Abstract
This paper proposes a novel approach that combines physics-based numerical simulation with deep-learning neural networks to create an AI-Physics hybrid model for reservoir simulation. Our primary objective is to reduce the time of history match and achieve the best match between model and observation data using our hybrid model. We trained our model using historical data and created a reliable forecasting model that predicts field behavior and proposes new scenarios to improve production in the upcoming years. To test our model, we combined AI physics history training with blind test prediction calculations of the remaining oil map, and the AI physics model created a forecast scenario definition based on this map. Our proposed simulation method can reduce the time of history matching and scenario evaluation by 90-95%. We created three improved forecast scenarios based on predefined scenarios that can produce millions of standard barrels more oil over three years than the original development scenario. The results demonstrate the potential of our AI-Physics hybrid model in revolutionizing reservoir simulation in the oil and gas industry. Our approach can significantly reduce simulation time, improve forecasting accuracy, and optimize production strategies, leading to significant economic benefits.
Published Version
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