{
  "abstract": "Forecasting a long horizon from only the first observations of a sequence is ill-posed: many trajectories are consistent with the same short history. We study this problem in oil and gas production forecasting, where forecasts made after roughly the first fifth of a well's producing life drive development and abandonment decisions, and where a usable forecast must describe a monotone decline. We present Physics-SIMS-TS, a conditional diffusion forecaster that combines negative guidance against synthetic artifacts, decline-curve constraints and an isotonic projection applied during sampling, spatial training augmentation, and an ensembled stochastic sampler yielding a full predictive distribution. Across three jurisdictions and more than 35,000 wells, under a shared-space, validation-frozen protocol, Physics-SIMS-TS is the most accurate diffusion forecaster in the comparison and is competitive with, but not superior to, ensembled transformer forecasters. Its forecasts are monotone by construction at a cost of at most 0.5% in mean squared error, and its trajectory ensemble yields calibrated intervals after one dispersion factor is fitted per jurisdiction. On six standard benchmarks a reversible-instance-normalization variant of the backbone is the leading diffusion baseline. We also quantify four protocol choices on which the measured ranking depends. Code and evaluation artifacts are released.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.22643",
  "author_total": 2,
  "authors": [
    {
      "name": "Temesgen Mikael Abraha"
    },
    {
      "name": "Yves Lucet"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5qfvzrfg7nwna.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.22643",
  "fulltext_url": null,
  "id": "5qfvzrfg7nwna",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.22643",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "2212|Reservoir Engineering and Simulation Methods"
    ]
  ],
  "openalex": "W7214019091",
  "pdf_url": "https://arxiv.org/pdf/2609.22643",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-18",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5qfvzrfg7nwna.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:33Z",
      "native_id": "2609.22643",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.22643"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Monotone-Constrained Diffusion Models for Long-Horizon Production Forecasting",
  "title_text": "Monotone-Constrained Diffusion Models for Long-Horizon Production Forecasting",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:33Z",
  "url": "https://research.draconitic.com/paper/5qfvzrfg7nwna",
  "venue": "arXiv",
  "versions": []
}