What this is:

GRA001, the original EBCDIC R-3 extract. RRC retired it on 2025-08-25 and now publishes the same reporting as the JSON dataload beside this entry — but the tape is the only place the history exists, so it is parsed and queryable here rather than sitting as 62 MB of packed decimal — 147,105 records of exactly 422 bytes.

How it is laid out:

The Railroad Commission publishes this as EBCDIC (IBM cp037). IBM mainframe encoding (cp037). Opening it as text gives you nonsense; it has to be transcoded first. We decode it into a table you can open anywhere. It has 192 columns and about 7.2K rows.

The columns you will most likely want:

  • district The district as the tape keys it: RRC's INTERNAL district number, not the designation the Commission prints.
  • serial_no The plant's four-digit serial number within its district.
  • district_code The district as RRC prints it -- 01-06, 7B, 7C, 08, 8A, 09, 10 -- derived by EZRRC from the tape's internal number.
  • plant_id The plant id RRC prints and every other product uses: the district designation, a hyphen and the four-digit serial, '7C-0286'.
  • operator_no The P-5 organization number of the plant operator.
  • county_code The three-digit RRC county code as a whole number, so Andrews is 3 and not '003'.

…and 186 more, all documented on the full page.

What we add:

Filter the plants by district, county, operator, type, capacity or the month they last filed, and export them with all eleven child tables — the remarks, the report cycles and the seven form sections.

There is more on the full page, including what this joins to.

  • Decoded out of the Commission's original format into something Excel opens.
  • Filterable before you download, so you can take one county instead of the whole state.
  • The Commission's single-letter codes translated into words.

Worth knowing before use:

Retired. RRC no longer refreshes it, so everything here is frozen at 2025-08-25 and the freshness badge will only get older. New R-3 filings are in the JSON dataset beside this one.

The full page lists every trap in this dataset.

Get it:

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