Measurement While Coring (MWC)—measurement of core acquisition in real time

2009 ◽  
Vol 49 (1) ◽  
pp. 25
Author(s):  
Greg Wheatley
Keyword(s):  
Real Time ◽  
Inner Core ◽  
Recovery Process ◽  
Drilling Fluids ◽  
Time Data ◽  
The Core ◽  
Core Recovery ◽  
Real Time Data ◽  
Level Recorder ◽  
Real Time Information

Coring technology has remained relatively unchanged for decades. One of the primary technology gaps has always been the lack of real-time data at the surface on core recovery. Coretrack has developed the Core Level Recorder (CLR), a core recovery tracking tool that delivers accurate core-to-reservoir correlation. The CLR can be coupled to a mud pulser system to deliver surface readout on the core recovery process delivering accurate core-to-reservoir correlation in real time. The CLR is placed inside the inner core barrel and moves upwards as core is recovered into the barrel recording the amount of rock captured in a time versus distance format. The transmission of this data to surface provides the real-time information on whether or not core is being recovered and accurately records any gaps in the process allowing the core to be correlated to the formation with ±50 mm accuracy. The CLR has been designed for use in both water and synthetic oil based drilling fluids of up to 125 °C and 10 kpsi with runs carried out in a variety of both on and offshore wells in Oman, Saudi Arabia and Australia. The CLR can be run in deviated holes and in all currently available core barrels. The most common size is for 4 inch core, however it is easily adapted for other barrel diameters. Real-time data transmission enables the coring technician to accurately state when core is being recovered. This greatly increases the ability to state when core milling or core jams have occurred—a notification that removes unnecessary round trips and brings significant savings. This paper will present case histories of runs made with the CLR.

Shifting the Paradigm: The Effective Way to Use Real-Time Data for Improved and Safer Oilwell Operations

2012 ◽  
Author(s):  
David M. Pritchard ◽  
Jesse Roye ◽  
J. C. Cunha
Keyword(s):  
Real Time ◽  
Control Analysis ◽  
Time Data ◽  
Well Control ◽  
Real Time Data ◽  
Drilling Operations ◽  
Recorded Data ◽  
Well Data ◽  
The Right ◽  
Real Time Information

When analyzing root causes for minor or major problems occurring in oilwell drilling operations, investigators almost always can track past events, step by step, using recorded data that was produced when the operation occurred. In recent catastrophic blow-outs, investigators were able not only to determine the causes of the accidents but also to indicate mitigating actions, which could have prevented the accident if they were taken when the operation actually took place. This is a strong indicator that, even though the industry has valuable real-time information available, it is not using it as a tool to avoid harmful events and improve performance. Real-time data is not about well control, it is about well control avoidance. Recent catastrophic events have underscored the value of having the right kind of experience to understand and interpret well data in real time, taking the necessary corrective actions before it escalates to more serious problems. What is the well telling us? How do we use real time data to ensure a stable wellbore? Real-time monitoring, integrated with rigorous total well control analysis, is required to embrace and achieve continuous improvements — and ensure the safest possible environment. Next generation monitoring requires a step change that includes hazards avoidance as a precursor to drilling optimization. Real-time data can be used effectively in operations to avoid, minimize, and better manage operational events associated with drilling and completion. Real-time data can also provide the foundational support to improve training in the industry as well as develop hands-on simulators for hazards avoidance.

Geospatial Analysis in Responding to a Nuclear Detonation Scenario in NYC: The Gotham Shield Exercise

2020 ◽  
Vol 17 (3) ◽  
Author(s):  
Luis Lowe ◽  
Adela Salame-Alfie ◽  
Bob Neurath ◽  
Celia Quinn ◽  
Armin Ansari ◽  
...  
Keyword(s):  
Public Health ◽  
Real Time ◽  
Geospatial Analysis ◽  
Federal State ◽  
Time Data ◽  
Response Planning ◽  
Real Time Data ◽  
State And Local ◽  
Real Time Information ◽  
Time Information

