This project used predictive analytics and machine learning-based modeling to detect drilling anomalies, namely stuck pipe events. Analysis focused on historical drilling data and real-time operational data to address the limitations of physics-based modeling. This project was designed to enable drilling crews to minimize downtime and non-productive time through real-time anomaly management. The solution used data science techniques to overcome data consistency/quality issues and flag drilling anomalies leading to a stuck pipe event. Predictive machine learning models were deployed across seven wells in different fields. The models analyzed both historical and real-time data across various data channels to identify anomalies (difficulties that impact non-productive time). The modeling approach mimicked the behavior of drillers using surface parameters. Small deviations from normal behavior were identified based on combinations of surface parameters, and automated machine learning was used to accelerate and optimize the modeling process. The output was a risk score that flags deviations in rig surface parameters. During the development phase, multiple data science approaches were attempted to monitor the overall health of the drilling process. They analyzed both historical and real-time data from torque, hole depth and deviation, standpipe pressure, and various other data channels. The models detected drilling anomalies with a harmonic model accuracy of 80% and produced valid alerts on 96% of stuck pipe and tight hole events. The average forewarning was two hours. This allowed personnel ample time to make corrections before stuck pipe events could occur. This also enabled the drilling operator to save the company upwards of millions of dollars in drilling costs and downtime. This project introduced novel data aggregation and deep learning-based normal behavior modeling methods. It demonstrates the benefits of adopting predictive analytics and machine learning in drilling operations. The approach enabled operators to mitigate data issues and demonstrate real-time, high-frequency and high-accuracy predictions. As a result, the operator was able to significantly reduce non-productive time.

For Drilling Contractors and Oil & Gas operators, daily drilling activity reporting is crucial to understand the underlying efficiency of the drilling process, identify risks and uncertainties at the well level and field level, look for opportunities to improve average rate of penetration and to be within the target drilling budget and target time. All the previous while increasing the safety of the people and assets and have the lowest impact in the environment. Daily drilling reports help drilling contractors and operators to understand what, where and when an activity was performed, helps managers to understand teams and contractors performance, and helps executives have a holistic view of the drilling process, and how it meets predefined Key Performance Indicator targets. Analysis does not only look into visible non-productive time, but extends into invisible non productive time. Examples of visible non productive time include drilling borehole assembly failure, critical rig equipment failure, etc. Examples of non visible non productive time include premature pulling out of hole of drilling equipment assuming high bit wear, variations between crew experience during drilling resulting in lower rate of penetration, etc. The challenge the industry faces is that daily drilling activity reporting; along with real time reporting, provides the foundation for drilling analytics. And inaccuracies in reporting the drilling activity, such as reporting the wrong activity, would lead to inaccurate analytics and insights. To overcome this challenge, operators standardize drilling activities reporting by defining drilling codes and subcodes that are used by Drillers, Tool Pushers and Company men to map a drilling activity to a usable code and subcode, and in turn, those codes and subcodes can be used to extract structured data out of the drilling reports for reporting purposes.