Anthropogenic emissions reached 37.4 Gt/a in 2023,intensifying the need for effective carbon storage in subsurface formations to mitigate global warming.Carbon Capture and Storage(CCS)has emerged as a viable solution,...Anthropogenic emissions reached 37.4 Gt/a in 2023,intensifying the need for effective carbon storage in subsurface formations to mitigate global warming.Carbon Capture and Storage(CCS)has emerged as a viable solution,with over 43 operational sites worldwide and projections for more than 840 projects by 2040,potentially storing 2225 Mt CO_(2) annually.This review provides a comprehensive analysis of CCS technologies,focusing on the integrity,safety,and economic viability of storage sites,which are crucial for long-term success.It identifies knowledge gaps in existing research,revealing that most studies address specific aspects of CCS but lack integrated approaches combining data,technologies,risks,and economic assessments.Some studies emphasize numerical modeling and fault reactivation risks but overlook issues such as cement degradation and casing corrosion,which are critical to preventing wellbore leakage.Others explore CO_(2)-rock interactions without considering cement integrity or focus on cement degradation without accounting for other field-scale risks.This review bridges these gaps by examining failures across wellbores,reservoirs,and caprocks,including cement integrity,casing corrosion,uplifting,fault activation,and seismicity due to injection.It also covers numerical modeling,experimental work,and monitoring techniques to ensure CCS integrity.Additionally,this review assesses economic risks to build confidence in CCS deployment,offering a comprehensive framework to ensure secure and long-term CO_(2) storage in subsurface formations.展开更多
This study discusses the benefits and challenges of well monitoring for Gulong shale oil production.It examines the Unified Transient Analysis(UTA)method,which integrates rate and pressure data to monitor changes in f...This study discusses the benefits and challenges of well monitoring for Gulong shale oil production.It examines the Unified Transient Analysis(UTA)method,which integrates rate and pressure data to monitor changes in fracture surface area and production efficiency in real-time.The UTA method allows for early detection of production impairments and provides feedback to optimize drawdown pressure,enhancing production without damaging fracture conductivity.Analysis of production data from Well A in the Daqing Oilfield demonstrates the method's efficacy,particularly in managing choke size adjustments and identifying fracture conductivity degradation.Despite its benefits,challenges such as data quality,manual data analysis,and the need for automated choke management are highlighted.The study underscores the necessity of integrating intelligent monitoring technologies and automating workflows to optimize Gulong shale oil production.展开更多
Nitrous oxide(N_(2)O)emissions pose a serious environmental problem when nitrogen(N)fertilizer is excessively applied to plantation systems to enhance tree growth.Although biochar can improve soil fertility and mitiga...Nitrous oxide(N_(2)O)emissions pose a serious environmental problem when nitrogen(N)fertilizer is excessively applied to plantation systems to enhance tree growth.Although biochar can improve soil fertility and mitigate soil N losses,our understanding of its interaction with N fertilizer and its long-term effects remains limited owing to experimental constraints.In this study,two microcosm incubation experiments were performed to evaluate the effect of fresh biochar,compared to 8-year field-aged biochar application in a poplar plantation,on soil N_(2)O emissions triggered by biogas slurry application.The experiments incorporated three biochar levels and four biogas slurry application rates,each with three replicates.The results demonstrated that fresh and aged biochar significantly reduced soil cumulative N_(2)O emissions by 31%–61%and 75%–99%,respectively,over 7 d following biogas slurry application.However,these mitigating effects diminished over incubation time.The application of fresh biochar significantly reduced soil available organic carbon and potential denitrification rates,suggesting that it primarily suppressed soil N_(2)O emissions by limiting the supply of electron donors.In contrast,aged biochar had minimal impact on soil available organic carbon and generally enhanced the abundances of bacterial amoA,nirS,nirK,and nosZ genes.This suggests that the aged biochar potentially suppressed soil N_(2)O emissions by promoting complete denitrification.Partial least squares structure equation model(PLS-SEM)analysis corroborated the two different mechanisms regulating the inhibitory influence of fresh and aged biochar on soil N_(2)O emissions.The lower R^(2)of PLS-SEM analysis for aged biochar(R^(2)=0.256)compared to that for fresh biochar(R^(2)=0.798)indicates that other factors,such as biochar properties,potentially affect soil N_(2)O emissions and warrant further investigation.This study highlights the need to evaluate the long-term effect of biochar on soil N_(2)O emissions,owing to the dynamic changes in biochar and soil properties over time.展开更多
