Offshore oil and gas platforms operate in some of the world's most demanding environments, where equipment failures can lead to production losses, safety incidents, environmental damage, and significant financial costs. Continuous monitoring of critical assets such as drilling equipment, rotating machinery, pressure vessels, subsea systems, structural components, pipelines, compressors, pumps, turbines, and power generation systems is essential for maintaining operational integrity. The Internet of Things (IoT) is transforming offshore operations by enabling real-time condition monitoring through connected sensors, edge computing, cloud platforms, artificial intelligence (AI), and predictive analytics.
This course equips participants with practical knowledge and technical skills to design, implement, and manage IoT-based condition monitoring systems for offshore platforms. Participants learn how to deploy industrial sensors, collect and analyze operational data, monitor equipment health, detect anomalies, predict equipment failures, integrate IoT platforms with Supervisory Control and Data Acquisition (SCADA), Distributed Control Systems (DCS), Enterprise Asset Management (EAM) systems, and digital twins, and improve maintenance decision-making using AI-powered analytics. The course also covers wireless communication technologies, offshore cybersecurity, edge computing, data governance, reliability engineering, and offshore asset integrity management.
Using industry-leading technologies such as Microsoft Azure IoT, AWS IoT Core, Siemens Industrial IoT, Honeywell Forge, AVEVA PI System, Emerson Plantweb, Schneider Electric EcoStruxure, IBM Maximo Application Suite, SAP Intelligent Asset Management, MQTT, OPC UA, LoRaWAN, Power BI, Python, TensorFlow, and industrial SCADA platforms, participants gain hands-on experience through practical laboratories, condition monitoring simulations, predictive maintenance exercises, and offshore industry case studies. By the end of the course, participants will be able to implement intelligent monitoring systems that improve equipment reliability, increase production availability, reduce maintenance costs, strengthen safety, and support digital transformation across offshore oil and gas operations.
Duration
10 Days
Who Should Attend
Individual Impact
Organizational Impact
By the end of this course, participants will be able to:
Topics
Practical Exercise: Assess opportunities for IoT deployment on an offshore platform.
Case Study: Digital transformation of an offshore production facility.
Topics
Practical Exercise: Select sensors for monitoring critical offshore equipment.
Case Study: Sensor deployment for rotating machinery.
Topics
Practical Exercise: Design an IoT communication architecture for an offshore platform.
Case Study: Reliable data transmission in offshore environments.
Topics
Practical Exercise: Develop a predictive maintenance model for offshore equipment.
Case Study: Improving turbine reliability using IoT.
Topics
Practical Exercise: Integrate IoT monitoring data with operational systems.
Case Study: Connected offshore operations using enterprise platforms.
Topics
Practical Exercise: Build an AI model to detect abnormal equipment behavior.
Case Study: AI-powered monitoring of offshore production systems.
Topics
Practical Exercise: Conduct a cybersecurity assessment for an offshore IoT environment.
Case Study: Securing connected offshore assets against cyber threats.
Topics
Practical Exercise: Develop a digital twin strategy for offshore equipment.
Case Study: Digital twins for offshore asset lifecycle management.
Topics
Practical Exercise: Develop an asset integrity monitoring framework.
Case Study: IoT-enabled compliance monitoring on offshore platforms.
Topics
Practical Exercise: Design and present a complete IoT-based condition monitoring solution for an offshore oil and gas platform.
Case Study: Building the next generation of intelligent offshore production facilities.
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