Carbon dioxide enhanced oil recovery(CO_(2)-EOR)technology is used for oil production and CO_(2) storage in reservoirs.Methods are being constantly developed to optimize oil recovery and CO_(2) storage during the CO_(...Carbon dioxide enhanced oil recovery(CO_(2)-EOR)technology is used for oil production and CO_(2) storage in reservoirs.Methods are being constantly developed to optimize oil recovery and CO_(2) storage during the CO_(2) displacement process,especially for low-permeability reservoirs under varying geological conditions.In this study,long-core experiments and trans-scale numerical simulations are employed to examine the characteristics of oil production and CO_(2) storage.Optimal production parameters for the target reservoir are also proposed.The results indicate that maintaining the pressure at 1.04 to 1.10 times the minimum miscible pressure(MMP)and increasing the injection rate can enhance oil production in the early stage of reservoir development.In contrast,reducing the injection rate at the later stages prevents CO_(2) channeling,thus improving oil recovery and CO_(2) storage efficiency.A solution-doubling factor is introduced to modify the calculation method for CO_(2) storage,increasing its accuracy to approximately 90%.Before CO_(2) breakthrough,prioritizing oil production is recommended to maximize the economic benefits of this process.In the middle stage of CO_(2) displacement,decreasing the injection rate optimizes the coordination between oil displacement and CO_(2) storage.Further,in the late stage,reduced pressure and injection rates are required as the focus shifts to CO_(2) storage.展开更多
Inland lakes are important water resources in arid and semiarid regions. Understanding climate effects on these lakes is critical to accurately evaluate the dynamic changes of water resources. This study focused on th...Inland lakes are important water resources in arid and semiarid regions. Understanding climate effects on these lakes is critical to accurately evaluate the dynamic changes of water resources. This study focused on the changes in Sayram Lake of Xinjiang, China, and addressed the effects of climate fluctuations on the inland lake based on long-term sequenced remote sensing images and meteorological data from the past 40 years. A geo- graphic information system （GIS） method was used to obtain the hypsometry of the basin area of Sayram Lake, and estimation methods for evaporation from rising temperature and water levels from increasing precipitation were proposed. Results showed that： （1）Areal values of Sayram Lake have increased over the past 40 years. （2） Both temperature and precipitation have increased with average increases of more than 1.8~C and 82 mm, respectively. Variation of the water levels in the lake was consistent with local climate changes, and the areal values show linear relationships with local temperature and precipitation data. （3） According to the hypsometry data of the basin area, the estimated lake water levels increased by 2.8 m, and the water volume increased by 12.9×108 m3 over the past 40 years. The increasing area of Sayram Lake correlated with local and regional climatic changes because it is hardly affected by human activities.展开更多
Spatial interpolation methods are frequently used to estimate values of meteorological data in locations where they are not measured. However, very little research has been investigated the relative performance of dif...Spatial interpolation methods are frequently used to estimate values of meteorological data in locations where they are not measured. However, very little research has been investigated the relative performance of different interpolation methods in meteorological data of Xinjiang Uygur Autonomous Region (Xinjiang). Actually, it has importantly practical significance to as far as possibly improve the accuracy of interpolation results for meteorological data, especially in mountainous Xinjiang. There- fore, this paper focuses on the performance of different spatial interpolation methods for monthly temperature data in Xinjiang. The daily observed data of temperature are collected from 38 meteorological stations for the period 1960- 2004. Inverse distance weighting (IDW), ordinary kriging (OK), temperature lapse rate method (TLR) and multiple linear regressions (MLR) are selected as interpolated methods. Two rasterized methods, multiple regression plus space residual error and directly interpolated observed temperature (DIOT) data, are used to analyze and compare the performance of these interpolation methods respectively. Moreover, cross-validation is used to evaluate the performance of different spatial interpolation methods. The results are as follows: 1) The method of DIOT is unsuitable for the study area in this paper. 2) It is important to process the observed data by local regression model before the spatial interpolation. 3) The MLR-IDW is the optimum spatial interpolation method for the monthly mean temperature based on cross-validation. For the authors, the reliability of results and the influence of measurement accuracy, density, distribution and spatial variability on the accuracy of the interpolation methods will be tested and analyzed in the future.展开更多