This study provides an in-depth comparative evaluation of landslide susceptibility using two distinct spatial units:and slope units(SUs)and hydrological response units(HRUs),within Goesan County,South Korea.Leveraging...This study provides an in-depth comparative evaluation of landslide susceptibility using two distinct spatial units:and slope units(SUs)and hydrological response units(HRUs),within Goesan County,South Korea.Leveraging the capabilities of the extreme gradient boosting(XGB)algorithm combined with Shapley Additive Explanations(SHAP),this work assesses the precision and clarity with which each unit predicts areas vulnerable to landslides.SUs focus on the geomorphological features like ridges and valleys,focusing on slope stability and landslide triggers.Conversely,HRUs are established based on a variety of hydrological factors,including land cover,soil type and slope gradients,to encapsulate the dynamic water processes of the region.The methodological framework includes the systematic gathering,preparation and analysis of data,ranging from historical landslide occurrences to topographical and environmental variables like elevation,slope angle and land curvature etc.The XGB algorithm used to construct the Landslide Susceptibility Model(LSM)was combined with SHAP for model interpretation and the results were evaluated using Random Cross-validation(RCV)to ensure accuracy and reliability.To ensure optimal model performance,the XGB algorithm’s hyperparameters were tuned using Differential Evolution,considering multicollinearity-free variables.The results show that SU and HRU are effective for LSM,but their effectiveness varies depending on landscape characteristics.The XGB algorithm demonstrates strong predictive power and SHAP enhances model transparency of the influential variables involved.This work underscores the importance of selecting appropriate assessment units tailored to specific landscape characteristics for accurate LSM.The integration of advanced machine learning techniques with interpretative tools offers a robust framework for landslide susceptibility assessment,improving both predictive capabilities and model interpretability.Future research should integrate broader data sets and explore hybrid analytical models to strengthen the generalizability of these findings across varied geographical settings.展开更多
Characterization and optimization of physical and chemical properties of drilling fluids are critical for the efficiency and success of drilling operations.In particular,maintaining the optimal levels of solids conten...Characterization and optimization of physical and chemical properties of drilling fluids are critical for the efficiency and success of drilling operations.In particular,maintaining the optimal levels of solids content is essential for achieving the most effective fluid performance.Proper management of solids content also reduces the risk of tool failures.Traditional solids content analysis methods,such as retort analysis,require substantial human intervention and time,which can lead to inaccuracies,time-management issues,and increased operational risks.In contrast to human-intensive methods,machine learning may offer a viable alternative for solids content estimation due to its pattern-recognition capability.In this study,a large set of laboratory reports of drilling-fluid analyses from 130 oil wells around the world were compiled to construct a comprehensive data set.The relationships among various rheological parameters were analyzed using statistical methods and machine learning algorithms.Several machine learning algorithms of diverse classes,namely linear(linear regression,ridge regression,and ElasticNet regression),kernel-based(support vector machine)and ensemble tree-based(gradient boosting,XGBoost,and random forests)algorithms,were trained and tuned to estimate solids content from other readily available drilling fluid properties.Input variables were kept consistent across all models for interpretation and comparison purposes.In the final stage,different evaluation metrics were employed to evaluate and compare the performance of different classes of machine learning models.Among all algorithms tested,random forests algorithm was found to be the best predictive model resulting in consistently high accuracy.Further optimization of the random forests model resulted in a mean absolute percentage error(MAPE)of 3.9%and 9.6%and R^(2) of 0.99 and 0.93 for the training and testing sets,respectively.Analysis of residuals,their histograms and Q-Q normality plots showed Gaussian distributions with residuals that are scattered around a mean of zero within error ranges of±1%and±4%,for training and testing,respectively.The selected