O&G Synergy Optimizer

A quantitative model utilizing constrained linear programming to sequence post-merger integration initiatives in the Oil & Gas sector.

The Business Problem

As highlighted by McKinsey's "Transact to Transform" M&A thesis, mega-mergers in the Permian basin often fail to capture full value because integration initiatives bottleneck physical resources. Companies attempt to extend laterals, standardize facilities, and deploy SimulFrac fleets simultaneously, exceeding the available rig fleet and engineering talent.

The Solution

This Python-based engine moves beyond static Excel DCF valuation. It uses PuLP to frame integration as an optimization problem: maximizing the Net Present Value (NPV) of realized synergies subject to absolute physical constraints (e.g., maximum concurrent rigs) and capital expenditure limits.

Note: The initiatives.csv dataset contains mock parameters designed strictly to demonstrate the mathematical constraints and logic of the optimization engine.

Beyond Oil & Gas: Adaptability for Quantitative Finance

The underlying mathematical engine (linear programming) is agnostic and can be easily adapted for general capital and resource allocation scenarios often found in trading and portfolio management. By modifying the input CSV and constraint variables, this tool solves similar optimization problems:

• Trade Ideas & Portfolio Construction: Swap O&G initiatives for specific trades (e.g., Long Equities, Arbitrage Strategies).
• Yield Maximization: Swap cash flow uplift for expected return/yield.
• Risk & Capital Constraints: Swap physical constraints (rigs) for margin requirements, sector risk limits, or total available capital.

How to Run

1. Clone the repository.
2. Install requirements: pip install -r requirements.txt
3. Run the Jupyter Notebook synergy_analysis.ipynb to view the optimal integration sequence.