Methodology for Optimizing Operational Performance and Life Management of Drilling Systems Using Real Time-Data and Predictive Analytics

2015 ◽  
Author(s):  
A. Kale ◽  
D. Zhang ◽  
A. David ◽  
L. Heuermann-Kuehn ◽  
O. Fanini
Keyword(s):  
Real Time ◽  
Predictive Analytics ◽  
Operational Performance ◽  
Time Data ◽  
Life Management ◽  
Real Time Data

Application of Artificial Intelligence and Machine Learning to Detect Drilling Anomalies Leading to Stuck Pipe Incidents

2021 ◽  
Author(s):  
Paulinus Abhyudaya Bimastianto ◽  
Shreepad Purushottam Khambete ◽  
Hamdan Mohamed Alsaadi ◽  
Suhail Mohammed Al Ameri ◽  
Erwan Couzigou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Data Science ◽  
Predictive Analytics ◽  
Drilling Process ◽  
Time Data ◽  
Surface Parameters ◽  
Normal Behavior ◽  
Real Time Data ◽  
Productive Time

Abstract This project used predictive analytics and machine learning-based modeling to detect drilling anomalies, namely stuck pipe events. Analysis focused on historical drilling data and real-time operational data to address the limitations of physics-based modeling. This project was designed to enable drilling crews to minimize downtime and non-productive time through real-time anomaly management. The solution used data science techniques to overcome data consistency/quality issues and flag drilling anomalies leading to a stuck pipe event. Predictive machine learning models were deployed across seven wells in different fields. The models analyzed both historical and real-time data across various data channels to identify anomalies (difficulties that impact non-productive time). The modeling approach mimicked the behavior of drillers using surface parameters. Small deviations from normal behavior were identified based on combinations of surface parameters, and automated machine learning was used to accelerate and optimize the modeling process. The output was a risk score that flags deviations in rig surface parameters. During the development phase, multiple data science approaches were attempted to monitor the overall health of the drilling process. They analyzed both historical and real-time data from torque, hole depth and deviation, standpipe pressure, and various other data channels. The models detected drilling anomalies with a harmonic model accuracy of 80% and produced valid alerts on 96% of stuck pipe and tight hole events. The average forewarning was two hours. This allowed personnel ample time to make corrections before stuck pipe events could occur. This also enabled the drilling operator to save the company upwards of millions of dollars in drilling costs and downtime. This project introduced novel data aggregation and deep learning-based normal behavior modeling methods. It demonstrates the benefits of adopting predictive analytics and machine learning in drilling operations. The approach enabled operators to mitigate data issues and demonstrate real-time, high-frequency and high-accuracy predictions. As a result, the operator was able to significantly reduce non-productive time.

scholarly journals High Performance Predictive Analytics in IoT

2019 ◽  
Vol 8 (4) ◽  
pp. 9266-9270
Keyword(s):  
Real Time ◽  
Network Architecture ◽  
High Performance ◽  
Predictive Analytics ◽  
Time Data ◽  
New Techniques ◽  
Real Time Data ◽  
Iot Devices ◽  
Indexing Technique ◽  
To Receive

Internet of things (IoT) is a quick-moving gathering of web associated sensors implanted in a wide-extending assortment of physical articles. While things can be any physical item (energize or lifeless) on the planet, to which you could associate or implant a sensor. Sensors can take countless potential estimations. Sensors produce gigantic measures of new, organized, unstructured, ongoing information, and structures enormous information. IoT information is exceptionally huge and confused, which can give genuine-time setting and supposition data about genuine articles or nature. Among the different challenges that the present IoT is facing, the three prime areas of concern are, need of efficient framework to receive IoT data, a need of a new scalable parallel indexing technique for efficiently storing IoT data and securing IoT generated data at all the stages i.e. from the edge devices to the cloud. A new efficient framework is introduced, which can retrieve meaningful information from these IoT devices and efficiently index it. For processing such enormous real time data generated from IoT devices, new techniques are introducing which are scalable and secure. The research proposes a general IoT network architecture. It describes the interconnectivity among the different things such as sensors, receivers and cloud. The proposed architecture efficiently receives real time data from all the sensors. The prime focus is on the elimination of the existing issues in IoT. Along with this, the provision has to make for standard future proofing against these new proposed schemes.

Company Data Governance Transformation to Support the Business Evolution

2021 ◽  
Author(s):  
Carla Sanasi ◽  
Luca Dal Forno ◽  
Giorgio Ricci Maccarini ◽  
Luigi Mutidieri ◽  
Pamela Tempone ◽  
...  
Keyword(s):  
Real Time ◽  
Performance Optimization ◽  
Predictive Analytics ◽  
Operating Efficiency ◽  
Time Data ◽  
Data Governance ◽  
Data Infrastructure ◽  
Real Time Data ◽  
New Business ◽  
Reduce Emissions

Abstract The evolution of the energy market requires companies to increase their operating efficiency, leveraging on collaborative environment and existing assets, including Data. A new focus on data governance and integration is needed to maximize the value of data and ensure "real-time" efficient response. The decoupling of data from applications enables organization by domain and data type in one cross-functional data hub. This scheme is independent from the scope of the activity and will therefore maintain its validity when dealing with new business requiring subsurface data utilization. The integrated data platform will feed advanced digital tools capable to control the risks, optimize performance and reduce emissions associated with the operations. Eni is putting this idea into practice with a new data infrastructure which is integrated across all the subsurface disciplines (G&G, Exploration, Upstream Laboratories, Reservoir and Well Operations departments). In this paper, the example of real time data exploitation will be discussed. Real time data workflow was first established in well operations for operational supervision and later developed for real time performance optimization, through the introduction of predictive analytics. Its latest evolution in the broader subsurface domain encompasses the application of AI to operations geology processes and the extension to all operated activities. This approach will equally support new company goals, such as decarbonization, increasing performance of subsurface activities related to underground storage of CO2 in depleted reservoirs.

Real‐Time Data and Predictive Analytics: Where Does IR Fit?

2020 ◽  
Vol 2020 (185-186) ◽  
pp. 11-24
Author(s):  
Kara Larkan‐Skinner ◽  
Jessica M. Shedd
Keyword(s):  
Real Time ◽  
Predictive Analytics ◽  
Time Data ◽  
Real Time Data

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
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