Corrosion Analysis and Leakage Detection

In the oil and gas industry, the early detection of corrosion and leaks is crucial for preventing environmental damage, ensuring operational safety, and minimizing costly repairs. Artificial intelligence (AI) plays a significant role in advancing the capabilities of monitoring systems for corrosion analysis and leakage detection.

INFORMATION
Use Case
Defect Detection
Industry
Oil and Gas
DETAILS
Challenge

 Streamlining Detection and Monitoring of Oil and Gas Infrastructure Integrity

The oil and gas industry frequently grapples with the challenge of monitoring their infrastructure for leaks and corrosion, critical for safe and efficient operations. The traditional approach—manual inspections—is not only cumbersome and slow but also prone to inaccuracies, particularly in hostile environments not suited for workers. These outdated methods often overlook minor leaks or early-stage corrosion and are susceptible to false positives. The diverse nature of pipeline systems, with their varying degrees of wear and size, further complicates the issue, making some degree of leakage almost a given. Left unchecked or detected too late, these issues can lead to serious safety concerns, environmental degradation, considerable financial losses, and operational disruptions due to maintenance setbacks.

Solution

Implementing Vision AI for Proactive Infrastructure Monitoring

To combat these challenges, our team explored the capabilities of machine learning and Vision AI to revolutionise infrastructure monitoring. We began with a detailed analysis of existing processes, the intricacies of pipeline systems, and potential data sources for monitoring. Our initial phase focused on understanding the specific requirements and the data environment, which informed the subsequent development and testing of our models.

We constructed a neural network capable of generating and interpreting image embeddings, effectively allowing us to compare and measure the ‘distance’ between various states of infrastructure integrity. Our engineers tapped into both labelled and unlabelled data, developing methods to extract valuable insights from the available imagery. By tackling both direct and ancillary visual classification challenges, we honed our system’s ability to discern subtle indications of leaks and corrosion.

Our iterative approach involved experimenting with a range of computer vision techniques, including both high-level and low-level image processing and Optical Character Recognition (OCR). Through this rigorous experimental process, we refined our model’s precision in detecting infrastructural anomalies, ultimately arriving at a robust solution for our client’s needs.

Results

Enhanced Operational Efficiency with AI-Driven Monitoring

Our intervention equipped the client with a cutting-edge, AI-powered monitoring tool that transformed their approach to infrastructure integrity. This innovative solution has delivered substantial benefits, including:

     1. Automated and continuous monitoring of leaks and corrosion.

     2. Precise and timely detection, leading to swift interventions.

     3. Operational acceleration, freeing personnel from repetitive manual checks.

     4. Significant reduction in the risk of errors, false positives, and associated costs.

A more reliable and enduring pipeline network, thanks to proactive maintenance strategies.

Techstacks Used

Technologies and Tools
Machine Learning (ML): TensorFlow, PyTorch, AutoML Computer Vision (CV): OpenCV, TensorFlow Object Detection API Deep Learning: TensorFlow, PyTorch, Keras Infrastructure: AWS, Azure, GCP, NVIDIA CUDA, Docker, Kubernetes Data Storage: AWS S3, Azure Blob Storage, PostgreSQL, NoSQL databases Data Ingestion: IoT Devices, UAVs/Drones, API Integrations Development: Python, C++, Jupyter Notebooks, Git CI/CD: Jenkins, GitLab CI Monitoring: Grafana, Kibana, Prometheus, PagerDuty, ELK Stack Deployment: TensorFlow Serving, ONNX, Terraform, Ansible

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