  Drilling activities in the oil and gas industry have been reported over
decades for thousands of wells on a daily basis, yet the analysis of this text
at large-scale for information retrieval, sequence mining, and pattern analysis
is very challenging. 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. We obtain
state-of-the-art classification accuracy within this technical language and
illustrate advanced queries enabled by the tool.
