Extracting events from Daily Drilling Reports using Fuzzy String Matching

Abstract

Continuous monitoring of oil drilling operations reduces process interruptions and equipment failure. It also contributes to the development of Key Performance Indicators, which leads to more efficient resource management. Daily Drilling Reports (DDRs) have long been the primary way of recording noticeable events, such as stuck pipe. DDRs came to constitute a valuable information base for most oil drilling companies. However, the task of extracting knowledge from DDRs can be costly and time-consuming. This work proposes an approach to recognise drilling events in DDRs using a rule-based language processing method called Fuzzy String Matching (FSM). We applied the FSM algorithm to search for a set of predefined keywords and key phrases to extract possible Invisible Lost Time (ILT) events from DDRs that may indicate risks or low operational efficiency. The fuzzy part of the algorithm allows the identification of terms or expressions that match the pre-established ones approximately rather than exactly, accounting for typos and different suffixes or prefixes. The proposed solution was applied on a data set of 392 real-world DDR records from a drilling company using a set of six ILT event’s key phrases annotated by Subject Matter Specialists. This process can be readily replicated to other events. The results show that in 116 reports tagged as normal, 92 records were identified as possible ILT events, which represents, in hours, 56% of the total drill normal time. Such promising results can lead to very significant improvements in identifying and extracting drilling events within DDRs.