Serial Number Recognition on Oil Pipes : Case Study Summary

This AI project focuses on automating the reading and recording of serial numbers on oil drilling pipes at rig sites. By replacing manual data entry with a computer vision–based solution, the system significantly improves accuracy, speed, and reliability in tracking pipe maintenance operations.

The solution was developed for Defender Tubular Services, enabling real-time digitization of pipe serial numbers and seamless integration with existing operational workflows.

AI Project Overview

At oil rig sites, pipe serial numbers were previously recorded manually after cleaning processes. This approach was time-consuming, error-prone, and difficult to scale across multiple locations.

The project aimed to digitally transform serial number recording using AI-powered optical character recognition (OCR), allowing serial numbers to be captured automatically, validated intelligently, and synchronized in real time for tracking and reporting.

Domain

Construction/Energy

AI Stack

OCR models / AI-based validation & error detection

Services

Computer Vision

Duration

4 Months

Defender Tubular Services is an oilfield services company based in Texas, USA, specializing in cleaning and maintaining oil drilling pipes. The company operates across multiple sites in the eastern United States, where accurate pipe tracking is critical for safety, compliance, and operational efficiency.

  • Manual serial number recording caused frequent errors

  • Low accuracy when recording worn or partially damaged serial numbers

  • Delays in updating tracking records across multiple sites

  • Limited visibility and accessibility of pipe data

  • Need for a scalable, digital solution compatible with existing workflows

The team developed a computer vision–based OCR system capable of detecting, reading, and validating serial numbers directly from pipe surfaces.

Key solution components include:

  • OCR models trained to recognize serial numbers under real-world conditions

  • AI-based validation to detect and correct potential recognition errors

  • Desktop application for on-site usage

  • Real-time data synchronization with Excel for tracking and reporting

  • Cloud deployment to ensure scalability and reliability

A Proof of Concept (PoC) was delivered rapidly to validate feasibility before full-scale development.

Results:

  • 85% recognition accuracy, compared to 30% with the traditional manual method

  • 53% faster logging process for serial number recording

  • 70% reduction in data entry errors

  • PoC delivered in 2 weeks

  • Full AI system developed and deployed in 8 weeks

The solution significantly improved operational efficiency while reducing human error and manual workload.

Key Business Impact:

  • Digitized a critical operational process

  • Improved data accuracy and traceability

  • Reduced manual workload at oil rig sites

  • Enabled real-time access to maintenance data

  • Established a scalable AI foundation for future expansion

  • Computer Vision Development

  • AI Model Training & Validation

  • Desktop & Mobile Application Development

  • Cloud Deployment (AWS)

  • AI Proof of Concept (PoC)

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