Development of a System for Remote Control and Monitoring of Wellheads

2021 ◽  
Author(s):  
Elijah Kiplimo ◽  
Daniel Oyoo ◽  
Antonio Tapia ◽  
Marseline Jepng’etich
Keyword(s):  
Remote Control ◽  
Data Analytics ◽  
Position Control ◽  
Fluid Pressure ◽  
Data Gathering ◽  
Machine Learning Algorithms ◽  
Sensor Technology ◽  
Online Platform ◽  
Well Integrity ◽  
Valve Position

Abstract Wellheads have a major role in ensuring well integrity, providing access to the wellbore and flow control. It is vital to constantly monitor the wellhead fluid pressure and temperature effectively in order to maintain the full control of wellbore fluids. Over the years, wellheads have remained purely mechanical and have heavily relied on physical on-site monitoring. There is need to develop a reliable and accessible monitoring solutions for the wellheads in order to increase the effectiveness of the well integrity management systems and to get the full benefits of the wellhead data by incorporating the emerging data analytics technologies. This project details the development of a system that gathers wellhead temperature, pressure and the accurate valve position at any given time. The data gathering systems used in this project entail smart sensor technology capable of withstanding the wellbore pressures and temperatures. The system transmits the data securely, using blockchain, to an online platform where advanced data analytics using MATLAB and machine learning algorithms are used for visual data representation. The online platform additionally provides a means of real-time valve position control and takes into account the exact revolutions required for the opening and closure of the valve hence keeping a record for maintenance purposes. The innovative use and analysis of the data gathered form the wellhead provides insights for the operators and service companies and gives a way for setting ang thresholds in order to get alerts based in their custom specifications. This paper documents the development of a system that gathers wellhead data and provides a means of remote control of the wellhead valves. It covers the design phase, selection of appropriate sensor placement locations on the wellhead, the design of valve actuators, offline data gathering systems and the online data analysis and valve control platform. The project also pays a key attention towards the secure data transmission techniques and highlights the benefits of incorporating such a system in the oil and gas upstream sector.

scholarly journals Is It Possible to Forecast the Price of Bitcoin?

Forecasting ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 377-420
Author(s):  
Julien Chevallier ◽  
Dominique Guégan ◽  
Stéphane Goutte
Keyword(s):  
Data Analytics ◽  
Daily Variation ◽  
A Priori ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Market Growth ◽  
Network Support ◽  
Market Participants ◽  
Nearest Neighbours ◽  
Stationary Behavior

This paper focuses on forecasting the price of Bitcoin, motivated by its market growth and the recent interest of market participants and academics. We deploy six machine learning algorithms (e.g., Artificial Neural Network, Support Vector Machine, Random Forest, k-Nearest Neighbours, AdaBoost, Ridge regression), without deciding a priori which one is the ‘best’ model. The main contribution is to use these data analytics techniques with great caution in the parameterization, instead of classical parametric modelings (AR), to disentangle the non-stationary behavior of the data. As soon as Bitcoin is also used for diversification in portfolios, we need to investigate its interactions with stocks, bonds, foreign exchange, and commodities. We identify that other cryptocurrencies convey enough information to explain the daily variation of Bitcoin’s spot and futures prices. Forecasting results point to the segmentation of Bitcoin concerning alternative assets. Finally, trading strategies are implemented.

scholarly journals HealthTEC Innovation Design - a proposal for a novel Master degree program based on Unmet Clinical Need, global Healthcare Challenges, and 21st century skills

2020 ◽  
Vol 6 (3) ◽  
pp. 599-603
Author(s):  
Michael Friebe
Keyword(s):  
Machine Learning ◽  
21St Century ◽  
21St Century Skills ◽  
Data Analytics ◽  
Industrial Revolution ◽  
Data Gathering ◽  
Healthcare Systems ◽  
Disruptive Innovation ◽  
Incremental Innovation ◽  
Healthcare Provision