model was further validated by applying the rheological measurements from mud samples taken from an offshore well from the Gulf of Mexico.The model was able to estimate total solids content in those four mud samples with an average absolute error of 1.08% of total solids content.The model was then used to develop a web-based graphical-user-interface(GUI)application,which can be practically used at the rig site by engineers to optimize drilling fluid programs.The proposed model can complement automation workflows that are designed to measure fundamental rheological properties in real time during drilling operations.While a standard retort test can take approximately 2 h at the rig site,such kind of real-time estimations can help the rig personnel to timely optimize drilling fluids,with a potential of saving 2920 man-hours in a given year for a single drilling rig.展开更多
The characterization of kerogen nanopores is crucial for predicting the geostorage capacity and recoverability of natural gas in unconventional gas shale reservoirs.Towards this end,a powerful technique is presented w...The characterization of kerogen nanopores is crucial for predicting the geostorage capacity and recoverability of natural gas in unconventional gas shale reservoirs.Towards this end,a powerful technique is presented which integrates 2D NMR T_(1)-T_(2) relaxation measurements with molecular dynamics(MD)simulations of hydrocarbons confined in the nanopores of kerogen.The integrated NMR-MD technique is demonstrated using T_(1)-T_(2) measurements of kerogen isolates and organic-rich chalks saturated with heptane,together with MD simulations of heptane completely dissolved in a realistic kerogen model.The NMR-MD results are used to extract the swelling ratio and nanopore size distribution of kerogen as a function of depth in the reservoir.The effects of organic nanoconfinement on the T_(1) relaxation dispersion and T_(2) residual dipolar coupling of heptane are investigated,as well as the effect of downhole effective stress on the kerogen nanopore size as a function of depth and compaction.Potential applications in partially depleted gas shale reservoirs are discussed,including CO_(2) utilization/geostorage,geostorage of green H_(2),and integration of the NMR-MD technique with thermodynamic models for predicting the competitive sorption of gas mixtures in kerogen.展开更多
Element profile was investigated for their use to trace the geographical origin of rice （Oryza sativa L.） samples. The concentrations of 13 elements （calcium （Ca）, potassium （K）, magnesium （Mg）, phosphorus （...Element profile was investigated for their use to trace the geographical origin of rice （Oryza sativa L.） samples. The concentrations of 13 elements （calcium （Ca）, potassium （K）, magnesium （Mg）, phosphorus （P）, boron （B）, manganese （Mn）, iron （Fe）, nickel （Ni）, copper （Cu）, arsenic （As）, selenium （Se）, molybdenum （Mo）, and cadmium （Cd）） were determined in the rice samples by inductively coupled plasma optical emission and mass spectrometry. Most of the essential elements for human health in rice were within normal ranges except for Mo and Se. Mo concentrations were twice as high as those in rice from Vietnam and Spain. Meanwhile, Se concentrations were three times lower in the whole province compared to the Chinese average level of 0.088 mg/kg. About 12% of the rice samples failed the Chinese national food safety standard of 0.2 mg/kg for Cd. Combined with the multi-elemental profile in rice, the principal component analysis （PCA）, discriminant function analysis （DFA） and Fibonacci index analysis （FIA） were applied to discriminate geographical origins of the samples. Results indicated that the FIA method could achieve a more effective geographical origin classification compared with PCA and DFA, due to its efficiency in making the grouping even when the elemental variability was so high that PCA and DFA showed little discriminatory power. Furthermore, some elements were identified as the most powerful indicators of geographical origin： Ca, Ni, Fe and Cd. This suggests that the newly established methodology of FIA based on the ionome profile can be applied to determine the geographical origin of rice.展开更多
For centuries,humans’capacity to capture and depict physical space has played a central role in industrial and societal development.However,the digital revolution and the emergence of networked devices and services a...For centuries,humans’capacity to capture and depict physical space has played a central role in industrial and societal development.However,the digital revolution and the emergence of networked devices and services accelerate geospatial capture,coordination,and intelligence in unprecedented ways.Underlying the digital transformation of industry and society is the fusion of the physical and digital worlds-‘perceptality’-where geospatial perception and reality merge.This paper analyzes the myriad forces that are driving perceptality and the future of geospatial