AI-powered Failure Analysis & Operational Efficiency Optimization 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.
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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%
