Fdas Economics
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)
Python API
Financial Calculations
from worldenergydata.fdas import (
calculate_npv,
excel_like_mirr,
calculate_irr,
calculate_payback,
calculate_all_metrics
)
import numpy as np
*See sub-skills for full details.*
### Assumptions Management
```python
from worldenergydata.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)
*See sub-skills for full details.*
### Production Processing
```python
from worldenergydata.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')
*See sub-skills for full details.*
### Drilling Timeline Extraction
```python
from worldenergydata.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
from worldenergydata.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,
*See sub-skills for full details.*
### BSEE Data Integration
```python
from worldenergydata.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']
*See sub-skills for full details.*
### Excel Report Generation
```python
from worldenergydata.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
from worldenergydata.fdas import (
AssumptionsManager,
BseeAdapter,
calculate_all_metrics
)
from worldenergydata.fdas.data import (
ProductionProcessor,
DrillingTimelineExtractor
)
*See sub-skills for full details.*
## Key Classes
| Class | Purpose |
|-------|---------|
| `calculate_npv` | Net Present Value calculation |
| `excel_like_mirr` | Excel-compatible MIRR calculation |
| `calculate_irr` | Internal Rate of Return calculation |
| `calculate_all_metrics` | Calculate all financial metrics at once |
| `AssumptionsManager` | Load and manage development assumptions |
| `ProductionProcessor` | Process and aggregate production data |
| `DrillingTimelineExtractor` | Extract drilling schedules |
| `CashflowEngine` | Generate monthly cashflow projections |
| `BseeAdapter` | BSEE data loading and integration |
| `FDASReportBuilder` | Excel report generation |
## Related Skills
- [npv-analyzer](../npv-analyzer/SKILL.md) - Simplified NPV calculations
- [production-forecaster](../production-forecaster/SKILL.md) - Production decline curves
- [bsee-data-extractor](../bsee-data-extractor/SKILL.md) - BSEE data loading
## References
- FDAS V30 Original Implementation
- DNV Financial Analysis Guidelines
- SPE Economic Evaluation Guidelines
## Sub-Skills
- [Best Practices](best-practices/SKILL.md)
## Sub-Skills
- [1. Financial Metrics Calculation (+3)](1-financial-metrics-calculation/SKILL.md)
- [Development Systems](development-systems/SKILL.md)
- [Financial Metrics JSON (+1)](financial-metrics-json/SKILL.md)
- [Validation](validation/SKILL.md)