intelligence and presents real-world implications and examples of its industrial application.Applications of sensors,robotics,cameras,machine learning,encryption,cloud computing and other software,and hardware intelligence are converging,enabling new ways for organizations and their equipment to perceive and capture reality.Meanwhile,demands for performance,reliability,and security are pushing compute‘to the edge’where real-time processing and coordination are vital.Big data place new restraints on economics,as pressures abound to actually use these data,both in real-time and for longer term strategic analysis and decision-making.These challenges require orchestration between information technology(IT)and operational technology(OT)and synchronization of diverse systems,data-sets,devices,environments,workflows,and people.展开更多
Increasing the production and utilization of shale gas is of great significance for building a clean and low-carbon energy system.Sharp decline of gas production has been widely observed in shale gas reservoirs.How to...Increasing the production and utilization of shale gas is of great significance for building a clean and low-carbon energy system.Sharp decline of gas production has been widely observed in shale gas reservoirs.How to forecast shale gas production is still challenging due to complex fracture networks,dynamic fracture properties,frac hits,complicated multiphase flow,and multi-scale flow as well as data quality and uncertainty.This work develops an integrated framework for evaluating shale gas well production based on data-driven models.Firstly,a comprehensive dominated-factor system has been established,including geological,drilling,fracturing,and production factors.Data processing and visualization are required to ensure data quality and determine final data set.A shale gas production evaluation model is developed to evaluate shale gas production levels.Finally,the random forest algorithm is used to forecast shale gas production.The prediction accuracy of shale gas production level is higher than 95%based on the shale gas reservoirs in China.Forty-one wells are randomly selected to predict cumulative gas production using the optimal regression model.The proposed shale gas production evaluation frame-work overcomes too many assumptions of analytical or semi-analytical models and avoids huge computation cost and poor generalization for numerical modelling.展开更多
The flow and seawater exchange rates have been predicted using a two-dimensional numerical model and a Lagrangian method for a semi-enclosed shallow bay where reclaiming and dredging works are scheduled. The wind effe...The flow and seawater exchange rates have been predicted using a two-dimensional numerical model and a Lagrangian method for a semi-enclosed shallow bay where reclaiming and dredging works are scheduled. The wind effect on the flow and material transport has been emphasized, and a thirty-year mean value of wind has been considered in the numerical simulation. As a whole, even after the reclaiming and dredging are conducted, the flow pattern looks similar to the original state. However, velocity variations up to 20% to 100% appear in the vicinity of the construction area. In the case of summcr wind forcing, the seawater exchange rate increases from 71.6% to 82.9% after the reclaiming and dredging, as indicated by a particle-tracking method. On the contrary, in the case of winter wind forcing, thc seawater cxchange rate appears to be 97.2% under natural conditions but decrcases slightly to 93.2% aftcr the rcclaiming and dredging. Thus, the wind forcing plays an important role in controlling the seawater exchangc rates. The seawater cxchange rate is further improved by 15% if the dredging is simultaneously carried out with the reclaiming. This suggests that the dredging can be an effective means to mitigate the variation of flow.展开更多
The capacitance-resistance model （CRM） is an alternative to conventional reservoir simulation. CRM, a simplification of complex numerical models, uses production and injection rates to infer a reservoir description....The capacitance-resistance model （CRM） is an alternative to conventional reservoir simulation. CRM, a simplification of complex numerical models, uses production and injection rates to infer a reservoir description. There is no prior geologic model. The principal output of CRM fitting is the fraction of injected fluid （usually water） that is produced at a producer at steady-state. These fractions are interwell connectivities. Interwell connectivities are fundamental information needed to manage waterfloods in oil reservoirs. The data-driven CRM is a fast tool to estimate these parameters in mature fields and allows one to make full use of the dynamic data available. This paper considers the problem of setting an upper bound on the uncertainty of interwell connectivities for linear-constrained models. Using analytical bounds and numerical simulations, we derive a consistent upper limit on the uncertainty of interwell connections that can be used to quantify the information content of a given dataset.展开更多
