Usage of IoT in Oil And Gas
Usage of IoT in Oil And Gas
This high-level overview and architecture focuses what IoT can bring to the oil and gas industry, or most any field, and how to get started.
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Deployment of IoT-based smart energy solutions results in better field communication, reduced costs of maintenance, real-time monitoring, digital oil field infrastructure, reduced power consumption, mine automation, greater safety and security of assets, and thus higher productivity.
IoT will improve energy efficiency, remote monitoring and control of physical assets, and productivity through applications as diverse as home security to condition monitoring on the factory floor.
- Predictive maintenance
- Pipeline and equipment monitoring
- Location Intelligence
- Emissions monitoring and control and release management
- Real-time machine and sensor integration
- Real-time alerts
- Link to enterprise resource planning data to trigger maintenance workflow
- Plant dashboards and trend analysis
- Asset information network
- Fleet operations monitoring
- IoT is removing the physical barriers so O&G companies helping reach broader target audiences and opening up new global business opportunities.
A typical reference architecture is depicted here for Oil and Gas Industry leveraging Microsoft Azure:
Two important aspects that need to be considered when architecting an IoT solution are scalability and security. The IoT solution should be scalable to support unpredictable traffic surge while security is important at the device level to ensure it is hack-proofed. Azure IoT Hub provides the reliability to secure the connection between device and cloud and vice-versa, but scalability has to be implemented at the architecture level.
- Device Management: After the device registers with the cloud gateway, it can send and receive the data to and from the hubs. It should have the device management feature to add, activate, deactivate, remove the device, and update the attributes of the device.
- Device Connectivity: There will be a huge amount of data that needs to be managed, with multiple messages being received in a second from a huge number of devices, which would result in 10s of thousands or possibly millions of messages a day. The platform should provide high-volume message ingestion using a single logical endpoint.
- Transformation and Storage: Once the messages arrive, the platform should provide a mechanism to select, transform, and route messages to various storage media for the purpose of archiving and staging for downstream processing.
- Analytics and Data Visualization: The value of collecting data in a continuous fashion is to build up a historical record for the purpose of performing analytics to gain business insights.
- Presentation and Action: The cloud solution should provide the ability to visualize the status of the messages in real time through tabular or graphical UI components. In addition, some messages may contain information of an alert status so the IoT solution must provide a mechanism for real-time notifications to actionable operation.
- Microsoft Azure IoT Suite is an enterprise-grade solution that lets you get started quickly through a set of extensible, preconfigured solutions that address common IoT scenarios, such as remote monitoring and predictive maintenance. These solutions are implementations of the IoT solution architecture described previously.
- The preconfigured solutions are complete, working, end-to-end solutions that include simulated devices to get you started, preconfigured Azure services such as Azure IoT Hub, Azure Event Hubs, Azure Stream Analytics, Azure Machine Learning, and Azure storage, and solution-specific management consoles. The preconfigured solutions contain proven, production-ready code that you can customize and extend to implement your own specific IoT scenarios.
HTML5-based UI to support a wider range of devices
Modernized, highly intuitive, and easy to use UI with internationalization in compliance with customer branding guidelines
Cloud-based, highly available, and scalable architecture to extend support for additional features in the future
Cloud-based architecture to eliminate the constraints with increasing users and data growth in the future
Standard REST-based integration architecture for future extensions and integrations with other on-premise or third-party systems
Support for event notification and online monitoring
Real-time/near real-time reporting and move towards self-service business intelligence
Predictive analytics to improve business process efficiency
Artificial Intelligence-based agents for learning and knowledge management
Standards (SAML)-based federated authentication of users to accept the user identities from social networks, like Google, Microsoft, etc.
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