Real-Time Optimization and Decarbonization of Oil and Gas Production Value Chain Enabled by Industry 4.0 Technologies: A Critical Review

Singh, Harpreet; Li, Chengxi; Cheng, Peng; Wang, Xunjie; Ge, Hao; Liu, Qing

doi:10.2118/214301-pa

Cited by 3 publications

(1 citation statement)

References 33 publications

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“…By implementing predictive maintenance, BP has reduced maintenance costs, improved equipment reliability, and enhanced safety performance on the Thunder Horse platform. Equinor's Mariner Field: Equinor implemented predictive maintenance on its Mariner Field, located in the UK North Sea (Singh et al, 2023). Using AI-driven analytics, Equinor monitors the condition of subsea equipment, including wellheads, control systems, and umbilicals, to predict maintenance needs and optimize operational efficiency.…”

Section: Predictive Maintenance With Aimentioning

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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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: Predictive Maintenance With Aimentioning

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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ERP Integration: Enhancing Collaboration in Virtual and Extended Enterprises

Usmani,

Happonen,

Watada

2024

Lecture Notes in Networks and Systems

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Tech-Business Analytics in Secondary Industry Sector

Kumar,

K.,

Aithal

2023

IJAEML

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Purpose: Businesses in all sectors, including the secondary industry, will turn to tech-business analytics as a crucial tool. Tech-Business Analytics' role in the secondary industrial sector is to support companies in making data-driven decisions that optimize their operations, boost productivity, and boost profitability. Businesses may optimize their supply chains by accessing data on suppliers, inventories, logistics, and other aspects to spot inefficiencies and areas for improvement. Organizations can use this information to reduce downtime and boost production to schedule maintenance in advance and predict when machinery and equipment will likely break. Examining data on product flaws, customer complaints, and other aspects can help firms improve their quality control systems by identifying root causes and implementing corrective measures. Studying data on consumer behaviour, industry trends, and other factors can help organizations optimize their sales and marketing activities and find chances for expansion and higher profitability. Design/Methodology/Approach: Businesses can use several processes in the tech-business analytics methodology to help them make decisions based on data in the secondary industry sector. This secondary industry sector can entail enhancing the effectiveness of the supply chain or decreasing equipment downtime. After identifying the issue, the necessary data must be gathered and prepared. Once the data is collected, it must be analyzed using statistical models and other analytical methods. This collected data might entail looking for relationships between multiple variables, spotting trends in consumer behaviour, or predicting outcomes using predictive models. Findings/Result: It is described in the article how tech-business analytics in the secondary industrial sector will have managed the growth itself from its inception to the present. The Tech-Business Analytics technique in the secondary industry sector offers a structured approach to problem-solving using data analysis to assist in better decision-making and improve business outcomes. Originality/Value: Exploring the evolutionary path of business analytics transforms into the advanced realm of technology-driven business analytics within the secondary industry sector. A generic architecture also examines 130 recently published Tech Business Analytics in Secondary Industry sector research projects for technical purposes. Tech-Business Analytics is a new field that applies ICCT-underpinning technologies in Tech-Business Analytics (TBA). TBA is intended to provide businesses with unprecedented opportunities for growth and innovation in secondary industry sectors. Paper Type: Exploratory research.

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Real-Time Optimization and Decarbonization of Oil and Gas Production Value Chain Enabled by Industry 4.0 Technologies: A Critical Review

Cited by 3 publications

References 33 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

ERP Integration: Enhancing Collaboration in Virtual and Extended Enterprises

Tech-Business Analytics in Secondary Industry Sector

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