文摘Anthropogenic emissions reached 37.4 Gt/a in 2023,intensifying the need for effective carbon storage in subsurface formations to mitigate global warming.Carbon Capture and Storage(CCS)has emerged as a viable solution,with over 43 operational sites worldwide and projections for more than 840 projects by 2040,potentially storing 2225 Mt CO_(2) annually.This review provides a comprehensive analysis of CCS technologies,focusing on the integrity,safety,and economic viability of storage sites,which are crucial for long-term success.It identifies knowledge gaps in existing research,revealing that most studies address specific aspects of CCS but lack integrated approaches combining data,technologies,risks,and economic assessments.Some studies emphasize numerical modeling and fault reactivation risks but overlook issues such as cement degradation and casing corrosion,which are critical to preventing wellbore leakage.Others explore CO_(2)-rock interactions without considering cement integrity or focus on cement degradation without accounting for other field-scale risks.This review bridges these gaps by examining failures across wellbores,reservoirs,and caprocks,including cement integrity,casing corrosion,uplifting,fault activation,and seismicity due to injection.It also covers numerical modeling,experimental work,and monitoring techniques to ensure CCS integrity.Additionally,this review assesses economic risks to build confidence in CCS deployment,offering a comprehensive framework to ensure secure and long-term CO_(2) storage in subsurface formations.
文摘This study discusses the benefits and challenges of well monitoring for Gulong shale oil production.It examines the Unified Transient Analysis(UTA)method,which integrates rate and pressure data to monitor changes in fracture surface area and production efficiency in real-time.The UTA method allows for early detection of production impairments and provides feedback to optimize drawdown pressure,enhancing production without damaging fracture conductivity.Analysis of production data from Well A in the Daqing Oilfield demonstrates the method's efficacy,particularly in managing choke size adjustments and identifying fracture conductivity degradation.Despite its benefits,challenges such as data quality,manual data analysis,and the need for automated choke management are highlighted.The study underscores the necessity of integrating intelligent monitoring technologies and automating workflows to optimize Gulong shale oil production.
基金supported by the Special Funds for Science and Technology Innovation on Carbon Peak Carbon Neutral of Jiangsu Province,China(No.BK20220017)the Natural Science Foundation of China(No.42007090)the National Key Research and Development Program of China(No.2021YFD22004)。
文摘Nitrous oxide(N_(2)O)emissions pose a serious environmental problem when nitrogen(N)fertilizer is excessively applied to plantation systems to enhance tree growth.Although biochar can improve soil fertility and mitigate soil N losses,our understanding of its interaction with N fertilizer and its long-term effects remains limited owing to experimental constraints.In this study,two microcosm incubation experiments were performed to evaluate the effect of fresh biochar,compared to 8-year field-aged biochar application in a poplar plantation,on soil N_(2)O emissions triggered by biogas slurry application.The experiments incorporated three biochar levels and four biogas slurry application rates,each with three replicates.The results demonstrated that fresh and aged biochar significantly reduced soil cumulative N_(2)O emissions by 31%–61%and 75%–99%,respectively,over 7 d following biogas slurry application.However,these mitigating effects diminished over incubation time.The application of fresh biochar significantly reduced soil available organic carbon and potential denitrification rates,suggesting that it primarily suppressed soil N_(2)O emissions by limiting the supply of electron donors.In contrast,aged biochar had minimal impact on soil available organic carbon and generally enhanced the abundances of bacterial amoA,nirS,nirK,and nosZ genes.This suggests that the aged biochar potentially suppressed soil N_(2)O emissions by promoting complete denitrification.Partial least squares structure equation model(PLS-SEM)analysis corroborated the two different mechanisms regulating the inhibitory influence of fresh and aged biochar on soil N_(2)O emissions.The lower R^(2)of PLS-SEM analysis for aged biochar(R^(2)=0.256)compared to that for fresh biochar(R^(2)=0.798)indicates that other factors,such as biochar properties,potentially affect soil N_(2)O emissions and warrant further investigation.This study highlights the need to evaluate the long-term effect of biochar on soil N_(2)O emissions,owing to the dynamic changes in biochar and soil properties over time.
