During the Vietnam War, millions of liters of six tactical herbicides were sprayed on the southern Vietnam landscape to defoliate forests, to clear military perimeters and to destroy enemy food supplies. The environme...During the Vietnam War, millions of liters of six tactical herbicides were sprayed on the southern Vietnam landscape to defoliate forests, to clear military perimeters and to destroy enemy food supplies. The environmental and human health impacts of spraying these herbicides, especially Agent Orange and those formulated with mixtures that included 2,4,5-trichlorophenoxyacetic acid (2,4,5-T) which was contaminated with 2,3,7,8-tetracholorodibenzo-p-dioxin (TCDD) have been documented over the last 60 years. The dioxin TCDD clean-up efforts at former military bases and other Vietnam hotspots are ongoing. However, the lesser-told story was the environmental and human health impacts on the communities and chemical plant workers who manufactured Agent Orange and other herbicides that became contaminated with dioxin TCDD in the manufacturing processes at seven locations in the United States and one site in Canada. The pollution at these chemical plant sites, adjacent rivers and groundwater is well known within each affected state or province but not widely recognized beyond their localities. In this paper we assess the national long-term effects on land, groundwater and river resources where Agent Orange and other agricultural herbicides containing 2,4,5-T with unknown amounts of dioxin TCDD were manufactured, transported, and temporarily stored. The sites where residual tactical herbicides with contaminated by-products were applied to public lands or disposed of by military and civilian workers within the United States and Canada are identified. After 60 years, these communities are still paying the price for the U.S. Government, DOD and USDA decisions to provide and use agricultural herbicides as tactical chemical weapons during the Vietnam War (1962-1971). There have been human health issues associated with the chemical manufacture, transport, storage and disposal of these herbicides related to workers who moved these chemical weapons from United States and Canada to SE Asia. Most of these dioxin contaminated tactica展开更多
Concentrations of redox-sensitive trace-element(RSTE) in marine shales have long been interpreted simply as redox proxies. However, the impact of other non-redox factors(e.g., sea-level fluctuation and seawater chemis...Concentrations of redox-sensitive trace-element(RSTE) in marine shales have long been interpreted simply as redox proxies. However, the impact of other non-redox factors(e.g., sea-level fluctuation and seawater chemistry) on the enrichment of RSTE, especially molybdenum(Mo) and uranium(U), in sediments has been rarely reported. This study presents newly obtained RSTE datasets from the Upper Pennsylvanian organic-rich Cline Shale in the silled Midland Basin, U.S., to illustrate the influence of sea-level fluctuation on the authigenic accumulation of RSTE in marine sediments. A previously established transgressive-regressive sequence of the Cline Shale, a well-constrained high-amplitude glacio-eustatic fluctuation curve, and an accompanying episodic resupply of aqueous RSTE from the Panthalassic Ocean provide an ideal stratigraphic framework for determining the spatial and temporal variations of sediment RSTE enrichment patterns that responded to the episodic variations of seawater chemistry in this marginal silled paleomarine basin. Results suggest that although slightly higher median RSTE concentrations were observed in sediments from more reducing environments, the overall variation ranges of RSTE concentrations largely overlap among sediments deposited from a wide redox spectrum(from oxic to euxinic conditions) or different sea-level statuses in the Cline Shale. In contrast to the sediment RSTE enrichment patterns, the variations of sediment Mo/TOC and U/TOC ratios are coupled with glacio-eustatic fluctuation. The highest Mo/TOC and U/TOC ratios are commonly observed in sediments deposited during the highest relative sea-level(RSTE resupply), whereas the lowest Mo/TOC and U/TOC ratios usually appear in sediments deposited during the lowest relative sea-level(RSTE depletion). Our findings suggest that the benthic redox conditions recorded in sediment Mo and U concentrations can be greatly obscured and weakened by depleted aqueous Mo and U concentrations in highly restricted basins. Thus, the use of sediment Mo a展开更多
