BOEM Offshore Energy Data Center

Use in your agent

Load BOEM pipeline masters into pandas and Parquet. Checked against the provider's documentation on Nov 1, 2026.

Access

• No account or key for the checked delimited pipeline-master ZIP.
• The ZIP contains a headerless, quoted, comma-delimited text file; BOEM publishes the field order on a separate record-layout page.

Request

curl -A 'Mozilla/5.0 (X11; Linux x86_64) SignalLabCheck/1.0' -O 'https://www.data.boem.gov/Pipeline/Files/pplmastdelimit.zip'

Format

ZIP containing one headerless comma-delimited TXT file. Its 42 fields describe pipeline segment identifiers, origin and destination areas, blocks and leases, approval and construction dates, status, size, product, operator, water depth and allowable pressure.

Load into pandas and Parquet

Needs Python 3 with requests, pandas and pyarrow.

"""BOEM pipeline master delimited ZIP -> pandas -> Parquet."""
import io
import zipfile
import requests
import pandas as pd

URL = "https://www.data.boem.gov/Pipeline/Files/pplmastdelimit.zip"
HEADERS = {"User-Agent": "Mozilla/5.0 (X11; Linux x86_64) SignalLabCheck/1.0"}
COLUMNS = ["segment_num", "segment_length", "orig_id_name", "orig_ar_code",
           "orig_blk_num", "orig_lse_num", "dest_id_name", "dest_ar_code",
           "dest_blk_num", "dest_lse_num", "aban_aprv_dt", "aban_date",
           "approved_date", "auth_code", "bd_ppl_sdv_fl", "bur_dsgn_fl",
           "cat_life_tm", "dep_flag", "ppl_const_date", "lk_detec_fl",
           "last_rev_date", "init_hs_dt", "fed_st_lgth", "status_code",
           "ppl_size_code", "row_number", "recv_maop_prss", "recv_seg_num",
           "prpsl_con_dt", "prod_code", "sys_desig_code", "ppl_row_prmte",
           "facil_operator", "min_wtr_dpth", "max_wtr_dpth", "prot_number",
           "maop_prss", "cathodic_code", "bidir_flag", "bd_ppl_fsv_fl",
           "aprv_code", "aban_type"]
r = requests.get(URL, headers=HEADERS, timeout=120)
r.raise_for_status()
with zipfile.ZipFile(io.BytesIO(r.content)) as archive:
    member = archive.namelist()[0]
    df = pd.read_csv(archive.open(member), header=None, names=COLUMNS,
                     dtype="string", skipinitialspace=True)
df = df.apply(lambda column: column.str.strip())
df.to_parquet("boem_pipeline_masters.parquet", index=False)
print(len(df), "pipeline segments", len(df.columns), "fields",
      df["last_rev_date"].max(), "latest revision date")

Run by us on 2026-11-01 (python 3.13.5 | pandas 3.1.0 | pyarrow 26.0.0 | requests 2.34.2). Result: 21314 pipeline segments 42 fields 20261030 latest revision date

Limits

• Rate limit: Not stated.
• Each subject and region is distributed as a separate file family.
• The checked pipeline-master ZIP is about 1.0 MB compressed.

Quirks

• The delimited file has no header row; keep the published record layout with the data version.
• Values are padded with spaces and dates use compact YYYYMMDD text; stripping as strings preserves identifiers and leading zeroes.
• BOEM says the working database can contain errors and is not for legal or navigational use.

Terms and attribution

• BOEM says federal-agency materials are generally public domain but cautions that some site materials may have separate restrictions.
• The Data Center disclaimer says the working data can contain errors and is not a legal or navigational record.

Sources

• https://www.data.boem.gov/Main/HtmlPage.aspx?page=FAQ: “A summary file provides a list of all data fields that are contained in the file selected to be downloaded.”
• https://www.data.boem.gov/Main/HtmlPage.aspx?page=Disclaimer: “Some errors may exist in this data and we are constantly working to find and eliminate them.”
• https://www.boem.gov/about-boem/copyright-restrictions-and-permissions: “Generally, materials produced by federal agencies are in the public domain and may be reproduced without permission.”