XGSleeve: detecting sleeve incidents in well completion by using XGBoost classifier.

ABSTRACT: The sliding sleeve holds a pivotal role in regulating fluid flow during hydraulic fracturing within shale oil extraction processes. However, concerns persist surrounding its reliability due to repeated attempts at opening the sleeve, resulting in process inefficiencies. While downhole cameras can verify sleeve states, their high cost poses a limitation. This study proposes an alternative approach, leveraging downhole data analysis for sleeve incident detection in lieu of cameras. This study introduces "XGSleeve," a novel machine-learning methodology. XGSleeve amalgamates hidden Markov model-based clustering with the XGBoost model, offering robust identification of sleeve incidents. This method serves as an operator-centric tool, addressing the domains of oil and gas, well completion

SUBMITTER: Somi S 

PROVIDER: S-EPMC10533988 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature