Poster Open Access

Combining Physics-Based and Data-Driven Modeling for Pressure Prediction in Well Construction

Oney Erge; Eric van Oort

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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Oney Erge</dc:creator>
  <dc:creator>Eric van Oort</dc:creator>
  <dc:description>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.</dc:description>
  <dc:subject>Deep Learning, Machine Learning, Combining Physics-Based Modeling and Data-Driven Modeling, Hydraulics Modeling, Frictional Pressure Loss Modeling.</dc:subject>
  <dc:title>Combining Physics-Based and Data-Driven Modeling for Pressure Prediction in Well Construction</dc:title>
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