In order to analyze and compare the differences in pore structures between shale gas and shale oil formations, a few samples from the Longmaxi and Bakken Formations were collected and studied using X-ray diffraction, ...In order to analyze and compare the differences in pore structures between shale gas and shale oil formations, a few samples from the Longmaxi and Bakken Formations were collected and studied using X-ray diffraction, LECO TOC measurement, gas adsorption and field-emission scanning electron microscope. The results show that samples from the Bakken Formation have a higher TOC than those from the Longmaxi Formation. The Longmaxi Formation has higher micropore volume and larger micropore surface area and exhibited a smaller average distribution of microsize pores compared to the Bakken Formation. Both formations have similar meso-macropore volume. The Longmaxi Formation has a much larger meso-macropore surface area, which is corresponding to a smaller average meso-macropore size. CO_2 adsorption data processing shows that the pore size of the majority of the micropores in the samples from the Longmaxi Formation is less than 1 nm, while the pore size of the most of the micropores in the samples from the Bakken Formation is larger than 1 nm. Both formations have the same number of pore clusters in the 2–20 nm range, but the Bakken Formation has two additional pore size groups with mean pore size diameters larger than 20 nm. Multifractal analysis of pore size distribution curves that was derived from gas adsorption indicates that the samples from the Longmaxi Formation have more significant micropore heterogeneity and less meso-macropore heterogeneity. Abundant micropores as well as mesomacropores exist in the organic matter in the Longmaxi Formation, while the organic matter of the Bakken Formation hosts mainly micropores.展开更多
Background:Digital hemispherical photography(DHP)is widely used to estimate the leaf area index(LAI)of forest plots due to its advantages of high efficiency and low cost.A crucial step in the LAI estimation of forest ...Background:Digital hemispherical photography(DHP)is widely used to estimate the leaf area index(LAI)of forest plots due to its advantages of high efficiency and low cost.A crucial step in the LAI estimation of forest plots via DHP is choosing a sampling scheme.However,various sampling schemes involving DHP have been used for the LAI estimation of forest plots.To date,the impact of sampling schemes on LAI estimation from DHP has not been comprehensively investigated.Methods:In this study,13 commonly used sampling schemes which belong to five sampling types(i.e.dispersed,square,cross,transect and circle)were adopted in the LAI estimation of five Larix principis-rupprechtii plots(25m×25 m).An additional sampling scheme(with a sample size of 89)was generated on the basis of all the sample points of the 13 sampling schemes.Three typical inversion models and four canopy element clumping index(Ωe)algorithms were involved in the LAI estimation.The impacts of the sampling schemes on four variables,including gap fraction,Ωe,effective plant area index(PAIe)and LAI estimation from DHP were analysed.The LAI estimates obtained with different sampling schemes were then compared with those obtained from litter collection measurements.Results:Large differences were observed for all four variable estimates(i.e.gap fraction,Ωe,PAIe and LAI)under different sampling schemes.The differences in impact of sampling schemes on LAI estimation were not obvious for the three inversion models,if the fourΩe algorithms,except for the traditional gap-size analysis algorithm were adopted in the estimation.The accuracy of LAI estimation was not always improved with an increase in sample size.Moreover,results indicated that with the appropriate inversion model,Ωe algorithm and sampling scheme,the maximum estimation error of DHP-estimated LAI at elementary sampling unit can be less than 20%,which is required by the global climate observing system,except in forest plots with extremely large LAI values(~>6.0).However,obtaining an LAI from DHP with an estimation error lower than 5%is impossible regardless of which combination of inversion model,Ωe algorithm and sampling scheme is used.Conclusion:The LAI estimation of L.principis-rupprechtii forests from DHP was largely affected by the sampling schemes adopted in the estimation.Thus,the sampling scheme should be seriously considered in the LAI estimation.One square and two transect sampling schemes(with sample sizes ranging from 3 to 9)were recommended to be used to estimate the LAI of L.principis-rupprechtii forests with the smallest mean relative error(MRE).By contrast,three cross and one dispersed sampling schemes were identified to provide LAI estimates with relatively large MREs.展开更多