Machine learning and artificial intelligence approaches have rapidly gained popularity for use in many subsurface energy applications.They are seen as novel methods that may enhance existing capabilities,providing for...Machine learning and artificial intelligence approaches have rapidly gained popularity for use in many subsurface energy applications.They are seen as novel methods that may enhance existing capabilities,providing for improved efficiency in exploration and production operations.Furthermore,their inte-gration into reservoir management workflows may shape the future landscape of the energy industry.This study implements a framework that generates predictive models using multiple machine learning classification-based algorithms which can identify specific stratigraphic units(i.e.,formations)as a function of total vertical depth and spatial positioning.The framework is applied in a case study to 13 specific formations of interest(Upper Spraberry through Atoka/Morrow reservoirs)in the Midland Basin,West Texas,United States;a prominent hydrocarbon producing sub-basin of the larger Permian Basin.The study dataset consists of over 275,000 records and includes data fields like formation iden-tifier,true vertical depth(in feet)of formations observed,and latitude and longitude coordinates(in decimal degrees).A subset of 134,374 data records were relevant to the 13 distinct formations of interest and were extracted and used for machine learning model training,validation,and testing.Four super-vised learning approaches including random forest(RF),gradient boosting(GB),support vector machine(SVM),and multilayer perceptron neural network(MLP)were evaluated and their prediction accuracy compared.The best performing model was ultimately built on the RF algorithm and is capable of an overall prediction accuracy of 93 percent on holdout data.The RF-based model demonstrated high prediction accuracy for major oil and gas producing zones including the San Andres,Upper Spraberry,Lower Spraberry,Clearfork,and Wolfcamp at 98,94,89,94,and 94 percent respectively.Overall,the resulting data-driven model provides a robust,cost-effective approach which can complement contemporary reservoir management approaches for multiple subsurface energy 展开更多
The dual structure of industrialization and urbanization in China causes domestic under-demand in recent years. The development strategy of the small town determines the economy can only meet lower equilibrium. The ro...The dual structure of industrialization and urbanization in China causes domestic under-demand in recent years. The development strategy of the small town determines the economy can only meet lower equilibrium. The route that emerges in the Midland is chosen to develop the regional key city or city to enclose actively, but this process will lead to new non-equilibrium that is pulled by the investment of government. Establishing a two-product model, this paper verifies that the endogenous motive force of growth of the urbanization is the division of labor, which also promotes the SME. So we draw a conclusion that the SME is the main force that can promotes the urbanization of the Midland.展开更多
文摘During the Vietnam War, millions of liters of six tactical herbicides were sprayed on the southern Vietnam landscape to defoliate forests, to clear military perimeters and to destroy enemy food supplies. The environmental and human health impacts of spraying these herbicides, especially Agent Orange and those formulated with mixtures that included 2,4,5-trichlorophenoxyacetic acid (2,4,5-T) which was contaminated with 2,3,7,8-tetracholorodibenzo-p-dioxin (TCDD) have been documented over the last 60 years. The dioxin TCDD clean-up efforts at former military bases and other Vietnam hotspots are ongoing. However, the lesser-told story was the environmental and human health impacts on the communities and chemical plant workers who manufactured Agent Orange and other herbicides that became contaminated with dioxin TCDD in the manufacturing processes at seven locations in the United States and one site in Canada. The pollution at these chemical plant sites, adjacent rivers and groundwater is well known within each affected state or province but not widely recognized beyond their localities. In this paper we assess the national long-term effects on land, groundwater and river resources where Agent Orange and other agricultural herbicides containing 2,4,5-T with unknown amounts of dioxin TCDD were manufactured, transported, and temporarily stored. The sites where residual tactical herbicides with contaminated by-products were applied to public lands or disposed of by military and civilian workers within the United States and Canada are identified. After 60 years, these communities are still paying the price for the U.S. Government, DOD and USDA decisions to provide and use agricultural herbicides as tactical chemical weapons during the Vietnam War (1962-1971). There have been human health issues associated with the chemical manufacture, transport, storage and disposal of these herbicides related to workers who moved these chemical weapons from United States and Canada to SE Asia. Most of these dioxin contaminated tactica
