DOI: 10.54287/gujsa.1992215 ISSN: 2147-9542

Predicting Minimum Miscibility Pressures in CO2 and N2 Gas Injection Systems by Explainable and Interpretable Neural Network and Support Vector Regression Models

Ifiok Etim, Anietie Okon, Kilaliba Tugwell, Wilfred Chinedu Okologume, Philip Asuquo
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Accurate minimum miscibility pressure (MMP) estimation is essential for designing and screening miscible gas injection EOR projects, particularly CO2 and N2 flooding. Empirical correlations provide quick estimates but are often inaccurate due to limited calibration ranges, oversimplified compositions, and poor representation of nonlinear gas-oil interactions. This study develops explicit machine-learning-based models: neural networks (NNs) and support vector regression (SVR), to estimate CO2 and N2 MMPs in petroleum reservoirs. A dataset of 586 samples from the literature, including reservoir temperature, compositional descriptors, and injected gas properties, was compiled and preprocessed. NN models used a feed-forward back-propagation architecture with the Levenberg-Marquardt algorithm, while SVR used a linear kernel for its explicit representation. The models’ performance was evaluated using statistical indices. The NN models achieved AARD, MSE, and R2 values of 0.1360, 3.260×10-3 and 0.9768, respectively, for CO2, and 0.1395, 3.260×10-3, 0.9752 for N2. The SVR models achieved AARD = 0.1326, MSE = 0.0444, and R2 = 0.9522 for CO2 and AARD = 0.1407, MSE = 0.0422, and R2 = 0.9835 for N2. These statistical indices indicate that the developed models’ predictions were consistent with the measured MMP dataset. The trained models were expressed explicitly in mathematical form using weights, biases, support vectors, and dual coefficients, enabling direct application in engineering calculations and software. Sensitivity analysis identified reservoir-injected gas temperature ratio (TR) and heavy-end molecular weight (MWC5+) as the most influential variables, with H2S showing relatively low importance. Compared with many existing ML models, the developed models offer competitive accuracy, improved reproducibility, and easier deployment, providing a practical and efficient tool for rapid MMP estimation in compositional screening and miscible EOR design.

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