---

name: bsee-data-extractor description: Extract and process BSEE (Bureau of Safety and Environmental Enforcement) production data. Use for querying oil/gas production data by API number, block, lease, or field with automatic data normalization and caching.

BSEE Data Extractor

Extract and process production data from BSEE (Bureau of Safety and Environmental Enforcement) databases for oil & gas analysis in the Gulf of Mexico.

When to Use

• Querying BSEE production data by API number, block, or lease
• Downloading and parsing BSEE ZIP file archives
• Normalizing production data across different time periods
• Building production timelines for specific wells or fields
• Tracking well status changes over time
• Preparing data for economic analysis (NPV, decline curves)

Core Pattern

CopyQuery Parameters → BSEE API/Files → Parse → Normalize → Cache → Output

Implementation

Data Models

Copyfrom dataclasses import dataclass, field
from datetime import date
from typing import Optional, List, Dict, Any
from enum import Enum
import pandas as pd

class ProductType(Enum):
    """Production fluid types."""
    OIL = "oil"
    GAS = "gas"
    WATER = "water"
    CONDENSATE = "condensate"

class WellStatus(Enum):
    """Well operational status."""
    PRODUCING = "producing"
    SHUT_IN = "shut_in"
    TEMPORARILY_ABANDONED = "ta"
    PERMANENTLY_ABANDONED = "pa"
    DRILLING = "drilling"
    COMPLETING = "completing"

@dataclass
class WellIdentifier:
    """Unique well identification."""
    api_number: str
    well_name: Optional[str] = None
    lease_number: Optional[str] = None
    area_code: Optional[str] = None
    block_number: Optional[str] = None

    @property
    def api_10(self) -> str:
        """Return 10-digit API number."""
        return self.api_number[:10] if len(self.api_number) >= 10 else self.api_number

    @property
    def api_14(self) -> str:
        """Return full 14-digit API number."""
        return self.api_number.ljust(14, '0')

@dataclass
class ProductionRecord:
    """Single month production record."""
    well_id: WellIdentifier
    production_date: date
    oil_bbls: float = 0.0
    gas_mcf: float = 0.0
    water_bbls: float = 0.0
    condensate_bbls: float = 0.0
    days_on_production: int = 0
    status: WellStatus = WellStatus.PRODUCING

    @property
    def oil_bopd(self) -> float:
        """Oil production in barrels per day."""
        if self.days_on_production > 0:
            return self.oil_bbls / self.days_on_production
        return 0.0

    @property
    def gas_mcfd(self) -> float:
        """Gas production in MCF per day."""
        if self.days_on_production > 0:
            return self.gas_mcf / self.days_on_production
        return 0.0

    @property
    def boe(self) -> float:
        """Barrels of oil equivalent (6:1 gas conversion)."""
        return self.oil_bbls + self.condensate_bbls + (self.gas_mcf / 6.0)

@dataclass
class WellProduction:
    """Complete production history for a well."""
    well_id: WellIdentifier
    records: List[ProductionRecord] = field(default_factory=list)
    first_production: Optional[date] = None
    last_production: Optional[date] = None

    def to_dataframe(self) -> pd.DataFrame:
        """Convert production records to DataFrame."""
        data = []
        for rec in self.records:
            data.append({
                'date': rec.production_date,
                'oil_bbls': rec.oil_bbls,
                'gas_mcf': rec.gas_mcf,
                'water_bbls': rec.water_bbls,
                'condensate_bbls': rec.condensate_bbls,
                'days': rec.days_on_production,
                'oil_bopd': rec.oil_bopd,
                'gas_mcfd': rec.gas_mcfd,
                'boe': rec.boe,
                'status': rec.status.value
            })
        return pd.DataFrame(data).sort_values('date')

    @property
    def cumulative_oil(self) -> float:
        """Total cumulative oil production."""
        return sum(r.oil_bbls for r in self.records)

    @property
    def cumulative_gas(self) -> float:
        """Total cumulative gas production."""
        return sum(r.gas_mcf for r in self.records)

    @property
    def cumulative_boe(self) -> float:
        """Total cumulative BOE."""
        return sum(r.boe for r in self.records)

BSEE Data Client

Copyimport requests
import zipfile
import io
from pathlib import Path
from typing import Optional, List, Dict, Generator
import pandas as pd
import logging

logger = logging.getLogger(__name__)

class BSEEDataClient:
    """
    Client for accessing BSEE production data.

