Closed-Loop Data Analytics for Wells Construction Management in Real Time Centre

2021 ◽  
Author(s):  
Azlesham Rosli ◽  
Whye Jin Mak ◽  
Bobbywadi Richard ◽  
Meor M Meor Hashim ◽  
M Faris Arriffin ◽  
...  
Keyword(s):  
Real Time ◽  
Information Exchange ◽  
Data Analytics ◽  
Dynamic Monitoring ◽  
Real Time Monitoring ◽  
The Real ◽  
Current Process ◽  
Execution Phase ◽  
Drilling Data ◽  
Monitoring Platform

Abstract The execution phase of the wells technical assurance process is a critical procedure where the drilling operation commences and the well planning program is implemented. During drilling operations, the real-time drilling data are streamed to a real-time centre where it is constantly monitored by a dedicated team of monitoring specialists. If any potential issues or possible opportunities arise, the team will communicate with the operation team on rig for an intervention. This workflow is further enhanced by digital initiatives via big data analytics implementation in PETRONAS. The Digital Standing Instruction to Driller (Digital SID) is a drilling operational procedures documentation tool meant to improve the current process by digitalizing information exchange between office and rig site. Boasting multi-operation usage, it is made fit to context and despite its automated generation, this tool allows flexibility for the operation team to customize the content and more importantly, monitor the execution in real-time. Another tool used in the real-time monitoring platform is the dynamic monitoring drilling system where it allows real-time drilling data to be more intuitive and gives the benefit of foresight. The dynamic nature of the system means that it will update existing roadmaps with extensive real-time data as they come in, hence improving its accuracy as we drill further. Furthermore, an automated drilling key performance indicator (KPI) and performance benchmarking system measures drilling performance to uncover areas of improvement. This will serve as the benchmark for further optimization. On top of that, an artificial intelligence (AI) driven Wells Augmented Stuck Pipe Indicator (WASP) is deployed in the real-time monitoring platform to improve the capability of monitoring specialists to identify stuck pipe symptoms way earlier before the occurrence of the incident. This proactive approach is an improvement to the current process workflow which is less timely and possibly missing the intervention opportunity. These four tools are integrated seamlessly with the real-time monitoring platform hence improving the project management efficiency during the execution phase. The tools are envisioned to offer an agile and efficient process workflow by integrating and tapering down multiple applications in different environments into a single web-based platform which enables better collaboration and faster decision making.

Research of Landslide Environment Monitoring Technology for Villages at Fluvial Terraces of the Gaoping River Basin of Taiwan

2020 ◽  
Author(s):  
Han Chung Yang ◽  
Chih Chiang Su ◽  
Yen Chang Chen
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Environmental Changes ◽  
Safety Information ◽  
Dynamic Monitoring ◽  
Real Time Monitoring ◽  
River Bank ◽  
The Real ◽  
Fluvial Terraces ◽  
Monitoring Stations

<p>A wireless tracer real-time monitoring system was developed and verified to be suitable for the real-time remote dynamic monitoring of typhoon- and flood-related scour at riverbeds and human-made structures (such as bridge abutments, spur dikes, and embedments). This study focused on the use of a wireless tracer to aid the real-time dynamic monitoring of natural disasters, including slope landslides, thus devising a real-time warning system for sediment disaster prevention and response. We selected Dajin Bridge, which is situated at Taiwan’s Zhoukou River, as the research site for deploying the monitoring system. Monitoring stations for detecting changes in the river’s course were established at both a downstream meander of the Dajin Bridge and a nearby revetment. Specifically, scour monitoring columns were separately buried at these two locations. Each column was equipped with five wireless tracers, and 16 coding sand jars were used to facilitate vertical installation of wireless tracers. Real-time monitoring stations for tracking slope changes were constructed using two methods. In both methods, an upright column was used to install the tracers, and a shielding net cover was additionally used in the second method to expand its monitoring range. After several heavy rain events, no slides or landslides were detected by the landslide stations; an on-site investigation corroborated this observation. As for the detection of the change in the river’s course, three wireless tracers were flushed away. Nonetheless, because the scour depth posed no immediate threat to river bank safety, additional safety measures were not required. The remaining wireless tracers were also adequate for the safety monitoring of river banks, bridges, and other structures within the research area. The aforementioned results demonstrate the effectiveness of the devised remote real-time monitoring system for detecting environmental changes. The system can thus provide real-time remote safety information on changes in slope and a river’s course for residents in mountainous areas.</p>

scholarly journals A Real-Time Monitoring Platform for Distributed Energy Resources in a Microgrid—Pilot Study in Oman

