Unlocking the Potential of Electrical Submersible Pumps: the Successful Testing and Deployment of a Real-Time Artificially Intelligent System, for Failure Prediction, Run Life Extension, and Production Optimization

2021 ◽  
Author(s):  
Mohammed Al Radhi ◽  
Fernando Angel Bermudez ◽  
Wael Al Madhoun ◽  
Khaled Al Blooshi ◽  
Noor Nasser Al Nahhas ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Intelligent System ◽  
New Technology ◽  
Collaborative Work ◽  
Real Life ◽  
Production Optimization ◽  
Petroleum Engineering ◽  
Life Extension ◽  
Electrical Submersible Pumps

Abstract This paper is a summary of the collaborative work between ADNOC (Abu Dhabi National Oil Company) and nybl, a deep tech development company, and the results of applying nybl's proprietary "Science-Based Artificial Intelligence" to ADNOC Electrical Submersible Pump (ESP) wells in real-time applications. The paper demonstrates the potential benefits of the real-life application of Artificial Intelligence (AI) / Machine Learning (ML) in conjunction with traditional Petroleum Engineering concepts and algorithms to predict imminent and future failures, extend and monitor run life, and maximize the production of ESPs. This paper will highlight ADNOC's innovative approach to pilot new technology through successful deployment on 27 wells, spread onshore and offshore, in real-time, with prescriptive actions.

Unlocking the Potential of Electrical Submersible Pumps: The Successful Testing and Deployment of a Real-Time Artificially Intelligent System, for Failure Prediction, Run Life Extension, and Production Optimization

2021 ◽  
Author(s):  
Fernando Bermudez ◽  
Noor Al Nahhas ◽  
Hafsa Yazdani ◽  
Michael LeTan ◽  
Mohammed Shono
Keyword(s):  
Real Time ◽  
Intelligent System ◽  
New Technology ◽  
Collaborative Work ◽  
Real Life ◽  
Production Optimization ◽  
Petroleum Engineering ◽  
Life Extension ◽  
Electrical Submersible Pumps ◽  
Successful Testing

Abstract This paper is a summary of the collaborative work between a Gulf Cooperation Council (GCC) national oil company (NOC) and Nybl, a deep tech development company, and the results of applying Nybl's proprietary science-based AI to the GCC NOC ESP wells in real-time applications. The paper demonstrates the potential benefits of the real-life application of AI / Machine Learning in conjunction with traditional Petroleum Engineering concepts and algorithms to predict imminent and future failures, extend and monitor run life, and maximize the production of Electrical Submersible Pumps (ESP's). This paper will highlight the NOC's innovative approach to pilot new technology through successful deployment on 27 wells, spread onshore and offshore, in real-time, with prescriptive actions.  

scholarly journals Intelligent System for Matured Coconut Identification

2020 ◽  
Vol 9 (1) ◽  
pp. 57-61
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
High Speed ◽  
Intelligent System ◽  
Previous Knowledge ◽  
Graphics Processors ◽  
Tree Crown ◽  
Coconut Tree

Vision is the key component in Artificial Intelligence and automated robotics. Identification of Matured coconuts in the coconut tree crown is one of the main expected functionality to be executed in Real Time for Automated coconut Harvesting Machine. This functionality is executed by an Intelligent System attached with that machine.. This project deals with the design of that intelligent system using the concept of Artificial Intelligence. Thus the prediction of matured coconut in the present captured image of coconut tree crown with the previous knowledge is done by that designed Intelligent System. In order to identify the coconut in the present capture image, a computing board and Jetson Nano board is used, which compares the captured image with a dataset and identifies the various stages of the coconut. In this paper we used two high speed graphics processors and identified which one has more accuracy.

scholarly journals Lynx: Automatic Elderly Behavior Prediction in Home Telecare

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Jose Manuel Lopez-Guede ◽  
Aitor Moreno-Fernandez-de-Leceta ◽  
Alexeiw Martinez-Garcia ◽  
Manuel Graña
Keyword(s):  
Real Time ◽  
Intelligent System ◽  
Expert Knowledge ◽  
Care Center ◽  
Real Life ◽  
Family Care ◽  
Machine Learning Algorithms ◽  
Wide Sense ◽  
Medical Recommendations ◽  
Personalized Health

