Practical Hybrid Forecasting Methods for Petroleum Reservoir Management
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2026-03-01
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Abstract
Accurately forecasting petroleum reservoir performance is essential for effective field management but remains challenging due to nonlinear dynamics, abrupt regime shifts, and data limitations. This chapter introduces a practical hybrid forecasting framework tailored for real-world petroleum reservoir operations. By combining statistical methods (e.g., Holt-Winters), specialized exponential growth models, and machine learning algorithms (e.g., Random Forest, XGBoost), the approach leverages the strengths of each method while mitigating their individual limitations. A core feature is an adaptive weighting mechanism that dynamically adjusts model contributions based on recent performance, enabling robust predictions across varying reservoir conditions. Through detailed implementation steps, including feature engineering, parameter tuning, and Python-based examples, this chapter equips practitioners with actionable tools for improving forecast accuracy and operational decision-making. An example of time recovery prediction validates the framework, showing that the hybrid model outperforms standalone methods in both accuracy and adaptability. This work bridges the gap between theoretical advances and field-ready solutions, offering a roadmap for deploying hybrid forecasting in modern reservoir management.
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Advances and Applications of Machine Learning in Fluid Flow Problems