Demonstration of Autonomous Drilling on a Full-Scale Test Rig

During recent years there has been an increased focus on automating drilling operations and several solutions are in daily use. We describe here results and lessons learned from testing on a full-scale test rig, the next step in drilling automation, namely autonomous drilling. By autonomous drilling we mean a system capable of taking its own decisions by evaluating the current conditions and adapting to them while considering multiple horizon strategies to fulfill the drilling operation goal. Autonomous drilling was demonstrated during a series of experiments at a full-scale test rig in Norway. The focus of the experiments was to reach the target depth as quickly and as safely as possible. Since the formation at the test rig is very hard, a previously drilled well was filled with weak cement of variable strengths to allow for fast drilling. As part of the experiments, it was planned to have drilling incidents to test the system capabilities in managing arising issues and recover from them. During the experiments no real-time downhole measurements were available, only surface data. In total 500 meters have been drilled in autonomous mode. The autonomous system is built as a hierarchical control system containing layers of protection for the machines, well and the commands, in addition to recovery procedures, optimization of the rate of penetration and autonomous decision-making. The system continuously evaluates the current situation and by balancing estimated risks and performance, e.g. risk of pack-off versus prognosed time to reach the target depth, decides the best action to perform next. The autonomous decision-making system is tightly connected with the control of the drilling machines and therefore it executes the necessary commands to follow up the computed decision. Drilling incidents may occur at any time and an autonomous system needs to be able to adapt to the current situation, such that it can manage drilling incidents by itself and recover from them, when possible. During the experiments, several drilling incidents occurred, and the system reacted as expected. Surface data, together with internally computed data from the autonomous decision-making algorithms, were logged during the experiments. Memory-based downhole data was available after the experiments were concluded. Based on all the data collected, an analysis of the behavior of the system was performed after the experiments. During the drilling experiments at the full-scale rig, the autonomous system adapted its decisions to the surrounding environment and tackled both smooth drilling situations and drilling incidents. To cope with possible lower situational awareness, the autonomous system manages by itself transitions from autonomous to manual mode if necessary. This feature, together with fault detection and isolation capabilities, are crucial for safe operation of an autonomous system.