Abstract
Offshore large-scale cluster extended reach wells (ERWs) are widely used to develop offshore oil & gas resources. Due to the complex downhole environments and complicated geological conditions, drilling parameters real-time optimization is challenging in offshore large-scale cluster ERWs drilling. In this paper, a real-time drilling parameters optimization method for offshore large-scale cluster extended reach drilling based on an intelligent optimization algorithm and machine learning (IOA-ML) is proposed. The method takes ROP as the objective function, establishes a ROP model based on long short-term memory (LSTM) neurons, and obtains drilling parameters optimization results asynchronously by combining the genetic algorithm, differential evolution algorithm, and particle swarm algorithm. The results show that ROP has been improved by 33.33% on average after the optimization by this real-time intelligent optimization method with an optimization time within 60s, which meets the requirements of real-time optimization of drilling parameters.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
