PHYSICS-DRIVEN FEATURE CREATION TO IMPROVE MACHINE LEARNING MODELS PERFORMANCE FOR OIL PRODUCTION RATE PREDICTION

PHYSICS-DRIVEN FEATURE CREATION TO IMPROVE MACHINE LEARNING MODELS PERFORMANCE FOR OIL PRODUCTION RATE PREDICTION

This paper aims to develop a machine learning-based model for oil production rate prediction. The significance of feature dimension reduction is addressed by applying well-established approaches like Principal Component Analysis (PCA) and the proposed physics-driven feature creation technique. The p...

Full description

Saved in:
Bibliographic Details
Main Authors: Eghbal Motaei, Seyed Mehdi Tabatabai, Tarek Ganat, Ahmad Khanifar, Sulaiman Dzaiy, Timur Chis
Format: Article
Language:English
Published: Petroleum-Gas University of Ploiesti 2024-12-01
Series:Romanian Journal of Petroleum & Gas Technology
Subjects:
oil rate prediction
feature engineering
principal component analysis
artificial intelligence
machine learning
Online Access:http://jpgt.upg-ploiesti.ro/wp-content/uploads/2024/12/22_RJPGT_no.2-2024-Physics-driven-feature-ML-models-performance-oil-production-prediction.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

http://jpgt.upg-ploiesti.ro/wp-content/uploads/2024/12/22_RJPGT_no.2-2024-Physics-driven-feature-ML-models-performance-oil-production-prediction.pdf

Similar Items