Data-Driven Modelling of Oil Flow Rates in Vertical Wells: Performance Comparisons of Neural Network and Multigene Genetic Programming Approaches

Kawu Yakubu; Anietie Okon; Okorie Agwu Ekwe

doi:10.54287/gujsa.1960311

Data-Driven Modelling of Oil Flow Rates in Vertical Wells: Performance Comparisons of Neural Network and Multigene Genetic Programming Approaches

Abstract

Accurate prediction of oil production rates remains challenging, especially in fields where direct measurement is not feasible. To address this issue, data-driven models were developed using neural networks (NN) and multigene genetic programming (MGGP) to provide real-time estimations of oil flow rates in vertical wells using routinely measured field parameters. The models were trained and tested on 70% and 30% of a dataset comprising 1,893 entries, respectively. The input variables for the models include oil API gravity, downstream temperature, upstream temperature, upstream pressure, and choke size. These developed models are simplified, reproducible, and suitable for practical application. Model performance was quantitatively evaluated using the correlation coefficient (R), the coefficient of determination (R2), the mean squared error (MSE), and the root mean square error (RMSE). The NN model demonstrated superior predictive capability, achieving R = 0.990, R2 = 0.980, MSE = 0.0001, and RMSE = 0.011, while the MGGP model achieved R = 0.980, R2 = 0.960, MSE = 0.0004, and RMSE = 0.020. Validation using an independent dataset further confirmed the robustness of the NN model, which outperformed the MGGP model across all evaluation metrics. Additional contributions of this study include sensitivity analysis of input variables, derivation of explicit predictive correlations, and evaluation of computational efficiency. Both models exhibited low computational cost, supporting real-time and field-scale applicability. Thus, the NN model outperforms the MGGP model in estimating oil field flow rates. Therefore, production engineers are recommended to utilise an NN-based model as a decision-support tool for estimating oil flow in vertically flowing wells.

Keywords

Supporting Institution

University of Uyo, Akwa Ibom State, Nigeria

Project Number

1

Ethical Statement

On behalf of all the co-authors, the corresponding author states that there are no ethical statements contained in the manuscript.

Thanks

The authors gratefully acknowledged the Department of Petroleum Engineering, University of Uyo, for providing a favorable setting for conducting this research

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Details

Primary Language

English

Subjects

Petroleum and Natural Gas

Journal Section

Research Article

Authors

Kawu Yakubu ^*
0000-0002-8013-8194
Nigeria

Anietie Okon
0000-0003-1744-3310
Nigeria

Okorie Agwu Ekwe
0000-0003-3855-8352
Malaysia

Early Pub Date

July 25, 2026

Publication Date

-

Submission Date

May 30, 2026

Acceptance Date

June 19, 2026

Published in Issue

Year 2026 Number: Advanced Online Publication

DOI

https://doi.org/10.54287/gujsa.1960311

IZ

https://izlik.org/JA88KW58NJ

Cite

RIS / Bibtex

APA

Yakubu, K., Okon, A., & Agwu Ekwe, O. (2026). Data-Driven Modelling of Oil Flow Rates in Vertical Wells: Performance Comparisons of Neural Network and Multigene Genetic Programming Approaches. Gazi University Journal of Science Part A: Engineering and Innovation, Advanced Online Publication, 966-1001. https://doi.org/10.54287/gujsa.1960311

AMA

1.Yakubu K, Okon A, Agwu Ekwe O. Data-Driven Modelling of Oil Flow Rates in Vertical Wells: Performance Comparisons of Neural Network and Multigene Genetic Programming Approaches. GU J Sci, Part A. 2026;(Advanced Online Publication):966-1001. doi:10.54287/gujsa.1960311

Chicago

Yakubu, Kawu, Anietie Okon, and Okorie Agwu Ekwe. 2026. “Data-Driven Modelling of Oil Flow Rates in Vertical Wells: Performance Comparisons of Neural Network and Multigene Genetic Programming Approaches”. Gazi University Journal of Science Part A: Engineering and Innovation, no. Advanced Online Publication: 966-1001. https://doi.org/10.54287/gujsa.1960311.

EndNote

Yakubu K, Okon A, Agwu Ekwe O (July 1, 2026) Data-Driven Modelling of Oil Flow Rates in Vertical Wells: Performance Comparisons of Neural Network and Multigene Genetic Programming Approaches. Gazi University Journal of Science Part A: Engineering and Innovation Advanced Online Publication 966–1001.

IEEE

[1]K. Yakubu, A. Okon, and O. Agwu Ekwe, “Data-Driven Modelling of Oil Flow Rates in Vertical Wells: Performance Comparisons of Neural Network and Multigene Genetic Programming Approaches”, GU J Sci, Part A, no. Advanced Online Publication, pp. 966–1001, July 2026, doi: 10.54287/gujsa.1960311.

ISNAD

Yakubu, Kawu - Okon, Anietie - Agwu Ekwe, Okorie. “Data-Driven Modelling of Oil Flow Rates in Vertical Wells: Performance Comparisons of Neural Network and Multigene Genetic Programming Approaches”. Gazi University Journal of Science Part A: Engineering and Innovation. Advanced Online Publication (July 1, 2026): 966-1001. https://doi.org/10.54287/gujsa.1960311.

JAMA

1.Yakubu K, Okon A, Agwu Ekwe O. Data-Driven Modelling of Oil Flow Rates in Vertical Wells: Performance Comparisons of Neural Network and Multigene Genetic Programming Approaches. GU J Sci, Part A. 2026;:966–1001.

MLA

Yakubu, Kawu, et al. “Data-Driven Modelling of Oil Flow Rates in Vertical Wells: Performance Comparisons of Neural Network and Multigene Genetic Programming Approaches”. Gazi University Journal of Science Part A: Engineering and Innovation, no. Advanced Online Publication, July 2026, pp. 966-1001, doi:10.54287/gujsa.1960311.

Vancouver

1.Kawu Yakubu, Anietie Okon, Okorie Agwu Ekwe. Data-Driven Modelling of Oil Flow Rates in Vertical Wells: Performance Comparisons of Neural Network and Multigene Genetic Programming Approaches. GU J Sci, Part A. 2026 Jul. 1;(Advanced Online Publication):966-1001. doi:10.54287/gujsa.1960311