2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 21 , Issue 2 , PP: 42-55, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Murodjon Sultanov 1 * , Botirjon Karimov 2 , Olimjon Uralov 3 , Nodir Akbarov 4
Doi: https://doi.org/10.54216/FPA.210203
Managing fuel and energy resources (FER) efficiently is still a major challenge for energy-intensive industries like oil and gas. This paper presents a practical framework that combines mathematical models with easy-to-run algorithms to plan and control FER use in real time. Our twin goals are to cut costs and keep equipment dependable. We first outline the main parts of an energy-management system for an oil-and-gas operation, and then list the key tasks, factors, and decision criteria. The framework has two complementary paths: Path 1 relates FER use to production output via Lagrange optimization, while Path 2 fine-tunes forecasts with a simple least-squares correction based on metered data. Both paths are implemented as executable algorithms and tested on real electricity and fuel-gas datasets. The new method cuts monthly FER-planning errors by up to 80 %, reducing penalties and helping equipment last longer.
Energy‑management system , Fuel and energy resources , Optimal planning , Operational control , Mathematical modelling , Algorithm design , Oil‑and‑gas industry
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