结构化每日钻井报告 (DDR) 是丰富的信息源，可以实现更好的规划、更准确的风险分析以及改进的关键绩效指标和合同。然而，此类信息最初以自由文本和非结构化格式存储，这使得高效的数据挖掘变得困难。随着人工智能（AI）技术，特别是人工智能语言模型的进步，将此类技术应用于非结构化数据已成为数字化转型的关键。完整的论文提出了一种结合人工智能新技术的自动 DDR 分类方法。
这项工作根据新提出的本体论解决了 DDR 自动分类的复杂任务。
Structured daily drilling reports (DDRs) are a rich source of information that allows better planning, more-accurate risk analysis, and improved key performance indicators and contracts. However, such information is originally stored in a free-text and unstructured format, which becomes difficult for efficient data mining. With the advance of artificial intelligence (AI) technologies, particularly AI language models, applying such techniques over unstructured data has become critical to digital transformation. The complete paper presents an approach for automatic DDR classification that incorporates new techniques of AI.
This work addresses the complex task of automatic classification of DDRs according to a newly proposed ontology.
