scholarly journals Artificial Intelligence Applications in Reservoir Engineering: A Status Check

Energies ◽  
2019 ◽  
Vol 12 (15) ◽  
pp. 2897 ◽  
Author(s):  
Turgay Ertekin ◽  
Qian Sun
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Systems ◽  
History Matching ◽  
Model Development ◽  
Reservoir Engineering ◽  
Design And Optimization ◽  
Engineering Problems ◽  
Learning Capabilities ◽  
Intelligence Models ◽  
Engineering Models

This article provides a comprehensive review of the state-of-art in the area of artificial intelligence applications to solve reservoir engineering problems. Research works including proxy model development, artificial-intelligence-assisted history-matching, project design, and optimization, etc. are presented to demonstrate the robustness of the intelligence systems. The successes of the developments prove the advantages of the AI approaches in terms of high computational efficacy and strong learning capabilities. Thus, the implementation of intelligence models enables reservoir engineers to accomplish many challenging and time-intensive works more effectively. However, it is not yet astute to completely replace the conventional reservoir engineering models with intelligent systems, since the defects of the technology cannot be ignored. The trend of research and industrial practices of reservoir engineering area would be establishing a hand-shaking protocol between the conventional modeling and the intelligent systems. Taking advantages of both methods, more robust solutions could be obtained with significantly less computational overheads.

Digital Solutions Suite: Big Data, Artificial Intelligence, and Digital Barrel

2021 ◽  
Author(s):  
Roberto Carlos Fuenmayor
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Process Management ◽  
Production Optimization ◽  
Multiple Sources ◽  
Multiple Systems ◽  
New Ways Of Working ◽  
Intelligence Models ◽  
Artificial Intelligence Models ◽  
Engineering Models

Abstract The concept of digital transformation is based on two principles: data driven—exploiting every bit of data source—and user focused. The objective is not only to consolidate data from multiple systems, but to apply an analytics approach to extract insights that are the product of the aggregation of multiple sources then present it to the user (field manager, production and surveillance engineer, region manager, and country) with criteria's of simplicity, specificity, novelty—and most importantly, clarity. The idea is to liberate the data across the whole upstream community and intended for production operations people by providing a one-stop production digital platform that taps into unstructured data and is transformed into structured to be used as input to engineering models and as a result provide data analytics and generate insights. There is three main key objectives: To have only one source of truth using cloud-based technology To incorporate artificial intelligence models to fill the data gaps of production and operations parameters such as pressure and temperature To incorporate multiple solutions for the upstream community that helps during the slow, medium, and fast loops of upstream operations. The new "way of working" helps multiple disciplines such as subsurface team, facilities, and operations, HSSE and business planning, combining business process management and technical workflows to generates insights and create value that impact the profit and losses (P&L) sheet of the operators. The "new ways of working" tackle values pillars such as production optimization, reduced unplanned deferment, cost avoidance, and improved process cycle efficiency. The use of big data and artificial intelligence algorithms are key to understand the production of the wells and fields, as well as anchoring on processing the data with automated engineering models, thus enabling better decision making including the span of time scale such as fast, medium, or slow loop actions.

Cyber Physical Control Systems

2015 ◽  
pp. 263-285
Keyword(s):  
Artificial Intelligence ◽  
Control Systems ◽  
Intelligent Systems ◽  
Control Strategies ◽  
Case Based Reasoning ◽  
Physical Control ◽  
Closed Loop Systems ◽  
Intelligence Models ◽  
Algorithmic Design ◽  
Case Based

The focus of algorithmic design is to solve composite problems. Intelligent systems use intellectual concepts like evolutionary computation, artificial neural networks, fuzzy systems, and swarm intelligence to process natural intelligence models. Artificial intelligence is used as a part of intelligent systems to perform logic- and case-based reasoning. Systems like mechanical and electrical support systems are operated by utilizing Supervisory Control and Data Acquisition (SCADA) systems. These systems cannot accomplish their purpose, provided the control system deals with the reliability of it. In CPSs, dimensions of physical processes are taken by sensors and are processed in cyber subsystems to drive the actuators that affect the physical processors. CPSs are closed-loop systems. The adaptation and the prediction are the properties to be followed by the control strategies that are implemented in cyber subsystems. This chapter explores cyber physical control systems.

