBB) model is critical for enabling rapid screening of the BBB permeability of the drugs targeting on the central nervous system.Though many models have been developed, their reproducibility and renewability remain a challenge. Furthermore, drug transport data from many of the models do not correlate well with the data for in vivo BBB drug transport.Induced-pluripotent stem cell(i PSC) technology provides reproducible cell resources for in vitro BBB modeling.Here, we generated a human in vitro BBB model by differentiating the human i PSC(hi PSC) line GM25256 into brain endothelial-type cells. The model displayed BBB characteristics including tight junction proteins(ZO-1,claudin-5, and occludin) and endothelial markers(von Willebrand factor and Ulex), as well as high transendothelial electrical resistance(TEER)(1560 X.cm2±230 X.cm2) and c-GTPase activity. Co-culture with primary rat astrocytes significantly increased the TEER of the model(2970 X.cm2 to 4185 X.cm2). RNAseq analysis confirmed the expression of key BBB-related genes in the hi PSC-derived endothelial cells in comparison with primary human brain microvascular endothelial cells,including P-glycoprotein(Pgp) and breast cancer resistant protein(BCRP). Drug transport assays for nine CNS compounds showed that the permeability of non-Pgp/BCRP and Pgp/BCRP substrates across the model was strongly correlated with rodent in situ brain perfusion data for these compounds(R2= 0.982 and R2= 0.9973,respectively), demonstrating the functionality of the drug transporters in the model. Thus, this model may be used to rapidly screen CNS compounds, to predict the in vivo BBB permeability of these compounds and to study the biology of the BBB.展开更多
文摘Permeability is affected by complex factors such as the subsurface geological structure and porosity-permeability correlation.For highly heterogeneous reservoirs with complex pore structures,it is extremely challenging to spatially characterize(predict)permeability using seismic data.The conventional way of permeability prediction intends to convert underground refl ection data into the elastic parameters sensitive to underground fluids,build a universal low-dimensional template via petrophysical modeling and ultimately deliver spatial prediction of permeability.However,this method is restrained by the actual subsurface condition,selected well-logging sensitive parameters and the accuracy of the computed elastic parameters and fails to simulate the petrophysical mechanisms of complex reservoir permeability,which reduces the permeability prediction accuracy.The method proposed in this paper combines petrophysics and artificial intelligence and integrates multiple types of information to build the high-dimensional petrophysical template for permeability,in an attempt to improve the spatial characterization and prediction accuracy of permeability.The field testing demonstrates the high application performance and effective improvement in prediction accuracy and fluvial channel characterization.
基金supported by the National Natural Science Foundation of China(Grant Nos.51839009 and 51679017)the Graduate Research and Innovation Foundation of Chongqing,China(Grant No.CYB18037).
文摘Pore scale variables(e.g.,porosity,grain size)are important indexes to predict the hydraulic properties of porous geomaterials.X-ray images from ten types of intact sandstones and another type of sandstone samples subjected to triaxial compression are used to investigate the permeability and fracture characteristics.A novel double threshold segmentation algorithm is proposed to segment cracks,pores and grains,and pore scale variables are defined and extracted from these X-ray CT images to study the geometric characteristics of microstructures of porous geomaterials.Moreover,novel relations among these pore scale variables for permeability prediction are established,and the evolution process of cracks is investigated.The results indicate that the porescale permeability is prominently improved by cracks.In addition,excellent agreements are found between the measured and the estimated pore scale variables and permeability.The established correlations can be employed to effectively identify the hydraulic properties of porous geomaterials.
基金supported by the National Key Research and Devel-opment Program of China (Grant No.2022YFC3005503)the National Natural Science Foundation of China (Grant Nos.52322907,52179141,U23B20149,U2340232)+1 种基金the Fundamental Research Funds for the Central Universities (Grant Nos.2042024kf1031,2042024kf0031)the Key Program of Science and Technology of Yunnan Province (Grant Nos.202202AF080004,202203AA080009).
