Аннотации:
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 Kazakhstan's 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 realworld 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.
