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

doi:10.54287/gujsa.1992215

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

Abstract

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 (T_R) and heavy-end molecular weight (MW_C5+) 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.

Keywords

Supporting Institution

The Tertiary Education Trust Fund (TETFund) Centre of Excellence in Computational Intelligence at the University of Uyo and the Institution-Based Grant

Project Number

TETF/DR&D/CE/UNIUYO/IBR/2025/VOL.1.

Ethical Statement

The authors declare no conflict of interest.

Thanks

The authors appreciate the support of the Tertiary Education Trust Fund (TETFund) Centre of Excellence in Computational Intelligence at the University of Uyo.

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Details

Primary Language

English

Subjects

Reservoir Engineering

Journal Section

Research Article

Authors

Ifiok Etim
0009-0009-5552-3558
Nigeria

Anietie Okon ^*
0000-0003-1744-3310
Nigeria

Kilaliba Tugwell
0009-0005-4698-0723
Nigeria

Wilfred Chinedu Okologume
0000-0002-6317-8936
Nigeria

Philip Asuquo
0000-0001-8084-3427
Nigeria

Early Pub Date

September 30, 2026

Publication Date

September 30, 2026

Submission Date

July 11, 2026

Acceptance Date

July 20, 2026

Published in Issue

Year 2026 Volume: 13 Number: 3

DOI

https://doi.org/10.54287/gujsa.1992215

IZ

https://izlik.org/JA36TC57DD

Cite

RIS / Bibtex

APA

Etim, I., Okon, A., Tugwell, K., Okologume, W. C., & Asuquo, P. (2026). Predicting Minimum Miscibility Pressures in CO2 and N2 Gas Injection Systems by Explainable and Interpretable Neural Network and Support Vector Regression Models. Gazi University Journal of Science Part A: Engineering and Innovation, 13(3), 1373-1421. https://doi.org/10.54287/gujsa.1992215

AMA

1.Etim I, Okon A, Tugwell K, Okologume WC, Asuquo P. Predicting Minimum Miscibility Pressures in CO2 and N2 Gas Injection Systems by Explainable and Interpretable Neural Network and Support Vector Regression Models. GU J Sci, Part A. 2026;13(3):1373-1421. doi:10.54287/gujsa.1992215

Chicago

Etim, Ifiok, Anietie Okon, Kilaliba Tugwell, Wilfred Chinedu Okologume, and Philip Asuquo. 2026. “Predicting Minimum Miscibility Pressures in CO2 and N2 Gas Injection Systems by Explainable and Interpretable Neural Network and Support Vector Regression Models”. Gazi University Journal of Science Part A: Engineering and Innovation 13 (3): 1373-1421. https://doi.org/10.54287/gujsa.1992215.

EndNote

Etim I, Okon A, Tugwell K, Okologume WC, Asuquo P (September 1, 2026) Predicting Minimum Miscibility Pressures in CO2 and N2 Gas Injection Systems by Explainable and Interpretable Neural Network and Support Vector Regression Models. Gazi University Journal of Science Part A: Engineering and Innovation 13 3 1373–1421.

IEEE

[1]I. Etim, A. Okon, K. Tugwell, W. C. Okologume, and P. Asuquo, “Predicting Minimum Miscibility Pressures in CO2 and N2 Gas Injection Systems by Explainable and Interpretable Neural Network and Support Vector Regression Models”, GU J Sci, Part A, vol. 13, no. 3, pp. 1373–1421, Sept. 2026, doi: 10.54287/gujsa.1992215.

ISNAD

Etim, Ifiok - Okon, Anietie - Tugwell, Kilaliba - Okologume, Wilfred Chinedu - Asuquo, Philip. “Predicting Minimum Miscibility Pressures in CO2 and N2 Gas Injection Systems by Explainable and Interpretable Neural Network and Support Vector Regression Models”. Gazi University Journal of Science Part A: Engineering and Innovation 13/3 (September 1, 2026): 1373-1421. https://doi.org/10.54287/gujsa.1992215.

JAMA

1.Etim I, Okon A, Tugwell K, Okologume WC, Asuquo P. Predicting Minimum Miscibility Pressures in CO2 and N2 Gas Injection Systems by Explainable and Interpretable Neural Network and Support Vector Regression Models. GU J Sci, Part A. 2026;13:1373–1421.

MLA

Etim, Ifiok, et al. “Predicting Minimum Miscibility Pressures in CO2 and N2 Gas Injection Systems by Explainable and Interpretable Neural Network and Support Vector Regression Models”. Gazi University Journal of Science Part A: Engineering and Innovation, vol. 13, no. 3, Sept. 2026, pp. 1373-21, doi:10.54287/gujsa.1992215.

Vancouver

1.Ifiok Etim, Anietie Okon, Kilaliba Tugwell, Wilfred Chinedu Okologume, Philip Asuquo. Predicting Minimum Miscibility Pressures in CO2 and N2 Gas Injection Systems by Explainable and Interpretable Neural Network and Support Vector Regression Models. GU J Sci, Part A. 2026 Sep. 1;13(3):1373-421. doi:10.54287/gujsa.1992215