Taxonomy Describing Levels of Autonomous Drilling Systems: Incorporating Complexity, Uncertainty, Sparse Data, With Human Interaction

Abstract

Abstract Drilling systems automation (DSA) involves multiple actors, each delivering functionality at different levels of automation, with system performance dependent on various input from human operators. Current automation classifications do not fully address the multi-agent nature of drilling operations. Marketing language in industry publications has also outstripped reality by boldly describing automated drilling operations as autonomous, leading to confusion. There is a need to define and include autonomous behavior in the taxonomy of drilling systems automation. A completely autonomous system without direct human interaction may not be a practical goal. Classification into levels of automation for drilling applies to the union of all functions used in a particular operation, and their interaction with humans. Various developed taxonomies showing the transition from manual to highly automated systems use the construct: acquire/observe, assess/orient, decide and act. This paper presents and analyzes taxonomies for their applicability to drilling systems automation, and their use to describe the level of autonomy in this discipline, considering the multi-agent nature and weak observability of drilling operations requiring human consideration. The authors initially collaborated under the SPE DSATS (Drilling Systems Automation Technical Section) to develop a classification applicable to drilling systems automation — and by extension, completions, intervention, and P&A automation — in which autonomous systems are recognized. The classification distinguishes the multi-agent drilling environment in which one agent may be concerned with hole cleaning, another with automated trajectory drilling, and yet another with optimizing rate-of-penetration, all while acting interdependently. Depending on the necessary collaboration between agents, this multi-agent construct can lead to a mixed-initiative autonomous system that is able to handle the complexity and uncertainty of the drilling environment. Drilling, however, also has an observability problem that necessitates a more stratified solution to taxonomy due to missing or lacking data and data attributes. This observability problem exists in both space and time: most measurements are at surface, some from the bottomhole assembly; the low bandwidth of traditional measurement-while-drilling telemetry methods delivers sparse measurements. This paper recommends a taxonomy for drilling systems automation from an enterprise to an execution level that considers the observability problem, complexity, and uncertainty, delivering the necessary capability to accurately classify and address autonomy within drilling systems automation. This taxonomy will greatly reduce the chance of miscommunication regarding drilling system automation capabilities. The complexity, uncertainty, and sparse observability inherent in drilling suggests that the levels of automation taxonomies adopted in other industries (aviation, automotive, etc.) may not appear directly applicable to drilling systems automation. However, the introduction of three levels of autonomous systems leaves the application of a drilling systems automation levels of taxonomy as an underlying model. A clearly communicated safe introduction of automated and autonomous drilling systems will directly benefit from this industry-specific taxonomy that recognizes the degree of needed human interaction at all levels across all interconnected systems.