Poster Open Access
This poster introduces a novel framework to combine the physics-based and data-driven modeling, aiming to attain the best features of both approaches for well construction. Gaussian processes, neural networks and deep learning models are trained and executed together with a physics model that is directly derived using the first principles. Then the results are combined through a decision-making algorithm, a hidden Markov model. The approach is tested within the scope of wellbore hydraulics on a dataset from an actual drilling operation. The results suggest the proposed approach has a good potential to allow safer, optimized drilling operations.
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SciPy2020_poster.pdf
md5:5bc9c2dc964c594d9cab8368b19cd0ff |
1.0 MB | Download |
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