Scientific knowledge of lunar lithologies was first acquired in the 1960s-1970s.The space race between the United States(U.S.)and Soviet Union has promoted numerous manned and robotic lunar exploration missions.Utiliz...Scientific knowledge of lunar lithologies was first acquired in the 1960s-1970s.The space race between the United States(U.S.)and Soviet Union has promoted numerous manned and robotic lunar exploration missions.Utilizing datasets from these missions,the first series of lunar geologic maps was prepared and published by the U.S.Geological Survey(USGS)The definition of lunar geological features in these maps was mostly based on morphological characteristics but lacked lithological constraints owing to the incompleteness of the compositional datasets avail-able.After two decades of silence,a new era of lunar exploration began in the 1990s when the Galileo spacecraft flew by the Moon during its gravity-assisted maneuvers.The very successful orbital missions,the Clementine and Lunar Prospector(LP),provided the first global geochemical and mineralogical(multispectral,gamma ray,neutron,etc.)datasets of the lunar surface.展开更多
The tectonic evolution of the Moon is driven by both endogenic(e.g.,magmatism)and exogenic(e.g.,meteorite impact)forces.A tectonic map of the Moon provides key information about the spatiotemporal distribution of stru...The tectonic evolution of the Moon is driven by both endogenic(e.g.,magmatism)and exogenic(e.g.,meteorite impact)forces.A tectonic map of the Moon provides key information about the spatiotemporal distribution of structures and tectonic units.Although the Moon has no plate tectonics,its surface can be divided into different terranes due to the uneven evolution[1].We define these terranes as tectonic units.Structures,such as craters and faults,are the basic elements of the tectonic units.As a synthesis of current knowledge on lunar tectonics and evolutionary history,lunar tectonic maps are a fundamental resource for scientific research,exploration planning,and landing site selection[2].展开更多
Hydro-morphological processes(HMP,any natural phenomenon contained within the spectrum defined between debris flows and flash floods)are globally occurring natural hazards which pose great threats to our society,leadi...Hydro-morphological processes(HMP,any natural phenomenon contained within the spectrum defined between debris flows and flash floods)are globally occurring natural hazards which pose great threats to our society,leading to fatalities and economical losses.For this reason,understanding the dynamics behind HMPs is needed to aid in hazard and risk assessment.In this work,we take advantage of an explainable deep learning model to extract global and local interpretations of the HMP occurrences across the whole Chinese territory.We use a deep neural network architecture and interpret the model results through the spatial pattern of SHAP values.In doing so,we can understand the model prediction on a hierarchical basis,looking at how the predictor set controls the overall susceptibility as well as doing the same at the level of the single mapping unit.Our model accurately predicts HMP occurrences with AUC values measured in a ten-fold cross-validation ranging between 0.83 and 0.86.This level of predictive performance attests for an excellent prediction skill.The main difference with respect to traditional statistical tools is that the latter usually lead to a clear interpretation at the expense of high performance,which is otherwise reached via machine/deep learning solutions,though at the expense of interpretation.The recent development of explainable Al is the key to combine both strengths.In this work,we explore this combination in the context of HMP susceptibility modeling.Specifically,we demonstrate the extent to which one can enter a new level of data-driven interpretation,supporting the decision-making process behind disaster risk mitigation and prevention actions.展开更多
基金funded by the National Science and Technology Major Project for the Exploration and Development of New Types of Oil and Gas(No.2024ZD14066)the National Natural Science Foundation of China(No.52274053)+1 种基金the Natural Science Foundation of Beijing Municipality(No.3173044)the Xinjiang Conglomerate Reservoir Laboratory Development Foundation Project(No.2020D04045)。
文摘Carbon dioxide enhanced oil recovery(CO_(2)-EOR)technology is used for oil production and CO_(2) storage in reservoirs.Methods are being constantly developed to optimize oil recovery and CO_(2) storage during the CO_(2) displacement process,especially for low-permeability reservoirs under varying geological conditions.In this study,long-core experiments and trans-scale numerical simulations are employed to examine the characteristics of oil production and CO_(2) storage.Optimal production parameters for the target reservoir are also proposed.The results indicate that maintaining the pressure at 1.04 to 1.10 times the minimum miscible pressure(MMP)and increasing the injection rate can enhance oil production in the early stage of reservoir development.In contrast,reducing the injection rate at the later stages prevents CO_(2) channeling,thus improving oil recovery and CO_(2) storage efficiency.A solution-doubling factor is introduced to modify the calculation method for CO_(2) storage,increasing its accuracy to approximately 90%.Before CO_(2) breakthrough,prioritizing oil production is recommended to maximize the economic benefits of this process.In the middle stage of CO_(2) displacement,decreasing the injection rate optimizes the coordination between oil displacement and CO_(2) storage.Further,in the late stage,reduced pressure and injection rates are required as the focus shifts to CO_(2) storage.
基金financially supported by the National Science Technology Support Plan Project (2012BAH28B01-03)the National Natural Science Foundation of China(41171332)+1 种基金the National Science Technology Basic Special Project (2011FY110400-2)the China Postdoctoral Science Foundation (2012M510526)
文摘Inland lakes are important water resources in arid and semiarid regions. Understanding climate effects on these lakes is critical to accurately evaluate the dynamic changes of water resources. This study focused on the changes in Sayram Lake of Xinjiang, China, and addressed the effects of climate fluctuations on the inland lake based on long-term sequenced remote sensing images and meteorological data from the past 40 years. A geo- graphic information system （GIS） method was used to obtain the hypsometry of the basin area of Sayram Lake, and estimation methods for evaporation from rising temperature and water levels from increasing precipitation were proposed. Results showed that： （1）Areal values of Sayram Lake have increased over the past 40 years. （2） Both temperature and precipitation have increased with average increases of more than 1.8~C and 82 mm, respectively. Variation of the water levels in the lake was consistent with local climate changes, and the areal values show linear relationships with local temperature and precipitation data. （3） According to the hypsometry data of the basin area, the estimated lake water levels increased by 2.8 m, and the water volume increased by 12.9×108 m3 over the past 40 years. The increasing area of Sayram Lake correlated with local and regional climatic changes because it is hardly affected by human activities.
文摘Spatial interpolation methods are frequently used to estimate values of meteorological data in locations where they are not measured. However, very little research has been investigated the relative performance of different interpolation methods in meteorological data of Xinjiang Uygur Autonomous Region (Xinjiang). Actually, it has importantly practical significance to as far as possibly improve the accuracy of interpolation results for meteorological data, especially in mountainous Xinjiang. There- fore, this paper focuses on the performance of different spatial interpolation methods for monthly temperature data in Xinjiang. The daily observed data of temperature are collected from 38 meteorological stations for the period 1960- 2004. Inverse distance weighting (IDW), ordinary kriging (OK), temperature lapse rate method (TLR) and multiple linear regressions (MLR) are selected as interpolated methods. Two rasterized methods, multiple regression plus space residual error and directly interpolated observed temperature (DIOT) data, are used to analyze and compare the performance of these interpolation methods respectively. Moreover, cross-validation is used to evaluate the performance of different spatial interpolation methods. The results are as follows: 1) The method of DIOT is unsuitable for the study area in this paper. 2) It is important to process the observed data by local regression model before the spatial interpolation. 3) The MLR-IDW is the optimum spatial interpolation method for the monthly mean temperature based on cross-validation. For the authors, the reliability of results and the influence of measurement accuracy, density, distribution and spatial variability on the accuracy of the interpolation methods will be tested and analyzed in the future.
基金supported by National Science and Technology Infrastructure Work Programs(2015FY210500)the Key Research Program of Frontier Sciences+3 种基金Chinese Academy of Sciences(QYZDY-SSW-DQC028)the Strategic Priority Program of the Chinese Academy of Sciences(XDB41000000)the National Natural Science Foundation of China(41773065,41941003,and 41902317)the Natural Science Foundation of Inner Mongolia,China(2020LH04002)。
基金supported by the National Science and Technology Infrastructure Work Projects(2015FY210500)Key Research Program of Frontier Sciences,Chinese Academy of Sciences(QYZDY-SSW-DQC028)+5 种基金Strategic Priority Program of Chinese Academy of Sciences(XDB41000000)the National Natural Science Foundation of China(42102280,41972322,and 11941001)the Natural Science Foundation of Shandong Province(ZR2021QD016)the China Postdoctoral Science Foundation(2020M682164)the State Scholarship Fund(201706220310)。
文摘Scientific knowledge of lunar lithologies was first acquired in the 1960s-1970s.The space race between the United States(U.S.)and Soviet Union has promoted numerous manned and robotic lunar exploration missions.Utilizing datasets from these missions,the first series of lunar geologic maps was prepared and published by the U.S.Geological Survey(USGS)The definition of lunar geological features in these maps was mostly based on morphological characteristics but lacked lithological constraints owing to the incompleteness of the compositional datasets avail-able.After two decades of silence,a new era of lunar exploration began in the 1990s when the Galileo spacecraft flew by the Moon during its gravity-assisted maneuvers.The very successful orbital missions,the Clementine and Lunar Prospector(LP),provided the first global geochemical and mineralogical(multispectral,gamma ray,neutron,etc.)datasets of the lunar surface.
基金supported by the National Science and Technology Infrastructure Work Projects(2015FY210500)the Key Research Program of Frontier Sciences,Chinese Academy of Sciences(QYZDY-SSW-DQC028)+1 种基金the Strategic Priority Program of Chinese Academy of Sciences(XDB41000000)the National Natural Science Foundation of China(41772346,41372337,and 40971187)。
文摘The tectonic evolution of the Moon is driven by both endogenic(e.g.,magmatism)and exogenic(e.g.,meteorite impact)forces.A tectonic map of the Moon provides key information about the spatiotemporal distribution of structures and tectonic units.Although the Moon has no plate tectonics,its surface can be divided into different terranes due to the uneven evolution[1].We define these terranes as tectonic units.Structures,such as craters and faults,are the basic elements of the tectonic units.As a synthesis of current knowledge on lunar tectonics and evolutionary history,lunar tectonic maps are a fundamental resource for scientific research,exploration planning,and landing site selection[2].
基金supported by the National Natural Science Foundation of China(grant no.42201452)the Fundamental Research Funds for the Central Universities(grant no.2412022QD003)the support from the China Institute of Water Resources and Hydropower Research(IWHR).
文摘Hydro-morphological processes(HMP,any natural phenomenon contained within the spectrum defined between debris flows and flash floods)are globally occurring natural hazards which pose great threats to our society,leading to fatalities and economical losses.For this reason,understanding the dynamics behind HMPs is needed to aid in hazard and risk assessment.In this work,we take advantage of an explainable deep learning model to extract global and local interpretations of the HMP occurrences across the whole Chinese territory.We use a deep neural network architecture and interpret the model results through the spatial pattern of SHAP values.In doing so,we can understand the model prediction on a hierarchical basis,looking at how the predictor set controls the overall susceptibility as well as doing the same at the level of the single mapping unit.Our model accurately predicts HMP occurrences with AUC values measured in a ten-fold cross-validation ranging between 0.83 and 0.86.This level of predictive performance attests for an excellent prediction skill.The main difference with respect to traditional statistical tools is that the latter usually lead to a clear interpretation at the expense of high performance,which is otherwise reached via machine/deep learning solutions,though at the expense of interpretation.The recent development of explainable Al is the key to combine both strengths.In this work,we explore this combination in the context of HMP susceptibility modeling.Specifically,we demonstrate the extent to which one can enter a new level of data-driven interpretation,supporting the decision-making process behind disaster risk mitigation and prevention actions.
