Transforming Offshore Oil and Gas Production Platforms into Smart Unmanned Installations

Tan, Jaime HuiChoo; Roberts, Brian; Sundararaju, Prabakaran; Sintive, Christophe; Facheris, Laurent; Bosch, Joel Vanden; Lehning, Virginie; Pegg, Mathew James

doi:10.4043/30160-ms

Cited by 4 publications

(3 citation statements)

References 1 publication

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“…These models can: detect anomalies, predict failures, and optimize maintenance intervals. Examples of IoT devices used in offshore maintenance logistics: fiber optic sensors, wireless sensor networks, remote operated vehicles (ROVs), and smart valves and actuators (Tan et al, 2020). By integrating IoT devices into maintenance logistics, offshore platforms can achieve greater visibility into equipment health and performance, enabling more effective maintenance planning and execution.…”

Section: Role Of Ai In Optimizing Maintenance Logisticsmentioning

confidence: 99%

Optimizing maintenance logistics on offshore platforms with AI: Current strategies and future innovations

Ayemere Ukato,

Oludayo Olatoye Sofoluwe,

Dazok Donald Jambol

et al. 2024

World J. Adv. Res. Rev.

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Offshore platforms are vital assets for the oil and gas industry, serving as the primary facilities for exploration, extraction, and processing. Maintenance logistics plays a crucial role in ensuring these platforms operate efficiently and safely. However, the remote and harsh environments of offshore platforms present significant challenges for maintenance activities. Traditional maintenance strategies often struggle to meet the demands of these environments, leading to inefficiencies, increased costs, and potential safety risks. This review discusses the application of Artificial Intelligence (AI) in optimizing maintenance logistics on offshore platforms. Current strategies involve a combination of preventive, predictive, and corrective maintenance approaches. Preventive maintenance schedules regular inspections and replacements based on predetermined intervals, while predictive maintenance utilizes data analytics to predict equipment failures and plan maintenance activities accordingly. Corrective maintenance addresses issues as they arise, often in response to unexpected failures. AI offers opportunities to enhance these strategies by leveraging advanced data analytics, machine learning, and optimization algorithms. AI-enabled predictive maintenance can analyze vast amounts of data from sensors, historical maintenance records, and environmental factors to forecast equipment failures with greater accuracy. This allows for proactive maintenance planning, minimizing downtime and reducing maintenance costs. Furthermore, AI can optimize maintenance logistics by improving resource allocation and scheduling. Through real-time monitoring and analysis, AI systems can prioritize maintenance tasks based on urgency, equipment criticality, and resource availability. This ensures that maintenance crews are deployed efficiently, reducing idle time and improving overall productivity. Future innovations in AI for maintenance logistics on offshore platforms include the integration of Internet of Things (IoT) devices and autonomous systems. IoT sensors can provide real-time data on equipment condition and environmental factors, enabling more precise predictive maintenance models. Autonomous maintenance robots equipped with AI algorithms can perform routine inspections and minor repairs, reducing the need for human intervention in hazardous environments. However, implementing AI in offshore maintenance logistics also poses challenges, including data quality, cybersecurity, and workforce readiness. Ensuring data accuracy and reliability is crucial for effective AI models, requiring robust data collection and management processes. Cybersecurity measures must be strengthened to protect AI systems from malicious attacks that could disrupt operations or compromise safety. Additionally, workforce training and education are essential to prepare personnel for working alongside AI systems and interpreting AI-generated insights. Optimizing maintenance logistics on offshore platforms with AI offers significant benefits in terms of efficiency, cost savings, and safety. By leveraging AI technologies, current maintenance strategies can be enhanced, and future innovations can revolutionize offshore maintenance practices, making operations more sustainable and resilient in the face of evolving challenges.

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Section: Role Of Ai In Optimizing Maintenance Logisticsmentioning

confidence: 99%

Optimizing maintenance logistics on offshore platforms with AI: Current strategies and future innovations

Ayemere Ukato,

Oludayo Olatoye Sofoluwe,

Dazok Donald Jambol

et al. 2024

World J. Adv. Res. Rev.