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1803
Author(s):  
Nasser Hosseinzadeh ◽  
Ahmed Al Maashri ◽  
Naser Tarhuni ◽  
Abdelsalam Elhaffar ◽  
Amer Al-Hinai
Keyword(s):  
Real Time ◽  
Communication Channel ◽  
Energy Resources ◽  
Distributed Energy Resources ◽  
Small Scale ◽  
Area Network ◽  
Distributed Energy ◽  
Real Time Monitoring ◽  
Holistic View ◽  
Monitoring Platform

This article presents the development of a platform for real-time monitoring of multi-microgrids. A small-scale platform has been developed and implemented as a prototype, which takes data from various types of devices located at a distance from each other. The monitoring platform is interoperable, as it allows several protocols to coexist. While the developed prototype is tested on small-scale distributed energy resources (DERs), it is done in a way to extend the concept for monitoring several microgrids in real scales. Monitoring strategies were developed for DERs by making a customized two-way communication channel between the microgrids and the monitoring center using a long-range bridged wireless local area network (WLAN). In addition, an informative and easy-to-use software dashboard was developed. The dashboard shows real-time information and measurements from the DERs—providing the user with a holistic view of the status of the DERs. The proposed system is scalable, modular, facilitates the interoperability of various types of inverters, and communicates data over a secure communication channel. All these features along with its relatively low cost make the developed real-time monitoring platform very useful for online monitoring of smart microgrids.

Observing the real time formation of phosphine-ligated gold clusters by electrospray ionization mass spectrometry

2017 ◽  
Vol 19 (26) ◽  
pp. 17187-17198 ◽  
Author(s):  
Marshall R. Ligare ◽  
Grant E. Johnson ◽  
Julia Laskin
Keyword(s):  
Mass Spectrometry ◽  
Electrospray Ionization ◽  
Real Time ◽  
Electrospray Ionization Mass Spectrometry ◽  
Gold Cluster ◽  
Gold Clusters ◽  
Ionization Mass Spectrometry ◽  
Ionization Mass ◽  
Real Time Monitoring ◽  
The Real

Real-time monitoring of the gold cluster synthesis by electrospray ionization mass spectrometry reveals distinct formation pathways for Au8, Au9 and Au10 clusters.

scholarly journals A Study of Detecting Broken Rail using the Real-time Monitoring System

2013 ◽  
Vol 28 (4) ◽  
pp. 1-7
Author(s):  
Tae Geon Kim ◽  
Beom Gyu Eom ◽  
Hi Sung Lee
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Real Time Monitoring ◽  
The Real

Design of Embedded Video Monitoring System Based on DM6437

2014 ◽  
Vol 1003 ◽  
pp. 249-253
Author(s):  
Hao Fang ◽  
Ai Hua Li ◽  
Yan Fei Liu
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Transmission Function ◽  
Video Signal ◽  
Video Encoding ◽  
Video Monitoring ◽  
Time Requirement ◽  
The Real ◽  
Monitoring Platform

To solve the difficulty of traditional video monitoring system in system upgrade and expansion, an solution of embedded video monitoring system based on DaVinci technology was put forward in this paper. By building the monitoring platform by DM6437 and DSP/BIOS in the solution, TVP5151 was used for receiving video signal in PAL/NTSC formats, and an JPEG Baseline Profile Encoder was integrated for video encoding, and the 10/100M Ethernet transmission function was realized based on NDK. Finally, the system is tested and the result shows that the system can capture and transmit D1 format signal in 25f/s and met the real-time requirement. At the same time, the system is easy to use and expand with a bright application prospect.

