{
  "abstract": "Accurate prediction of hydraulic fracturing initiation pressure ( $$P_b$$ ) in natural gas hydrate-bearing sediments provides screening-level support for laboratory-scale hydraulic fracturing design in NGH-bearing sediments. This study demonstrates that systematic feature engineering and neural architecture search yield substantial improvements for predicting $$P_b$$ on a 114-sample published dataset. The framework first expands the feature space from 12 to 208 variables via polynomial transformations, interaction terms, and domain-specific features, followed by deduplication. Multi-method feature selection identifies a 14-feature subset, and extended evaluation identifies a verified 12 $$\\times $$ 32 model as the best-performing saved configuration. The reproducible-by-retraining reference is R $$^2$$ $$\\approx $$ 0.87; the verified 12 $$\\times $$ 32 saved model achieves R $$^2$$ = 0.92 on its original held-out split and is reported as an upper bound on the dataset. The best of 60 retraining runs reaches 0.85—none reproduces 0.92, confirming that 0.92 is a saved-artifact result, not a reproducible target. The verified 12 $$\\times $$ 32 model reduces MSE from 19.34 to 8.22 MPa $$^2$$ and MAE from 3.16 to 2.14 MPa compared to the best prior reported result (R $$^2$$ = 0.81). Feature importance and SHAP analysis show that the model assigns highest importance to stress-composition interactions and stress-derived statistical terms, consistent with known geomechanical controls on fracture initiation. Generalization is modest (CV mean R $$^2 \\approx $$ 0.50), and simpler models (RandomForest, SVR) are recommended as production-ready baselines; the 12 $$\\times $$ 32 DNN is retained as the best verified saved artifact. These findings confirm that the dataset contains untapped predictive capacity, and that methodological refinement through domain-informed feature engineering combined with systematic architecture search provides an effective pathway for improving prediction fidelity in small-sample geomechanical problems.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "Thi H. Ho"
    },
    {
      "name": "Thuat T. Trinh",
      "orcid": "0000-0002-1721-6786"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0bm1j28jx11sw.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-75431-1",
  "fulltext_url": null,
  "id": "0bm1j28jx11sw",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-75431-1",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2210|Hydraulic Fracturing and Reservoir Analysis"
    ]
  ],
  "openalex": "W7222606376",
  "pdf_url": "https://www.nature.com/articles/s41598-026-75431-1_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-16",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/0bm1j28jx11sw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:30Z",
      "native_id": "10.1038/s41598-026-75431-1",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-75431-1"
    }
  ],
  "subjects": [],
  "title": "Improving deep neural network prediction of hydraulic fracturing initiation pressure in hydrate-bearing sediments through feature engineering and architecture search",
  "title_text": "Improving deep neural network prediction of hydraulic fracturing initiation pressure in hydrate-bearing sediments through feature engineering and architecture search",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:30Z",
  "url": "https://research.draconitic.com/paper/0bm1j28jx11sw",
  "venue": "Scientific Reports",
  "versions": []
}