Mathematical Models for Oil Production Optimization in Fuzzy Environments: Well Stock Forecasting and Regulation
Issa I. Orazbayev B. Tuleuova R. Makhatova V.
February 2024International Information and Engineering Technology Association
Mathematical Modelling of Engineering Problems
2024#11Issue 2340 - 348 pp.
The relevance of this study is the importance of investigating mathematical models and systems to optimize oil production in forecasting and regulating well stock in fuzzy environments. The purpose was to assess the practical application of Markov chain models and fuzzy set theory to optimize oil production. This study specifically analyzed operating and idle well stocks in Kazakhstans Kenkiyak oil field using a Markov chain system of equations. Fuzzy set theory was then applied to model linguistic relationships between oil production parameters like depth and porosity. The Markov model successfully predicted linear asymptotes of well stock over time and assessed impacts of changing repair crew productivity. The fuzzy approach effectively modeled the dependence of production efficiency on depth and reservoir rock porosity. Results showed a 15% improvement in forecasting accuracy and a 10% increase in production efficiency. This demonstrates the value of mathematical models in optimizing real-world oil production processes and their ability to enhance management system performance. The models provide oil field designers with tools to better regulate well stock and staff operations.
fuzzy sets theory , Markov chain theory , membership function , oil wells , production technology
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Department of System Analysis and Control, L.N. Gumilyov Eurasian National University, Astana, 010008, Kazakhstan
Department of Mathematics and Methods of Teaching Mathematics, Kh. Dosmukhamedov Atyrau University, Atyrau, 060011, Kazakhstan
Department of Software Engineering, Kh. Dosmukhamedov Atyrau University, Atyrau, 060011, Kazakhstan
Department of System Analysis and Control
Department of Mathematics and Methods of Teaching Mathematics
Department of Software Engineering
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