This paper introduces Lynx, an intelligent system for personal safety at home environments, oriented to elderly people living independently, which encompasses a decision support machine for automatic home risk prevention, tested in real-life environments to respond to real time situations. The automatic system described in this paper prevents such risks by an advanced analytic methods supported by an expert knowledge system. It is minimally intrusive, using plug-and-play sensors and machine learning algorithms to learn the elder’s daily activity taking into account even his health records. If the system detects that something unusual happens (in a wide sense) or if something is wrong relative to the user’s health habits or medical recommendations, it sends at real-time alarm to the family, care center, or medical agents, without human intervention. The system feeds on information from sensors deployed in the home and knowledge of subject physical activities, which can be collected by mobile applications and enriched by personalized health information from clinical reports encoded in the system. The system usability and reliability have been tested in real-life conditions, with an accuracy larger than 81%.

scholarly journals Belief Change without Compactness

2021 ◽  
Vol 24 (1) ◽  
Author(s):  
Jandson S Ribeiro
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Finite Subset ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Belief Change ◽  
Temporal Logics ◽  
Sensing Actions ◽  
Rational Behaviour ◽  
Do So

Dealing with dynamics is a vital problem in Artificial Intelligence (AI). An intelligent system should be able to perceive and interact with its environment to perform its tasks satisfactorily. To do so, it must sense external actions that might interfere with its tasks, demanding the agent to self-adapt to the environment dynamics. In AI, the field that studies how a rational agent should change its knowledge in order to respond to a new piece of information is known as Belief Change. It assumes that an agent’s knowledge is specified in an underlying logic that satisfies some properties including compactness: if an information is entailed by a set X of formulae, then this information should also be entailed by a finite subset of X. Several logics with applications in AI, however, do not respect this property. This is the case of many temporal logics such as LTL and CTL. Extending Belief Change to these logics would provide ways to devise self-adaptive intelligent systems that could respond to change in real time. This is a big challenge in AI areas such as planning, and reasoning with sensing actions. Extending belief change beyond the classical spectrum has been shown to be a tough challenge, and existing approaches usually put some constraints upon the system, which are either too restrictive or dispense some of the so desired rational behaviour an intelligent system should present. This is a summary of the thesis “Belief Change without Compactness” by Jandson S Ribeiro. The thesis extends Belief Change to accommodate non-compact logics, keeping the rationality criteria and without imposing extra constraints. We provide complete new semantic perspectives for Belief Change by extending to non-compact logics its three main pillars: the AGM paradigm, the KM paradigm and Non-monotonic Reasoning.

Ambulatory Assessment

2009 ◽  
Vol 14 (2) ◽  
pp. 109-119 ◽  
Author(s):  
Ulrich W. Ebner-Priemer ◽  
Timothy J. Trull
Keyword(s):  
Real Time ◽  
Clinical Psychology ◽  
Real Life ◽  
Ambulatory Assessment ◽  
Computer Assisted ◽  
Affective Valence ◽  
Time Data ◽  
Physiological Processes ◽  
Context Specific ◽  
Questionnaire Studies

Convergent experimental data, autobiographical studies, and investigations on daily life have all demonstrated that gathering information retrospectively is a highly dubious methodology. Retrospection is subject to multiple systematic distortions (i.e., affective valence effect, mood congruent memory effect, duration neglect; peak end rule) as it is based on (often biased) storage and recollection of memories of the original experience or the behavior that are of interest. The method of choice to circumvent these biases is the use of electronic diaries to collect self-reported symptoms, behaviors, or physiological processes in real time. Different terms have been used for this kind of methodology: ambulatory assessment, ecological momentary assessment, experience sampling method, and real-time data capture. Even though the terms differ, they have in common the use of computer-assisted methodology to assess self-reported symptoms, behaviors, or physiological processes, while the participant undergoes normal daily activities. In this review we discuss the main features and advantages of ambulatory assessment regarding clinical psychology and psychiatry: (a) the use of realtime assessment to circumvent biased recollection, (b) assessment in real life to enhance generalizability, (c) repeated assessment to investigate within person processes, (d) multimodal assessment, including psychological, physiological and behavioral data, (e) the opportunity to assess and investigate context-specific relationships, and (f) the possibility of giving feedback in real time. Using prototypic examples from the literature of clinical psychology and psychiatry, we demonstrate that ambulatory assessment can answer specific research questions better than laboratory or questionnaire studies.

