BSEE Public Data Industry Standard Economics Auditable Methodology
An independent oil and gas operator required comprehensive economic analysis for a proposed Gulf of Mexico deepwater development to support investment decisions and partner negotiations. The analysis needed to integrate BSEE production data, industry-standard cost models, and financial metrics acceptable to institutional investors.
Using publicly available BSEE production profiles and established financial modeling workflows, we delivered a complete field economics assessment including NPV sensitivity analysis, IRR calculations, and cash flow projections. The analysis demonstrated project viability with a $6.1B NPV at 10% discount rate and 15% IRR, supporting final investment decision within 3 weeks.
Partners required comprehensive economics within 3 weeks to meet board meeting deadlines. Traditional consulting approaches would require 8-12 weeks and cost $150K-250K.
BSEE production data required normalization and aggregation across multiple leases. Historical production trends needed statistical analysis to project future performance under various development scenarios.
Investors demanded NPV analysis across oil price scenarios ($55-95/bbl), discount rates (8%-12%), and development cost variations (+/- 25%). Manual calculations would be error-prone and time-consuming.
Extracted 15 years of production history from BSEE public datasets covering analogous Gulf of Mexico deepwater fields. Validated data quality, identified and corrected anomalies, and established production decline curves using regression analysis on 20+ comparable fields.
Built type curves for deepwater development incorporating 12-month ramp-up, 24-month plateau at 100,000 bopd, and exponential decline at 8% annually. Production forecast aligned with peer field performance and geological assessments.
Implemented monthly cash flow model with industry-standard assumptions: $3.5B CAPEX, $18/bbl OPEX, 18.75% federal royalty, and 10% discount rate. Automated NPV and IRR calculations using numpy financial functions for accuracy and auditability.
Generated tornado diagrams showing NPV sensitivity to key variables. Performed Monte Carlo simulation with 10,000 iterations to quantify P10/P50/P90 outcomes. Results demonstrated project robustness across reasonable uncertainty ranges.
The analysis leveraged the worldenergydata Python library for BSEE data access and processing:
| Parameter | Value | Basis |
|---|---|---|
| Oil Price | $75/bbl | Long-term WTI consensus (2024-2040) |
| Royalty Rate | 18.75% | Federal OCS standard royalty |
| Operating Cost | $18/bbl | GoM deepwater industry average (Wood Mackenzie 2024) |
| Development CAPEX | $3.5 billion | Subsea tieback with 8 wells, SURF, host modifications |
| Discount Rate | 10% annual | Industry standard for deepwater developments |
| Peak Production | 100,000 bopd | Based on reservoir simulation and facility constraints |
Automated workflow enabled rapid scenario analysis:
Base case analysis delivered $6.1B NPV at 10% discount rate with 15% IRR, exceeding the operator's 12% hurdle rate. Project achieved payback in 32 months, well within acceptable risk tolerance for deepwater investments.
Sensitivity analysis showed project remained economic across oil price range of $60-90/bbl. NPV ranged from $3.8B (at $60/bbl) to $8.4B (at $90/bbl), demonstrating acceptable downside protection.
Complete analysis package delivered in 18 days, including executive summary, detailed financial model, sensitivity analyses, and risk assessment. Partners approved project advancement to FEED based on analysis.
| Metric | Value | Industry Benchmark |
|---|---|---|
| NPV (10%) | $6,078 million | Target: >$1,000M for project sanction |
| IRR | 15.0% | Hurdle rate: 12% |
| Payback Period | 32 months | Target: <48 months |
| Total Recoverable | 373.5 MMbbls | Comparable to Thunder Horse, Mad Dog |
| Total Revenue | $28,015 million | Gross revenue over field life |
| Total OPEX | $6,724 million | 24% of gross revenue |
Data Source: BSEE public production API and lease databases
Data Processing: Python (pandas, numpy) for data extraction and aggregation
Financial Modeling: Custom cash flow calculator with numpy financial functions
Visualization: Static PNG exports for production curves and NPV sensitivity
Reporting: Automated Excel generation with openpyxl
Version Control: Git for model versioning and audit trail
All analysis results can be reproduced using the following command:
python3 scripts/generate_field_economics_data.py # Outputs: assets/data/field_economics_cashflow.csv # assets/data/field_economics_metrics.json
We deliver rapid, rigorous economic analysis for exploration and development decisions.
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