Energy Investing

Oil & Gas Moneyball: Why Statistical Independence is Beneficial

Statistical independence can lower production risk for investors in unconventional oil and gas wells through diversification in low-permeability wells.

Oil & Gas Moneyball: Why Statistical Independence is Beneficial

Published on

October 12, 2023

Over the last two decades, unconventional oil and natural gas wells have emerged as the dominant source of energy production in the United States. Despite the complexity of drilling these wells and forecasting production volumes, investors in such assets can take advantage of several mathematical principles to reduce the risk in their portfolios. This article dives into the practical application of the Central Limit Theorem and Law of Large Numbers within the energy sector and explores how the characteristics of unconventional reservoirs and diversified assets can work together to reduce production volatility for investors. 

Reservoir Permeability - Friend or Foe?

As discussed in this article, the permeability of a reservoir determines if it’s better suited for conventional or unconventional drilling. Drilling in a conventional reservoir (i.e. one with high permeability) isn’t always a lower-risk option. The high permeability in conventional reservoirs means that every well drilled extracts from the same collective pool of hydrocarbons. This means that every additional well drilled can negatively impact the performance of surrounding wells. As an analogy, if two people are sharing a milkshake, there is less to go around. In unconventional reservoirs, everyone gets their own milkshake.

An unconventional reservoir's low permeability can actually be more enticing to an operator for exactly this reason. Because the low permeability inhibits the movement of hydrocarbons within the reservoir, each individual well interacts less with the others, with minimal impact on production. If the distances between laterals, or spacing, is sufficiently large, then the performance of wells in the reservoir are more independent from one another.

How Statistical Independence Reduces Risk

Enter the Central Limit Theorem (CLT) and the Law of Large Numbers (LLN). The CLT states that selecting samples from a population (i.e. wells from a geographic area) that are statistically independent from one another and relatively similar results in sample means that trend towards a normal distribution. The LLN states that the confidence interval of a given sample of wells (i.e. package) is inversely proportionate to the square root of the number of wells or samples within the package. For energy investors, this means that portfolio volatility is expected to decrease as more wells are added to one's portfolio.

For example, if an investor who owns an interest in a single well with a 30% annual production volatility adds more wells with the same level of volatility to their portfolio, we expect their portfolio’s overall production volatility to decrease below 30%.

This table demonstrates how a portfolio’s production volatility might change as the number of wells in the portfolio increases.

Despite the fact that individual wells can have large production volatilities, increasing the number of wells gradually decreases that production volatility for the portfolio as a whole. Thus, investors can develop a portfolio with reduced production risk - all thanks to the geophysical properties of low permeability rock formations and their implication on statistical analysis. 

Energia: Fractional Ownership In Many, Diversified Assets

Investors in oil and gas can mitigate much of the production risk by taking a diversified approach to their portfolio. Offerings on Energia’s platform have been carefully curated by sponsors to take ownership interests across as many wells as possible in order to reduce production volatility risk for investors. Energia’s online platform is the first commercially available tool for investors to quantify this risk, offering greater transparency into the investing process. For more information, go to 

The information provided in this article is for educational and informational purposes only. It does not constitute financial, investment, or professional advice. Readers should conduct their own research and consult with relevant experts before making any financial decisions. The author and publisher are not liable for any actions or decisions taken based on the contents of this article.

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