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Assistant professor Shadfar Davoodi developed an AI framework that optimizes drilling in heterogeneous carbonate reservoirs. The model predicted a potential 68.4% increase in drilling speed for one well, alongside a 41.2% torque reduction. Published in Results in Engineering, the study used 5,738 records to train LSTM, random forest, and neural network models, with LSTM proving most accurate.
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Stuut Raises $52.5M for AI Automation