Real-Time Emergency Response Fleet Deployment: Concepts, Systems, Simulation & Case Studies

2008 ◽  
pp. 133-162 ◽  
Author(s):  
Ali Haghani ◽  
Saini Yang
Keyword(s):  
Real Time ◽  
Case Studies ◽  
Emergency Response ◽  
Systems Simulation ◽  
Fleet Deployment

Real-time seismic damage prediction and comparison of various ground motion intensity measures based on machine learning

2021 ◽  
Author(s):  
Yongjia Xu ◽  
Xinzheng Lu ◽  
Yuan Tian ◽  
Yuli Huang
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Case Studies ◽  
Ground Motion ◽  
Emergency Response ◽  
Prediction Method ◽  
Seismic Damage ◽  
Intensity Measures ◽  
Damage Prediction ◽  
Model Training

<p>After earthquakes, an accurate and efficient seismic damage prediction is indispensable for emergency response. Existing methods face the dilemma between accuracy and efficiency. A real-time and accurate seismic damage prediction method based on machine-learning is proposed here. 48 intensity measures are used as input to represent the ground motion comprehensively. Besides, the workload of the NLTHA method is replaced by model training/testing and moved to a non-urgent stage to promote efficiency. Case studies with various building cases prove the accuracy and efficiency of the proposed method. Key intensity measures for each building are identified by iteratively using the proposed framework.</p>

A Pandemic of Violence? The Impact of COVID-19 on Conflict

2020 ◽  
Vol 26 (3) ◽  
Author(s):  
Sara M.T. Polo
Keyword(s):  
Spatial Distribution ◽  
Real Time ◽  
Case Studies ◽  
Armed Conflict ◽  
Event Data ◽  
Social Unrest ◽  
Johns Hopkins University ◽  
The World ◽  
Political Advantage ◽  
The Impact

AbstractThis article examines the impact and repercussions of the COVID-19 pandemic on patterns of armed conflict around the world. It argues that there are two main ways in which the pandemic is likely to fuel, rather than mitigate, conflict and engender further violence in conflict-prone countries: (1) the exacerbating effect of COVID-19 on the underlying root causes of conflict and (2) the exploitation of the crisis by governments and non-state actors who have used the coronavirus to gain political advantage and territorial control. The article uses data collected in real-time by the Armed Conflict Location & Event Data Project (ACLED) and the Johns Hopkins University to illustrate the unfolding and spatial distribution of conflict events before and during the pandemic and combine this with three brief case studies of Afghanistan, Nigeria, and Libya. Descriptive evidence shows how levels of violence have remained unabated or even escalated during the first five months of the pandemic and how COVID-19-related social unrest has spread beyond conflict-affected countries.

SOSPO-SP: Secure Operation of Sustainable Power Systems Simulation Platform for Real-Time System State Evaluation and Control

2014 ◽  
Vol 10 (4) ◽  
pp. 2318-2329 ◽  
Author(s):  
Hugo Morais ◽  
Pieter Vancraeyveld ◽  
Allan Henning Birger Pedersen ◽  
Morten Lind ◽  
Hjortur Johannsson ◽  
...  
Keyword(s):  
Power Systems ◽  
Real Time ◽  
Simulation Platform ◽  
System State ◽  
Time System ◽  
Real Time System ◽  
State Evaluation ◽  
Systems Simulation ◽  
And Control ◽  
Sustainable Power

IIOT and Real Time Data Analytics - Maximizing the Impact on Safety and Productivity

2021 ◽  
Author(s):  
Ryan Daher ◽  
Nesma Aldash
Keyword(s):  
Real Time ◽  
Case Studies ◽  
Data Analytics ◽  
Contact Tracing ◽  
Time Data ◽  
Seamless Integration ◽  
Location Services ◽  
Industrial Internet ◽  
Potential Applications ◽  
The Impact

