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Scientific, Technological and Production Journal
ISSN 2079–3332 (print)
ISSN 3034-6835 (online)
founded in 2001

Neural network optimization of mixed solid fuel compositions to improve the economic efficiency of thermohydrodynamic impact on oil reservoirs

Original articles: 2025 No 4

Neural network optimization of mixed solid fuel compositions to improve the economic efficiency of thermohydrodynamic impact on oil reservoirs

DOI: 10.56195/20793332-2025-25-4-53-58

Read in pdf M. G. Efimov1, A. R. Mukhutdinov2
  1. Kazan Innovation University named after V. G. Timiryasov, Kazan, Republic of Tatarstan, Russian Federation
  2. Kazan National Research Technological University, Kazan, Republic of Tatarstan, Russian Federation
p. 53-58, No.4/2025
Abstract:
An urgent task for the oil and gas industry is improving the efficiency of hydrocarbon recovery from difficult-torecover reserves (DTR). The study aimed to investigate the influence of component composition and initial conditions on operational characteristics (combustion rate, volume of gaseous products) of composite solid fuels (MSF) with an ammonium nitrate composition (ANC) and to develop recommendations for their optimization for use in downhole gas generators. A neural network software module validated on experimental data was employed. It was demonstrated that pressure and charge density are key factors governing the combustion process. Compositions of MSF ANC adapted to typical conditions in Western Siberia, the Volga-Ural region, and the Arctic were proposed. The results align with the Russian Federation’s Mineral Resource Complex Development Strategy (MRC) until 2035 and ISO 14001 requirements.
Keywords:
composite solid fuel, ammonium nitrate composition, neural network modeling, combustion rate, ecological safety, surveyor monitoring, MRC Strategy, economic efficiency
For citation:
Efimov MG, Mukhutdinov AR. Neural network optimization of mixed solid fuel compositions to improve the economic efficiency of thermo-hydrodynamic impact on oil reservoirs. Mine Surveying and Subsurface Use. 2025;25(4):53-58. (In Russ.). https://doi.org/10.56195/20793332-2025-25-4-53-58.
Information about the authors:
  1. Maksim G. Efimov – Senior Lecturer, Department of Computer Modeling and Technosphere Safety, Private Educational Institution of Higher Education “Kazan Innovative University named after V.G. Timiryasov”, 420111, Republic of Tatarstan, Kazan, Russian Federation, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
  2. Aglyam R. Mukhutdinov – Dr. Sci. (Eng.), Professor of the Department of Technology of Solid Chemicals, Federal State Budgetary Educational Institution of Higher Education “Kazan National Research Technological University”, 420015, Republic of Tatarstan, Kazan, Russian Federation, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
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