Predicting the Surveillance Data in a Low-Permeability Carbonate Reservoir with the Machine-Learning Tree Boosting Method and the Time-Segmented Feature Extraction

Predicting the Surveillance Data in a Low-Permeability Carbonate Reservoir with the Machine-Learning Tree Boosting Method and the Time-Segmented Feature Extraction

Predictive analysis of the reservoir surveillance data is crucial for the high-efficiency management of oil and gas reservoirs. Here we introduce a new approach to reservoir surveillance that uses the machine learning tree boosting method to forecast production data. In this method, the prediction t...

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Bibliographic Details
Main Authors: Cong Wang, Lisha Zhao, Shuhong Wu, Xinmin Song
Format: Article
Language:English
Published: MDPI AG 2020-11-01
Series:Energies
Subjects:
oil and gas reservoir development and management
automatic surveillance
machine learning
Online Access:https://www.mdpi.com/1996-1073/13/23/6307

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