基金supported by a National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(RS-2023-00222536).
文摘This study provides an in-depth comparative evaluation of landslide susceptibility using two distinct spatial units:and slope units(SUs)and hydrological response units(HRUs),within Goesan County,South Korea.Leveraging the capabilities of the extreme gradient boosting(XGB)algorithm combined with Shapley Additive Explanations(SHAP),this work assesses the precision and clarity with which each unit predicts areas vulnerable to landslides.SUs focus on the geomorphological features like ridges and valleys,focusing on slope stability and landslide triggers.Conversely,HRUs are established based on a variety of hydrological factors,including land cover,soil type and slope gradients,to encapsulate the dynamic water processes of the region.The methodological framework includes the systematic gathering,preparation and analysis of data,ranging from historical landslide occurrences to topographical and environmental variables like elevation,slope angle and land curvature etc.The XGB algorithm used to construct the Landslide Susceptibility Model(LSM)was combined with SHAP for model interpretation and the results were evaluated using Random Cross-validation(RCV)to ensure accuracy and reliability.To ensure optimal model performance,the XGB algorithm’s hyperparameters were tuned using Differential Evolution,considering multicollinearity-free variables.The results show that SU and HRU are effective for LSM,but their effectiveness varies depending on landscape characteristics.The XGB algorithm demonstrates strong predictive power and SHAP enhances model transparency of the influential variables involved.This work underscores the importance of selecting appropriate assessment units tailored to specific landscape characteristics for accurate LSM.The integration of advanced machine learning techniques with interpretative tools offers a robust framework for landslide susceptibility assessment,improving both predictive capabilities and model interpretability.Future research should integrate broader data sets and explore hybrid analytical models to strengthen the generalizability of these findings across varied geographical settings.
文摘Characterization and optimization of physical and chemical properties of drilling fluids are critical for the efficiency and success of drilling operations.In particular,maintaining the optimal levels of solids content is essential for achieving the most effective fluid performance.Proper management of solids content also reduces the risk of tool failures.Traditional solids content analysis methods,such as retort analysis,require substantial human intervention and time,which can lead to inaccuracies,time-management issues,and increased operational risks.In contrast to human-intensive methods,machine learning may offer a viable alternative for solids content estimation due to its pattern-recognition capability.In this study,a large set of laboratory reports of drilling-fluid analyses from 130 oil wells around the world were compiled to construct a comprehensive data set.The relationships among various rheological parameters were analyzed using statistical methods and machine learning algorithms.Several machine learning algorithms of diverse classes,namely linear(linear regression,ridge regression,and ElasticNet regression),kernel-based(support vector machine)and ensemble tree-based(gradient boosting,XGBoost,and random forests)algorithms,were trained and tuned to estimate solids content from other readily available drilling fluid properties.Input variables were kept consistent across all models for interpretation and comparison purposes.In the final stage,different evaluation metrics were employed to evaluate and compare the performance of different classes of machine learning models.Among all algorithms tested,random forests algorithm was found to be the best predictive model resulting in consistently high accuracy.Further optimization of the random forests model resulted in a mean absolute percentage error(MAPE)of 3.9%and 9.6%and R^(2) of 0.99 and 0.93 for the training and testing sets,respectively.Analysis of residuals,their histograms and Q-Q normality plots showed Gaussian distributions with residuals that are scattered around a mean of zero within error ranges of±1%and±4%,for training and testing,respectively.The selected model was further validated by applying the rheological measurements from mud samples taken from an offshore well from the Gulf of Mexico.The model was able to estimate total solids content in those four mud samples with an average absolute error of 1.08% of total solids content.The model was then used to develop a web-based graphical-user-interface(GUI)application,which can be practically used at the rig site by engineers to optimize drilling fluid programs.The proposed model can complement automation workflows that are designed to measure fundamental rheological properties in real time during drilling operations.While a standard retort test can take approximately 2 h at the rig site,such kind of real-time estimations can help the rig personnel to timely optimize drilling fluids,with a potential of saving 2920 man-hours in a given year for a single drilling rig.
基金Vinegar Technologies LLC,Chevron Energy Technology Company,Rice University Consortium for Processes in Porous Media,and the American Chemical Society Petroleum Research Fund(No.ACS PRF 58859-ND6)for their financial support。
文摘The characterization of kerogen nanopores is crucial for predicting the geostorage capacity and recoverability of natural gas in unconventional gas shale reservoirs.Towards this end,a powerful technique is presented which integrates 2D NMR T_(1)-T_(2) relaxation measurements with molecular dynamics(MD)simulations of hydrocarbons confined in the nanopores of kerogen.The integrated NMR-MD technique is demonstrated using T_(1)-T_(2) measurements of kerogen isolates and organic-rich chalks saturated with heptane,together with MD simulations of heptane completely dissolved in a realistic kerogen model.The NMR-MD results are used to extract the swelling ratio and nanopore size distribution of kerogen as a function of depth in the reservoir.The effects of organic nanoconfinement on the T_(1) relaxation dispersion and T_(2) residual dipolar coupling of heptane are investigated,as well as the effect of downhole effective stress on the kerogen nanopore size as a function of depth and compaction.Potential applications in partially depleted gas shale reservoirs are discussed,including CO_(2) utilization/geostorage,geostorage of green H_(2),and integration of the NMR-MD technique with thermodynamic models for predicting the competitive sorption of gas mixtures in kerogen.
基金supported by the Ministry of Science and Technology,China (No.2009DFB90120)
文摘Element profile was investigated for their use to trace the geographical origin of rice （Oryza sativa L.） samples. The concentrations of 13 elements （calcium （Ca）, potassium （K）, magnesium （Mg）, phosphorus （P）, boron （B）, manganese （Mn）, iron （Fe）, nickel （Ni）, copper （Cu）, arsenic （As）, selenium （Se）, molybdenum （Mo）, and cadmium （Cd）） were determined in the rice samples by inductively coupled plasma optical emission and mass spectrometry. Most of the essential elements for human health in rice were within normal ranges except for Mo and Se. Mo concentrations were twice as high as those in rice from Vietnam and Spain. Meanwhile, Se concentrations were three times lower in the whole province compared to the Chinese average level of 0.088 mg/kg. About 12% of the rice samples failed the Chinese national food safety standard of 0.2 mg/kg for Cd. Combined with the multi-elemental profile in rice, the principal component analysis （PCA）, discriminant function analysis （DFA） and Fibonacci index analysis （FIA） were applied to discriminate geographical origins of the samples. Results indicated that the FIA method could achieve a more effective geographical origin classification compared with PCA and DFA, due to its efficiency in making the grouping even when the elemental variability was so high that PCA and DFA showed little discriminatory power. Furthermore, some elements were identified as the most powerful indicators of geographical origin： Ca, Ni, Fe and Cd. This suggests that the newly established methodology of FIA based on the ionome profile can be applied to determine the geographical origin of rice.
基金supported by Hexagon AB,a global provider of information technologies for geospatial and industrial enterprises.