    Supports both API access and file-based data extraction.
    """

    # BSEE data URLs
    BASE_URL = "https://www.data.bsee.gov"
    PRODUCTION_URL = f"{BASE_URL}/Production/Files"

    # Column mappings for BSEE data files
    PRODUCTION_COLUMNS = {
        'API_WELL_NUMBER': 'api_number',
        'PRODUCTION_DATE': 'production_date',
        'OIL': 'oil_bbls',
        'GAS': 'gas_mcf',
        'WATER': 'water_bbls',
        'CONDENSATE': 'condensate_bbls',
        'DAYS_ON_PROD': 'days_on_production',
        'WELL_STAT_CD': 'status_code'
    }

    def __init__(self, cache_dir: Path = None):
        """
        Initialize BSEE data client.

        Args:
            cache_dir: Directory for caching downloaded data
        """
        self.cache_dir = cache_dir or Path("data/bsee_cache")
        self.cache_dir.mkdir(parents=True, exist_ok=True)
        self.session = requests.Session()

    def download_production_file(self, year: int, month: int = None) -> Path:
        """
        Download BSEE production data file.

        Args:
            year: Production year
            month: Optional month (downloads full year if not specified)

        Returns:
            Path to downloaded file
        """
        if month:
            filename = f"ogoramon{year}{month:02d}.zip"
        else:
            filename = f"ogoramon{year}.zip"

        cache_path = self.cache_dir / filename

        if cache_path.exists():
            logger.info(f"Using cached file: {cache_path}")
            return cache_path

        url = f"{self.PRODUCTION_URL}/{filename}"
        logger.info(f"Downloading: {url}")

        response = self.session.get(url)
        response.raise_for_status()

        cache_path.write_bytes(response.content)
        logger.info(f"Saved to: {cache_path}")

        return cache_path

    def extract_production_data(self, zip_path: Path) -> pd.DataFrame:
        """
        Extract production data from BSEE ZIP file.

        Args:
            zip_path: Path to ZIP file

        Returns:
            DataFrame with production records
        """
        with zipfile.ZipFile(zip_path, 'r') as zf:
            # Find the data file (usually .txt or .csv)
            data_files = [f for f in zf.namelist()
                         if f.endswith(('.txt', '.csv', '.dat'))]

            if not data_files:
                raise ValueError(f"No data file found in {zip_path}")

            with zf.open(data_files[0]) as f:
                # Try different delimiters
                try:
                    df = pd.read_csv(f, delimiter='\t', low_memory=False)
                except:
                    f.seek(0)
                    df = pd.read_csv(f, delimiter=',', low_memory=False)

        # Rename columns to standard names
        df = df.rename(columns={
            k: v for k, v in self.PRODUCTION_COLUMNS.items()
            if k in df.columns
        })

        return df

    def query_by_api(self, api_number: str,
                     start_year: int = None,
                     end_year: int = None) -> WellProduction:
        """
        Query production data for specific API number.

        Args:
            api_number: Well API number (10 or 14 digit)
            start_year: Start year for query
            end_year: End year for query

        Returns:
            WellProduction object with complete history
        """
        from datetime import datetime

        end_year = end_year or datetime.now().year
        start_year = start_year or (end_year - 20)  # Default 20 years

        well_id = WellIdentifier(api_number=api_number)
        records = []

        for year in range(start_year, end_year + 1):
            try:
                zip_path = self.download_production_file(year)
                df = self.extract_production_data(zip_path)

                # Filter for API number (match on first 10 digits)
                api_10 = well_id.api_10
                df_well = df[df['api_number'].astype(str).str[:10] == api_10]

                for _, row in df_well.iterrows():
                    records.append(self._row_to_record(row, well_id))

            except Exception as e:
                logger.warning(f"Error processing year {year}: {e}")
                continue

        production = WellProduction(well_id=well_id, records=records)

        if records:
            dates = [r.production_date for r in records]
            production.first_production = min(dates)
            production.last_production = max(dates)

        return production

    def query_by_block(self, area_code: str, block_number: str,
                       year: int) -> List[WellProduction]:
        """
        Query all wells in a specific OCS block.

