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Abstract Advanced AI planner and connected intelligent systems are used to obtain a continuous feedback workflow between surface and downhole. This significantly increases the consistency and efficiency of well construction operations while minimizing human involvement and footprint, advancing efforts towards autonomous operations, and lowering the operational carbon footprints by an order of magnitude. To send steering instructions to a rotary steerable system (RSS), one often uses modulations of flow or rotation per minute (RPM) called downlinks. The proposed workflow presented here includes an automated downlink detection and an AI planner to determine the optimum downlinking sequence for executing directional drilling decisions. The automated downlink detection is based on an intelligent system that will continuously monitor pressure and RPM waves at the surface and raise an automation event when it detects downlink patterns that match predefined patterns with a certain confidence level. Once the current steering mode is clearly identified and the geometric trajectory to follow has been generated, the next step is to define the optimum downlinking sequence to follow that trajectory path. This is done by the AI planner, which carefully analyzes the current state, the available downlinking map, and the desired state to derive the most favorable path to the desired state. The autonomous workflow has been tested in the field in many different countries across the world. The automated downlink detection located at the surface has an average detection rate of more than 98% with more than 10,000 downlinks to be detected every month across numerous field locations. On rare occasions, where the surface downlink detection is not picking up the downlink signal, the downhole downlink detection will send downlink confirmation from downhole to surface via the measurement-while-drilling telemetry system. This makes the overall system exceptionally robust. In addition, the intelligent downlink sequence optimization will often find the shortest path from the current tool state to the desired state, thus increasing the efficiency of the drilling operations. With the native cloud monitoring system, domain experts can access the system at any time from wherever they can log into the secure robust network. The synergy between surface systems and downhole systems has enabled the development of a unique autonomous well construction system that improves performance while reducing the carbon footprint, allowing domain experts from all over the world to remotely monitor their operations without any traveling needed.
Liu et al. (Tue,) studied this question.