Modeling soil salinity in an arid salt-affected ecosystem is a difficult task when using remote sensing data because of the complicated soil context （vegetation cover, moisture, surface roughness, and organic matter...Modeling soil salinity in an arid salt-affected ecosystem is a difficult task when using remote sensing data because of the complicated soil context （vegetation cover, moisture, surface roughness, and organic matter） and the weak spectral features of salinized soil. Therefore, an index such as the salinity index （SI） that only uses soil spectra may not detect soil salinity effectively and quantitatively. The use of vegetation reflectance as an indirect indicator can avoid limitations associated with the direct use of soil reflectance. The normalized difference vegetation index （NDVI）, as the most common vegetation index, was found to be responsive to salinity but may not be available for retrieving sparse vegetation due to its sensitivity to background soil in arid areas. Therefore, the arid fraction integrated index （AFⅡ） was created as supported by the spectral mixture analysis （SMA）, which is more appropriate for analyzing variations in vegetation cover （particularly halophytes） than NDVI in the study area. Using soil and vegetation separately for detecting salinity perhaps is not feasible. Then, we developed a new and operational model, the soil salinity detecting model （SDM） that combines AFⅡ and SI to quantitatively estimate the salt content in the surface soil. SDMs, including SDM1 and SDM2, were constructed through analyzing the spatial characteristics of soils with different salinization degree by integrating AFⅡ and SI using a scatterplot. The SDMs were then compared to the combined spectral response index （COSRI） from field measurements with respect to the soil salt content. The results indicate that the SDM values are highly correlated with soil salinity, in contrast to the performance of COSRI. Strong exponential relationships were observed between soil salinity and SDMs （R2〉0.86, RMSE〈6.86） compared to COSRI （R2=0.71, RMSE=16.21）. These results suggest that the feature space related to biophysical properties combined with AFII and SI can effectively provide information on soil salinity.展开更多
The first pilot test of polyacrylamide microsphere alternate surfactant flood(PMAS)with mixtures of anionic-cationic surfactants(Sa/c)was carried out for a high-temperature,high-salinity,and highhardness sandstone res...The first pilot test of polyacrylamide microsphere alternate surfactant flood(PMAS)with mixtures of anionic-cationic surfactants(Sa/c)was carried out for a high-temperature,high-salinity,and highhardness sandstone reservoir to demonstrate the potential of this novel technique to improve oil recovery.A critical micelle concentration(CMC)of 4.82 mg/L,an ultralow interfacial tension(IFT)of 8104 mN/m,and a high oil solubilization of 22 were obtained.Static and dynamic adsorptions of Sa/c on natural core containing 15 wt%clay were reduced to about 2.20 and 0.30 mg/g-core,respectively,with the addition of adsorption inhibitor(AI).Since June 2014,the pilot test of PMAS was carried out in a Sinopec reservoir with a temperature of 87C,a salinity of 260,393 mg/L,and a hardness of 6,401 mg/L.Twelve cycles of alternative injection of 0.0125 PV Sa/c with a concentration of 0.1%and 0.0125 PV polyacrylamide microsphere with a concentration of 0.2%were conducted at an injection rate of 0.1 PV/yr,for a total of 0.3 PV chemical injection.As a result,the net daily oil production increased from 0 t to 6.5 t,and the water cut decreased from 96.3%to 93.8%,leading to an ultimate improved oil recovery of 6.3%original oil-in-place.展开更多
基金financially supported by the National Basic Research Program of China (2009CB825105)the National Natural Science Foundation of China (41261090)
文摘Modeling soil salinity in an arid salt-affected ecosystem is a difficult task when using remote sensing data because of the complicated soil context （vegetation cover, moisture, surface roughness, and organic matter） and the weak spectral features of salinized soil. Therefore, an index such as the salinity index （SI） that only uses soil spectra may not detect soil salinity effectively and quantitatively. The use of vegetation reflectance as an indirect indicator can avoid limitations associated with the direct use of soil reflectance. The normalized difference vegetation index （NDVI）, as the most common vegetation index, was found to be responsive to salinity but may not be available for retrieving sparse vegetation due to its sensitivity to background soil in arid areas. Therefore, the arid fraction integrated index （AFⅡ） was created as supported by the spectral mixture analysis （SMA）, which is more appropriate for analyzing variations in vegetation cover （particularly halophytes） than NDVI in the study area. Using soil and vegetation separately for detecting salinity perhaps is not feasible. Then, we developed a new and operational model, the soil salinity detecting model （SDM） that combines AFⅡ and SI to quantitatively estimate the salt content in the surface soil. SDMs, including SDM1 and SDM2, were constructed through analyzing the spatial characteristics of soils with different salinization degree by integrating AFⅡ and SI using a scatterplot. The SDMs were then compared to the combined spectral response index （COSRI） from field measurements with respect to the soil salt content. The results indicate that the SDM values are highly correlated with soil salinity, in contrast to the performance of COSRI. Strong exponential relationships were observed between soil salinity and SDMs （R2〉0.86, RMSE〈6.86） compared to COSRI （R2=0.71, RMSE=16.21）. These results suggest that the feature space related to biophysical properties combined with AFII and SI can effectively provide information on soil salinity.
文摘The first pilot test of polyacrylamide microsphere alternate surfactant flood(PMAS)with mixtures of anionic-cationic surfactants(Sa/c)was carried out for a high-temperature,high-salinity,and highhardness sandstone reservoir to demonstrate the potential of this novel technique to improve oil recovery.A critical micelle concentration(CMC)of 4.82 mg/L,an ultralow interfacial tension(IFT)of 8104 mN/m,and a high oil solubilization of 22 were obtained.Static and dynamic adsorptions of Sa/c on natural core containing 15 wt%clay were reduced to about 2.20 and 0.30 mg/g-core,respectively,with the addition of adsorption inhibitor(AI).Since June 2014,the pilot test of PMAS was carried out in a Sinopec reservoir with a temperature of 87C,a salinity of 260,393 mg/L,and a hardness of 6,401 mg/L.Twelve cycles of alternative injection of 0.0125 PV Sa/c with a concentration of 0.1%and 0.0125 PV polyacrylamide microsphere with a concentration of 0.2%were conducted at an injection rate of 0.1 PV/yr,for a total of 0.3 PV chemical injection.As a result,the net daily oil production increased from 0 t to 6.5 t,and the water cut decreased from 96.3%to 93.8%,leading to an ultimate improved oil recovery of 6.3%original oil-in-place.
