August 16, 2025

A Deep Learning Framework for Accurate Liquid Rate Prediction & Back Allocation in Oil Production

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The innovative deep learning framework significantly improves liquid rate prediction accuracy in oil production systems.
XGBoost achieved an exceptional mean absolute error (MAE) of 5.7549, among the various machine learning models tested.
Comprehensive data collection and preprocessing involved essential features such as choke size and separator pressure.
Implementing this model in production practices may enable enhanced predictive capabilities and operational flexibility.

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Deep learning enhances accuracy in liquid rate prediction and back allocation in oil production, suggesting practical advancements.

Vahyalajah et al. (2025) studied this question.

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