---

name: fdas-economics description: Perform offshore field development economic analysis with NPV, MIRR, IRR, and payback calculations. Use for investment analysis, cashflow modeling, BSEE data integration, development system classification, and Excel report generation.

FDAS Economics Skill

Field Development Analysis System (FDAS) for offshore oil & gas economic evaluation with Excel-compatible NPV, MIRR, IRR, and payback calculations.

When to Use

• NPV (Net Present Value) calculations for field developments
• MIRR (Modified Internal Rate of Return) analysis
• IRR (Internal Rate of Return) evaluation
• Cashflow modeling for offshore projects
• Development system classification (dry, subsea15, subsea20)
• Production forecasting and analysis
• Drilling timeline extraction and cost modeling
• BSEE data integration for economic analysis
• Excel report generation for stakeholder presentation

Prerequisites

• Python environment with worldenergydata package installed
• BSEE production and well data (optional, for real field analysis)
• Lease assumptions Excel file (optional, for custom assumptions)

Analysis Types

1. Financial Metrics Calculation

Calculate core investment metrics from cashflows.

Copyfdas_economics:
  financial_metrics:
    flag: true
    cashflows:
      type: "array"  # or from_production
      values: [-1500, -500, 200, 800, 1200, 1000, 800, 600, 400, 200]
      units: "MM_USD"
    discount_rate: 0.10
    metrics:
      - npv
      - mirr
      - irr
      - payback
    output:
      summary_file: "results/financial_metrics.json"
      report_file: "results/metrics_report.html"

2. Development System Classification

Classify fields by water depth for cost estimation.

Copyfdas_economics:
  classification:
    flag: true
    water_depths:
      - development: "Field_A"
        depth_ft: 4500
      - development: "Field_B"
        depth_ft: 7200
    output:
      classification_file: "results/dev_system_classification.csv"

3. Cashflow Modeling

Generate monthly cashflow projections.

Copyfdas_economics:
  cashflow_modeling:
    flag: true
    development_name: "ANCHOR"
    assumptions_file: "config/lease_assumptions.xlsx"
    dev_system: "subsea15"
    wti_price: 75.0  # or price_curve file
    analysis_period_years: 20
    output:
      cashflow_file: "results/monthly_cashflows.csv"
      summary_file: "results/cashflow_summary.json"

4. Complete Field Analysis

End-to-end economic analysis with BSEE data.

Copyfdas_economics:
  field_analysis:
    flag: true
    development_name: "ANCHOR"
    bsee_data_path: "data/modules/bsee/current"
    assumptions_file: "config/lease_assumptions.xlsx"
    discount_rate: 0.10
    wti_scenario:
      base: 75.0
      low: 55.0
      high: 95.0
    output:
      excel_report: "results/anchor_economics.xlsx"
      html_report: "results/anchor_analysis.html"
      json_summary: "results/anchor_summary.json"

Python API

Financial Calculations

Copyfrom worldenergydata.modules.fdas import (
    calculate_npv,
    excel_like_mirr,
    calculate_irr,
    calculate_payback,
    calculate_all_metrics
)
import numpy as np

# Define cashflows (negative = investment, positive = returns)
cashflows = np.array([-1500, -500, 200, 800, 1200, 1000, 800, 600, 400, 200])
discount_rate = 0.10  # 10%

# Calculate NPV
npv = calculate_npv(cashflows, discount_rate, period='annual')
print(f"NPV: ${npv:,.2f}M")

# Calculate MIRR (Excel-compatible)
mirr_monthly, mirr_annual = excel_like_mirr(cashflows, discount_rate)
print(f"MIRR (Annual): {mirr_annual:.2%}")

# Calculate IRR
irr_monthly, irr_annual = calculate_irr(cashflows)
print(f"IRR (Annual): {irr_annual:.2%}")

# Calculate Payback Period
payback = calculate_payback(cashflows)
print(f"Payback: {payback:.1f} years")

# Calculate all metrics at once
metrics = calculate_all_metrics(cashflows, discount_rate=0.10)
# Returns: {npv, mirr_monthly, mirr_annual, irr_monthly, irr_annual, payback_years}

