Meana Petro
DF-03FoundationalWave 1AI

Archive Auto-Tagging & Classification

In one sentence

Works out what each document is and which well, field and date it belongs to, so the archive becomes navigable rather than a folder tree.

The problem

Archives are organised by whoever filed them: by year in one directory, by contractor in another, by a person’s initials in a third. Two copies of the same report sit in three places under different names, and nobody knows which is current.

How it works

Classification assigns document type, then entity resolution links each document to the well, field, reservoir and equipment it concerns, including matching the many name variants the same well has accumulated. Near-duplicate detection identifies redundant copies and proposes a canonical version for the records team to confirm.

Data required

  • The document archive with its existing folder structure preserved
  • Master well and field registers for entity matching
  • Known name variants and legacy naming conventions
  • A labelled sample covering the main document types

What you get

  • Document metadata: type, entities, date, confidence, canonical status
  • Duplicate and near-duplicate clusters with a proposed canonical copy
  • Archive map showing coverage and gaps by well and by year

What it cannot do

Entity resolution on wells with inconsistent historical naming is the hardest part and is never fully automatic; a residual share of documents requires a records specialist to disambiguate. The system surfaces those rather than guessing, because a document attached to the wrong well is more damaging than a document attached to nothing.

Every capability page states a real limitation. If a vendor cannot tell you where their product stops, you are not being told the truth about where it starts.

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