Application of natural language processing for spill reduction in an exploration and production company

Abstract

Spill reduction is a key focus for exploration and production (E&P) companies to protect people, the environment, and to maintain oil production. Learning from previous incidents enable companies to identify ways to lower the severity and frequency of spills. Root cause failure analysis (RCFA) is a key element in process safety management, and this is often focused on high severity incidents because of the time required to properly conduct the analysis. Incidents that have lower severity occur more frequently, but typically less time and resources are spent analyzing these events. Learning from lower severity, high frequency events can be useful in improving process safety programs and reduce spill frequency and impacts because the data set is more representative of the big picture. Natural language processing (NLP) is applied to identify causes and contributing factors of spills and leaks associated with E&P activity to reduce the frequency and severity of these events. Reviewing incident reports allow us to identify trends and areas of improvement, and NLP can be used to process the data quickly and improve data quality. Incident reports are typically free text that includes a description and cause of the event. One challenge with free-text descriptions is that further analysis and interpretation is needed in order to effectively analyze, trend, and learn from the available data. In addition, identifying the causes and contributing factors of a spill accurately can be time intensive. Two natural language processing methods, rule-based entity extraction and a machine learning model approach have been utilized to analyze a greater number of incident reports, which enables us to pinpoint causes of leaks, reduce the frequency of spills, and mitigate the impact of these events. This paper presents the challenges associated with learning from incident reports, particularly spills, and offers solutions that natural language processing can provide to improve data quality and contribute to spill reduction efforts in the context of an exploration and production company.