Wired drill pipe（WDP）technology is one of the most promising data acquisition technologies in today s oil and gas industry.For the first time it allows sensors to be positioned along the drill string which enables c...Wired drill pipe（WDP）technology is one of the most promising data acquisition technologies in today s oil and gas industry.For the first time it allows sensors to be positioned along the drill string which enables collecting and transmitting valuable data not only from the bottom hole assembly（BHA）,but also along the entire length of the wellbore to the drill floor.The technology has received industry acceptance as a viable alternative to the typical logging while drilling（LWD）method.Recently more and more WDP applications can be found in the challenging drilling environments around the world,leading to many innovations to the industry.Nevertheless most of the data acquired from WDP can be noisy and in some circumstances of very poor quality.Diverse factors contribute to the poor data quality.Most common sources include mis-calibrated sensors,sensor drifting,errors during data transmission,or some abnormal conditions in the well,etc.The challenge of improving the data quality has attracted more and more focus from many researchers during the past decade.This paper has proposed a promising solution to address such challenge by making corrections of the raw WDP data and estimating unmeasurable parameters to reveal downhole behaviors.An advanced data processing method,data validation and reconciliation（DVR）has been employed,which makes use of the redundant data from multiple WDP sensors to filter/remove the noise from the measurements and ensures the coherence of all sensors and models.Moreover it has the ability to distinguish the accurate measurements from the inaccurate ones.In addition,the data with improved quality can be used for estimating some crucial parameters in the drilling process which are unmeasurable in the first place,hence provide better model calibrations for integrated well planning and realtime operations.展开更多
In this paper,a case study is carried out in comparison of pipes-and-filters architecture and batch sequential architecture.Concepts on a data flow system and the two mentioned architectures are presented.A Java templ...In this paper,a case study is carried out in comparison of pipes-and-filters architecture and batch sequential architecture.Concepts on a data flow system and the two mentioned architectures are presented.A Java template class design in implementing the "pipes" and "filters" in the pipes-and-filters architecture is given at the design level.Finally,this paper uses a concrete example to show how to use Java to implement the pipesand-filters architecture.Using varied amount of data from text files,performance and memory usage of the two architectures are illustrated.展开更多
Climate change and global warming results in natural hazards, including flash floods. Flash floods can create blue spots;areas where transport networks (roads, tunnels, bridges, passageways) and other engineering stru...Climate change and global warming results in natural hazards, including flash floods. Flash floods can create blue spots;areas where transport networks (roads, tunnels, bridges, passageways) and other engineering structures within them are at flood risk. The economic and social impact of flooding revealed that the damage caused by flash floods leading to blue spots is very high in terms of dollar amount and direct impacts on people’s lives. The impact of flooding within blue spots is either infrastructural or social, affecting lives and properties. Currently, more than 16.1 million properties in the U.S are vulnerable to flooding, and this is projected to increase by 3.2% within the next 30 years. Some models have been developed for flood risks analysis and management including some hydrological models, algorithms and machine learning and geospatial models. The models and methods reviewed are based on location data collection, statistical analysis and computation, and visualization (mapping). This research aims to create blue spots model for the State of Tennessee using ArcGIS visual programming language (model) and data analytics pipeline.展开更多
基金supported by University of Stavanger, NorwaySINTEF,the Center for Integrated Operations in the Petroleum Industry and the management of National Oilwell Varco Intelli Serv
文摘Wired drill pipe（WDP）technology is one of the most promising data acquisition technologies in today s oil and gas industry.For the first time it allows sensors to be positioned along the drill string which enables collecting and transmitting valuable data not only from the bottom hole assembly（BHA）,but also along the entire length of the wellbore to the drill floor.The technology has received industry acceptance as a viable alternative to the typical logging while drilling（LWD）method.Recently more and more WDP applications can be found in the challenging drilling environments around the world,leading to many innovations to the industry.Nevertheless most of the data acquired from WDP can be noisy and in some circumstances of very poor quality.Diverse factors contribute to the poor data quality.Most common sources include mis-calibrated sensors,sensor drifting,errors during data transmission,or some abnormal conditions in the well,etc.The challenge of improving the data quality has attracted more and more focus from many researchers during the past decade.This paper has proposed a promising solution to address such challenge by making corrections of the raw WDP data and estimating unmeasurable parameters to reveal downhole behaviors.An advanced data processing method,data validation and reconciliation（DVR）has been employed,which makes use of the redundant data from multiple WDP sensors to filter/remove the noise from the measurements and ensures the coherence of all sensors and models.Moreover it has the ability to distinguish the accurate measurements from the inaccurate ones.In addition,the data with improved quality can be used for estimating some crucial parameters in the drilling process which are unmeasurable in the first place,hence provide better model calibrations for integrated well planning and realtime operations.
文摘In this paper,a case study is carried out in comparison of pipes-and-filters architecture and batch sequential architecture.Concepts on a data flow system and the two mentioned architectures are presented.A Java template class design in implementing the "pipes" and "filters" in the pipes-and-filters architecture is given at the design level.Finally,this paper uses a concrete example to show how to use Java to implement the pipesand-filters architecture.Using varied amount of data from text files,performance and memory usage of the two architectures are illustrated.
文摘Climate change and global warming results in natural hazards, including flash floods. Flash floods can create blue spots;areas where transport networks (roads, tunnels, bridges, passageways) and other engineering structures within them are at flood risk. The economic and social impact of flooding revealed that the damage caused by flash floods leading to blue spots is very high in terms of dollar amount and direct impacts on people’s lives. The impact of flooding within blue spots is either infrastructural or social, affecting lives and properties. Currently, more than 16.1 million properties in the U.S are vulnerable to flooding, and this is projected to increase by 3.2% within the next 30 years. Some models have been developed for flood risks analysis and management including some hydrological models, algorithms and machine learning and geospatial models. The models and methods reviewed are based on location data collection, statistical analysis and computation, and visualization (mapping). This research aims to create blue spots model for the State of Tennessee using ArcGIS visual programming language (model) and data analytics pipeline.