        Args:
            area_code: OCS area code (e.g., 'GC', 'WR', 'MC')
            block_number: Block number
            year: Year to query

        Returns:
            List of WellProduction objects
        """
        zip_path = self.download_production_file(year)
        df = self.extract_production_data(zip_path)

        # Filter by area and block (column names may vary)
        area_col = next((c for c in df.columns if 'AREA' in c.upper()), None)
        block_col = next((c for c in df.columns if 'BLOCK' in c.upper()), None)

        if area_col and block_col:
            df_block = df[
                (df[area_col].astype(str).str.upper() == area_code.upper()) &
                (df[block_col].astype(str) == str(block_number))
            ]
        else:
            logger.warning("Could not find area/block columns")
            return []

        # Group by API number
        wells = {}
        for _, row in df_block.iterrows():
            api = str(row.get('api_number', ''))
            if api not in wells:
                well_id = WellIdentifier(
                    api_number=api,
                    area_code=area_code,
                    block_number=block_number
                )
                wells[api] = WellProduction(well_id=well_id, records=[])

            wells[api].records.append(self._row_to_record(row, wells[api].well_id))

        return list(wells.values())

    def _row_to_record(self, row: pd.Series, well_id: WellIdentifier) -> ProductionRecord:
        """Convert DataFrame row to ProductionRecord."""
        prod_date = row.get('production_date')
        if isinstance(prod_date, str):
            # Parse various date formats
            for fmt in ['%Y%m', '%Y-%m', '%Y/%m', '%Y%m%d', '%Y-%m-%d']:
                try:
                    prod_date = pd.to_datetime(prod_date, format=fmt).date()
                    break
                except:
                    continue
        elif hasattr(prod_date, 'date'):
            prod_date = prod_date.date()
        else:
            prod_date = date.today()

        return ProductionRecord(
            well_id=well_id,
            production_date=prod_date,
            oil_bbls=float(row.get('oil_bbls', 0) or 0),
            gas_mcf=float(row.get('gas_mcf', 0) or 0),
            water_bbls=float(row.get('water_bbls', 0) or 0),
            condensate_bbls=float(row.get('condensate_bbls', 0) or 0),
            days_on_production=int(row.get('days_on_production', 0) or 0),
            status=self._parse_status(row.get('status_code', ''))
        )

    def _parse_status(self, code: str) -> WellStatus:
        """Parse BSEE status code to WellStatus enum."""
        code = str(code).upper().strip()
        status_map = {
            'P': WellStatus.PRODUCING,
            'SI': WellStatus.SHUT_IN,
            'TA': WellStatus.TEMPORARILY_ABANDONED,
            'PA': WellStatus.PERMANENTLY_ABANDONED,
            'DR': WellStatus.DRILLING,
            'CO': WellStatus.COMPLETING
        }
        return status_map.get(code, WellStatus.PRODUCING)

Production Aggregator

Copyfrom typing import Dict, List, Tuple
import pandas as pd
import numpy as np
from datetime import date

class ProductionAggregator:
    """
    Aggregate production data across wells, fields, or time periods.
    """

    def __init__(self, wells: List[WellProduction]):
        """
        Initialize aggregator with well production data.

        Args:
            wells: List of WellProduction objects
        """
        self.wells = wells
        self._combined_df = None

    @property
    def combined_dataframe(self) -> pd.DataFrame:
        """Get combined production data as DataFrame."""
        if self._combined_df is None:
            dfs = []
            for well in self.wells:
                df = well.to_dataframe()
                df['api_number'] = well.well_id.api_number
                df['well_name'] = well.well_id.well_name
                dfs.append(df)

            self._combined_df = pd.concat(dfs, ignore_index=True)

        return self._combined_df

    def monthly_totals(self) -> pd.DataFrame:
        """Aggregate production by month."""
        df = self.combined_dataframe.copy()
        df['year_month'] = pd.to_datetime(df['date']).dt.to_period('M')

        return df.groupby('year_month').agg({
            'oil_bbls': 'sum',
            'gas_mcf': 'sum',
            'water_bbls': 'sum',
            'boe': 'sum',
            'days': 'sum',
            'api_number': 'nunique'
        }).rename(columns={'api_number': 'well_count'}).reset_index()

    def annual_totals(self) -> pd.DataFrame:
        """Aggregate production by year."""
        df = self.combined_dataframe.copy()
        df['year'] = pd.to_datetime(df['date']).dt.year

