Sequence Mining and Pattern Analysis in Drilling Reports with Deep Natural Language Processing
This work addresses the problem of information retrieval and pattern analysis for drilling operations, enabling optimization and accident mitigation, but it is incremental as it applies existing NLP methods to a new dataset.
The paper tackled the challenge of analyzing large-scale drilling reports in the oil and gas industry by proposing a methodology for automatic sentence classification into EVENT, SYMPTOM, and ACTION labels, achieving state-of-the-art classification accuracy within this technical domain.
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.