Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
Summary In the actual construction process, well path control is a challenging task mainly due to the inevitable well deflection caused by geological factors, drilling tools as well as borehole enlargement. Most conventional well path control methods focus on elaborate mechanism model construction. The methods are typically constructed on the basis of certain constraints or assumptions, which reflect their limited ability to accurately capture the actual drilling process, low level of intelligence, poor anti-interference performance, and weak adaptive capacity. To address these challenges, this paper proposes a target-aware well path control method that integrates reinforcement learning and transfer learning. The proposed method employs a deep deterministic policy gradient model based on the prioritized experience replay mechanism and leverages transfer learning to accelerate model learning. This enables the construction of a target-aware well path adaptive control system with strong anti-interference capability. The proposed target-aware control method of well path based on reinforcement learning and transfer learning can accurately track the preset trajectory in diverse geological environments, reach the target area with high precision, and make reasonable trajectory optimization decisions with measurement while drilling (MWD) even when the target trajectory does not match the actual distribution of the reservoir. This approach exhibits excellent anti-interference and adaptive abilities.
Summary In the actual construction process, well path control is a challenging task mainly due to the inevitable well deflection caused by geological factors, drilling tools as well as borehole enlargement. Most conventional well path control methods focus on elaborate mechanism model construction. The methods are typically constructed on the basis of certain constraints or assumptions, which reflect their limited ability to accurately capture the actual drilling process, low level of intelligence, poor anti-interference performance, and weak adaptive capacity. To address these challenges, this paper proposes a target-aware well path control method that integrates reinforcement learning and transfer learning. The proposed method employs a deep deterministic policy gradient model based on the prioritized experience replay mechanism and leverages transfer learning to accelerate model learning. This enables the construction of a target-aware well path adaptive control system with strong anti-interference capability. The proposed target-aware control method of well path based on reinforcement learning and transfer learning can accurately track the preset trajectory in diverse geological environments, reach the target area with high precision, and make reasonable trajectory optimization decisions with measurement while drilling (MWD) even when the target trajectory does not match the actual distribution of the reservoir. This approach exhibits excellent anti-interference and adaptive abilities.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.