E&P companies frequently mention portfolio resilience when describing their strategic plans or the composition of their opportunity inventories. As a general concept, the ability to recover from adversity quickly and deliver performance objectives under multiple scenarios is a critical characteristic in any strategic plan. While there is little disagreement in the value of ‘resilience’ as a portfolio characteristic, there is significant variance in the ways this attribute is described or measured as related to E&P portfolios. In many cases, available planning software or valuation tools drive the ways that resilience is considered, resulting in measures that may or may not actually reflect the ability to adapt and still deliver desired performance objectives. This paper provides a structured and repeatable method for quantifying portfolio resilience, with examples provided to clarify the value in applying these techniques.

To demonstrate the methodology, a simple portfolio model reflecting a typical E&P asset portfolio was developed. Resilience as measured using this approach is the ability for a given strategy to deliver set performance expectations when exposed to a broad range of uncertainties or adverse scenarios (above and below ground). These adverse scenarios or ‘torpedoes’ are applied to the portfolio and the cumulative performance difference is computed across multiple objectives and used as a measure of aggregate downside impact. Measuring ‘resilience’ as the ability of a portfolio to recover from selected ‘torpedoes’ in delivering performance is quantified, and portfolio selections are then optimized using linear programming to maximize the resilience measure. The resulting performance of this portfolio is then compared to other optimized portfolios to quantify the trade-offs between performance, resilience, value, and risk. The examples provided clearly depict the value in quantifying a resilience measure and introducing this into the decision-making process. Some of the alternative methods of assessing resilience will also be assessed in this same frame, allowing a direct comparison of these techniques.

While the concepts and techniques outlined in this paper are not entirely new, the combination of aggregate variance computation, linear programming, and rapid assessment of alternative scenarios (torpedoes) provides a practical and replicable measure of E&P portfolio resilience. As objectives and key performance measures are typically fluid, these methods may be adapted quickly for real-time insights into portfolio decisions and value versus risk trade-offs between competing strategic plans.

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