AI-Powered Automated Defect Recognition for Weld Radiography: A Transformation to the Traditional approach in Oil & Gas Industry

Whitepaper

SAUDI ARAMCO

ZULUF WATER INJECTION PROJECTS

Author: Mohammed AlJaber

Co- Author: Abdulaziz AlQahtani

Co- Author: Mohammed Youssef

Contents

1.  Executive Summary..................................................................... 3

2.  The Challenge.............................................................................. 4

3.  Methodology................................................................................ 5

4.  Technical Breakthrough.............................................................. 6

5.  Case Study: Real-World Validation of ADR Technology.......... 7

6.  Impact and Benefits..................................................................... 8

7.  Project Achievements: Results and Success Stories................... 9

8. ADR Scope and Limitations........................................................ 11

9.  Conclusion: The Future of AI-Driven Weld Inspection.............. 12

10.References................................................................................... 13

1.   Executive Summary

Weld inspection in the oil and gas industry is crucial for ensuring the integrity of infrastructure operating under harsh conditions, high pressures, and corrosive environments. Historically, this has relied on manual interpretation by certified inspectors, a process that is time-consuming and susceptible to human variability and inefficiencies such as fatigue-induced errors and inconsistent defect classification.

To address these limitations, the Zuluf Offshore Water Injection Project team successfully validated an innovative AI-driven Automated Defect Recognition (ADR) system. This solution leverages a hybrid approach, combining advanced machine learning models with rule-based algorithms to automate the weld radiography interpretation process.

The core of the technology is a multi-stage processing model that includes:

• A Preprocessing Module for image enhancement and standardization.
• An AI Detection Core trained on a large, annotated dataset to detect and classify defects. The IQI and defect detection models use YOLOv5, while the ROI model uses YOLOv8. The system utilizes both the YOLOv5 and YOLOv8 models for high-speed object detection and instance segmentation, enabling the identification of six key defect types: porosities, slag inclusions, lack of fusion (LOF), lack of penetration (LOP), and root/external undercuts.
• A Post-Processing Engine that applies a rule-based engine to automate compliance checks, perform measurements, and generate digital reports.
• Rigorous testing and validation, including a comparative field trial against manual inspectors, have demonstrated the system's superior performance. Key results include:
• Enhanced Accuracy: The ADR system achieved a defect detection accuracy in the range of 90~95%, a significant improvement from the 85-90% range of traditional manual methods. In a comparative field trial, ADR consistently outperformed manual inspection across all defect types, achieving 97% POD for porosity, >91% POD for lack of fusion and 90 % for lack of penetration, >90% POD for slag inclusions, and 98 % for IQI/ROI.
• Operational Efficiency: Inspection time was reduced to under 1 minute per radiograph, facilitating faster project timelines.
• Assured Compliance: The system ensures 100% automated and reliable adherence to critical industry standards, including ASME B31.3, API 1104, ASME Section V & AWS D1.1.

The deployment of this ADR solution represents a paradigm shift in Non-Destructive Testing (NDT), delivering tangible benefits such as cost savings through reduced manual labor and rework, as well as significant ESG impact by enhancing worker safety and protecting environment. As a testament to its success, the system was validated through Saudi Aramco’s Zuluf Project, processing over 5,000 radiographs using scientific sampling with a 90-95% accuracy.

Looking ahead, the model is designed to be scalable and adaptable. Future work includes fine-tuning the model to handle Digital Radiography (DR) and Computed Radiography testing (CRT) images and integrating with cloud-based workflows for remote collaboration. This innovation sets a new benchmark for quality and efficiency, aligning with Saudi Aramco's strategic initiatives in digital transformation and sustainable practices.