A Machine Learning-Based Optimisation Framework for Estimating Gas Injection and Enhancing Oil Recovery in Petroleum Reservoirs
DOI:
https://doi.org/10.26437/k3fncq31Keywords:
Deep learning. energy efficiency. gas injection. oil reservoirs. optimisationAbstract
Purpose: This study presents an integrated data-driven framework for optimising gas injection strategies in enhanced oil recovery processes.
Design/Methodology/Approach: The proposed framework uses Long Short-Term Memory (LSTM) to model nonlinear temporal dependencies. The key operational and reservoir variables, such as gas injection rate (GIR), bottom-hole pressure, separator pressure, reservoir temperature, tubing inner diameter, gas-oil ratio (GOR), and gas composition, are considered. In the proposed method, after preprocessing, feature selection is done using the Sequential Forward Selection (SFS) method. Then, the Whale Optimisation Algorithm (WOA) was employed to optimise injection strategies and tune LSTM hyperparameters. The objective is to maximise the net present value (NPV) subject to operational constraints.
Research Limitation: Uncertainties and changing reservoir conditions may limit the generalisability of the proposed framework without further real-time adaptation.
Findings: The results demonstrate that the hybrid WOA–LSTM framework outperforms LSTM and GRU models in both prediction accuracy and economic evaluation. In the multivariate scenario, the model's RMSE is 2.22, MAE is 1.09, accuracy is 97.20%, and NPV is $27.42 million. The results confirm the effectiveness of integrating metaheuristic optimisation and deep learning to enhance production forecasting and decision-making.
Practical Implication: It can enable oil field operators to improve the production efficiency and maximise economic returns while respecting the operational constraints.
Social Implication: Optimising gas injection resources reduces waste and enhances energy efficiency. In air pollution, the proposed model reduces hydrocarbon production and improves air quality.
Originality/Value: This study introduces a novel hybrid framework that combines LSTM-based forecasting with WOA for prediction. The model offers a powerful solution for complex reservoir management problems.
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