scholarly journals Stigma, biomarkers, and algorithmic bias: recommendations for precision behavioral health with artificial intelligence

JAMIA Open ◽  
2020 ◽  
Vol 3 (1) ◽  
pp. 9-15 ◽  
Author(s):  
Colin G Walsh ◽  
Beenish Chaudhry ◽  
Prerna Dua ◽  
Kenneth W Goodman ◽  
Bonnie Kaplan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Systems ◽  
Social Stigma ◽  
Model Development ◽  
Healthcare Delivery ◽  
Clinical Markers ◽  
Behavioral Healthcare ◽  
Effective Implementation ◽  
Design And Implementation ◽  
Algorithmic Bias

Abstract Effective implementation of artificial intelligence in behavioral healthcare delivery depends on overcoming challenges that are pronounced in this domain. Self and social stigma contribute to under-reported symptoms, and under-coding worsens ascertainment. Health disparities contribute to algorithmic bias. Lack of reliable biological and clinical markers hinders model development, and model explainability challenges impede trust among users. In this perspective, we describe these challenges and discuss design and implementation recommendations to overcome them in intelligent systems for behavioral and mental health.

Artificial Intelligence Ethics in Biomedical-Engineering-Oriented Problems

Biotechnology ◽  
2019 ◽  
pp. 1675-1687
Author(s):  
Alice Pavaloiu
Keyword(s):  
Artificial Intelligence ◽  
Biomedical Engineering ◽  
Intelligent Systems ◽  
Machine Ethics ◽  
Design And Development ◽  
Essential Information ◽  
Engineering Problems ◽  
The Future

The field of artificial intelligence has recently encountered some ethical questions associated with the future of humankind. Although it is a common question that has been asked for years, the existence of humankind against badly configured intelligent systems is more important nowadays. As a result of rapid developments in intelligent systems and their increasing role in our life, there is a remarkable anxiety about dangerous artificial intelligence. Because of that, some research interests gathered under some topics like machine ethics, future of artificial intelligence, and even existential risks are drawing researchers' interest. As associated with this state, the objective of this chapter is to examine ethical factors in using intelligent systems for biomedical-engineering-oriented purposes. The chapter firstly gives essential information about the background and then considers possible scenarios that may require ethical adjustments during design and development of artificial-intelligence-oriented systems for biomedical engineering problems.

scholarly journals An automated COVID-19 triage pipeline using artificial intelligence based on chest radiographs and clinical data

2022 ◽  
Vol 5 (1) ◽  
Author(s):  
Chris K. Kim ◽  
Ji Whae Choi ◽  
Zhicheng Jiao ◽  
Dongcui Wang ◽  
Jing Wu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Model Development ◽  
Chest Radiographs ◽  
X Rays ◽  
Diagnosis And Prognosis ◽  
Radiology Reports ◽  
Intelligence Models ◽  
Artificial Intelligence Models ◽  
Image Repository ◽  
External Test

AbstractWhile COVID-19 diagnosis and prognosis artificial intelligence models exist, very few can be implemented for practical use given their high risk of bias. We aimed to develop a diagnosis model that addresses notable shortcomings of prior studies, integrating it into a fully automated triage pipeline that examines chest radiographs for the presence, severity, and progression of COVID-19 pneumonia. Scans were collected using the DICOM Image Analysis and Archive, a system that communicates with a hospital’s image repository. The authors collected over 6,500 non-public chest X-rays comprising diverse COVID-19 severities, along with radiology reports and RT-PCR data. The authors provisioned one internally held-out and two external test sets to assess model generalizability and compare performance to traditional radiologist interpretation. The pipeline was evaluated on a prospective cohort of 80 radiographs, reporting a 95% diagnostic accuracy. The study mitigates bias in AI model development and demonstrates the value of an end-to-end COVID-19 triage platform.

