TADI: Tool-Augmented Drilling Intelligence via Agentic LLM Orchestration over Heterogeneous Wellsite Data

2013-03-24 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Drilling Operations · Depth: Expert, extended

Summary

TADI (Tool-Augmented Drilling Intelligence) is an agentic AI system designed to convert drilling operational data into evidence-based analytical intelligence. It processes the Equinor Volve Field dataset, integrating 1,759 daily drilling reports (DDRs), selected WITSML real-time objects, 15,634 production records, formation tops, and perforations. The system utilizes a dual-store architecture, employing DuckDB for structured queries over 12 tables with 65,447 rows and ChromaDB for semantic search across 36,709 embedded documents. TADI features 12 domain-specialized tools, orchestrated by a large language model (LLM) through iterative function calls, to gather multi-step evidence by cross-referencing structured drilling measurements with narrative reports. It achieves zero-error parsing of all 1,759 DDR XML files, handles three incompatible well naming conventions, and is supported by 95 automated tests and a 130-question stress-question taxonomy. The system formalizes agent behavior as a sequential tool-selection problem and introduces the Evidence Grounding Score (EGS) as a grounding-compliance proxy.

Key takeaway

For AI Architects designing solutions for complex technical operations, you should prioritize developing domain-specialized tools and meticulously engineered system prompts over solely relying on LLM scale or fine-tuning. Your systems must enforce dual-source evidence grounding, combining structured data queries with narrative text retrieval, to ensure robust and verifiable analytical intelligence. This approach will accelerate post-well reviews, enhance cross-well learning, and preserve critical operational knowledge.

Key insights

Domain-specialized tools and structured prompts drive analytical quality in agentic LLM systems for technical operations.

Principles

• Encapsulate domain knowledge in deterministic tools, not just LLM fine-tuning.
• Enforce dual-source evidence grounding to prevent "evidence monoculture."
• Utilize a dual-store architecture for hybrid structured and semantic retrieval.

Method

TADI employs an LLM orchestrator to iteratively select and invoke 12 domain-specific tools over a dual-store backend (DuckDB for SQL, ChromaDB for vector search), guided by a 168-line system prompt to synthesize evidence-backed answers.

In practice

• Implement dual-method phase detection for robust operational analysis.
• Use statistical mud property comparison to diagnose operational issues.
• Apply composite difficulty indices for cross-well benchmarking.

Topics

• Agentic AI Systems
• Drilling Operations
• Tool-Augmented LLMs
• Volve Field Dataset
• Dual-Store Architecture

Best for: AI Architect, AI Scientist, AI Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.