        return df.groupby('year').agg({
            'oil_bbls': 'sum',
            'gas_mcf': 'sum',
            'water_bbls': 'sum',
            'boe': 'sum',
            'days': 'sum',
            'api_number': 'nunique'
        }).rename(columns={'api_number': 'well_count'}).reset_index()

    def well_summary(self) -> pd.DataFrame:
        """Get summary statistics per well."""
        summaries = []
        for well in self.wells:
            df = well.to_dataframe()

            if len(df) == 0:
                continue

            summary = {
                'api_number': well.well_id.api_number,
                'well_name': well.well_id.well_name,
                'first_production': df['date'].min(),
                'last_production': df['date'].max(),
                'months_producing': len(df),
                'cumulative_oil': df['oil_bbls'].sum(),
                'cumulative_gas': df['gas_mcf'].sum(),
                'cumulative_boe': df['boe'].sum(),
                'peak_oil_bopd': df['oil_bopd'].max(),
                'peak_gas_mcfd': df['gas_mcfd'].max(),
                'avg_oil_bopd': df['oil_bopd'].mean(),
                'avg_gas_mcfd': df['gas_mcfd'].mean()
            }
            summaries.append(summary)

        return pd.DataFrame(summaries)

    def decline_curve_data(self, well: WellProduction) -> Dict:
        """
        Prepare data for decline curve analysis.

        Returns time on production and rate data.
        """
        df = well.to_dataframe()
        df = df[df['oil_bopd'] > 0].sort_values('date')

        if len(df) == 0:
            return {'time': [], 'rate': []}

        # Calculate months from first production
        first_date = df['date'].iloc[0]
        df['months'] = ((pd.to_datetime(df['date']) - pd.to_datetime(first_date))
                       .dt.days / 30.44).astype(int)

        return {
            'time': df['months'].tolist(),
            'rate': df['oil_bopd'].tolist(),
            'dates': df['date'].tolist()
        }

Report Generator

Copyimport plotly.graph_objects as go
from plotly.subplots import make_subplots
from pathlib import Path

class BSEEReportGenerator:
    """Generate interactive HTML reports for BSEE data."""

    def __init__(self, aggregator: ProductionAggregator):
        """
        Initialize report generator.

        Args:
            aggregator: ProductionAggregator with well data
        """
        self.aggregator = aggregator

    def generate_field_report(self, output_path: Path, field_name: str = "Field"):
        """
        Generate comprehensive field production report.

        Args:
            output_path: Path for HTML output
            field_name: Name of the field for report title
        """
        fig = make_subplots(
            rows=3, cols=2,
            subplot_titles=(
                'Monthly Oil Production',
                'Monthly Gas Production',
                'Cumulative Production',
                'Well Count Over Time',
                'Water Cut Trend',
                'GOR Trend'
            ),
            vertical_spacing=0.08
        )

        monthly = self.aggregator.monthly_totals()
        monthly['date'] = monthly['year_month'].dt.to_timestamp()

        # Monthly oil production
        fig.add_trace(
            go.Bar(x=monthly['date'], y=monthly['oil_bbls']/1000,
                   name='Oil (Mbbls)', marker_color='green'),
            row=1, col=1
        )

        # Monthly gas production
        fig.add_trace(
            go.Bar(x=monthly['date'], y=monthly['gas_mcf']/1000,
                   name='Gas (MMCF)', marker_color='red'),
            row=1, col=2
        )

        # Cumulative production
        monthly['cum_oil'] = monthly['oil_bbls'].cumsum() / 1e6
        monthly['cum_gas'] = monthly['gas_mcf'].cumsum() / 1e6

        fig.add_trace(
            go.Scatter(x=monthly['date'], y=monthly['cum_oil'],
                      name='Cum Oil (MMbbls)', line=dict(color='green')),
            row=2, col=1
        )
        fig.add_trace(
            go.Scatter(x=monthly['date'], y=monthly['cum_gas'],
                      name='Cum Gas (BCF)', line=dict(color='red')),
            row=2, col=1
        )

        # Well count
        fig.add_trace(
            go.Scatter(x=monthly['date'], y=monthly['well_count'],
                      name='Active Wells', fill='tozeroy'),
            row=2, col=2
        )

