Speech Recognition Technology Used to Detect Drillstring Breakover and Optimize Drilling Tasks

Abstract

Detecting when the entirety of a drillstring is moving—referred to as breakover—is necessary for automating several tasks in the drilling process. This paper provides an overview of how cross-industry application of machine learning (ML) technology helped solve challenges related to real-time pattern recognition of breakover and how this solution assisted with providing immediate metrics and control of the drilling process. This project leveraged Hidden Markov Models (HMMs), used frequently in other industries for speech recognition and pattern recognition over time series data, to create a statistical classifier that detects drillstring breakover in real time. Although these techniques have not seen widespread adoption in the oil and gas industry, they provide a flexible solution to many automation problems. Model features correlated with string stiffness were constructed that allowed for accurate classification of pre-breakover and post-breakover states. Subject matter experts were enlisted to label 500+ examples of breakover, which were used to train and test models for both ascending and descending drillstrings. The models were then deployed and integrated into the drilling control system to provide monitoring capabilities and control certain processes. The models provided accurate detections of breakover more than 90% of the time when measured against several wells studied for this project and provided hookload values for both breakover and general pickup and slackoff operations. This high accuracy allows for broad application of the model to several use cases. Applications include reducing an operator's 20-ft standard pickup distance, thereby reducing overall connection times, and using the associated models improved the quality of tares in deep lateral sections. The model also provided additional benefits, including automated drag monitoring for rotary drilling and tripping as well as hole condition monitoring during cleanup cycles. Both offer opportunities to optimize flat time and will be discussed in detail.