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…
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…
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…
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…
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…
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…
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,…
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…
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,…
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…