scholarly journals The BABAR Prompt Reconstruction System, or Getting the Results out Fast: an Evaluation of Nine Months Experience Operating a Near Real-time Bulk Data Production System

2000 ◽  
Author(s):  
Thomas Glanzman
Keyword(s):  
Real Time ◽  
Production System ◽  
Bulk Data ◽  
Data Production

A Continuous Data Production Approach to Flow Estimation in Canada

2021 ◽  
Author(s):  
Francois Rainville ◽  
Alain Pietroniro ◽  
Andre Bouchard ◽  
Amber Brown ◽  
Douglas Stiff
Keyword(s):  
Real Time ◽  
Production System ◽  
Water Levels ◽  
Continuous Data ◽  
Production Cycle ◽  
Time Data ◽  
The World ◽  
Data Production ◽  
Real Time Data ◽  
Rating Curves

<p>The world has entered an era of immense water-related threats due to climate warming and human actions.  Changing precipitation patterns, reducing snowpack, accelerating glacial melt, intensifying floods and droughts have made the need for timely hydrometric information indispensable. Climate change thus introduced requirements for adaptive management and timely water resource information at the municipal, regional and national levels. Over the last 10 years, it became evident that demands from users had moved towards best available hydrometric data in near real-time.  As with most hydrometric services around the world, the WSC was a legacy and archive-driven organization that published approved data on an annual basis.  Real-time data was an after-thought simply equated with the application of rating curves onto telemetry water levels, while hydrographers remained focused on approving data months after the facts. To address this challenge, the Meteorological Service of Canada‘s National Hydrological Services, and specifically the Water Survey of Canada (WSC) has developed a near real-time continuous data production system to meet the evolving needs of stakeholders.  To meet this challenge, WSC developed solutions where data would be improved as field-measurements were being acquired. Corrections to data and rating curves are applied within hours of field discharge measurements, allowing for near-real time publication of corrected discharge information.  Moreover, station conditions and performance are constantly monitored with “eyes-on-data” production tools that allow the program to optimize its field visits, costs and data publication. These tools were developed in-house to enable effective network time-management while communicating important information with partner agencies.  This was made possible with a cloud-based hydrometric data production system and modern telecommunications tools.  As a result of this work, the improved near real-time data became the catalyst to revamp a multi-decade approach to final data approval. This improved overall efficiency and is now leading to less delays in the approved data production cycle.  This paper describes the design and implementation of the continuous data production system adopted at WSC and highlights some of the benefits noted since program implementation. This paper also identifies future investments that could help the sustainability of this new system in the long term.</p>

Proxy Model for Real-Time Estimation of Cool Down Time of Subsea Production System to Prevent Hydrates Formation

2021 ◽  
Author(s):  
Joseph Rizzo Cascio ◽  
Antonio Da Silva ◽  
Martino Ghetti ◽  
Martino Corti ◽  
Marco Montini
Keyword(s):  
Real Time ◽  
Production System ◽  
Field Data ◽  
Oil And Gas ◽  
Time Estimation ◽  
Integrated Approach ◽  
Proxy Model ◽  
Real Time Estimation ◽  
Subsea Production System ◽  
Formation Of Hydrates

Abstract Objectives/Scope The benefits of real-time estimation of the cool down time of Subsea Production System (SPS) to prevent formation of hydrates are shown on a real oil and gas facility. The innovative tool developed is based on an integrated approach, which embeds a proxy model of SPS and hydrate curves, exploiting real-time field data from the Eni Digital Oil Field (eDOF, an OSIsoft PI based application developed and managed by Eni) to continuously estimate the cool down time before hydrates are formed during the shutdown. Methods, Procedures, Process The Asset value optimization and the Asset integrity of hydrocarbon production systems are complex and multi-disciplinary tasks in the oil and gas industry, due to the high number of variables and their synergy. An accurate physical model of SPS is built and, then, used to develop a proxy model, which integrates hydrate curves at different MeOH concentration, being able to estimate in real time the cool down time of SPS during the shutdown exploiting data from subsea transmitters made available by eDOF in order to prevent formation of hydrates. The tool is also integrated with a user-friendly interface, making all relevant information readily available to the operators on field. Results, Observations, Conclusions The integrated approach provides a continues estimation of cool down time based on real time field data (eDOF) in order to prevent formation of hydrates and activate preservation actions. An accurate physical model of SPS is built on a real business case using Olga software and cool down curves simulated considering different operating shutdown scenarios. Hydrate curves of the considered production fluid are also simulated at different MeOH concentration using PVTsim NOVA software. Off-line simulated curves are then implemented as numerical tables combined with eDOF data by an Eni developed fast executing proxy model to produce estimated cool down time before hydrates are formed. A graphic representation of SPS behavior and its cool down time estimation during shutdown are displayed and ready to use by the operators on field in support of the operations, saving cost and time. Novel/Additive Information The benefits of real time estimation of the cool down time of SPS to prevent hydrates formation are shown in terms of saving of time and cost during the shutdown operations on a real case application. This integrated approach allows to rely on a continue, automatic and acceptably accurate estimate of the available time before hydrates are formed in SPS, including the possibility to be further developed for cases where subsea transmitters are not available or extended to other flow assurance issues.

scholarly journals Construction of Intelligent Integrated Model Framework for the Workshop Manufacturing System Via Digital Twin

2021 ◽  
Author(s):  
Zhongyu Zhang ◽  
Zhenjie Zhu ◽  
Jinsheng Zhang ◽  
Jingkun Wang
Keyword(s):  
Real Time ◽  
Production System ◽  
Manufacturing System ◽  
System Optimization ◽  
Production Cycle ◽  
Model Framework ◽  
The Real ◽  
Digital Twin ◽  
Twin Model ◽  
Information Layer

Abstract With the drastic development of the globally advanced manufacturing industry, transition of the original production pattern from traditional industries to advanced intelligence is completed with the least delay possible, which are still facing new challenges. Because the timeliness, stability and reliability of them is significantly restricted due to lack of the real-time communication. Therefore, an intelligent workshop manufacturing system model framework based on digital twin is proposed in this paper, driving the deep inform integration among the physical entity, data collection, and information decision-making. The conceptual and obscure of the traditional digital twin is refined, optimized, and upgraded on the basis of the four-dimension collaborative model thinking. A refined nine-layer intelligent digital twin model framework is established. Firstly, the physical evaluation is refined into entity layer, auxiliary layer and interface layer, scientifically managing the physical resources as well as the operation and maintenance of the instrument, and coordinating the overall system. Secondly, dividing the data evaluation into the data layer and the processing layer can greatly improve the flexible response-ability and ensure the synchronization of the real-time data. Finally, the system evaluation is subdivided into information layer, algorithm layer, scheduling layer, and functional layer, developing flexible manufacturing plan more reasonably, shortening production cycle, and reducing logistics cost. Simultaneously, combining SLP and artificial bee colony are applied to investigate the production system optimization of the textile workshop. The results indicate that the production efficiency of the optimized production system is increased by 34.46%.

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