Meana Petro
DF-02FundacionalOla 1IADominio

Extracción Estructurada de Informes

En una frase

Pulls the numbers out of the reports — depths, rates, pressures, volumes, dates — and puts them into a database with a link back to the page they came from.

El problema

Searchable text is a large improvement over scanned images and it is still not data. Nobody can plot a trend, build a model or run a query against prose. The numbers have to become rows.

Cómo funciona

Document type is classified first, then a type-specific extraction schema is applied — a daily drilling report, a PVT study and a well test report each have their own. Extraction combines layout-aware models with domain validation rules: a depth that decreases, a rate outside physical range or a date out of sequence is rejected rather than stored. Every extracted value keeps a citation to its source page and coordinates.

Datos necesarios

  • OCR output from the document pipeline
  • A defined target schema per document type, agreed with the domain team
  • Validation rules and physical ranges for each field
  • A labelled sample per document type, typically fifty to two hundred documents

Qué obtiene

  • Structured records in the relational store with full source citation
  • Extraction confidence per field with a review queue for low-confidence values
  • Coverage report: how many documents of each type were processed and what share of fields were populated

Qué no puede hacer

Each new document type needs its own schema and labelled examples — this is not a single model that reads everything. A document type with only a handful of examples in the archive is not worth automating, and we say so rather than delivering a fragile extractor. Values that were wrong in the original report are extracted faithfully wrong; validation catches the physically impossible, not the merely mistaken.

Cada página de capacidad declara una limitación real. Si un proveedor no puede decirle dónde termina su producto, no le están diciendo la verdad sobre dónde empieza.

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