        # Water cut
        df = self.aggregator.combined_dataframe.copy()
        df['date'] = pd.to_datetime(df['date'])
        monthly_avg = df.groupby(df['date'].dt.to_period('M')).agg({
            'oil_bbls': 'sum',
            'water_bbls': 'sum',
            'gas_mcf': 'sum'
        }).reset_index()
        monthly_avg['date'] = monthly_avg['date'].dt.to_timestamp()
        monthly_avg['water_cut'] = (monthly_avg['water_bbls'] /
                                    (monthly_avg['oil_bbls'] + monthly_avg['water_bbls'])) * 100
        monthly_avg['gor'] = monthly_avg['gas_mcf'] / monthly_avg['oil_bbls'].replace(0, np.nan)

        fig.add_trace(
            go.Scatter(x=monthly_avg['date'], y=monthly_avg['water_cut'],
                      name='Water Cut (%)', line=dict(color='blue')),
            row=3, col=1
        )

        # GOR
        fig.add_trace(
            go.Scatter(x=monthly_avg['date'], y=monthly_avg['gor'],
                      name='GOR (MCF/bbl)', line=dict(color='orange')),
            row=3, col=2
        )

        fig.update_layout(
            height=1000,
            title_text=f"{field_name} Production Analysis",
            showlegend=True
        )

        # Write report
        output_path.parent.mkdir(parents=True, exist_ok=True)
        fig.write_html(str(output_path))

        return output_path

    def generate_well_report(self, well: WellProduction, output_path: Path):
        """Generate individual well production report."""
        df = well.to_dataframe()

        fig = make_subplots(
            rows=2, cols=2,
            subplot_titles=(
                'Daily Oil Rate',
                'Daily Gas Rate',
                'Cumulative Production',
                'Production Decline'
            )
        )

        # Oil rate
        fig.add_trace(
            go.Scatter(x=df['date'], y=df['oil_bopd'],
                      name='Oil (BOPD)', line=dict(color='green')),
            row=1, col=1
        )

        # Gas rate
        fig.add_trace(
            go.Scatter(x=df['date'], y=df['gas_mcfd'],
                      name='Gas (MCFD)', line=dict(color='red')),
            row=1, col=2
        )

        # Cumulative
        df['cum_oil'] = df['oil_bbls'].cumsum() / 1000
        df['cum_gas'] = df['gas_mcf'].cumsum() / 1000

        fig.add_trace(
            go.Scatter(x=df['date'], y=df['cum_oil'],
                      name='Cum Oil (Mbbls)'),
            row=2, col=1
        )

        # Semi-log decline plot
        df_producing = df[df['oil_bopd'] > 0]
        fig.add_trace(
            go.Scatter(x=df_producing['date'], y=df_producing['oil_bopd'],
                      name='Rate (BOPD)', mode='markers'),
            row=2, col=2
        )
        fig.update_yaxes(type='log', row=2, col=2)

        fig.update_layout(
            height=800,
            title_text=f"Well {well.well_id.api_number} Production History",
            showlegend=True
        )

        fig.write_html(str(output_path))
        return output_path

YAML Configuration

Query Configuration

Copy# config/bsee_query.yaml

metadata:
  task: bsee_data_extraction
  created: "2024-01-15"

query:
  type: by_block  # by_api, by_block, by_lease, by_field

  # API query parameters
  api_number: "1771049130"

  # Block query parameters
  area_code: "GC"  # Green Canyon
  block_number: "640"

  # Time range
  start_year: 2010
  end_year: 2024

data_options:
  include_gas: true
  include_water: true
  include_condensate: true
  normalize_rates: true  # Convert to daily rates

cache:
  enabled: true
  directory: "data/bsee_cache"
  expiry_days: 30

output:
  format: "csv"  # csv, parquet, json
  path: "data/results/bsee_extract.csv"

  report:
    enabled: true
    path: "reports/bsee_production.html"
    title: "BSEE Production Analysis"

Multi-Well Configuration

Copy# config/field_analysis.yaml

metadata:
  task: field_analysis
  field_name: "Lower Tertiary Development"

wells:
  - api_number: "1771049130"
    name: "Well A-1"
    type: "producer"

  - api_number: "1771049131"
    name: "Well A-2"
    type: "producer"

  - api_number: "1771049132"
    name: "Well B-1"
    type: "injector"

analysis:
  production_summary: true
  decline_analysis: true
  water_cut_trend: true
  gor_trend: true

time_range:
  start: "2015-01-01"
  end: "2024-12-31"

output:
  summary_csv: "data/results/field_summary.csv"
  well_reports: "reports/wells/"
  field_report: "reports/field_production.html"

