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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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.3906912", 
  "language": "eng", 
  "title": "Combining Physics-Based and Data-Driven Modeling for Pressure Prediction in Well Construction", 
  "issued": {
    "date-parts": [
      [
        2020, 
        6, 
        24
      ]
    ]
  }, 
  "abstract": "<p>This poster introduces&nbsp;a novel framework to combine&nbsp;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.</p>", 
  "author": [
    {
      "family": "Oney Erge"
    }, 
    {
      "family": "Eric van Oort"
    }
  ], 
  "type": "graphic", 
  "id": "3906912"
}
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