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DF-02أساسيةالموجة 1الذكاء الاصطناعيالمجال

الاستخراج المهيكل من التقارير

في جملة واحدة

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.

المشكلة

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.

كيف تعمل

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.

البيانات المطلوبة

  • 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

ما الذي تحصل عليه

  • 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

ما لا تستطيع فعله

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.

كل صفحة قدرة تذكر حداً حقيقياً. إن لم يستطع المورّد إخبارك أين يتوقف منتجه، فلم يُقل لك الحق عن أين يبدأ.

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