AI-Powered Failure Mode Classification for a Global Oil & Gas Service Provider
Overview
A UK-based oil and gas services provider wanted to automate the classification of service notifications to improve operational efficiency and failure analysis. ACL Digital developed an NLP-based data model and a web application to categorize unstructured service records into defined failure modes, enabling faster diagnostics and data-driven decision-making.
Download Case Study
Challenges
Inability to efficiently analyze large volumes of unstructured service notification (SN) data
Lack of automation in failure mode classification, leading to delays in root cause analysis
Absence of a centralized tool for visualizing categorized failure insights across maintenance operations
Solution
- Developed a hierarchical data model for Tubing Hanger with category mappings aligned to key business value areas
- Integrated data elements from multiple enterprise systems including TCE and SAP, enabling comprehensive search across part numbers and TCE-specific fields
- Defined frequently requested categories and subcategories based on stakeholder inputs, with future scalability to accommodate a broader set of field issue queries
- Built a user-friendly web interface allowing stakeholders to search, retrieve metrics, and generate statistics based on specific input parameters
Outcomes
- Enabled faster root cause analysis and proactive product improvements by categorizing service notifications using NLP-driven search and failure mode mapping
- Delivered actionable insights through a centralized dashboard and advanced search interface, enhancing service efficiency and reducing manual analysis time by over 60%
