Tools, Technologies and Frameworks for Digital Twins in the Oil and Gas Industry: An In-Depth Analysis.

The digital twin (DT), which involves creating a virtual replica of a physical asset or system, has emerged as a transformative set of tools across various industries. In the oil and gas (O&G) industry, the development of DTs represents a significant evolution in how companies manage complex operations, enhance safety, and optimize decision-making processes. Despite these significant advancements, the underlying tools, technologies, and frameworks for developing DTs in O&G applications remain non-standardized and unfamiliar to many O&G practitioners, highlighting the need for a systematic literature review (SLR) on the topic. Thus, this paper offers an SLR of the existing literature on DT development for O&G from 2018 onwards, utilizing Scopus and Web of Science Core Collection. We provide a comprehensive overview of this field, demonstrate how it is evolving, and highlight standard practices and research opportunities in the area. We perform broad classifications of the 98 studies, categorizing the DTs by their development methodologies, implementation objectives, data acquisition, asset digital development, data integration and preprocessing, data analysis and modeling, evaluation and validation, and deployment tools. We also include a bibliometric analysis of the selected papers, highlighting trends and key contributors. Given the increasing number of new DT developments in O&G and the many new technologies available, we hope to provide guidance on the topic and promote knowledge production and growth concerning the development of DTs for O&G.

Machine Learning Algorithms for Activity-Intensity Recognition Using Accelerometer Data.

In pervasive healthcare monitoring, activity recognition is critical information for adequate management of the patient. Despite the great number of studies on this topic, a contextually relevant parameter that has received less attention is intensity recognition. In the present study, we investigated the potential advantage of coupling activity and intensity, namely, Activity-Intensity, in accelerometer data to improve the description of daily activities of individuals. We further tested two alternatives for supervised classification. In the first alternative, the activity and intensity are inferred together by applying a single classifier algorithm. In the other alternative, the activity and intensity are classified separately. In both cases, the algorithms used for classification are k-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF). The results showed the viability of the classification with good accuracy for Activity-Intensity recognition. The best approach was KNN implemented in the single classifier alternative, which resulted in 79% of accuracy. Using two classifiers, the result was 97% accuracy for activity recognition (Random Forest), and 80% for intensity recognition (KNN), which resulted in 78% for activity-intensity coupled. These findings have potential applications to improve the contextualized evaluation of movement by health professionals in the form of a decision system with expert rules.

Sistema de computação ubíqua na assistência domiciliar à saúde / Sistema de computación ubicua en la asistencia de salud domiciliaria / Ubiquitous computing system in home health care

Objetivo: concepção de um sistema computacional inteligente para interligar o paciente em sua casa e os profissionais de saúde, por meio da integração de diversos serviços relacionados ao monitoramento remoto da saúde do paciente. Método: análise das principais experiências e trabalhos relacionados à área de computação ubíqua na assistência domiciliar à saúde, em conjunto com especialistas da área de saúde, com o objetivo de identificar os requisitos essenciais que devem ser tratados para a viabilização de um sistema dessa classe. Resultados: um sistema com uma arquitetura flexível que integra uma infraestrutura de sensores, dispositivos, e serviços inteligentes para (i) contínua identificação da situação de saúde do paciente e envio de alertas; (ii) envio de notificações e lembretes direcionados ao paciente e associados ao plano de cuidados, elaborado pelo profissional de saúde. Um protótipo implementado com foco na manipulação de dados de pressão arterial e frequência cardíaca é apresentado para demonstrar a viabilidade da proposta. Conclusão: o sistema proposto executa serviços integrados que permitem o acompanhamento contínuo da evolução do tratamento do paciente em sua casa.

Objective: To design an intelligent computer system to link patients in their home with health care professionals through the integration of various services related to remote monitoring of a patient?s health. Method: analysis of the key experiences and work related to the area of ubiquitous computing in home care, together with the support from specialists in home care, with the aim of identifying the essential requirements that must be addressed for building a system of this class. Results: a system was implemented with a flexible architecture that integrates an infrastructure of sensors, devices, and intelligent services for (i) continuous identification of the patient?s health situation and sending alerts when appropriate, (ii) sending to patients notifications and reminders associated with the care plan, drawn up by health professionals. A prototype was implemented with a focus on processing blood pressure and heart rate data, and is presented as a demonstration of the feasibility of the proposed system. Conclusion: The proposed system implements integrated services that allow continuous monitoring of the evolution of patient at home.

Objetivo: Concepción de un sistema inteligente para interconectar al paciente, que se encuentra en su domicilio, con los profesionales de salud a través de la integración de diversos servicios relacionados al monitoriamiento de la salud del paciente. Método: Análisis de las principales experiencias y trabajos relacionados al área de computación ubicua en la asistencia de salud domiciliaria, conjuntamente con especialistas en el área de salud, con el objetivo de identificar los requisitos esenciales que deben ser tratados para viabilizar este tipo de sistema. Resultados: Un sistema con una arquitectura flexible que integra una infraestructura de sensores, dispositivos, y servicios inteligentes para (i) identificación continua de la situación de salud del paciente y envío de alertas; (ii) envío de notificaciones y recordatorios direccionados al paciente y asociados al plan de cuidados, elaborado por el profesional de salud. Un prototipo implementado focalizando la manipulación de datos de presión arterial y frecuencia cardiaca es presentado para demostrar la viabilidad de la propuesta. Conclusión: El sistema propuesto implementa servicios integrados que permiten el acompañamiento continuo de la evolución del tratamiento del paciente en su domicilio.