Applying Specialized AI Agents for Plug and Abandonment Operation Analysis and Regulatory Compliance: A Fundamental Shift Towards Automating Workflows and Augmenting Engineer's Capabilities

Abstract

Analysis of well plug and abandonment operations in the oil and gas industry requires comprehensive interpretation of historical data distributed across numerous heterogeneous sources, including daily drilling reports, well completion reports, cementing data, intervention reports, schematics, and well integrity assessment materials. In conventional practice, this process is largely performed manually, takes from several days to several weeks, and depends heavily on the individual experience of the engineer. Variations in document structure, data incompleteness, inconsistencies between sources, and limited traceability of engineering conclusions introduce risks of error and hinder the scalability of analysis. This paper presents an agent-oriented approach to automating the analysis of well P&A operations. The proposed system transforms fragmented processing of historical documentation into a structured and traceable workflow, including document ingestion, parsing, data extraction, reconstruction of well state, validation, well schematic generation, and regulatory compliance assessment. Unlike monolithic solutions based on large language models (LLMs), the system decomposes a complex engineering task into a set of specialized agents coordinated by a central orchestration mechanism. This approach ensures modularity, controllability, reproducibility, and auditability of each processing stage. The system was applied to representative sets of historical well documentation, including the Petrel-1 case study. The results demonstrate a significant reduction in initial analysis time: data extraction and consolidation were completed in 30–45 minutes compared to 1–3 working days, while a preliminary full P&A analysis required 60–90 minutes instead of 3–5 working days. At the same time, 85–95% of the required engineering parameters were automatically extracted from the documents, depending on input data quality, and all extracted values were linked to their original sources. The system also identified inter-document inconsistencies, missing data, and ambiguous parameters, not replacing engineering judgment but supporting it with more complete and structured information. The results demonstrate that multi-agent architectures can serve as a practical foundation for automating complex engineering processes that require a combination of unstructured data interpretation, deterministic validation, domain-specific logic, and strict traceability. The proposed system does not replace engineers but augments their capabilities by reducing manual workload, improving analytical consistency, and providing a transparent basis for regulatory compliance verification.