Deployment of a Hybrid Machine Learning and Physics Based Drilling Advisory System at the Rig Site for ROP Optimization

written by: Michael Behounek; Kirt McKenna; Taylor Thetford; Trey Peroyea; Michael Roberts; Julie Pearce; Gary Hickin; Pradeepkumar Ashok; Michael Yi; Dawson Ramos.

During well construction, automatic monitoring of the sensor signals for drilling dysfunction detection through pattern recognition algorithms is key to improving rate of penetration (ROP) and preventing tool failure. The addition of physics-based models can enable further improvement, but often one is limited by the contextual data needed by these models, as well as the…


Automated Merging of Time Series and Textual Operations Data to Extract Technical Limiter Re-Design Recommendations

written by: Michael Yi; Pradeepkumar Ashok; Dawson Ramos; Spencer Bohlander; Taylor Thetford; Mojtaba Shahri; Mickey Noworyta; Trey Peroyea; Michael Behounek.

Paper presented at the IADC/SPE International Drilling Conference and Exhibition, Galveston, Texas, USA, March 2022. Paper Number: SPE-208745-MS. Published: March 01 2022. During the construction planning phase of any new well, drilling engineers often look at offset well data to identify information that could be used to drill the new well more efficiently. This is generally a…


A Real-Time Probabilistic Slide Drilling Dysfunction Advisory to Assist Remote Directional Drilling Operations

written by: Dawson Ramos; Pradeepkumar Ashok; Michael Yi; John D’ Angelo; Ian Rostagno; Spencer Bohlander; Taylor Thetford; James Moisan; Michael Behounek; Mickey Noworyta; Jason Beasley; Joshua Wilson.

Paper presented at the SPE Annual Technical Conference and Exhibition, Dubai, UAE, September 2021. Paper Number: SPE-205984-MS. Published: September 15 2021. Current slide drilling practices rely heavily on the intuition of the directional drillers to identify and correct drilling dysfunctions. Monitoring numerous dysfunctions simultaneously requires more complex analysis than can be done manually in real-time. There is…


Natural Language Processing Applied to Reduction of False and Missed Alarms in Kick and Lost Circulation Detection

written by: Michael Yi; Pradeepkumar Ashok; Dawson Ramos; Taylor Thetford; Spencer Bohlander; James Moisan; Michael Behounek.

Paper presented at the SPE Annual Technical Conference and Exhibition, Dubai, UAE, September 2021. Paper Number: SPE-206340-MS. Published: September 15 2021. Kick and lost circulation events are large contributors to non-productive time. Therefore, early detection of these events is crucial. In the absence of good flow in and flow out sensors, pit volume trends offer the best…


A Probabilistic Belief System to Track the Cleanliness of a Hole in Real-Time

written by: Pradeepkumar Ashok, John D' Angelo, Dawson Ramos, Michael Yi, Taylor Thetford, Nathaniel Younk, Spencer Bohlander, Mojtaba Shahri, Michael Behounek.

Paper SPE-204125-MS, presented at the SPE/IADC International Drilling Conference and Exhibition, Virtual, March 2021. The paper proposes a method that relies on the detection of events over a long time horizon and the use of key parameters relating to such events to quantify hole cleanliness. These events are then related through duration and frequency to…


Time-Series Data Augmentation Techniques for Improving Automated Drilling Dysfunction Classifiers

written by: Michael Yi, Dawson Ramos, Pradeepkumar Ashok, Taylor Thetford, Spencer Bohlander, Michael Behounek.

Paper SPE-204063-MS, presented at the SPE/IADC International Drilling Conference and Exhibition, Virtual, March 2021. Detecting drilling dysfunctions from surface data is not always easy as downhole vibrations tend to get damped before they reach surface sensors. Building machine learning models to recognize patterns in the surface data requires vibration signals captured by downhole sensors for…


Change Management Challenges Deploying a Rig-Based Drilling Advisory System

written by: Michael Behounek, Blake Millican, Brian Nelson, Matthew Wicks, Eugene Rintala, Matthew White, Taylor Thetford, Pradeepkumar Ashok, Dawson Ramos

Paper SPE-194184-MS, presented at SPE/IADC International Drilling Conference and Exhibition, 5-7 March, The Hague, Netherlands, 2019. The system used for this paper consists of a Rig-based Drilling Advisory System (RDAS) where new advisory information is displayed in the driller’s cabin running real-time pattern recognition algorithms to detect drilling dysfunctions. When a drilling dysfunction is encountered,…


Implementation of a Fully Automated Real-Time Torque and Drag Model for Improving Drilling Performance: Case Study

written by: M. Shahri, T. Wilson, Taylor Thetford, B. Nelson, M. Behounek, A. Ambrus, J. D’Angelo, P Ashok

Paper SPE-191426-MS, presented at the SPE Annual Technical Conference and Exhibition, 24-26 September, Dallas, Texas, 2018. The drilling industry has made significant progress on physics-based torque and drag (T&D) models that can run either offline (pre-job or post-job) or in real-time. Despite its numerous benefits, real-time T&D analysis is not prevalent since it requires merging…


Adopting Physical Models in Real-Time Drilling Application: Wellbore Hydraulics

written by: A. Ambrus, P. Ashok, D. Ramos, A. Chintapalli, A. Susich, T. Thetford, B. Nelson, M. Shahri, J. McNab, M. Behounek

Paper SPE-191797-MS, presented at the SPE Liquids-Rich Basins Conference – North America, 5-6 September, Midland, Texas, 2018. The first step towards drilling optimization and automation is a reliable data acquisition and handling system. This includes receiving and processing different frequency data across multiple platforms and ensuring proper data quality. Once we implement such a platform,…


Automatic Sensor Data Validation: Improving the Quality and Reliability of Rig Data

written by: Ashok P, van Oort E, and Ambrus A.

Paper SPE-163726, presented at the SPE Digital Energy Conference, The Woodlands, Texas, March 5–7. The hostile and uncertain environment in which sensors are used and the low priority typically given to regular calibration of these sensors are the primary reasons for poor quality drilling data. Today, online streaming of sensor data from primary sensors on…