AbstractIn April 2017, the Centers for Disease Control and Prevention (CDC) participated in the Gotham Shield Exercise, led by the Federal Emergency Management Agency (FEMA) and in collaboration with other federal agencies to test the federal, state and local government’s ability to respond to an improvised nuclear device (IND). With active engagement from CDC leadership, 266 scientific and support staff from across the agency participated in the Gotham Shield exercise. The scenario involved a 10-kiloton detonation near the Lincoln Tunnel in New Jersey. This nuclear detonation scenario provided CDC with the opportunity to test some of the all-hazards tools the agency uses during response to other national or international emergencies, such as Geographic Information Systems (GIS) and mapping tools, and apply these tools to a nuclear emergency. Geospatial analysis associated with real time data can provide near real time information for individuals and entities associated with response and recovery activities. This type of analysis can provide timely data in regard to maps and information used to properly place staging areas for Community Reception Centers (CRC), mass care locations, and other medical care and countermeasure related services. Maps showing locations of power loss, such as locations of lost or inoperable main electrical grid and substations, combined with real time data on where power is available provides valuable information for first responders and emergency managers as well as responders engaged in communicating critical public messages to affected populations in these areas. By using real-time information, response officials can direct the response, allocate scarce resources, aid in coordination efforts, and provide a more efficient means of providing critical public health and medical services. The results of the exercise highlight the importance of using geospatial analysis for response planning and effect mitigation before, during, and after a public health event of this magnitude, and the value they represent in informed decision making.

399-P: Nurse-Driven Intervention Using Real-Time Data to Improve Hypoglycemia Rates in a Respiratory Care Unit

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 399-P
Author(s):  
ANN MARIE HASSE ◽  
RIFKA SCHULMAN ◽  
TORI CALDER
Keyword(s):  
Real Time ◽  
Respiratory Care ◽  
Time Data ◽  
Real Time Data

MHA releases largest real‐time data from MH help‐seekers

2021 ◽  
Vol 31 (6) ◽  
pp. 7-7
Author(s):  
Valerie A. Canady
Keyword(s):  
Real Time ◽  
Time Data ◽  
Real Time Data

Why Ecological Momentary Assessment Surveys Go Incomplete?: When It Happens and How It Impacts Data

2020 ◽  
Author(s):  
Yu-Hsiang Wu ◽  
Jingjing Xu ◽  
Elizabeth Stangl ◽  
Shareka Pentony ◽  
Dhruv Vyas ◽  
...  
Keyword(s):  
Real Time ◽  
Self Report ◽  
Data Logging ◽  
Time Data ◽  
Real Time Data ◽  
Report Data ◽  
Self Report Data ◽  
Ecological Momentary ◽  
The Impact ◽  
Momentary Assessment

Abstract Background Ecological momentary assessment (EMA) often requires respondents to complete surveys in the moment to report real-time experiences. Because EMA may seem disruptive or intrusive, respondents may not complete surveys as directed in certain circumstances. Purpose This article aims to determine the effect of environmental characteristics on the likelihood of instances where respondents do not complete EMA surveys (referred to as survey incompletion), and to estimate the impact of survey incompletion on EMA self-report data. Research Design An observational study. Study Sample Ten adults hearing aid (HA) users. Data Collection and Analysis Experienced, bilateral HA users were recruited and fit with study HAs. The study HAs were equipped with real-time data loggers, an algorithm that logged the data generated by HAs (e.g., overall sound level, environment classification, and feature status including microphone mode and amount of gain reduction). The study HAs were also connected via Bluetooth to a smartphone app, which collected the real-time data logging data as well as presented the participants with EMA surveys about their listening environments and experiences. The participants were sent out to wear the HAs and complete surveys for 1 week. Real-time data logging was triggered when participants completed surveys and when participants ignored or snoozed surveys. Data logging data were used to estimate the effect of environmental characteristics on the likelihood of survey incompletion, and to predict participants' responses to survey questions in the instances of survey incompletion. Results Across the 10 participants, 715 surveys were completed and survey incompletion occurred 228 times. Mixed effects logistic regression models indicated that survey incompletion was more likely to happen in the environments that were less quiet and contained more speech, noise, and machine sounds, and in the environments wherein directional microphones and noise reduction algorithms were enabled. The results of survey response prediction further indicated that the participants could have reported more challenging environments and more listening difficulty in the instances of survey incompletion. However, the difference in the distribution of survey responses between the observed responses and the combined observed and predicted responses was small. Conclusion The present study indicates that EMA survey incompletion occurs systematically. Although survey incompletion could bias EMA self-report data, the impact is likely to be small.

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