{
  "abstract": "A successful drilling operation plays a vital role in the profitability of exploration and production of hydrocarbon reserves. However, these operations can come with high drilling rig rental costs. Reliable predictions of Rate of Penetration (ROP) can avoid unnecessary drilling equipment and rig rental expenses. In this study, Gradient Boosting Regressor, Random Forest, and K-Nearest Neighbours were developed to predict rate of penetration based on drilling operational data. The machine learning models were benchmarked with established empirical models, such as the Bingham and Bourgoyne and Young models, which are typically used to predict the rate of penetration. All machine learning models achieved, coefficient of determination (R2) > 0.93 and Average Absolute Percentage Error (AAPE) < 3% on the test dataset. Random Forest under Scenario 1 was identified as the most physically consistent model, attaining strong predictive performance with R2 = 0.9494 & AAPE = 1.96%, satisfying both the Weight on Bit (WOB) flounder point, and rotary speed optimum criteria used within the industry. These results support the practical use of machine learning-based ROP prediction as a reliable tool for real-time optimization of drilling parameters, offering a pathway to reduce non-productive time and rig costs while preserving the physical interpretability that drilling engineers require for field decision making.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Chemical and Petroleum Engineering, United Arab Emirates University, Al Ain, United Arab Emirates",
    "Hildebrand Austin Department of Petroleum and Geosystems Engineering, The University of Texas at Austin, Austin, Texas, United States of America",
    "Chemical and Process Engineering, Faculty of Engineering, University of Strathclyde Bahrain, Kingdom of Bahrain"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Musaab Magzoub"
    },
    {
      "affs": [
        1
      ],
      "name": "Noora Al Mansoori"
    },
    {
      "affs": [
        2
      ],
      "name": "Mohammed Abdalla Ayoub Mohammed"
    },
    {
      "name": "Almostafa Alhadi"
    },
    {
      "name": "Kaushik Sivaramakrishnan"
    },
    {
      "name": "Munawar Shaik"
    },
    {
      "name": "Abu Bakar Adni"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/41vazd9c8wrxg.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0360046",
  "fulltext_url": null,
  "id": "41vazd9c8wrxg",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0360046",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2103|Drilling and Well Engineering"
    ],
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      "plos",
      "/Biology and life sciences/Organisms/Eukaryota/Plants/Trees"
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      "/Computer and information sciences/Artificial intelligence/Machine learning"
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      "/Computer and information sciences/Artificial intelligence/Machine learning/Decision tree learning"
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    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning/Machine learning algorithms"
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      "/Earth sciences/Geology"
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      "/Engineering and technology/Management engineering/Decision analysis/Decision trees/Decision tree learning"
    ],
    [
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      "/Physical sciences/Materials science/Materials/Organic materials/Petroleum"
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    ],
    [
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      "/Physical sciences/Physics/Classical mechanics/Continuum mechanics/Fluid mechanics/Fluid dynamics/Flow rate"
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      "/Physical sciences/Physics/Classical mechanics/Motion/Torque"
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      "plos",
      "/Research and analysis methods/Decision analysis/Decision trees/Decision tree learning"
    ],
    [
      "plos",
      "/Research and analysis methods/Simulation and modeling/Algorithms/Machine learning algorithms"
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  "openalex": "W7225394108",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-06",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/41vazd9c8wrxg.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T12:20:24Z",
      "native_id": "10.1371/journal.pone.0360046",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0360046"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "plos:materials science",
      "subfield": "materials-polymers"
    },
    {
      "field": "earth",
      "origin": "plos:geology",
      "subfield": "geology-geochemistry"
    },
    {
      "field": "physics",
      "origin": "plos:fluid dynamics",
      "subfield": "fluid-dynamics"
    }
  ],
  "title": "Predicting rate of penetration (ROP) with machine learning models: A physics-validated comparative case study of Gradient Boosting Regressor (GBR), Random Forest (RF), and K-Nearest Neighbours (KNN)",
  "title_text": "Predicting rate of penetration (ROP) with machine learning models: A physics-validated comparative case study of Gradient Boosting Regressor (GBR), Random Forest (RF), and K-Nearest Neighbours (KNN)",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T12:20:48Z",
  "url": "https://research.draconitic.com/paper/41vazd9c8wrxg",
  "venue": "PLOS One",
  "versions": []
}