Based on structural surface normal vector spherical distance and the pole stereographic projection Euclidean distance,two distance functions were established.The cluster analysis of structure surface was conducted by ...Based on structural surface normal vector spherical distance and the pole stereographic projection Euclidean distance,two distance functions were established.The cluster analysis of structure surface was conducted by the use of ATTA clustering methods based on ant colony piles,and Silhouette index was introduced to evaluate the clustering effect.The clustering analysis of the measured data of Sanshandao Gold Mine shows that ant colony ATTA-based clustering method does better than K-mean clustering analysis.Meanwhile,clustering results of ATTA method based on pole Euclidean distance and ATTA method based on normal vector spherical distance have a great consistence.The clustering results are most close to the pole isopycnic graph.It can efficiently realize grouping of structural plane and determination of the dominant structural surface direction.It is made up for the defects of subjectivity and inaccuracy in icon measurement approach and has great engineering value.展开更多
The traditional reservoir classification methods based on conventional well logging are inefficient for determining the properties,such as the porosity,shale volume,J function,and flow zone index,of the tight sandston...The traditional reservoir classification methods based on conventional well logging are inefficient for determining the properties,such as the porosity,shale volume,J function,and flow zone index,of the tight sandstone reservoirs because of their complex pore structure and large heterogeneity.Specifically,the method that is commonly used to characterize the reservoir pore structure is dependent on the nuclear magnetic resonance(NMR)transverse relaxation time(T2)distribution,which is closely related to the pore size distribution.Further,the pore structure parameters(displacement pressure,maximum pore-throat radius,and median pore-throat radius)can be determined and applied to reservoir classification based on the empirical linear or power function obtained from the NMR T2 distributions and the mercury intrusion capillary pressure ourves.However,the effective generalization of these empirical functions is difficult because they differ according to the region and are limited by the representative samples of different regions.A lognormal distribution is commonly used to describe the pore size and particle size distributions of the rock and quantitatively characterize the reservoir pore structure based on the volume,mean radius,and standard deviation of the small and large pores.In this study,we obtain six parameters(the volume,mean radius,and standard deviation of the small and large pores)that represent the characteristics of pore distribution and rock heterogeneity,calculate the total porosity via NMR logging,and classify the reservoirs via cluster analysis by adopting a bimodal lognormal distribution to fit the NMR T2 spectrum.Finally,based on the data obtained from the core tests and the NMR logs,the proposed method,which is readily applicable,can effectively classify the tight sandstone reservoirs.展开更多
Gap statistic is a well-known index of clustering validity, but its realization is difficult to be comprehended and accurately determined. A direct method is presented to improve the performance of the Gap statistic, ...Gap statistic is a well-known index of clustering validity, but its realization is difficult to be comprehended and accurately determined. A direct method is presented to improve the performance of the Gap statistic, which applies the two-order difference of within-cluster dispersion to replace the constructed null reference distribution in the Gap statistic. Hence, the realization of the Gap statistic becomes easy and is reformulated, and its uncertainty in applications is reduced. Also, the limitation of the Gap statistic is analyzed by two typical examples, that is, the Gap statistic is difficult to be applied to the dataset that contains strong-overlap or uneven-density clusters. Experiments verify the usefulness of the proposed method.展开更多
基金Project(41272304)supported by the National Natural Science Foundation of ChinaProject(51074177)jointly supported by the National Natural Science Foundation and Shanghai Baosteel Group Corporation,ChinaProject(CX2012B070)supported by Hunan Provincial Innovation Fund for Postgraduated Students,China
文摘Based on structural surface normal vector spherical distance and the pole stereographic projection Euclidean distance,two distance functions were established.The cluster analysis of structure surface was conducted by the use of ATTA clustering methods based on ant colony piles,and Silhouette index was introduced to evaluate the clustering effect.The clustering analysis of the measured data of Sanshandao Gold Mine shows that ant colony ATTA-based clustering method does better than K-mean clustering analysis.Meanwhile,clustering results of ATTA method based on pole Euclidean distance and ATTA method based on normal vector spherical distance have a great consistence.The clustering results are most close to the pole isopycnic graph.It can efficiently realize grouping of structural plane and determination of the dominant structural surface direction.It is made up for the defects of subjectivity and inaccuracy in icon measurement approach and has great engineering value.
基金supported by the by the National Science and Technology Major Project “Prediction Technique and Evaluation of Tight Oil Sweet Spot”(2016ZX05046-002)
文摘The traditional reservoir classification methods based on conventional well logging are inefficient for determining the properties,such as the porosity,shale volume,J function,and flow zone index,of the tight sandstone reservoirs because of their complex pore structure and large heterogeneity.Specifically,the method that is commonly used to characterize the reservoir pore structure is dependent on the nuclear magnetic resonance(NMR)transverse relaxation time(T2)distribution,which is closely related to the pore size distribution.Further,the pore structure parameters(displacement pressure,maximum pore-throat radius,and median pore-throat radius)can be determined and applied to reservoir classification based on the empirical linear or power function obtained from the NMR T2 distributions and the mercury intrusion capillary pressure ourves.However,the effective generalization of these empirical functions is difficult because they differ according to the region and are limited by the representative samples of different regions.A lognormal distribution is commonly used to describe the pore size and particle size distributions of the rock and quantitatively characterize the reservoir pore structure based on the volume,mean radius,and standard deviation of the small and large pores.In this study,we obtain six parameters(the volume,mean radius,and standard deviation of the small and large pores)that represent the characteristics of pore distribution and rock heterogeneity,calculate the total porosity via NMR logging,and classify the reservoirs via cluster analysis by adopting a bimodal lognormal distribution to fit the NMR T2 spectrum.Finally,based on the data obtained from the core tests and the NMR logs,the proposed method,which is readily applicable,can effectively classify the tight sandstone reservoirs.
基金National Natural Science Foundation of China(No.60572065, 60772080, 60532020)
文摘Gap statistic is a well-known index of clustering validity, but its realization is difficult to be comprehended and accurately determined. A direct method is presented to improve the performance of the Gap statistic, which applies the two-order difference of within-cluster dispersion to replace the constructed null reference distribution in the Gap statistic. Hence, the realization of the Gap statistic becomes easy and is reformulated, and its uncertainty in applications is reduced. Also, the limitation of the Gap statistic is analyzed by two typical examples, that is, the Gap statistic is difficult to be applied to the dataset that contains strong-overlap or uneven-density clusters. Experiments verify the usefulness of the proposed method.