Assumptions Management

Copyfrom worldenergydata.modules.fdas import AssumptionsManager, classify_dev_system_by_depth

# Load assumptions from Excel
mgr = AssumptionsManager.from_excel('lease_assumptions.xlsx')

# Classify development system by water depth
dev_system = classify_dev_system_by_depth(water_depth=4500)
# Returns: 'subsea15' (500-6000 ft)

# Get specific assumption parameters
host_capex = mgr.get(dev_system, 'HOST_CAPEX_MM')
surf_per_well = mgr.get(dev_system, 'SURF_PER_WELL_MM')
royalty_rate = mgr.get(dev_system, 'ROYALTY_RATE')
variable_opex = mgr.get(dev_system, 'VARIABLE_OPEX_$/BBL')

print(f"Host CAPEX: ${host_capex}M")
print(f"SURF per well: ${surf_per_well}M")
print(f"Royalty rate: {royalty_rate:.1%}")

Production Processing

Copyfrom worldenergydata.modules.fdas.data import ProductionProcessor
import pandas as pd

# Load production data
production_df = pd.read_csv('production_data.csv')
processor = ProductionProcessor(production_df)

# Monthly aggregation by development
monthly = processor.aggregate_monthly(by='DEV_NAME')

# First oil identification
first_oil = processor.identify_first_oil(by='DEV_NAME')
print(f"First Oil Date: {first_oil['FIRST_OIL_DATE'].iloc[0]}")

# Cumulative production
cumulative = processor.calculate_cumulative_production(by='DEV_NAME')

# Production statistics
stats = processor.get_production_statistics(by='DEV_NAME')
print(f"Peak Rate: {stats['PEAK_RATE_BBL'].iloc[0]:,.0f} BBL/day")
print(f"Total Production: {stats['TOTAL_OIL_BBL'].iloc[0]:,.0f} BBL")

Drilling Timeline Extraction

Copyfrom worldenergydata.modules.fdas.data import DrillingTimelineExtractor

# Extract drilling timeline
extractor = DrillingTimelineExtractor(well_data)

timeline = extractor.extract_timeline(
    development_name='ANCHOR',
    gap_months=3  # Campaign gap threshold
)

print(f"First Spud: {timeline['first_spud']}")
print(f"Last Completion: {timeline['last_completion']}")
print(f"Total Drilling Months: {len(timeline['drilling_monthly'])}")

Cashflow Engine

Copyfrom worldenergydata.modules.fdas.analysis import CashflowEngine
from datetime import datetime

# Initialize cashflow engine
engine = CashflowEngine(assumptions_mgr, dev_system='subsea15')

# Generate monthly cashflows
cashflows = engine.generate_monthly_cashflow(
    production_monthly=monthly_production,
    drilling_timeline=timeline,
    wti_prices=wti_price_dict,  # {month_str: price}
    first_oil_date=datetime(2025, 1, 1)
)

# Each cashflow contains:
# - Revenue (oil sales)
# - Royalties
# - OPEX (variable and fixed)
# - CAPEX (drilling, facilities, host)
# - Net cashflow
for cf in cashflows[:3]:
    print(f"{cf.month}: Revenue=${cf.revenue_usd:,.0f}, "
          f"OPEX=${cf.opex_usd:,.0f}, Net=${cf.net_cashflow_usd:,.0f}")

BSEE Data Integration

Copyfrom worldenergydata.modules.fdas import BseeAdapter
from pathlib import Path

# Initialize BSEE adapter
adapter = BseeAdapter(Path('data/modules/bsee/current'))

# Load data by development
dev_data = adapter.load_by_development('ANCHOR')
production = dev_data['production']
wells = dev_data['wells']

print(f"Production records: {len(production)}")
print(f"Wells: {len(wells)}")

# Load production for date range
production = adapter.load_production(
    start_date='2020-01-01',
    end_date='2024-12-31'
)

