Hybrid Approach to Managing Oilfield Development Processes under Uncertainty

Abstract Effective management of oil field development processes is a complex challenge due to geological heterogeneity, uncertain petrophysical properties, unstable reservoir pressure, and fluctuating production and injection rates. Economic and technological factors further complicate decision-making. Conventional optimization methods based on deterministic models and standalone artificial intelligence techniques cannot fully account for uncertainties in initial data and expert estimates. This paper proposes a novel hybrid approach that combines artificial intelligence, fuzzy logic, neuro-fuzzy modeling, and evolutionary optimization. The approach integrates fuzzy models for representing linguistic uncertainty, the adaptive ANFIS model for learning from field data, and a genetic algorithm for optimizing field operating modes. A mathematical model for multi-criteria management is developed, considering cumulative oil production, oil recovery factor, produced water volumes, and operational costs. Membership functions and an inference engine are used to formalize expert knowledge and account for uncertainty in technological parameters. Optimization is performed using a genetic algorithm to identify effective solutions under interconnected parameters and nonlinear constraints. The results demonstrate that integrating fuzzy logic, neuro-fuzzy modeling, and evolutionary optimization improves the robustness of the control system to uncertain information. The proposed approach enhances decision-making, improves forecasting of field development indicators, and supports rational control of reservoir operating modes under changing field conditions.