View full text Add to dashboard Cite

show abstract

“…Similar application areas are foreseeable in the development of unmanned and minimum manned platforms in the North Sea such as the Oseberg H from Equinor in the Norwegian Continental Shelf (NCS). Such platforms are built on the concept of minimum visit policy with an ambition to visit the platforms only once or twice a year and thus both CBM and multi-component maintenance become relevant (Tan et al 2020). Costs of these visits are often very high for remote operations and Nachimuthu et al (2019) reports that a corrective maintenance trip at an offshore wind farm can be expected to cost $70, 000 − $130, 000 .…”

Section: Motivationmentioning

confidence: 99%

Condition-based multi-component maintenance decision support under degradation uncertainties

Islam

Vatn

2023

Int J Syst Assur Eng Manag

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Multi-component maintenance optimization is a well-studied area for age-based failure models but in contrast, incorporation of condition-based maintenance (CBM) is still an open area of research. Taking advantage of condition monitoring information for updating components’ health conditions demands a dynamic short-term approach when grouping multiple activities subject to CBM policy. Degradation models are commonly utilized in CBM for predicting the future condition of a given component to decide appropriate maintenance actions where inherent uncertainties exist in the degradation processes. There are a limited number of works in literature that account for degradation uncertainties where maintenance cost is a function of such uncertainty. This paper aims to develop a maintenance decision support for a multi-component system by incorporating CBM while considering the degradation uncertainties. In this paper, a two-stage stochastic programming is proposed to address such an issue and the problem is formulated for situations where maintenance opportunities are limited due to practical constraints (e.g., remote offshore maintenance operations of wind farms, unmanned platforms in oil and gas industries, etc.). The concept of marginal cost is used in developing the equation of optimality. This is a combinatorial problem and becomes intractable when the number of components is large therefore a heuristic is proposed to reduce the problem size which reduces the required computational time substantially. It is shown that significant cost savings are possible, especially, when the downtime cost and common setup cost are significant. A numerical example is provided with a system of six components achieving above $$10\%$$ 10 % cost reduction when the degradation uncertainties are taken into account.

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“…The industry of offshore oil and gas production is a complex sector that comprises exploration, extraction, and transport of natural gas and crude oil from the seabed [1,2]. It is widely known that this sector plays an essential role in the global energy supply.…”

Section: Introductionmentioning

confidence: 99%

Hybrid methodology based on computational vision and sensor fusion for assisting autonomous UAV on offshore messenger cable transfer operation

et al. 2022

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The recent development of new offshore projects in pre-salt deepwater fields has placed offshore loading operations as the main production outflow alternative, increasing the operational complexity and risks. Numerous dangerous situations are associated with oil offloading, such as the messenger line transfer during the mooring stage. Nowadays, this critical task is realized by launching a thin messenger cable using the pneumatic line throwing apparatus. This is a complex and slow process since the operation usually occurs with the ship opposite to the wind. This work proposes a hybrid flight methodology based on computer vision and sensor fusion techniques for autonomous unmanned aerial vehicles (UAVs). The UAV takes off from an oil rig and precisely reaches a specific point in the shuttle tanker without using expensive positioning devices and augmenting UAV’s orientation (yaw) precision since the compass can suffer from severe interference due to naval metallic structures near the vehicle. The proposed framework was tested in a realistic simulated environment considering several practical operational constraints. The results demonstrated both the robustness and efficiency of the methodology.

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Transforming Offshore Oil and Gas Production Platforms into Smart Unmanned Installations

Cited by 4 publications

References 1 publication

Optimizing maintenance logistics on offshore platforms with AI: Current strategies and future innovations

Optimizing maintenance logistics on offshore platforms with AI: Current strategies and future innovations

Condition-based multi-component maintenance decision support under degradation uncertainties

Hybrid methodology based on computational vision and sensor fusion for assisting autonomous UAV on offshore messenger cable transfer operation

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