Excel Report Generation

Copyfrom worldenergydata.modules.fdas.reports import FDASReportBuilder

# Generate formatted Excel report
builder = FDASReportBuilder(
    development_name='ANCHOR',
    cashflows=cashflows,
    assumptions=assumptions_mgr,
    dev_system='subsea15'
)

builder.generate_report('anchor_economics.xlsx')
print("Excel report generated: anchor_economics.xlsx")

Complete Workflow Example

Copyfrom worldenergydata.modules.fdas import (
    AssumptionsManager,
    BseeAdapter,
    calculate_all_metrics
)
from worldenergydata.modules.fdas.data import (
    ProductionProcessor,
    DrillingTimelineExtractor
)
from worldenergydata.modules.fdas.analysis import CashflowEngine
from pathlib import Path
import numpy as np

# 1. Load assumptions
mgr = AssumptionsManager.from_excel('lease_assumptions.xlsx')

# 2. Load BSEE data
adapter = BseeAdapter(Path('data/modules/bsee/current'))
dev_data = adapter.load_by_development('ANCHOR')

# 3. Process production
processor = ProductionProcessor(dev_data['production'])
monthly_production = processor.aggregate_monthly(by='DEV_NAME')

# 4. Extract drilling timeline
extractor = DrillingTimelineExtractor(dev_data['wells'])
timeline = extractor.extract_timeline('ANCHOR')

# 5. Determine development system
water_depth = dev_data['wells']['WATER_DEPTH'].mean()
dev_system = 'subsea15' if water_depth < 6000 else 'subsea20'

# 6. Generate cashflows
engine = CashflowEngine(mgr, dev_system)
wti_prices = {str(m): 75.0 for m in monthly_production['YEAR_MONTH']}
first_oil = processor.identify_first_oil(by='DEV_NAME')['FIRST_OIL_DATE'].iloc[0]

cashflows = engine.generate_monthly_cashflow(
    monthly_production,
    timeline,
    wti_prices,
    first_oil
)

# 7. Calculate financial metrics
cf_array = np.array([cf.net_cashflow_usd for cf in cashflows])
metrics = calculate_all_metrics(cf_array, 0.10)

print(f"NPV (10%): ${metrics['npv']/1e6:,.1f}M")
print(f"MIRR: {metrics['mirr_annual']:.2%}")
print(f"IRR: {metrics['irr_annual']:.2%}")
print(f"Payback: {metrics['payback_years']:.1f} years")

Key Classes

Development Systems

Output Formats

Financial Metrics JSON

Copy{
  "development": "ANCHOR",
  "discount_rate": 0.10,
  "npv_mm_usd": 1523.45,
  "mirr_annual": 0.1823,
  "irr_annual": 0.2156,
  "payback_years": 4.2,
  "total_capex_mm": 2500.0,
  "total_revenue_mm": 8500.0,
  "profitability_index": 1.61
}

Cashflow Summary CSV

Copymonth,revenue_mm,royalty_mm,opex_mm,capex_mm,net_cashflow_mm,cumulative_mm
2025-01,45.2,5.9,8.5,0.0,30.8,-1969.2
2025-02,48.5,6.3,8.8,0.0,33.4,-1935.8
2025-03,52.1,6.8,9.2,0.0,36.1,-1899.7

Best Practices

1. Use Excel-compatible MIRR - Always use excel_like_mirr() for consistency with spreadsheet models
2. Validate assumptions - Review assumptions file before running analysis
3. Check data quality - Verify BSEE data completeness before economic analysis
4. Document scenarios - Track WTI price and discount rate assumptions
5. Version control - Keep assumptions files and reports versioned

Validation

FDAS module validated against original implementation:

• NPV: 100% match
• MIRR: 100% match (0.00e+00 difference)
• Performance: 3x faster than original

Related Skills

• npv-analyzer - Simplified NPV calculations
• production-forecaster - Production decline curves
• bsee-data-extractor - BSEE data loading

References

• FDAS V30 Original Implementation
• DNV Financial Analysis Guidelines
• SPE Economic Evaluation Guidelines