Guidelines for life extension process management in oil and gas facilities

Ferreira, Nayara Nunes; Martins, Marcelo Ramos; Figueiredo, Marco Antônio Gaya de; Gagno, Victor Hugo

doi:10.1016/j.jlp.2020.104290

Cited by 17 publications

(5 citation statements)

References 24 publications

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“…Cost savings are achieved through better resource allocation and reduced downtime. Optimized maintenance schedules contribute to extending the lifespan of subsea equipment by ensuring that maintenance is performed in a timely manner (Ferreira et al, 2020). Preventive maintenance interventions prevent premature degradation and reduce the frequency of costly repairs.…”

Section: Optimization Of Maintenance Schedules With Aimentioning

confidence: 99%

“…This review provides an in-depth exploration of the strategic approaches to implementing Artificial Intelligence (AI) for improved safety and efficiency in subsea maintenance. Subsea maintenance is indispensable in the oil and gas industry due to several reasons: asset integrity, safety, environmental protection, operational continuity, regulatory compliance (Ferreira et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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AI-enhanced subsea maintenance for improved safety and efficiency: Exploring strategic approaches

Oludayo Olatoye Sofoluwe,

Obinna Joshua Ochulor,

Ayemere Ukato

et al. 2024

Int. J. Sci. Res. Arch.

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As the oil and gas industry increasingly explores deeper and more remote offshore sites, the maintenance of subsea infrastructure becomes paramount. The use of Artificial Intelligence (AI) in subsea maintenance offers promising solutions to enhance safety and efficiency in these challenging environments. This review explores strategic approaches to integrating AI into subsea maintenance operations. AI facilitates predictive maintenance by analyzing vast amounts of data collected from sensors and historical maintenance records. Machine learning algorithms can detect patterns and predict equipment failures before they occur, enabling proactive maintenance scheduling. This predictive capability reduces downtime and minimizes the risk of accidents by addressing potential issues before they escalate. AI-enabled autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs) play a crucial role in subsea inspections and repairs. These AI-enhanced robots can navigate complex subsea environments, perform inspections, and execute maintenance tasks with greater precision and efficiency than human divers. By reducing the need for human intervention in hazardous environments, AI-driven AUVs and ROVs significantly improve safety. Furthermore, AI algorithms can optimize maintenance schedules based on factors such as equipment condition, environmental conditions, and operational requirements. By dynamically adjusting maintenance plans, operators can maximize equipment uptime while minimizing costs and risks. This proactive approach ensures that maintenance activities are conducted at the most opportune times, reducing the likelihood of unplanned downtime and improving overall efficiency. Moreover, AI facilitates condition-based maintenance strategies, where equipment health is continuously monitored in real-time. Sensors installed on subsea infrastructure collect data on factors such as temperature, pressure, and vibration, which is then analyzed by AI algorithms to assess equipment condition. By detecting early signs of degradation or malfunction, AI enables timely interventions, preventing costly breakdowns and ensuring optimal performance. In addition to predictive and condition-based maintenance, AI-driven analytics offer insights into operational performance and asset integrity. By analyzing data from various sources, including sensors, historical records, and operational logs, AI can identify trends, anomalies, and optimization opportunities. These insights enable operators to make data-driven decisions that enhance overall system reliability and efficiency. Strategic approaches to implementing AI in subsea maintenance require collaboration between technology providers, operators, and regulatory bodies. Establishing industry standards and guidelines for AI applications in subsea operations is crucial to ensure safety, reliability, and interoperability. Furthermore, investing in research and development to enhance AI algorithms and robotics technology is essential to unlock the full potential of AI in subsea maintenance. AI-enhanced subsea maintenance offers significant benefits in terms of safety and efficiency. By leveraging predictive analytics, autonomous robotics, and real-time monitoring, operators can optimize maintenance activities, reduce downtime, and minimize risks. Strategic approaches to integrating AI into subsea operations require collaboration, investment, and a commitment to advancing technology to meet the challenges of offshore environments.

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Section: Optimization Of Maintenance Schedules With Aimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

AI-enhanced subsea maintenance for improved safety and efficiency: Exploring strategic approaches

Oludayo Olatoye Sofoluwe,

Obinna Joshua Ochulor,

Ayemere Ukato

et al. 2024

Int. J. Sci. Res. Arch.

