A novel machine learning model for early operational anomaly detection using LWD/MWD data

Drilling and workover operations represent a crucial part of a well’s life cycle in terms of deliverability and economics. Understanding the underlying phenomena that cause operational anomalies is the stepping stone into early detection and control of undesired events, such as a kick. The evolution of artificial intelligence and machine learning applications lend itself to well operations, to gain new efficiencies and unveil hidden insightful observations about downhole and surface operating conditions. Incorporating the mechanisms of natural phenomena and big data, retrieved from sources such as logging while drilling (LWD) and measurement while drilling (MWD) logs, and placed into machine learning models, boost capabilities for early detection of operational anomalies, and mitigation of potential negative consequences, while eliminating human bias. This article highlights a novel machine learning model developed to streamline early detection for the operational anomaly of uncontrolled hydrocarbon flow during well operations, such as drilling. The proposed technique detects and classifies the risk level of a kick before it reaches the surface, to extend the safe response time limit. When this method is integrated with LWD data in real-time mode by means of software, an alarm system can be embedded to alert field hands about downhole conditions. This does not only promote safer operations, but also significantly improves the availability and reliability of critical information. To further fine-tune the accuracy of the predictive model, multiple rounds of cross-validation were executed on the training data. It is evident that training machine learning models allow for more learning through practice. The technique presented shows that big data and machine learning algorithms are powerful tools to uncover hidden information, and enable improvement in operational leadership. © 2019 Aramco Services Company. All rights reserved.