基金supported by the State of Texas Advanced Resource Recovery (STARR) program at the Bureau of Economic Geology (BEG)minor financial support from the China Scholarship Council (Grant No. 201606440062)the Geological Society of America Graduate Student Research Grant (Grant No. 9244823)。
文摘Concentrations of redox-sensitive trace-element(RSTE) in marine shales have long been interpreted simply as redox proxies. However, the impact of other non-redox factors(e.g., sea-level fluctuation and seawater chemistry) on the enrichment of RSTE, especially molybdenum(Mo) and uranium(U), in sediments has been rarely reported. This study presents newly obtained RSTE datasets from the Upper Pennsylvanian organic-rich Cline Shale in the silled Midland Basin, U.S., to illustrate the influence of sea-level fluctuation on the authigenic accumulation of RSTE in marine sediments. A previously established transgressive-regressive sequence of the Cline Shale, a well-constrained high-amplitude glacio-eustatic fluctuation curve, and an accompanying episodic resupply of aqueous RSTE from the Panthalassic Ocean provide an ideal stratigraphic framework for determining the spatial and temporal variations of sediment RSTE enrichment patterns that responded to the episodic variations of seawater chemistry in this marginal silled paleomarine basin. Results suggest that although slightly higher median RSTE concentrations were observed in sediments from more reducing environments, the overall variation ranges of RSTE concentrations largely overlap among sediments deposited from a wide redox spectrum(from oxic to euxinic conditions) or different sea-level statuses in the Cline Shale. In contrast to the sediment RSTE enrichment patterns, the variations of sediment Mo/TOC and U/TOC ratios are coupled with glacio-eustatic fluctuation. The highest Mo/TOC and U/TOC ratios are commonly observed in sediments deposited during the highest relative sea-level(RSTE resupply), whereas the lowest Mo/TOC and U/TOC ratios usually appear in sediments deposited during the lowest relative sea-level(RSTE depletion). Our findings suggest that the benthic redox conditions recorded in sediment Mo and U concentrations can be greatly obscured and weakened by depleted aqueous Mo and U concentrations in highly restricted basins. Thus, the use of sediment Mo a
文摘Machine learning and artificial intelligence approaches have rapidly gained popularity for use in many subsurface energy applications.They are seen as novel methods that may enhance existing capabilities,providing for improved efficiency in exploration and production operations.Furthermore,their inte-gration into reservoir management workflows may shape the future landscape of the energy industry.This study implements a framework that generates predictive models using multiple machine learning classification-based algorithms which can identify specific stratigraphic units(i.e.,formations)as a function of total vertical depth and spatial positioning.The framework is applied in a case study to 13 specific formations of interest(Upper Spraberry through Atoka/Morrow reservoirs)in the Midland Basin,West Texas,United States;a prominent hydrocarbon producing sub-basin of the larger Permian Basin.The study dataset consists of over 275,000 records and includes data fields like formation iden-tifier,true vertical depth(in feet)of formations observed,and latitude and longitude coordinates(in decimal degrees).A subset of 134,374 data records were relevant to the 13 distinct formations of interest and were extracted and used for machine learning model training,validation,and testing.Four super-vised learning approaches including random forest(RF),gradient boosting(GB),support vector machine(SVM),and multilayer perceptron neural network(MLP)were evaluated and their prediction accuracy compared.The best performing model was ultimately built on the RF algorithm and is capable of an overall prediction accuracy of 93 percent on holdout data.The RF-based model demonstrated high prediction accuracy for major oil and gas producing zones including the San Andres,Upper Spraberry,Lower Spraberry,Clearfork,and Wolfcamp at 98,94,89,94,and 94 percent respectively.Overall,the resulting data-driven model provides a robust,cost-effective approach which can complement contemporary reservoir management approaches for multiple subsurface energy
文摘The dual structure of industrialization and urbanization in China causes domestic under-demand in recent years. The development strategy of the small town determines the economy can only meet lower equilibrium. The route that emerges in the Midland is chosen to develop the regional key city or city to enclose actively, but this process will lead to new non-equilibrium that is pulled by the investment of government. Establishing a two-product model, this paper verifies that the endogenous motive force of growth of the urbanization is the division of labor, which also promotes the SME. So we draw a conclusion that the SME is the main force that can promotes the urbanization of the Midland.
