_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 227955, “First Global Deepwater Autonomous Drilling Project: Artificial Intelligence Application To Derive Optimal Drilling Parameters Within integration of Rig-Orchestration System, ” by Salvatore Spagnolo, SPE, Valerio Bruni, and Carmelo Camillieri, Eni, et al. The paper has not been peer‑reviewed. _ The complete paper presents the first application globally for the oil and gas industry of autonomous drilling in deep water and highlights the journey to reach optimal drilling parameters while integrating proprietary tools from project partners. The described autonomous solution facilitates seamless integration and interface with the rig-control system (RCS) and downhole tools, achieving peak automation on a drillship and fostering a forward-looking intelligent execution of standard operating procedures. Introduction In this drilling campaign, an artificial intelligence (AI) -driven autonomous system was deployed on a drillship designed to operate at water depths up to 12, 000 ft. This autonomous drilling was integrated with two other automation systems deployed onboard. Transfer protocols between the systems were performed seamlessly, enabling closed-loop coordination and optimized workflow execution. With the integrated automation and autonomous system deployed, full Degree 5 autonomy was achieved on the drillship. Methodology System A: Rig Automation. The multimachine control (MMC) system provides centralized, automated control of multiple critical drilling machines on the drilling floor. MMC enables a single operator to manage hoisting, rotating, and pipe-handling systems through a unified interface, typically with a single joystick. System B: Advanced Control System (ACS). NOVOS is a drilling system specifically engineered to relieve drillers from manual, repetitive tasks. The application of an advanced ACS solution plays a pivotal role in optimization because of the possibility to execute drilling instructions per predefined parameters or, as in the study case, with input from an orchestration system. In addition, the ACS has the capability to handle transitions between drilling tasks. System C: Orchestrator Solution. DrillOps is an autonomous drilling-performance system designed for on-bottom operations. It leverages a physics-based model of bit behavior with AI models and advanced pattern-recognition techniques to adapt in real time to changes in information and drilling response. By monitoring real-time data, the system continuously recalibrates itself, identifies optimal drilling parameters, and adjusts them automatically within the safety limits defined in the well plan. System C also proactively addresses drilling dysfunctions early and applies operator-defined mitigation strategies. Once stability is restored, the system re-optimizes to resume efficient drilling. Well-Planning Phase: Envelope Definition With Digital Well‑Planning Solution To fully leverage the expected potential of autonomous System C, a wider operating envelope was given to the system. In principle, the concept was to push the entire drilling system to an engineered limit, and not to evaluate each well-design element independently. The process consisted of the identification of technical specifications of various equipment, the capture of operational and technical constraints and limiting factors, and the application of multidimensional automated engineering analysis to validate planning. To implement this new workflow for analysis, strong collaboration and objective alignment across departments, service providers, and rig providers are required. Repetitive sensitivity analysis was necessary. To enhance workflow efficiency, all engineering inputs were required to be in a single platform to achieve more-accurate analysis.
Chris Carpenter (Fri,) studied this question.