This paper discusses the technical challenges related to implementing a rig-site, real-time drilling advisory system and current solutions to these challenges. The system uses a data-driven response surface model based on physics-based calculations to optimize rate of penetration (ROP) while minimizing drilling vibration dysfunction with regards to lateral (whirl) and torsional (stick-slip) vibrational modes. Minimizing these vibrations is important to mitigate bit damage that can lead to reduced ROP and bit trips. By incorporating drilling vibrations with ROP optimization, the system helps operations identify drilling limiters and support the bit and BHA redesign process to improve drilling on subsequent wells.
Throughout development, the team has identified and managed challenges related to estimating drilling performance from data, making optimization trade-offs, guiding the driller to characterize performance, and handling formation variability. Surface sensors provide real-time drilling data measurements to an electronic drilling recorder which converts data into Wellsite Information Transfer Specification (WITS) records. These measurements often require filtering, averaging, and transformation with physical models to estimate drilling performance with suitable accuracy for driller guidance. Improving drilling performance involves a tradeoff between optimizing ROP and avoiding drilling dysfunction. The system currently uses a drilling efficiency term, such as mechanical specific energy (MSE),to incorporate whirl and other energy losses, and it computes an absolute estimate of bit stick-slip with the torsional severity estimate (TSE). This drilling optimization tradeoff requires comparing relative measures of performance, such as ROP and MSE, with an absolute measure of performance as TSE. A drill-off test involving changing the drilling parameters and observing performance is central to create performance trends versus the drilling parameters. A method for guiding the driller to conduct a drill-off test should accommodate driller human factors, capture sufficient data to provide an accurate trend, and complete the drill-off test in a sufficient amount of time to use the test results to optimize the current formation. Data-driven trends should be relevant for the formation the bit is currently drilling. Formation changes may occur with varying magnitude and frequency, and the system addresses aspects of formation change.
This paper discusses these drilling challenges using field and simulation data and provides results from recent North America land operations targeting unconventional oil and gasdevelopments. Field feedback has been incorporated for continuous improvement to algorithms. This deployment demonstrates the benefit of using the rig-site system for real-time drilling set point decisions and post-well drillstring redesign decisions.