Hybrid Foam EOR Performance Prediction and Optimization Using Machine Learning: Emphasis on Injection Strategy

As conventional oil and gas reserves decline, enhanced
oil recovery
(EOR) becomes vital for improving oil recovery efficiency. Foam flooding
reduces gas mobility and improves sweep efficiency, but its performance
depends heavily on reservoir conditions and injection strategies.
Most existing machine learning (ML) research on foam flooding has
primarily focused on pure CO2 foam; the potential of N2–CO2 hybrid foam has not been sufficiently
investigated. The hybrid foam flooding offers synergistic benefits
by simultaneously reducing oil viscosity and stabilizing the foam,
making it an effective EOR method. This study utilizes a data set
of 420 laboratory data points gathered from literature sources. This
study presents a data-driven ML framework that integrates three algorithms
and two improved optimization methods, the mapping hiking optimization
algorithm (MHOA) and mapping mountain gazelle optimizer (MMGO), to
develop six hybrid predictive models. Among the six models, MHOA-XGBoost
(extreme gradient boosting) delivered the highest test accuracy, with
a coefficient of determination (R2) of
0.9917 and a mean absolute error (MAE) of 0.72. Among all the input
variables, SHAP analysis identified injected volume as the most influential
factor. Compared with the injection of a single foam, the injection
of hybrid foam can recover more oil. Adjusting the injection ratio
of the hybrid foam resulted in greater improvements, with the maximum
recovery (>38%) observed at a ratio of 20:80 (N2/CO2). In summary, by integrating advanced ML techniques with
optimization algorithms, this study has established a reliable and
efficient framework for predicting the performance of hybrid foam
flooding and optimizing its injection strategies.