Abstract
As automated directional drilling matures, it is increasingly accepted by operators as a technology to improve drilling performance. The maturation of one service provider's slide automation technology was driven largely by development and experience in one US onshore basin. This approach produced a system that was robust and configurable – but configured initially for basin-specific challenges. This paper describes how automated directional drilling practices from different basins are captured to drive performance improvements and collapse the learning curve in each subsequent deployment.
The implementation of this technology beyond the development basin was guided by an understanding of directional drilling best practices and challenges in each new area. Basin-specific factors like wellbore geometry and trajectory, formation tendency, and BHA objectives were assessed and codified to drive adjustments to the automation configuration. Engineering studies of offset wells preceded initial deployments in each new basin to identify necessary adjustments; changes were iteratively implemented and monitored via novel KPIs, such as pre-slide flat time, steering control precision, and performance consistency, alongside other industry-standard metrics.
Results from deployments in multiple US onshore basins areas show that because of variations in drilling practices, basin-specific configurations are a critical part of delivering high-performance automated directional drilling. A key early finding was the importance of identifying similar and dissimilar practices between basins so that adjustments to the system's logic were focused on high-impact activities, while minimizing startup time. Furthermore, as parameters and equipment change within a basin, it is necessary to continuously monitor and revise the slide automation system to accommodate these changes. One case study is presented to show how differences in manual slide performance between basins drive changes to the automated steering system's behavior. Another case study is presented that outlines the relationship between different BHA equipment and the automation configuration.
