BERTopic-Based Countermeasure Categorization in HAZOP Reports

 

The oil and gas industry presents a complex matrix of operations where safety is critical due to the inherent risks of fires, explosions, and toxic releases. Despite the deployment of sophisticated safety management systems, incidents continue to occur, underlining the limitations of traditional hazard evaluation and response frameworks. Historical HAZOP reports are valuable repositories of operational learnings, offering a rich source of countermeasures. However, the industry struggles with the sheer volume of these countermeasures, which makes manual analysis impractical. To address this gap, this study proposes an NLP-based framework using the BERTopic algorithm to categorize and automate the interpretation of HAZOP countermeasures, significantly improving the efficiency and reach of safety systems during the design phase.

Leveraging Natural Language Processing for Safety Data Interpretation

The advent of Natural Language Processing (NLP) has introduced novel possibilities in parsing large volumes of unstructured safety data. This research showcases how the integration of Sentence-BERT (SBERT), UMAP, and HDBSCAN into the BERTopic pipeline offers a powerful tool for extracting meaningful topics from HAZOP countermeasures. By transforming textual data into contextual embeddings and clustering them efficiently, the method enables safety professionals to automatically identify critical issues and prioritize preventive actions. This represents a significant step toward digitizing industrial safety intelligence for better decision-making and reduced human error in the interpretation of hazard data.

Model Performance Comparison: BERTopic vs. Traditional Topic Models

A vital component of this research is the comparative evaluation of BERTopic against widely-used topic modeling techniques such as Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA). The results clearly show that BERTopic outperforms traditional models, achieving a coherence score of 84.6% and topic diversity of 90.7%—compared to LDA’s 45.3% and LSA’s 53% coherence. This superior performance highlights the importance of contextual embeddings and modern NLP pipelines in accurately understanding the thematic structure of safety countermeasures, thus supporting more actionable and department-specific interventions in the design phase.

Integration of Countermeasures into Design Through CBS and API Standards

To ensure that extracted countermeasures can be practically applied in engineering and safety design, the study introduces a novel framework—the Countermeasures Breakdown Structure (CBS). This hierarchical system maps clustered topics directly to safety system categories and aligns them with regulatory standards such as API RP 750 and API RP 752. The integration of CBS not only streamlines safety planning but also ensures traceability and compliance, allowing HAZOP participants and designers to build protection layers in a more structured, automated, and accountable manner.

Multi-Site Model Validation and Transferability

A critical strength of the proposed BERTopic-based approach is its demonstrated robustness across datasets. When applied to a secondary dataset from an oil and gas production facility, the model maintained high performance, achieving 85.29% coherence and 98.33% topic diversity. This cross-site validation confirms the generalizability and scalability of the model, proving its potential as a standard analytical tool in HAZOP studies for diverse facilities. The adaptability of the system encourages broader industrial adoption and could be a stepping stone for the standardization of AI-driven hazard analysis protocols.

Implications for Hazard Management and Interdepartmental Collaboration

This research presents transformative implications for hazard management, particularly in enhancing cross-functional collaboration. By automating the classification of safety recommendations and aligning them with departmental responsibilities, the model ensures timely and precise communication during the design phase. Departments can focus on relevant countermeasures with minimal manual filtering, enabling faster response cycles and better allocation of resources. Moreover, emphasizing high-risk topics through clustering enables prioritization of interventions that carry the greatest impact, contributing to a more proactive and intelligent safety culture in the oil and gas sector.

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