Artificial Intelligence Ethics in Biomedical-Engineering-Oriented Problems

2018 ◽  
pp. 219-231
Author(s):  
Alice Pavaloiu
Keyword(s):  
Artificial Intelligence ◽  
Biomedical Engineering ◽  
Intelligent Systems ◽  
Machine Ethics ◽  
Design And Development ◽  
Essential Information ◽  
Engineering Problems ◽  
The Future

The field of artificial intelligence has recently encountered some ethical questions associated with the future of humankind. Although it is a common question that has been asked for years, the existence of humankind against badly configured intelligent systems is more important nowadays. As a result of rapid developments in intelligent systems and their increasing role in our life, there is a remarkable anxiety about dangerous artificial intelligence. Because of that, some research interests gathered under some topics like machine ethics, future of artificial intelligence, and even existential risks are drawing researchers' interest. As associated with this state, the objective of this chapter is to examine ethical factors in using intelligent systems for biomedical-engineering-oriented purposes. The chapter firstly gives essential information about the background and then considers possible scenarios that may require ethical adjustments during design and development of artificial-intelligence-oriented systems for biomedical engineering problems.

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.

scholarly journals Group Agency and Artificial Intelligence

2021 ◽  
Author(s):  
Christian List
Keyword(s):  
Artificial Intelligence ◽  
Moral Status ◽  
Intelligent Systems ◽  
Group Agency ◽  
High Stakes ◽  
Moral Agents ◽  
Legal Personhood ◽  
The Social ◽  
Intentional Agents ◽  
Regulatory Challenges

AbstractThe aim of this exploratory paper is to review an under-appreciated parallel between group agency and artificial intelligence. As both phenomena involve non-human goal-directed agents that can make a difference to the social world, they raise some similar moral and regulatory challenges, which require us to rethink some of our anthropocentric moral assumptions. Are humans always responsible for those entities’ actions, or could the entities bear responsibility themselves? Could the entities engage in normative reasoning? Could they even have rights and a moral status? I will tentatively defend the (increasingly widely held) view that, under certain conditions, artificial intelligent systems, like corporate entities, might qualify as responsible moral agents and as holders of limited rights and legal personhood. I will further suggest that regulators should permit the use of autonomous artificial systems in high-stakes settings only if they are engineered to function as moral (not just intentional) agents and/or there is some liability-transfer arrangement in place. I will finally raise the possibility that if artificial systems ever became phenomenally conscious, there might be a case for extending a stronger moral status to them, but argue that, as of now, this remains very hypothetical.

scholarly journals Stress testing reveals gaps in clinic readiness of image-based diagnostic artificial intelligence models

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Albert T. Young ◽  
Kristen Fernandez ◽  
Jacob Pfau ◽  
Rasika Reddy ◽  
Nhat Anh Cao ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Stress Testing ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Skin Lesions ◽  
Stress Tests ◽  
Performance Metric ◽  
Intelligence Models ◽  
Artificial Intelligence Models

AbstractArtificial intelligence models match or exceed dermatologists in melanoma image classification. Less is known about their robustness against real-world variations, and clinicians may incorrectly assume that a model with an acceptable area under the receiver operating characteristic curve or related performance metric is ready for clinical use. Here, we systematically assessed the performance of dermatologist-level convolutional neural networks (CNNs) on real-world non-curated images by applying computational “stress tests”. Our goal was to create a proxy environment in which to comprehensively test the generalizability of off-the-shelf CNNs developed without training or evaluation protocols specific to individual clinics. We found inconsistent predictions on images captured repeatedly in the same setting or subjected to simple transformations (e.g., rotation). Such transformations resulted in false positive or negative predictions for 6.5–22% of skin lesions across test datasets. Our findings indicate that models meeting conventionally reported metrics need further validation with computational stress tests to assess clinic readiness.

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