Most multiphase flow separation detection methods used commonly in oilfields are low in efficiency and accuracy,and have data delay.An online multiphase flow detection method is proposed based on magnetic resonance te...Most multiphase flow separation detection methods used commonly in oilfields are low in efficiency and accuracy,and have data delay.An online multiphase flow detection method is proposed based on magnetic resonance technology,and its supporting device has been made and tested in lab and field.The detection technology works in two parts:measure phase holdup in static state and measure flow rate in flowing state.Oil-water ratio is first measured and then gas holdup.The device is composed of a segmented magnet structure and a dual antenna structure for measuring flowing fluid.A highly compact magnetic resonance spectrometer system and intelligent software are developed.Lab experiments and field application show that the online detection system has the following merits:it can measure flow rate and phase holdup only based on magnetic resonance technology;it can detect in-place transient fluid production at high frequency and thus monitor transient fluid production in real time;it can detect oil,gas and water in a full range at high precision,the detection isn’t affected by salinity and emulsification.It is a green,safe and energy-saving system.展开更多
The detection of informal settlements is the first step in planning and upgrading deprived areas in order to leave no one behind in SDGs.Very High-Resolution satellite images(VHR),have been extensively used for this p...The detection of informal settlements is the first step in planning and upgrading deprived areas in order to leave no one behind in SDGs.Very High-Resolution satellite images(VHR),have been extensively used for this purpose.However,as a cost-prohibitive data source,VHR might not be available to all,particularly nations that are home to many informal settlements.This study examines the application of open and freely available data sources to detect the structure and pattern of informal settlements.Here,in a case study of Jakarta,Indonesia,Medium Resolution satellite imagery(MR)derived from Landsat 8(2020)was classified to detect these settlements.The classification was done using Random Forest(RF)classifier through two complementary approaches to develop the training set.In the first approach,available survey data sets(Jakarta’s informal settlements map for 2015)and visual interpreta-tion using High-Resolution Google Map imagery have been used to build the training set.Throughout the second round of classifica-tion,OpenStreetMap(OSM)layers were used as the complementary approach for training.Results from the validation test for the second round revealed better accuracy and precision in classi-fication.The proposed method provides an opportunity to use open data for informal settlements detection,when:1)more expen-sive high resolution data sources are not accessible;2)the area of interest is not larger than a city;and 3)the physical characteristics of the settlements differ significantly from their surrounding formal area.The method presents the application of globally accessible data to help the achievement of resilience and SDGs in informal settlements.展开更多
利用GNSS-MR(Global Navigation Satellite System Multipath Reflectometry)技术反演积雪深度是近年来一种新兴的卫星遥感技术。目前大多数研究仅使用GPS(Global Position System)数据限制了该技术的发展,为了扩展GNSS-MR算法的应用,...利用GNSS-MR(Global Navigation Satellite System Multipath Reflectometry)技术反演积雪深度是近年来一种新兴的卫星遥感技术。目前大多数研究仅使用GPS(Global Position System)数据限制了该技术的发展,为了扩展GNSS-MR算法的应用,介绍了基于GNSS-MR算法的雪深反演模型。首先,通过多项式拟合分解GLONASS观测数据获取高精度的信噪比残差序列;然后,利用Lomb-Scargle谱分析法对其进行频谱分析可解算雪深值。选取IGS中心的YEL2站2015年11月到2016年6月共243天的GLONASS卫星L1波段反射信号的SNR数据进行实例分析,并以美国国家气象数据中心提供的加拿大Y-H (Yellowknife Henderson)气象站的实测雪深数据为真值,将反演雪深与实测雪深进行对比验证。所得实验结果如下:(1)与GPS卫星的反演值相比,基于GLONASS-MR(GLONASS Multipath Reflectometry)技术反演积雪深度的精度同样能达到厘米级,RMSE仅3.3 cm,反演值与实测值的空间分布趋势一致且相关性较强,其相关系数R2高达0.969;(2)不同的积雪深度对信噪比的振幅频率与垂直反射距离具有直接影响;(3)对同一卫星而言,信噪比的频谱振幅强度峰值与其对应的反演值存在线性相关;(4)在相同条件下,采用多颗GLONASS卫星数据比单颗GLONASS卫星数据反演雪深的效果明显更优。基于反演的高时间分辨率产品,分析该地区雪深日变化的情况,实验结果表明基于陆基CORS站的GLONASS-MR技术在用于实时、连续的雪深变化监测方面具有良好的潜力和可行性。展开更多
基金Supported by the National Natural Science Foundation of China(51704327)
文摘Most multiphase flow separation detection methods used commonly in oilfields are low in efficiency and accuracy,and have data delay.An online multiphase flow detection method is proposed based on magnetic resonance technology,and its supporting device has been made and tested in lab and field.The detection technology works in two parts:measure phase holdup in static state and measure flow rate in flowing state.Oil-water ratio is first measured and then gas holdup.The device is composed of a segmented magnet structure and a dual antenna structure for measuring flowing fluid.A highly compact magnetic resonance spectrometer system and intelligent software are developed.Lab experiments and field application show that the online detection system has the following merits:it can measure flow rate and phase holdup only based on magnetic resonance technology;it can detect in-place transient fluid production at high frequency and thus monitor transient fluid production in real time;it can detect oil,gas and water in a full range at high precision,the detection isn’t affected by salinity and emulsification.It is a green,safe and energy-saving system.
文摘The detection of informal settlements is the first step in planning and upgrading deprived areas in order to leave no one behind in SDGs.Very High-Resolution satellite images(VHR),have been extensively used for this purpose.However,as a cost-prohibitive data source,VHR might not be available to all,particularly nations that are home to many informal settlements.This study examines the application of open and freely available data sources to detect the structure and pattern of informal settlements.Here,in a case study of Jakarta,Indonesia,Medium Resolution satellite imagery(MR)derived from Landsat 8(2020)was classified to detect these settlements.The classification was done using Random Forest(RF)classifier through two complementary approaches to develop the training set.In the first approach,available survey data sets(Jakarta’s informal settlements map for 2015)and visual interpreta-tion using High-Resolution Google Map imagery have been used to build the training set.Throughout the second round of classifica-tion,OpenStreetMap(OSM)layers were used as the complementary approach for training.Results from the validation test for the second round revealed better accuracy and precision in classi-fication.The proposed method provides an opportunity to use open data for informal settlements detection,when:1)more expen-sive high resolution data sources are not accessible;2)the area of interest is not larger than a city;and 3)the physical characteristics of the settlements differ significantly from their surrounding formal area.The method presents the application of globally accessible data to help the achievement of resilience and SDGs in informal settlements.