文摘Accurately and efficiently predicting the permeability of porous media is essential for addressing a wide range of hydrogeological issues.However,the complexity of porous media often limits the effectiveness of individual prediction methods.This study introduces a novel Particle Swarm Optimization-based Permeability Integrated Prediction model(PSO-PIP),which incorporates a particle swarm optimization algorithm enhanced with dy-namic clustering and adaptive parameter tuning(KGPSO).The model integrates multi-source data from the Lattice Boltzmann Method(LBM),Pore Network Modeling(PNM),and Finite Difference Method(FDM).By assigning optimal weight coefficients to the outputs of these methods,the model minimizes deviations from actual values and enhances permeability prediction performance.Initially,the computational performances of the LBM,PNM,and FDM are comparatively analyzed on datasets consisting of sphere packings and real rock samples.It is observed that these methods exhibit computational biases in certain permeability ranges.The PSOPIP model is proposed to combine the strengths of each computational approach and mitigate their limitations.The PSO-PIP model consistently produces predictions that are highly congruent with actual permeability values across all prediction intervals,significantly enhancing prediction accuracy.The outcomes of this study provide a new tool and perspective for the comprehensive,rapid,and accurate prediction of permeability in porous media.
基金supported by the by the National Natural Science Foundation(No.60874069,60634020)the National High Technology Research and Development Programme of China(No.2009AA04Z124)Hunan Provincial Natural Science Foundation of China(No.09JJ3122)
文摘To effectively predict the permeability index of smelting process in the imperial smelting furnace, an intelligent prediction model is proposed. It integrates the case-based reasoning （CBR） with adaptive par- ticle swarm optimization （PSO）. The nmnber of nearest neighbors and the weighted features vector are optimized online using the adaptive PSO to improve the prediction accuracy of CBR. The adaptive inertia weight and mutation operation are used to overcome the premature convergence of the PSO. The proposed method is validated a compared with the basic weighted CBR. The results show that the proposed model has higher prediction accuracy and better performance than the basic CBR model.
基金support of the National Natural Science Foundation of China (Grant Nos. 52174133 and 51809263)China Atomic Energy Authority。
文摘The sealing performance of a bentonite barrier is highly dependent on its seepage characteristics, which are directly related to the characteristics of its pore structure. Based on scanning electron microscopy(SEM) and focused ion beam-SEM(FIB-SEM), the pore structure of bentonite was characterized at different scales. First, a reasonable gray threshold was determined through back analysis, and the image was binarized based on the threshold. In addition, binary images were used to analyze bentonite’s pore structure(porosity and pore size distribution). Furthermore, the effects of different algorithms on the pore structure characterization were evaluated. Then, permeability calculations were performed based on the previous pore structure characteristics and a modified permeability prediction model. For permeability prediction based on the three-dimensional model, the effect of pore tortuosity was also considered. Finally, the accuracy of numerical calculations was verified by conducting macroscopic gas and alcohol permeability experiments. This approach provides a better understanding of the microscale mechanism of gas transport in bentonite and the importance of pore structures at different scales in determining its seepage characteristics.
文摘An in vitro blood-brain barrier(BBB) model is critical for enabling rapid screening of the BBB permeability of the drugs targeting on the central nervous system.Though many models have been developed, their reproducibility and renewability remain a challenge. Furthermore, drug transport data from many of the models do not correlate well with the data for in vivo BBB drug transport.Induced-pluripotent stem cell(i PSC) technology provides reproducible cell resources for in vitro BBB modeling.Here, we generated a human in vitro BBB model by differentiating the human i PSC(hi PSC) line GM25256 into brain endothelial-type cells. The model displayed BBB characteristics including tight junction proteins(ZO-1,claudin-5, and occludin) and endothelial markers(von Willebrand factor and Ulex), as well as high transendothelial electrical resistance(TEER)(1560 X.cm2±230 X.cm2) and c-GTPase activity. Co-culture with primary rat astrocytes significantly increased the TEER of the model(2970 X.cm2 to 4185 X.cm2). RNAseq analysis confirmed the expression of key BBB-related genes in the hi PSC-derived endothelial cells in comparison with primary human brain microvascular endothelial cells,including P-glycoprotein(Pgp) and breast cancer resistant protein(BCRP). Drug transport assays for nine CNS compounds showed that the permeability of non-Pgp/BCRP and Pgp/BCRP substrates across the model was strongly correlated with rodent in situ brain perfusion data for these compounds(R2= 0.982 and R2= 0.9973,respectively), demonstrating the functionality of the drug transporters in the model. Thus, this model may be used to rapidly screen CNS compounds, to predict the in vivo BBB permeability of these compounds and to study the biology of the BBB.