Artificial intelligence as a moving force of improvement and innovative development in education and pedagogic

2019 ◽  
pp. 21-30
Author(s):  
M. G. Koliada ◽  
T. I. Bugayova
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Interaction Analysis ◽  
Educational Data Mining ◽  
Main Task ◽  
Intellectual System ◽  
Will Force ◽  
To Come ◽  
Artificial Intelligence Systems

The article discusses the history of the development of the problem of using artificial intelligence systems in education and pedagogic. Two directions of its development are shown: “Computational Pedagogic” and “Educational Data Mining”, in which poorly studied aspects of the internal mechanisms of functioning of artificial intelligence systems in this field of activity are revealed. The main task is a problem of interface of a kernel of the system with blocks of pedagogical and thematic databases, as well as with the blocks of pedagogical diagnostics of a student and a teacher. The role of the pedagogical diagnosis as evident reflection of the complex influence of factors and reasons is shown. It provides the intelligent system with operative and reliable information on how various reasons intertwine in the interaction, which of them are dangerous at present, where recession of characteristics of efficiency is planned. All components of the teaching and educational system are subject to diagnosis; without it, it is impossible to own any pedagogical situation optimum. The means in obtaining information about students, as well as the “mechanisms” of work of intelligent systems based on innovative ideas of advanced pedagogical experience in diagnostics of the professionalism of a teacher, are considered. Ways of realization of skill of the teacher on the basis of the ideas developed by the American scientists are shown. Among them, the approaches of researchers D. Rajonz and U. Bronfenbrenner who put at the forefront the teacher’s attitude towards students, their views, intellectual and emotional characteristics are allocated. An assessment of the teacher’s work according to N. Flanders’s system, in the form of the so-called “The Interaction Analysis”, through the mechanism of fixing such elements as: the verbal behavior of the teacher, events at the lesson and their sequence is also proposed. A system for assessing the professionalism of a teacher according to B. O. Smith and M. O. Meux is examined — through the study of the logic of teaching, using logical operations at the lesson. Samples of forms of external communication of the intellectual system with the learning environment are given. It is indicated that the conclusion of the found productive solutions can have the most acceptable and comfortable form both for students and for the teacher in the form of three approaches. The first shows that artificial intelligence in this area can be represented in the form of robotized being in the shape of a person; the second indicates that it is enough to confine oneself only to specially organized input-output systems for targeted transmission of effective methodological recommendations and instructions to both students and teachers; the third demonstrates that life will force one to come up with completely new hybrid forms of interaction between both sides in the form of interactive educational environments, to some extent resembling the educational spaces of virtual reality.

Setting Health-Care Priorities

2019 ◽  
Author(s):  
Torbjörn Tännsjö
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Real Life ◽  
Thought Experiments ◽  
Life Extension ◽  
Weighted Sum ◽  
Absolute Priority ◽  
Abstract Thought

The three most promising theories of distributive ethics are presented: Utilitarianism, with or without a prioritarian amendment. The maximin/leximin theory. Egalitarianism. Utilitarianism urges us to maximize the sum-total of happiness. When prioritarianism is added to utilitarianism we are instead urged to maximize a weighted sum of happiness, where happiness weighs less the happier you are and unhappiness weighs more the more miserable you are. The maximin/leximin theory urges us to give absolute priority to those who are worst off. Egalitarianism gives us two goals: to maximize happiness but also to level out differences with regard to happiness between persons. All of these theories are justifiable. In abstract thought experiments they conflict. When applied in real life they converge in an unexpected manner: more resources should be directed to mental health and less to marginal life extension. It is doubtful if the desired change will take place, however. What gets in its way is human irrationality.

scholarly journals BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13849-13850
Author(s):  
Donghyeon Lee ◽  
Man-Je Kim ◽  
Chang Wook Ahn
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Real Time ◽  
Large Scale ◽  
Outcome Predictor ◽  
Short Term ◽  
Rts Game

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

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