rrc-fields 1.1.0

A parser for Texas RRC field data in EBCDIC format

rrc-fields

A comprehensive Python parser for Texas Railroad Commission (RRC) field data in EBCDIC format.

This library parses the raw EBCDIC tape files from the Texas Railroad Commission's Oil & Gas Division, converting them into structured, human-readable JSON. It supports 30 distinct record segments, handling complex COBOL data types like signed zoned decimals, packed decimals (COMP-3), and implied decimal scaling.

Note: This project was created completely with minimal effort and Gemini 3 Pro.

Installation

pip install rrc-fields

Usage Examples

1. Basic Usage (Simple Parsing)

The simplest way to use rrc-fields is to parse a file and get the raw records.

from rrc_fields import RRCFieldParser
import json

parser = RRCFieldParser()
# Parse the tape file
# Returns a generator or list of dictionaries
records = parser.parse_file('path/to/field_tape.ebc')

for record in records:
    print(record)

2. Advanced Usage (Value Conversion & Filtering)

You can enable value conversion to get human-readable labels instead of raw codes, and filter by record type to process only specific data segments (e.g., just the Field Root records).

from rrc_fields import RRCFieldParser

parser = RRCFieldParser()

# 'convert_values=True': Converts codes like 'G' to 'GAS FIELD'
# 'include': Only return records with these IDs
# Note: layout is optional and defaults to loading all known segments
records = parser.parse_file(
    'path/to/field_tape.ebc', 
    convert_values=True,
    include=['01']  # Only get Field Root (01) records
)

for record in records:
    print(f"Field: {record.get('FL_FIELD_NAME')} ({record.get('FL_FIELD_TYPE')})")

3. Complex Example: Generating a Hierarchical JSON

In many cases, you want to group related records (like Gas Info segments) under their parent Field record. Here is how you might structure the data.

from rrc_fields import RRCFieldParser
import json

def process_tape_to_hierarchy(file_path):
    parser = RRCFieldParser()
    all_records = parser.parse_file(file_path, convert_values=True)
    
    fields = {}
    current_field_id = None
    
    for rec in all_records:
        rec_id = rec.get('RRC_TAPE_RECORD_ID')
        
        # Record 01 is the Field Root
        if rec_id == '01':
            field_no = rec.get('FL_FIELD_NUMBER')
            current_field_id = field_no
            fields[field_no] = {
                'info': rec,
                'gas_segments': [],
                'oil_segments': [],
                'remarks': []
            }
            
        # Other records belong to the current field
        elif current_field_id:
            if rec_id == '02': # Gas Segment
                fields[current_field_id]['gas_segments'].append(rec)
            elif rec_id == '19': # Oil Segment
                fields[current_field_id]['oil_segments'].append(rec)
            elif rec_id in ['05', '06', '10']: # Remarks
                fields[current_field_id]['remarks'].append(rec)

    return fields

# Run the processing
hierarchy = process_tape_to_hierarchy('field_tape.ebc')

# Save to file
with open('fields_hierarchy.json', 'w') as f:
    json.dump(hierarchy, f, indent=2)

Output Format

The parser returns a flat list of dictionaries by default. Below is an expanded example of what a parsed record looks like.

Example Output (JSON)

[
  {
    "RRC_TAPE_RECORD_ID": "01",
    "FL_FIELD_DISTRICT": "01",
    "FL_FIELD_NUMBER": 123456,
    "FL_FIELD_NAME": "EXAMPLE FIELD",
    "FL_FIELD_TYPE": "GAS FIELD",
    "FL_FIELD_CLASS": "GAS",
    "FL_RAILROAD_COMM_DISTRICT": "01",
    "FL_FIELD_RULE_TYPE": "REGULAR"
  },
  {
    "RRC_TAPE_RECORD_ID": "02",
    "FL_GAS_DISC_COUNTY_CODE": "123",
    "FL_GAS_DISCOVERY_DATE": "19950101",
    "FL_GAS_AVG_GRAVITY": 0.65,
    "FL_GAS_AVG_BTU": 1050,
    "FL_GAS_ALLOCATION_FORMULA": "100% ACREAGE",
    "FL_GAS_WELL_COUNT": 5
  }
]

Supported Record Segments

This parser supports all 30 documented field tape record usage types:

ID	Segment Name	Description
01	FLDROOT	Field Root Information (District, Field Name, Type)
02	GASSEG	Gas Field Information
03	FLMKTDMD	Field Market Demand Forecast
04	OPRMKTDM	Operator Market Demand Forecast
05	FLMKTRMK	Field Market Demand Remarks
06	FLSUPRMK	Field Market Demand Supplement Remarks
07	GASCYCLE	Field Gas Cycle Information
08	GSFLDRUL	Field Gas Rules (Spacing, allocation)
09	GASAFORM	Field Gas Allocation Formula
10	GASRMRKS	Field Gas Remarks
11	GSCOUNTY	Field Gas County Codes
12	GASAFACT	Field Gas Allocation Factors
13	ASHEET	Allowable Sheet Root
14	ASHEETMO	Allowable Sheet Monthly Information
15	FLT3ROOT	Field T-3 Form Root
16	FLDT3	Field T-3 Form Data
17	FLDMO	Field Monthly Stats
18	CALC49B	49(b) Calculation Information
19	OILSEG	Oil Field Information
20	OILCYCLE	Oil Cycle Information
21	OLFLDRUL	Oil Field Rules
22	OILAFORM	Oil Allocation Formula
23	OILRMRKS	Oil Remarks
24	OILFTROT	Oil Factors Root
25	OILAFACT	Oil Allocation Factors
26	OLCOUNTY	Oil Field County
27	ASSOCGAS	Associated Gas Fields
28	FLDMAP	Field Map Index
29	GSOPTRUL	Gas Optional Rules
30	OLOPTRUL	Oil Optional Rules

Getting sample ebc from large.ebc

dd if=main.ebc of=sample.ebc bs=1024 count=100