AbstractThe effectiveness, efficiency, availability, agility, and equality of global healthcare systems are in question. The COVID-19 pandemic have further highlighted some of these issues and also shown that healthcare provision is in many parts of the world paternalistic, nimble, and often governed too extensively by revenue and profit motivations. The 4th industrial revolution - the machine learning age - with data gathering, analysis, optimisation, and delivery changes has not yet reached Healthcare / Health provision. We are still treating patients when they are sick rather then to use advanced sensors, data analytics, machine learning, genetic information, and other exponential technologies to prevent people from becoming patients or to help and support a clinicians decision. We are trying to optimise and improve traditional medicine (incremental innovation) rather than to use technologies to find new medical and clinical approaches (disruptive innovation). Education of future stakeholders from the clinical and from the technology side has not been updated to Health 4.0 demands and the needed 21st century skills. This paper presents a novel proposal for a university and innovation lab based interdisciplinary Master education of HealthTEC innovation designers.

scholarly journals Data Analytics for Monitoring the Satisfactory Parameters of Airline Passengers using Machine Learning Algorithms in Python

2020 ◽  
Vol 9 (3) ◽  
pp. 1231-1235
Keyword(s):  
Machine Learning ◽  
Data Analytics ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Complex Information ◽  
Huge Data ◽  
Gradient Boosting Machine ◽  
Airline Passengers ◽  
Effective Representation

An effective representation by machine learning algorithms is to obtain the results especially in Big Data, there are numerous applications can produce outcome, whereas a Random Forest Algorithm (RF) Gradient Boosting Machine (GBM), Decision tree (DT) in Python will able to give the higher accuracy in regard with classifying various parameters of Airliner Passengers satisfactory levels. The complex information of airline passengers has provided huge data for interpretation through different parameters of satisfaction that contains large information in quantity wise. An algorithm has to support in classifying these data’s with accuracies. As a result some of the methods may provide less precision and there is an opportunity of information cancellation and furthermore information missing utilizing conventional techniques. Subsequently RF and GBM used to conquer the unpredictability and exactness about the information provided. The aim of this study is to identify an Algorithm which is suitable for classifying the satisfactory level of airline passengers with data analytics using python by knowing the output. The optimization and Implementation of independent variables by training and testing for accuracy in python platform determined the variation between the each parameters and also recognized RF and GBM as a better algorithm in comparison with other classifying algorithms.

scholarly journals Advanced Cluster and Predictive Analysis Tool Development for Commercial Office Real Estate Energy Usage

2021 ◽  
Author(s):  
Carleen Lawson
Keyword(s):  
Energy Use ◽  
Data Gathering ◽  
Machine Learning Algorithms ◽  
Analysis Tool ◽  
Tool Development ◽  
Energy Usage ◽  
Future Data ◽  
Building Characteristics ◽  
Energy Benchmarking ◽  
Physical Features

From 2009-2015, REALPAC collected monthly energy usage and building characteristics for over 500 buildings in the 20 by ‘15 Energy Benchmarking Survey (REALPAC, 2009). While preliminary analysis had been completed on this dataset, this research undertook an in-depth statistical analysis of the data to identify trends and important variables. Eight machine learning algorithms were employed to predict energy usage as a function of previous energy use and select physical features. The dataset did not possess the appropriate variables to predict such usage accurately. Characteristics such as building system efficiency, construction assemblies, condition, compactness, and window to wall ratio are thus recommended for inclusion in future data-gathering initiatives. https://digital.library.ryerson.ca/islandora/object/RULA:8631/datastream/LAW_RSCR-4.80MB/view https://digital.library.ryerson.ca/islandora/object/RULA:8631/datastream/LAW-ExTa-428KB/view https://digital.library.ryerson.ca/islandora/object/RULA:8631/datastream/LAW-ExGa-5.62MB/view https://digital.library.ryerson.ca/islandora/object/RULA:8631/datastream/LAW-DATA-1.9MB/view

Development of Failure Prediction Models for Subsea Blowout Preventers Using Data Analytics and AI