CLI Usage

Basic Queries

Copy# Query by API number
python -m bsee_extractor query --api 1771049130 --output data/well_production.csv

# Query by block
python -m bsee_extractor query --area GC --block 640 --year 2023

# Query multiple years
python -m bsee_extractor query --api 1771049130 --start-year 2010 --end-year 2024

Report Generation

Copy# Generate well report
python -m bsee_extractor report --api 1771049130 --output reports/well_report.html

# Generate field report from config
python -m bsee_extractor report --config config/field_analysis.yaml

Data Export

Copy# Export to CSV
python -m bsee_extractor export --api 1771049130 --format csv --output data/export.csv

# Export to Parquet (for large datasets)
python -m bsee_extractor export --area GC --block 640 --format parquet --output data/block_production.parquet

Usage Examples

Example 1: Single Well Analysis

Copyfrom bsee_extractor import BSEEDataClient, BSEEReportGenerator

# Initialize client
client = BSEEDataClient(cache_dir=Path("data/bsee_cache"))

# Query well production
well = client.query_by_api("1771049130", start_year=2010, end_year=2024)

# Get production DataFrame
df = well.to_dataframe()
print(f"Records: {len(df)}")
print(f"Cumulative Oil: {well.cumulative_oil:,.0f} bbls")
print(f"Cumulative Gas: {well.cumulative_gas:,.0f} MCF")

# Generate report
from production_aggregator import ProductionAggregator
aggregator = ProductionAggregator([well])
reporter = BSEEReportGenerator(aggregator)
reporter.generate_well_report(well, Path("reports/well_analysis.html"))

Example 2: Block-Level Analysis

Copy# Query all wells in Green Canyon Block 640
wells = client.query_by_block("GC", "640", year=2023)
print(f"Found {len(wells)} wells in GC 640")

# Aggregate production
aggregator = ProductionAggregator(wells)

# Monthly totals
monthly = aggregator.monthly_totals()
print(f"\n2023 Production:")
print(f"  Total Oil: {monthly['oil_bbls'].sum():,.0f} bbls")
print(f"  Total Gas: {monthly['gas_mcf'].sum():,.0f} MCF")

# Well summary
summary = aggregator.well_summary()
print(f"\nWell Rankings by Cumulative Oil:")
print(summary.sort_values('cumulative_oil', ascending=False)[
    ['api_number', 'cumulative_oil', 'peak_oil_bopd']
].head(10))

# Generate field report
reporter = BSEEReportGenerator(aggregator)
reporter.generate_field_report(
    Path("reports/gc640_production.html"),
    field_name="Green Canyon 640"
)

Example 3: Decline Curve Preparation

Copy# Get decline curve data for type curve analysis
well = client.query_by_api("1771049130")
decline_data = aggregator.decline_curve_data(well)

# Prepare for Arps decline fitting
import numpy as np

time = np.array(decline_data['time'])
rate = np.array(decline_data['rate'])

# Filter to producing periods only
mask = rate > 0
time_producing = time[mask]
rate_producing = rate[mask]

# Export for external decline curve software
decline_df = pd.DataFrame({
    'months': time_producing,
    'oil_bopd': rate_producing
})
decline_df.to_csv("data/decline_input.csv", index=False)

Best Practices

Data Caching

• Enable caching to avoid repeated downloads
• Set appropriate expiry for frequently updated data
• Use local cache for development, clear for production runs

Query Optimization

• Query by year ranges to limit data volume
• Use block/area queries for field-level analysis
• Filter early in pipeline to reduce memory usage

Error Handling

• Handle missing data periods gracefully
• Log warnings for data quality issues
• Validate API numbers before querying

File Organization

Copyproject/
├── config/
│   ├── bsee_query.yaml
│   └── field_analysis.yaml
├── data/
│   ├── bsee_cache/         # Downloaded BSEE files
│   ├── raw/                # Raw extracted data
│   ├── processed/          # Cleaned data
│   └── results/            # Analysis outputs
├── reports/
│   ├── wells/              # Individual well reports
│   └── field_production.html
└── src/
    └── bsee_extractor/
        ├── client.py
        ├── models.py
        ├── aggregator.py
        └── reports.py

Related Skills

• npv-analyzer - Economic analysis using BSEE data
• data-pipeline-processor - General data processing
• engineering-report-generator - Report generation