Finding an accurate method for estimating permeability aside from well logs has been a difficult task for many years.The most commonly used methods targeted towards regression technique to understand the correlation b...Finding an accurate method for estimating permeability aside from well logs has been a difficult task for many years.The most commonly used methods targeted towards regression technique to understand the correlation between pore throat radii,porosity and permeability are Winland and Pittman equation approaches.While these methods are very common among petrophysicists,they do not give a good prediction in certain cases.Consequently,this paper investigates the relationship among porosity,permeability,and pore throat radii using three methods such as multiple regression analysis,artificial neural network(ANN),and adaptive neuro-fuzzy inference system(ANFIS)for application in transition zone permeability modeling.Firstly,a comprehensive mercury injection capillary pressure(MICP)test was conducted using 228 transition zone carbonate core samples from a field located in the Middle-East region.Multiple regression analysis was later performed to estimate the permeability using pore throat and porosity measurement.For the ANN,a two-layer feed-forward neural network with sigmoid hidden neurons and a linear output neuron was used.The technique involves training,validation,and testing of input/output data.However,for the ANFIS method,a hybrid optimization consisting of least-square and backpropagation gradient descent methods with a subtractive clustering technique was used.The ANFIS combines both the artificial neural network and fuzzy logic inference system(FIS)for the training,validation,and testing of input/output data.The results show that the best correlation for the multiple regression technique is achieved for pore throat radii with 35%mercury saturation(R35).However,for both the ANN and ANFIS techniques,pore throat radii with 55%mercury saturation(R55)gives the best result.Both the ANN and ANFIS are later found to be more effective and efficient and thus recommended as compared with the multiple regression technique commonly used in the industry.展开更多
The tight sandstones of the Upper Triassic Xujiahe Formation（T_3x） constitute important gas reservoirs in western Sichuan.The Xujiahe sandstones are characterized by low to very low porosity （av.5.22%and 3.62%） fo...The tight sandstones of the Upper Triassic Xujiahe Formation（T_3x） constitute important gas reservoirs in western Sichuan.The Xujiahe sandstones are characterized by low to very low porosity （av.5.22%and 3.62%） for the T_3x^4 and T_3x^2 sandstones,respectively）,extremely low permeability（av. 0.060 mD and 0.058 mD for the T_3x^4 and T_3x^2 sandstones,respectively）,strong heterogeneity,micronano pore throat,and poor pore throat sorting.As a result of complex pore structure and the occurrence of fractures,weak correlations exist between petrophysical properties and pore throat size,demonstrating that porosity or pore throat size alone does not serve as a good permeability predictor.Much improved correlations can be obtained between permeability and porosity when pore throat radii are incorporated. Correlations between porosity,permeability,and pore throat radii corresponding to different saturations of mercury were established,showing that the pore throat radius at 20%mercury saturation（R_（20）） is the best permeability predictor.Multivariate regression analysis and artificial neural network（ANN） methods were used to establish permeability prediction models and the unique characteristics of neural networks enable them to be more successful in predicting permeability than the multivariate regression model.In addition, four petrophysical rock types can be identified based on the distributions of R_（20）,each exhibiting distinct petrophysical properties and corresponding to different flow units.展开更多
基金The authors appreciate the Abu Dhabi National Oil Company(ADNOC)the ADNOC R&D Oil-Subcommittee for funding and supporting this work(RDProj.084-RCM)。
文摘Finding an accurate method for estimating permeability aside from well logs has been a difficult task for many years.The most commonly used methods targeted towards regression technique to understand the correlation between pore throat radii,porosity and permeability are Winland and Pittman equation approaches.While these methods are very common among petrophysicists,they do not give a good prediction in certain cases.Consequently,this paper investigates the relationship among porosity,permeability,and pore throat radii using three methods such as multiple regression analysis,artificial neural network(ANN),and adaptive neuro-fuzzy inference system(ANFIS)for application in transition zone permeability modeling.Firstly,a comprehensive mercury injection capillary pressure(MICP)test was conducted using 228 transition zone carbonate core samples from a field located in the Middle-East region.Multiple regression analysis was later performed to estimate the permeability using pore throat and porosity measurement.For the ANN,a two-layer feed-forward neural network with sigmoid hidden neurons and a linear output neuron was used.The technique involves training,validation,and testing of input/output data.However,for the ANFIS method,a hybrid optimization consisting of least-square and backpropagation gradient descent methods with a subtractive clustering technique was used.The ANFIS combines both the artificial neural network and fuzzy logic inference system(FIS)for the training,validation,and testing of input/output data.The results show that the best correlation for the multiple regression technique is achieved for pore throat radii with 35%mercury saturation(R35).However,for both the ANN and ANFIS techniques,pore throat radii with 55%mercury saturation(R55)gives the best result.Both the ANN and ANFIS are later found to be more effective and efficient and thus recommended as compared with the multiple regression technique commonly used in the industry.
基金supported by the Important National Science&Technology Specific Project (2008ZX05002-004)
文摘The tight sandstones of the Upper Triassic Xujiahe Formation（T_3x） constitute important gas reservoirs in western Sichuan.The Xujiahe sandstones are characterized by low to very low porosity （av.5.22%and 3.62%） for the T_3x^4 and T_3x^2 sandstones,respectively）,extremely low permeability（av. 0.060 mD and 0.058 mD for the T_3x^4 and T_3x^2 sandstones,respectively）,strong heterogeneity,micronano pore throat,and poor pore throat sorting.As a result of complex pore structure and the occurrence of fractures,weak correlations exist between petrophysical properties and pore throat size,demonstrating that porosity or pore throat size alone does not serve as a good permeability predictor.Much improved correlations can be obtained between permeability and porosity when pore throat radii are incorporated. Correlations between porosity,permeability,and pore throat radii corresponding to different saturations of mercury were established,showing that the pore throat radius at 20%mercury saturation（R_（20）） is the best permeability predictor.Multivariate regression analysis and artificial neural network（ANN） methods were used to establish permeability prediction models and the unique characteristics of neural networks enable them to be more successful in predicting permeability than the multivariate regression model.In addition, four petrophysical rock types can be identified based on the distributions of R_（20）,each exhibiting distinct petrophysical properties and corresponding to different flow units.