文摘For centuries,humans’capacity to capture and depict physical space has played a central role in industrial and societal development.However,the digital revolution and the emergence of networked devices and services accelerate geospatial capture,coordination,and intelligence in unprecedented ways.Underlying the digital transformation of industry and society is the fusion of the physical and digital worlds-‘perceptality’-where geospatial perception and reality merge.This paper analyzes the myriad forces that are driving perceptality and the future of geospatial intelligence and presents real-world implications and examples of its industrial application.Applications of sensors,robotics,cameras,machine learning,encryption,cloud computing and other software,and hardware intelligence are converging,enabling new ways for organizations and their equipment to perceive and capture reality.Meanwhile,demands for performance,reliability,and security are pushing compute‘to the edge’where real-time processing and coordination are vital.Big data place new restraints on economics,as pressures abound to actually use these data,both in real-time and for longer term strategic analysis and decision-making.These challenges require orchestration between information technology(IT)and operational technology(OT)and synchronization of diverse systems,data-sets,devices,environments,workflows,and people.
基金funded by National Natural Science Foundation of China(52004238)China Postdoctoral Science Foundation(2019M663561).
文摘Increasing the production and utilization of shale gas is of great significance for building a clean and low-carbon energy system.Sharp decline of gas production has been widely observed in shale gas reservoirs.How to forecast shale gas production is still challenging due to complex fracture networks,dynamic fracture properties,frac hits,complicated multiphase flow,and multi-scale flow as well as data quality and uncertainty.This work develops an integrated framework for evaluating shale gas well production based on data-driven models.Firstly,a comprehensive dominated-factor system has been established,including geological,drilling,fracturing,and production factors.Data processing and visualization are required to ensure data quality and determine final data set.A shale gas production evaluation model is developed to evaluate shale gas production levels.Finally,the random forest algorithm is used to forecast shale gas production.The prediction accuracy of shale gas production level is higher than 95%based on the shale gas reservoirs in China.Forty-one wells are randomly selected to predict cumulative gas production using the optimal regression model.The proposed shale gas production evaluation frame-work overcomes too many assumptions of analytical or semi-analytical models and avoids huge computation cost and poor generalization for numerical modelling.
文摘The flow and seawater exchange rates have been predicted using a two-dimensional numerical model and a Lagrangian method for a semi-enclosed shallow bay where reclaiming and dredging works are scheduled. The wind effect on the flow and material transport has been emphasized, and a thirty-year mean value of wind has been considered in the numerical simulation. As a whole, even after the reclaiming and dredging are conducted, the flow pattern looks similar to the original state. However, velocity variations up to 20% to 100% appear in the vicinity of the construction area. In the case of summcr wind forcing, the seawater exchange rate increases from 71.6% to 82.9% after the reclaiming and dredging, as indicated by a particle-tracking method. On the contrary, in the case of winter wind forcing, thc seawater cxchange rate appears to be 97.2% under natural conditions but decrcases slightly to 93.2% aftcr the rcclaiming and dredging. Thus, the wind forcing plays an important role in controlling the seawater exchangc rates. The seawater cxchange rate is further improved by 15% if the dredging is simultaneously carried out with the reclaiming. This suggests that the dredging can be an effective means to mitigate the variation of flow.
基金YPF for financial support and to the Center for Petroleum Asset Risk Management of the University of Texas at Austin for hospitality and an exciting research environment
文摘The capacitance-resistance model （CRM） is an alternative to conventional reservoir simulation. CRM, a simplification of complex numerical models, uses production and injection rates to infer a reservoir description. There is no prior geologic model. The principal output of CRM fitting is the fraction of injected fluid （usually water） that is produced at a producer at steady-state. These fractions are interwell connectivities. Interwell connectivities are fundamental information needed to manage waterfloods in oil reservoirs. The data-driven CRM is a fast tool to estimate these parameters in mature fields and allows one to make full use of the dynamic data available. This paper considers the problem of setting an upper bound on the uncertainty of interwell connectivities for linear-constrained models. Using analytical bounds and numerical simulations, we derive a consistent upper limit on the uncertainty of interwell connections that can be used to quantify the information content of a given dataset.
