Sequence Mining and Pattern Analysis in Drilling Reports with Deep Natural Language Processing

Drilling activities in the oil and gas industry have been reported over decades for thousands of wells on a daily basis, yet little effort was made to analyze this text in large-scale for information retrieval, sequence mining, and pattern analysis. Drilling reports contain interpretations written by drillers from noting measurements in downhole sensors and surface equipment, and can be used for operation optimization and accident mitigation. In this initial work, a methodology is proposed for automatic classification of sentences written in drilling reports into three relevant labels (EVENT, SYMPTOM, and ACTION) for hundreds of wells in an actual field. Some of the main challenges in the text corpus were overcome, which include the high frequency of technical symbols, mistyping/abbreviation of technical terms, and the presence of incomplete sentences in the drilling reports. This work describes state-of-the-art classification accuracy obtained within this technical language and illustrates advanced queries enabled by the tool.