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.  

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.

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%.

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.

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 Diagnostics and Prognostics of Energy Conversion Processes via Knowledge-Based Systems

Proceedings ◽  
2020 ◽  
Vol 58 (1) ◽  
pp. 1
Author(s):  
Roberto Melli ◽  
Enrico Sciubba
Keyword(s):  
Expert System ◽  
Knowledge Base ◽  
Real Time ◽  
System Analysis ◽  
Intelligent System ◽  
Time Derivative ◽  
Time History ◽  
Control Procedure ◽  
Time Data ◽  
Ongoing Research

This paper presents a critical and analytical description of an ongoing research program aimed at the implementation of an expert system capable of monitoring, through an Intelligent Health Control procedure, the instantaneous performance of a cogeneration plant. The expert system is implemented in the CLIPS environment and is denominated PROMISA as the acronym for Prognostic Module for Intelligent System Analysis. It generates, in real time and in a form directly useful to the plant manager, information on the existence and severity of faults, forecasts on the future time history of both detected and likely faults, and suggestions on how to control the problem. The expert procedure, working where and if necessary with the support of a process simulator, derives from the available real-time data a list of selected performance indicators for each plant component. For a set of faults, pre-defined with the help of the plant operator (Domain Expert), proper rules are defined in order to establish whether the component is working correctly; in several instances, since one single failure (symptom) can originate from more than one fault (cause), complex sets of rules expressing the combination of multiple indices have been introduced in the knowledge base as well. Creeping faults are detected by analyzing the trend of the variation of an indicator over a pre-assigned interval of time. Whenever the value of this ‘‘discrete time derivative’’ becomes ‘‘high’’ with respect to a specified limit value, a ‘‘latent creeping fault’’ condition is prognosticated. The expert system architecture is based on an object-oriented paradigm. The knowledge base (facts and rules) is clustered—the chunks of knowledge pertain to individual components. A graphic user interface (GUI) allows the user to interrogate PROMISA about its rules, procedures, classes and objects, and about its inference path. The paper also presents the results of some simulation tests.

scholarly journals DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 405
Author(s):  
Marcos Lupión ◽  
Javier Medina-Quero ◽  
Juan F. Sanjuan ◽  
Pilar M. Ortigosa
Keyword(s):  
Real Time ◽  
Activity Recognition ◽  
Real Life ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Heterogeneous Sensors ◽  
On Line ◽  
The University

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.

scholarly journals A Probabilistic Model for Real-Time Semantic Prediction of Human Motion Intentions from RGBD-Data

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4141
Author(s):  
Wouter Houtman ◽  
Gosse Bijlenga ◽  
Elena Torta ◽  
René van de Molengraft
Keyword(s):  
Real Time ◽  
Collision Avoidance ◽  
Probabilistic Model ◽  
Real Life ◽  
Human Motion ◽  
Model Based ◽  
Multiple Hypotheses ◽  
Navigation Algorithms ◽  
The Right ◽  
Motion Behavior

For robots to execute their navigation tasks both fast and safely in the presence of humans, it is necessary to make predictions about the route those humans intend to follow. Within this work, a model-based method is proposed that relates human motion behavior perceived from RGBD input to the constraints imposed by the environment by considering typical human routing alternatives. Multiple hypotheses about routing options of a human towards local semantic goal locations are created and validated, including explicit collision avoidance routes. It is demonstrated, with real-time, real-life experiments, that a coarse discretization based on the semantics of the environment suffices to make a proper distinction between a person going, for example, to the left or the right on an intersection. As such, a scalable and explainable solution is presented, which is suitable for incorporation within navigation algorithms.

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