Abstract With the global push towards Industry 4.0, a number of leading companies and organizations have invested heavily in Industrial Internet of Things (IIOT's) and acquired a massive amount of data. But data without proper analysis that converts it into actionable insights is just more information. With the advancement of Data analytics, machine learning, artificial intelligence, numerous methods can be used to better extract value out of the amassed data from various IIOTs and leverage the analysis to better make decisions impacting efficiency, productivity, optimization and safety. This paper focuses on two case studies- one from upstream and one from downstream using RTLS (Real Time Location Services). Two types of challenges were present: the first one being the identification of the location of all personnel on site in case of emergency and ensuring that all have mustered in a timely fashion hence reducing the time to muster and lessening the risks of Leaving someone behind. The second challenge being the identification of personnel and various contractors, the time they entered in productive or nonproductive areas and time it took to complete various tasks within their crafts while on the job hence accounting for efficiency, productivity and cost reduction. In both case studies, advanced analytics were used, and data collection issues were encountered highlighting the need for further and seamless integration between data, analytics and intelligence is needed. Achievements from both cases were visible increase in productivity and efficiency along with the heightened safety awareness hence lowering the overall risk and liability of the operation. Novel/Additive Information: The results presented from both studies have highlighted other potential applications of the IIOT and its related analytics. Pertinent to COVID-19, new application of such approach was tested in contact tracing identifying workers who could have tested positive and tracing back to personnel that have been in close proximity and contact therefore reducing the spread of COVID. Other application of the IIOT and its related analytics has also been tested in crane, forklift and heavy machinery proximity alert reducing the risk of accidents.

scholarly journals Processing Collections of Geo-Referenced Images for Natural Disasters

2018 ◽  
Vol 18 (03) ◽  
pp. e22
Author(s):  
Fernando Loor ◽  
Veronica Gil-Costa ◽  
Mauricio Marin
Keyword(s):  
High Risk ◽  
Natural Disasters ◽  
Real Time ◽  
Citizen Science ◽  
Emergency Response ◽  
P2p Network ◽  
Limited Time ◽  
Real Time Processing ◽  
Time Processing ◽  
Powerful Mechanism

After disaster strikes, emergency response teams need to work fast. In this context, crowdsourcing has emerged as a powerful mechanism where volunteers can help to process different tasks such as processing complex images using labeling and classification techniques. In this work we propose to address the  problem of how to efficiently process large volumes of georeferenced images using crowdsourcing in the context of high risk such as natural disasters. Research on citizen science and crowdsourcing indicates that volunteers should be able to contribute in a useful way with a limited time to a project, supported by the results of usability studies. We present the design of a platform for real-time processing of georeferenced images. In particular, we focus on the interaction between the crowdsourcing and the volunteers connected to a P2P network.

scholarly journals Marine measurement and real-time control systems with case studies

2018 ◽  
Vol 159 ◽  
pp. 457-469 ◽  
Author(s):  
Melek Ertogan ◽  
Gokhan Tansel Tayyar ◽  
Philip A. Wilson ◽  
Seniz Ertugrul
Keyword(s):  
Control Systems ◽  
Real Time ◽  
Case Studies ◽  
Real Time Control ◽  
Time Control

Delivering Pressure Transient Analysis During Drawdown on ESP Wells: Case Studies and Lessons Learned

2021 ◽  
Author(s):  
Lawrence Camilleri ◽  
Mohammed Al-Jorani ◽  
Mohammed Kamal Aal Najar ◽  
Joseph Ayoub
Keyword(s):  
Real Time ◽  
Case Studies ◽  
Flow Rate ◽  
High Frequency ◽  
Transient Analysis ◽  
Continuous Production ◽  
Complete Analysis ◽  
Pressure Transient Analysis ◽  
Pressure Transient ◽  
Production History

