fdas-economics

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.
type	reference
capabilities	[]
requires	[]
tags	[]
category	data
version	1.0.0

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']}")
()

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)