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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{
  "inLanguage": {
    "alternateName": "eng", 
    "@type": "Language", 
    "name": "English"
  }, 
  "description": "<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>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "The University of Texas", 
      "@id": "https://orcid.org/0000-0002-2687-9135", 
      "@type": "Person", 
      "name": "Oney Erge"
    }, 
    {
      "affiliation": "The University of Texas", 
      "@type": "Person", 
      "name": "Eric van Oort"
    }
  ], 
  "url": "https://zenodo.org/record/3906912", 
  "datePublished": "2020-06-24", 
  "keywords": [
    "Deep Learning, Machine Learning, Combining Physics-Based Modeling and Data-Driven Modeling, Hydraulics Modeling, Frictional Pressure Loss Modeling."
  ], 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.5281/zenodo.3906912", 
  "@id": "https://doi.org/10.5281/zenodo.3906912", 
  "@type": "CreativeWork", 
  "name": "Combining Physics-Based and Data-Driven Modeling for Pressure Prediction in Well Construction"
}
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