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“…Aging assets create economic and operational risks in facility management. In order to manage these risks and ensure facility management in a holistic manner, decision support mechanisms should be created using technologies such as artificial intelligence, the Internet of Things (IoT), and GIS [94][95][96].…”

Section: Systematic Literature Review Findingsmentioning

confidence: 99%

“…Moreover, the exploration of decision support mechanisms for physical asset management suggests a growing acknowledgment of the need for sophisticated tools that can handle complex data and assist in making informed decisions [90][91][92][93][94][95][96]. This is particularly relevant given the advancement of technologies that can potentially transform traditional asset management practices [100][101][102][103].…”

Section: Energy Markets 2%mentioning

confidence: 99%

Bridging Geo-Data and Natural Gas Pipeline Design Standards: A Systematic Review of BIM-GIS Integration for Natural Gas Pipeline Asset Management

Demir,

Yomralioglu

2024

Energies

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In today’s world, effective management and the use of spatial data are of great importance in many sectors. Industries such as land management, asset management, and infrastructure management are areas where spatial data are heavily utilized. Advanced technologies such as Geographic Information Systems (GISs) and Building Information Modeling (BIM) are used in the processes of collecting, analyzing, and managing geographically enabled data (geo-data). These technologies enable the effective processing of large datasets, improve decision-making processes based on geographic information, and facilitate more efficient collaboration across sectors. This study conducts an in-depth examination of the existing literature on asset management, infrastructure management, and BIM-GIS integration using bibliometric analysis and systematic literature review methods. Bibliometric analysis is employed to determine statistical values such as current research trends, frequently cited authors, most used keywords, and country performances in the relevant field. This study’s results highlight future research trends and significant gaps in the areas of asset management, infrastructure management, natural gas pipelines, and BIM-GIS integration. In particular, this study demonstrates the critical importance of asset management and BIM-GIS integration for sustainable infrastructure design, construction, and management. In this context, attention is drawn to the importance of data standardization, digitization, systematic integration, and contemporary land management requirements.

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“…Esta estratégia é a mais apropriada quando os reservatórios do campo ainda dispõe de boa quantidade de petróleo e gás a ser explorado de maneira economicamente satisfatória. Segundo Ferreira et al (2020), órgãos reguladores, mesmo em países mais avançados do setor de petróleo e gás ou que já passaram pelo processo de extensão, desenvolveram apenas material superficial sobre extensão de vida útil, sem definir uma metodologia estruturada para avaliação da possibilidade prolongação das atividades. No Brasil, segundo ANP (2021a), cerca de 55% das plataformas em operação têm mais de 20 anos e chegarão ao final do seu ciclo de vida em 2025.…”

Section: Motivação E Justificativaunclassified

Desenvolvimento de um Gêmeo Digital para previsão de falhas estruturais em casco de FPSO.

Neves

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Aos meus amados pais. AgradecimentosO financiamento desta pesquisa pela Enauta Participações S.A. (contrato n.º TC-0219-02113-001) e a parceira entre a Escola Politécnica da Universidade de São Paulo e Technomar Engenharia Ltda. é gratamente reconhecido.Gostaria de expressar meus sinceros agradecimentos ao meu orientador Professor Luís Bitencourt Jr. pelo conhecimento compartilhado, disponibilidade, compreensão, paciência e colaboração à pesquisa durante a realização deste trabalho.Também sou grato ao meu coorientador Professor Guilherme Rosa Franzini, especialmente pelas contribuições e sugestões da perspectiva da Engenharia Naval.Ao Professor Alfredo Gay Neto, pelas recomendações na utilização das ferramentas de análise estrutural e comentários que foram de grande importância no enriquecimento deste trabalho, meu reconhecimento e gratidão.

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Guidelines for life extension process management in oil and gas facilities

Cited by 17 publications

References 24 publications

AI-enhanced subsea maintenance for improved safety and efficiency: Exploring strategic approaches

AI-enhanced subsea maintenance for improved safety and efficiency: Exploring strategic approaches

Bridging Geo-Data and Natural Gas Pipeline Design Standards: A Systematic Review of BIM-GIS Integration for Natural Gas Pipeline Asset Management

Desenvolvimento de um Gêmeo Digital para previsão de falhas estruturais em casco de FPSO.

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