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

TL;DR

This paper presents a deep NLP-based methodology for classifying sentences in drilling reports into categories like EVENT, SYMPTOM, and ACTION, enabling large-scale analysis for operational insights in the oil and gas industry.

Contribution

It introduces a novel approach for automatic sentence classification in complex technical drilling reports, addressing challenges like symbols, abbreviations, and incomplete sentences.

Findings

Achieved state-of-the-art classification accuracy.

Enabled advanced query capabilities in drilling report analysis.

Demonstrated effectiveness on real-world field data.

Abstract

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.