2021 ◽  
Author(s):  
Rodrigo Chamusca Machado ◽  
Fabbio Leite ◽  
Cristiano Xavier ◽  
Alberto Albuquerque ◽  
Samuel Lima ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Data Analytics ◽  
Historical Data ◽  
Learning Algorithms ◽  
Failure Prediction ◽  
Machine Learning Algorithms ◽  
Time Data ◽  
Real Time Data ◽  
Operational Data

Objectives/Scope This paper presents how a brazilian Drilling Contractor and a startup built a partnership to optimize the maintenance window of subsea blowout preventers (BOPs) using condition-based maintenance (CBM). It showcases examples of insights about the operational conditions of its components, which were obtained by applying machine learning techniques in real time and historic, structured or unstructured, data. Methods, Procedures, Process From unstructured and structured historical data, which are generated daily from BOP operations, a knowledge bank was built and used to develop normal functioning models. This has been possible even without real-time data, as it has been tested with large sets of operational data collected from event log text files. Software retrieves the data from Event Loggers and creates structured database, comprising analog variables, warnings, alarms and system information. Using machine learning algorithms, the historical data is then used to develop normal behavior modeling for the target components. Thereby, it is possible to use the event logger or real time data to identify abnormal operation moments and detect failure patterns. Critical situations are immediately transmitted to the RTOC (Real-time Operations Center) and management team, while less critical alerts are recorded in the system for further investigation. Results, Observations, Conclusions During the implementation period, Drilling Contractor was able to identify a BOP failure using the detection algorithms and used 100% of the information generated by the system and reports to efficiently plan for equipment maintenance. The system has also been intensively used for incident investigation, helping to identify root causes through data analytics and retro-feeding the machine learning algorithms for future automated failure predictions. This development is expected to significantly reduce the risk of BOP retrieval during the operation for corrective maintenance, increased staff efficiency in maintenance activities, reducing the risk of downtime and improving the scope of maintenance during operational windows, and finally reduction in the cost of spare parts replacementduring maintenance without impact on operational safety. Novel/Additive Information For the near future, the plan is to integrate the system with the Computerized Maintenance Management System (CMMS), checking for historical maintenance, overdue maintenance, certifications, at the same place and time that we are getting real-time operational data and insights. Using real-time data as input, we expect to expand the failure prediction application for other BOP parts (such as regulators, shuttle valves, SPMs (Submounted Plate valves), etc) and increase the applicability for other critical equipment on the rig.

scholarly journals Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1029 ◽  
Author(s):  
Thomas Kundinger ◽  
Nikoletta Sofra ◽  
Andreas Riener
Keyword(s):  
Machine Learning ◽  
User Study ◽  
Driving Simulator ◽  
Machine Learning Algorithms ◽  
Sensor Technology ◽  
Physiological Data ◽  
Promising Alternative ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
Medical Grade

Drowsy driving imposes a high safety risk. Current systems often use driving behavior parameters for driver drowsiness detection. The continuous driving automation reduces the availability of these parameters, therefore reducing the scope of such methods. Especially, techniques that include physiological measurements seem to be a promising alternative. However, in a dynamic environment such as driving, only non- or minimal intrusive methods are accepted, and vibrations from the roadbed could lead to degraded sensor technology. This work contributes to driver drowsiness detection with a machine learning approach applied solely to physiological data collected from a non-intrusive retrofittable system in the form of a wrist-worn wearable sensor. To check accuracy and feasibility, results are compared with reference data from a medical-grade ECG device. A user study with 30 participants in a high-fidelity driving simulator was conducted. Several machine learning algorithms for binary classification were applied in user-dependent and independent tests. Results provide evidence that the non-intrusive setting achieves a similar accuracy as compared to the medical-grade device, and high accuracies (>92%) could be achieved, especially in a user-dependent scenario. The proposed approach offers new possibilities for human–machine interaction in a car and especially for driver state monitoring in the field of automated driving.

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