用智能体系统整合钻井数据，实现精准分析与自动验证。
TADI: Tool-Augmented Drilling Intelligence via Agentic LLM Orchestration over Heterogeneous Wellsite Data
- 通过大模型调度12个专业工具，跨结构化数据与报告文本进行多步推理。
- 零错误解析1759份钻井报告，支持三种命名不一致的井名处理。
- 强调领域工具设计比模型规模更关键，适合工程智能化研究者。
我们提出TADI（Tool-Augmented Drilling Intelligence），一个将钻井运营数据转化为证据驱动分析智能的智能体AI系统。基于Equinor Volve油田数据集，TADI整合了1,759份每日钻井报告（DDR）、选定的WITSML实时数据对象、15,634条生产记录、地层顶面及射孔信息，采用双存储架构：用DuckDB处理12张表共65,447行的结构化查询，用ChromaDB对36,709个嵌入文档进行语义检索。十二个领域专用工具由大语言模型通过迭代函数调用协同调度，实现跨结构化测量与报告叙述的多步证据收集。系统零错误解析全部1,759份DDR XML文件，兼容三种不一致的井名命名规范，并配备95个自动化测试和涵盖六个操作类别的130个压力测试问题。我们形式化智能体行为为顺序工具选择问题，提出证据锚定得分（EGS）作为基于测量值、引用的DDR段落及必答部分的锚定合规性代理指标。完整6,084行代码实现无需框架依赖，给定公开Volve数据下载与API密钥即可复现。案例研究与定性消融分析表明，领域专用工具设计是技术作业分析质量的主要驱动力，而非模型规模本身。
原文摘要 · Abstract (English)
We present TADI (Tool-Augmented Drilling Intelligence), an agentic AI system that transforms drilling operational data into evidence-based analytical intelligence. Applied to the Equinor Volve Field dataset, TADI integrates 1,759 daily drilling reports, selected WITSML real-time objects, 15,634 production records, formation tops, and perforations into a dual-store architecture: DuckDB for structured queries over 12 tables with 65,447 rows, and ChromaDB for semantic search over 36,709 embedded documents. Twelve domain-specialized tools, orchestrated by a large language model via iterative function calling, support multi-step evidence gathering that cross-references structured drilling measurements with daily report narratives. The system parses all 1,759 DDR XML files with zero errors, handles three incompatible well naming conventions, and is backed by 95 automated tests plus a 130-question stress-question taxonomy spanning six operational categories. We formalize the agent's behavior as a sequential tool-selection problem and propose the Evidence Grounding Score (EGS) as a simple grounding-compliance proxy based on measurements, attributed DDR quotations, and required answer sections. The complete 6,084-line, framework-free implementation is reproducible given the public Volve download and an API key, and the case studies and qualitative ablation analysis suggest that domain-specialized tool design, rather than model scale alone, is the primary driver of analytical quality in technical operations.
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