Improving Real-Time Drilling Data Quality Using Artificial Intelligence and Machine Learning Techniques

2021 ◽  
Author(s):  
S. H. Al Gharbi ◽  
A. A. Al-Majed ◽  
A. Abdulraheem ◽  
S. Patil ◽  
S. M. Elkatatny
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Quality ◽  
Real Time ◽  
Input Data ◽  
Support Vector ◽  
The Real ◽  
Drilling Data ◽  
Drilling Operations

Abstract Due to high demand for energy, oil and gas companies started to drill wells in remote areas and unconventional environments. This raised the complexity of drilling operations, which were already challenging and complex. To adapt, drilling companies expanded their use of the real-time operation center (RTOC) concept, in which real-time drilling data are transmitted from remote sites to companies’ headquarters. In RTOC, groups of subject matter experts monitor the drilling live and provide real-time advice to improve operations. With the increase of drilling operations, processing the volume of generated data is beyond a human's capability, limiting the RTOC impact on certain components of drilling operations. To overcome this limitation, artificial intelligence and machine learning (AI/ML) technologies were introduced to monitor and analyze the real-time drilling data, discover hidden patterns, and provide fast decision-support responses. AI/ML technologies are data-driven technologies, and their quality relies on the quality of the input data: if the quality of the input data is good, the generated output will be good; if not, the generated output will be bad. Unfortunately, due to the harsh environments of drilling sites and the transmission setups, not all of the drilling data is good, which negatively affects the AI/ML results. The objective of this paper is to utilize AI/ML technologies to improve the quality of real-time drilling data. The paper fed a large real-time drilling dataset, consisting of over 150,000 raw data points, into Artificial Neural Network (ANN), Support Vector Machine (SVM) and Decision Tree (DT) models. The models were trained on the valid and not-valid datapoints. The confusion matrix was used to evaluate the different AI/ML models including different internal architectures. Despite the slowness of ANN, it achieved the best result with an accuracy of 78%, compared to 73% and 41% for DT and SVM, respectively. The paper concludes by presenting a process for using AI technology to improve real-time drilling data quality. To the author's knowledge based on literature in the public domain, this paper is one of the first to compare the use of multiple AI/ML techniques for quality improvement of real-time drilling data. The paper provides a guide for improving the quality of real-time drilling data.

A Novel Hash Chain-Based Data Availability Monitoring Method for Off-site Disaster Recovery Architecture

2021 ◽  
pp. 2150294
Author(s):  
Neng Huang ◽  
Junxing Zhu ◽  
Chaonian Guo ◽  
Shuhan Cheng ◽  
Xiaoyong Li
Keyword(s):  
Information Systems ◽  
Real Time ◽  
Data Center ◽  
Disaster Recovery ◽  
Distributed Storage ◽  
Data Availability ◽  
Data Consistency ◽  
Real Time Monitoring ◽  
The Real ◽  
Hash Chain

With the rapid development of mobile Internet, there is a higher demand for the real-time, reliability and availability of information systems and to prevent the possible systemic risks of information systems, various business consistency standards and regulatory guidelines have been published, such as Recovery Time Object (RTO) and Recovery Point Object (RPO). Some of the current related researches focus on the standards, methods, management tools and technical frameworks of business consistency, while others study the data consistency algorithms in the cases of large data, cloud computing and distributed storage. However, few researchers have studied on how to monitor the data consistency and RPO of production-disaster recovery, and what architecture and technology should be applied in the monitoring. Moreover, in some information systems, due to the complex structures and distributions of data, it is difficult for traditional methods to quickly detect and accurately locate the first error data. Besides, due to the separation of production data center (PDC) and disaster recovery data center (DRDC), it is difficult to calculate the data difference and RPO between the two centers. This paper first discusses the architecture of remote distributed DRDCs. The architecture can make the disaster recovery (DR) system always online and the data always readable, and support the real-time monitoring of data availability, consistency as well as other related indicators, in this way to make DRDC out-of-the-box in disasters. Second, inspired by blockchain, this paper proposes a method to realize real-time monitoring of data consistency and RTO by building hash chains for PDC and DRDC. Third, this paper evaluates the hash chain operations from the algorithm time complexity, the data consistency, and the validity of RPO monitoring algorithms and since DR system is actually a kind of distributed system, the proposed approach can also be applied to the data consistency detection and data difference monitoring in other distributed systems.

An automated apparatus for the real-time monitoring of bioluminescence in plants

2005 ◽  
Vol 340 (2) ◽  
pp. 187-192 ◽  
Author(s):  
Kazuhisa Okamoto ◽  
Kiyoshi Onai ◽  
Norihiko Ezaki ◽  
Toru Ofuchi ◽  
Masahiro Ishiura
Keyword(s):  
Real Time ◽  
Real Time Monitoring ◽  
The Real
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