Predicting Bubble Point Pressure and Oil Formation Volume Factor by Explainable and Reproducible Support Vector Regression-Based Models from Multi-Regional PVT Data

Anietie Okon; Moses Ekpenyong; Kilaliba Tugwell; Philip Asuquo

doi:10.54287/gujsa.1977786

Predicting Bubble Point Pressure and Oil Formation Volume Factor by Explainable and Reproducible Support Vector Regression-Based Models from Multi-Regional PVT Data

Abstract

Accurate estimation of reservoir pressure-volume-temperature (PVT) properties underpins reserves evaluation, reservoir simulation, production forecasting, and surface facility design. Bubble point pressure (P_bp) and oil formation volume factor (B_ob) are especially critical because they control hydrocarbon phase behaviour, fluid expansion, and depletion performance. Laboratory PVT analysis is the most reliable but often costly, slow, and unavailable. Empirical correlations are more practical; however, they suffer from regional bias, limited calibration ranges, and poor representation of nonlinear fluid behaviour. This study develops explainable, reproducible support vector regression (SVR) models to predict P_bp and B_ob using four common descriptors: solution gas-oil ratio (Rs), gas specific gravity (γ_g), API oil gravity (γ_API), and reservoir temperature (T_R). From 953 global PVT samples, 797 were preprocessed, normalised, and split using hold-out and k-fold cross-validation. Linear-kernel SVR delivered high accuracy: for P_bp, (R² = 0.9826, R = 0.9913, RMSE = 0.0586, MSE = 0.0034); for B_ob, (R² = 0.9494, R = 0.9744, RMSE = 0.0882, MSE = 0.0078). To overcome the “black-box” limitation, the SVR models are fully specified using their support vectors, dual coefficients, and bias terms, enabling independent reproduction and implementation. Perturbation-based explainability shows that Rs is the dominant predictor for both properties, accounting for 71.87% of P_bp and 76.76% of B_ob, consistent with thermodynamic expectations. Compared with widely used empirical correlations, the new models perform better on a multi-regional dataset. Thus, the explainable SVR models offer a transparent, accurate, and practical tool for reservoir PVT estimation, combining predictive performance with engineering interpretability.

Keywords

Supporting Institution

The funding for this research was provided by the Tertiary Education Trust Fund (TETFund), Nigeria, under 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 acknowledge the support of the 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

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

Moses Ekpenyong
0000-0001-6774-5259
Nigeria

Kilaliba Tugwell
0009-0005-4698-0723
Nigeria

Philip Asuquo
0000-0001-8084-3427
Nigeria

Early Pub Date

September 20, 2026

Publication Date

September 30, 2026

Submission Date

June 24, 2026

Acceptance Date

July 3, 2026

Published in Issue

Year 2026 Volume: 13 Number: 3

DOI

https://doi.org/10.54287/gujsa.1977786

IZ

https://izlik.org/JA27YB65KP

Cite

RIS / Bibtex

APA

Okon, A., Ekpenyong, M., Tugwell, K., & Asuquo, P. (2026). Predicting Bubble Point Pressure and Oil Formation Volume Factor by Explainable and Reproducible Support Vector Regression-Based Models from Multi-Regional PVT Data. Gazi University Journal of Science Part A: Engineering and Innovation, 13(3), 1002-1038. https://doi.org/10.54287/gujsa.1977786

AMA

1.Okon A, Ekpenyong M, Tugwell K, Asuquo P. Predicting Bubble Point Pressure and Oil Formation Volume Factor by Explainable and Reproducible Support Vector Regression-Based Models from Multi-Regional PVT Data. GU J Sci, Part A. 2026;13(3):1002-1038. doi:10.54287/gujsa.1977786

Chicago

Okon, Anietie, Moses Ekpenyong, Kilaliba Tugwell, and Philip Asuquo. 2026. “Predicting Bubble Point Pressure and Oil Formation Volume Factor by Explainable and Reproducible Support Vector Regression-Based Models from Multi-Regional PVT Data”. Gazi University Journal of Science Part A: Engineering and Innovation 13 (3): 1002-38. https://doi.org/10.54287/gujsa.1977786.

EndNote

Okon A, Ekpenyong M, Tugwell K, Asuquo P (September 1, 2026) Predicting Bubble Point Pressure and Oil Formation Volume Factor by Explainable and Reproducible Support Vector Regression-Based Models from Multi-Regional PVT Data. Gazi University Journal of Science Part A: Engineering and Innovation 13 3 1002–1038.

IEEE

[1]A. Okon, M. Ekpenyong, K. Tugwell, and P. Asuquo, “Predicting Bubble Point Pressure and Oil Formation Volume Factor by Explainable and Reproducible Support Vector Regression-Based Models from Multi-Regional PVT Data”, GU J Sci, Part A, vol. 13, no. 3, pp. 1002–1038, Sept. 2026, doi: 10.54287/gujsa.1977786.

ISNAD

Okon, Anietie - Ekpenyong, Moses - Tugwell, Kilaliba - Asuquo, Philip. “Predicting Bubble Point Pressure and Oil Formation Volume Factor by Explainable and Reproducible Support Vector Regression-Based Models from Multi-Regional PVT Data”. Gazi University Journal of Science Part A: Engineering and Innovation 13/3 (September 1, 2026): 1002-1038. https://doi.org/10.54287/gujsa.1977786.

JAMA

1.Okon A, Ekpenyong M, Tugwell K, Asuquo P. Predicting Bubble Point Pressure and Oil Formation Volume Factor by Explainable and Reproducible Support Vector Regression-Based Models from Multi-Regional PVT Data. GU J Sci, Part A. 2026;13:1002–1038.

MLA

Okon, Anietie, et al. “Predicting Bubble Point Pressure and Oil Formation Volume Factor by Explainable and Reproducible Support Vector Regression-Based Models from Multi-Regional PVT Data”. Gazi University Journal of Science Part A: Engineering and Innovation, vol. 13, no. 3, Sept. 2026, pp. 1002-38, doi:10.54287/gujsa.1977786.

Vancouver

1.Anietie Okon, Moses Ekpenyong, Kilaliba Tugwell, Philip Asuquo. Predicting Bubble Point Pressure and Oil Formation Volume Factor by Explainable and Reproducible Support Vector Regression-Based Models from Multi-Regional PVT Data. GU J Sci, Part A. 2026 Sep. 1;13(3):1002-38. doi:10.54287/gujsa.1977786