基金the joint support from China Scholarship Council(201406450029)National Natural Science Foundation of China(Grant No.41504108)China Postdoctoral Science Foundation(Grant No.2015M582568)
文摘In order to analyze and compare the differences in pore structures between shale gas and shale oil formations, a few samples from the Longmaxi and Bakken Formations were collected and studied using X-ray diffraction, LECO TOC measurement, gas adsorption and field-emission scanning electron microscope. The results show that samples from the Bakken Formation have a higher TOC than those from the Longmaxi Formation. The Longmaxi Formation has higher micropore volume and larger micropore surface area and exhibited a smaller average distribution of microsize pores compared to the Bakken Formation. Both formations have similar meso-macropore volume. The Longmaxi Formation has a much larger meso-macropore surface area, which is corresponding to a smaller average meso-macropore size. CO_2 adsorption data processing shows that the pore size of the majority of the micropores in the samples from the Longmaxi Formation is less than 1 nm, while the pore size of the most of the micropores in the samples from the Bakken Formation is larger than 1 nm. Both formations have the same number of pore clusters in the 2–20 nm range, but the Bakken Formation has two additional pore size groups with mean pore size diameters larger than 20 nm. Multifractal analysis of pore size distribution curves that was derived from gas adsorption indicates that the samples from the Longmaxi Formation have more significant micropore heterogeneity and less meso-macropore heterogeneity. Abundant micropores as well as mesomacropores exist in the organic matter in the Longmaxi Formation, while the organic matter of the Bakken Formation hosts mainly micropores.
基金the National Science Foundation of China(Grant Nos.41871233,41371330 , 41001203).
文摘Background:Digital hemispherical photography(DHP)is widely used to estimate the leaf area index(LAI)of forest plots due to its advantages of high efficiency and low cost.A crucial step in the LAI estimation of forest plots via DHP is choosing a sampling scheme.However,various sampling schemes involving DHP have been used for the LAI estimation of forest plots.To date,the impact of sampling schemes on LAI estimation from DHP has not been comprehensively investigated.Methods:In this study,13 commonly used sampling schemes which belong to five sampling types(i.e.dispersed,square,cross,transect and circle)were adopted in the LAI estimation of five Larix principis-rupprechtii plots(25m×25 m).An additional sampling scheme(with a sample size of 89)was generated on the basis of all the sample points of the 13 sampling schemes.Three typical inversion models and four canopy element clumping index(Ωe)algorithms were involved in the LAI estimation.The impacts of the sampling schemes on four variables,including gap fraction,Ωe,effective plant area index(PAIe)and LAI estimation from DHP were analysed.The LAI estimates obtained with different sampling schemes were then compared with those obtained from litter collection measurements.Results:Large differences were observed for all four variable estimates(i.e.gap fraction,Ωe,PAIe and LAI)under different sampling schemes.The differences in impact of sampling schemes on LAI estimation were not obvious for the three inversion models,if the fourΩe algorithms,except for the traditional gap-size analysis algorithm were adopted in the estimation.The accuracy of LAI estimation was not always improved with an increase in sample size.Moreover,results indicated that with the appropriate inversion model,Ωe algorithm and sampling scheme,the maximum estimation error of DHP-estimated LAI at elementary sampling unit can be less than 20%,which is required by the global climate observing system,except in forest plots with extremely large LAI values(~>6.0).However,obtaining an LAI from DHP with an estimation error lower than 5%is impossible regardless of which combination of inversion model,Ωe algorithm and sampling scheme is used.Conclusion:The LAI estimation of L.principis-rupprechtii forests from DHP was largely affected by the sampling schemes adopted in the estimation.Thus,the sampling scheme should be seriously considered in the LAI estimation.One square and two transect sampling schemes(with sample sizes ranging from 3 to 9)were recommended to be used to estimate the LAI of L.principis-rupprechtii forests with the smallest mean relative error(MRE).By contrast,three cross and one dispersed sampling schemes were identified to provide LAI estimates with relatively large MREs.
