Improving Safety and Logistics at Well Pads with AWS AI Computer Vision
AWS customers in the upstream oil and gas industry faced challenges monitoring safety and logistics at remote well pads with limited connectivity. They deployed an AI solution using AWS DeepLens cameras running custom ML models locally to detect trucks, license plates, and PPE compliance such as hard hats. The solution leverages Amazon Rekognition for image analysis, Amazon Textract for extracting vehicle markings, and AWS IoT Greengrass to manage edge runtime and connectivity. Processed images and alerts are sent to the cloud via AWS IoT Core, integrated with Amazon Aurora for data storage, Amazon QuickSight for dashboards and visualization, Amazon SNS for notifications, and Amazon CloudWatch for logging and monitoring. This enables real-time validation of vendor truck visits and PPE compliance, reduces operational costs, and improves safety monitoring without continuous cloud connectivity.
- Organization
- AWS customers in upstream oil and gas industry
- Industry
- Energy & Utilities
- Location
- United States
- Published
- August 2021
Reported outcomes
Strategic outcomes
Primary read
Use case focus
Showing 3 of 3
- 1Edge AI
- 2Safety Monitoring
- 3Computer Vision
- Remote well pads require effective safety and logistics monitoring but suffer from limited connectivity and power constraints.
- Verifying vendor truck visits and PPE usage is difficult with manual or disconnected processes.
- Deployed custom computer vision models on AWS DeepLens edge cameras to detect trucks, license plates, and PPE compliance.
- Used Amazon Rekognition and Amazon Textract for image processing and object recognition in the cloud.
- Integrated edge inference with cloud services using AWS IoT Greengrass, AWS IoT Core, and Lambda for event processing and alerting.
- Stored and analyzed data with Amazon Aurora and visualized results with Amazon QuickSight dashboards.
- Monitored system health and generated alerts via Amazon CloudWatch and Amazon SNS.
- Enables real-time safety and logistics monitoring at remote oil and gas well pads.
- Reduces operational costs compared to manual or disconnected methods.
- Provides accurate and timely information on vendor visits and PPE compliance to improve safety.
- Operates with minimal data transmission to cloud, suitable for limited connectivity environments.
- Estimated cost of $161 per month to run at 1,000 well pads excluding hardware costs.
Architecture
The solution architecture includes AWS DeepLens running ML models on cameras at the edge to perform object detection and PPE compliance checks locally. Images with objects of interest are sent via AWS IoT Core to cloud services. Amazon Rekognition and Amazon Textract perform image analysis and text extraction to identify vehicle and vendor information. Data is stored in Amazon Aurora to support dashboards and reporting via Amazon QuickSight. AWS Lambda and Amazon SNS handle event processing and notifications. Amazon CloudWatch monitors system health and logs.
Sources & evidence1
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