Abstract While pressure transient analysis (PTA) is a proven interpretation technique, it is mostly used on buildups because drawdowns are difficult to interpret. However, the deferred production associated with buildups discourages regular application of PTA to determine skin and identify boundary conditions. Several case studies are presented covering a range of well configurations to illustrate how downhole transient liquid rate measurements with electrical submersible pump (ESP) gauges enable PTA during drawdown and therefore real-time optimization. The calculation of high-frequency transient flow rates using ESP gauge real-time data is based on the principle that the power absorbed by the pump is equal to that generated by the motor. This technique is independent of fluid specific gravity and therefore is self-calibrating with changes in water cut and phase segregation. Analytical equations ensure that the physics is always respected, thereby providing the necessary repeatability. The combination of downhole transient high-frequency flow rate and permanent pressure gauge data enables PTA using commonly available analytical techniques and software, especially because superposition time is calculated accurately. The availability of continuous production history brings significant value for PTA. It makes it possible to perform history matching and to deploy semilog analysis using an accurate set of superposition time functions. However, the application of log-log analysis techniques is usually more challenging because of imperfections in input data such as noise, oversimplified production history, time-synchronization issues, or wellbore effects. These limitations are solved by utilizing high-frequency downhole data from ESP. This is possible first as superposition time is effectively an integral function, which dampens any noise in the flow rate signal. Another important finding is that wellbore effects in subhydrostatic wells are less impactful in drawdowns than in buildups where compressibility and redistribution can mask reservoir response. Key reservoir properties, in particular mobility, can nearly always be estimated, leading to better skin factor determination even without downhole shut-in. Finally, with the constraint of production deferment eliminated, drawdowns can be monitored for extended durations to identify boundaries and to perform time-lapse interpretation more efficiently. Confirming a constant pressure boundary or a change in skin enables more effective and proactive production management. In all cases considered, a complete analysis was possible, including buildup and drawdown data comparison. With the development of downhole flow rate calculation technology, it is now possible to provide full inflow characterization in a matter of days following an ESP workover, without any additional hardware or staff mobilization to the wellsite and no deferred production. More importantly, the technique provides the necessary information to diagnose the cause of underproduction, identify stimulation candidates, and manage drawdown.

Case Studies for the Successful Deployment of Wells Augmented Stuck Pipe Indicator in Wells Real Time Centre

2021 ◽  
Author(s):  
Meor M. Meor Hashim ◽  
M. Hazwan Yusoff ◽  
M. Faris Arriffin ◽  
Azlan Mohamad ◽  
Tengku Ezharuddin Tengku Bidin ◽  
...  
Keyword(s):  
Real Time ◽  
Case Studies ◽  
Drilling Fluid ◽  
Drill String ◽  
Drilling Operation ◽  
Cost Impact ◽  
Drilling Parameters ◽  
The Impact ◽  
Productive Time ◽  
Specific Challenge

Abstract The restriction or inability of the drill string to reciprocate or rotate while in the borehole is commonly known as a stuck pipe. This event is typically accompanied by constraints in drilling fluid flow, except for differential sticking. The stuck pipe can manifest based on three different mechanisms, i.e. pack-off, differential sticking, and wellbore geometry. Despite its infrequent occurrence, non-productive time (NPT) events have a massive cost impact. Nevertheless, stuck pipe incidents can be evaded with proper identification of its unique symptoms which allows an early intervention and remediation action. Over the decades, multiple analytical studies have been attempted to predict stuck pipe occurrences. The latest venture into this drilling operational challenge now utilizes Machine Learning (ML) algorithms in forecasting stuck pipe risk. An ML solution namely, Wells Augmented Stuck Pipe Indicator (WASP), is developed to tackle this specific challenge. The solution leverages on real-time drilling database and supplementary engineering design information to estimate proxy drilling parameters which provide active and impartial pattern recognition of prospective stuck pipe events. The solution is built to assist Wells Real Time Centre (WRTC) personnel in proactively providing a holistic perspective in anticipating potential anomalies and recommending remedial countermeasures before incidents happen. Several case studies are outlined to exhibit the impact of WASP in real-time drilling operation monitoring and intervention where WASP is capable to identify stuck pipe symptoms a few hours earlier and provide warnings for stuck pipe avoidance. The presented case studies were run on various live wells where restrictions are predicted stands ahead of the incidents. Warnings and alarms were generated, allowing further analysis by the personnel to verify and assess the situation before delivering a precautionary procedure to the rig site. The implementation of the WASP will reduce analysis time and provide timely prescriptive action in the proactive real-time drilling operation monitoring and intervention hub, subsequently creating value through cost containment and operational efficiency.

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