Predictive analytics gives oil investors edge in M&A valuations

Potential investors are increasingly using predictive analytics in their valuations of oil producers, OAG Analytics Chief Executive Luther Birdzell has told Upstream Intelligence.

Investors are showing more interest in predictive analytics as they eye oil and gas M&A (Image credit: rs-photo / iStock)

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The oil and gas industry has typically used predictive software for operational purposes, such as estimating and improving the output of a well or predicting when equipment may require maintenance.

In the past 12 months, OAG has also started receiving requests from financial-sector firms for its analytics platforms. This trend has been precipitated by the decline in oil prices which has pushed many small- and medium-sized exploration and production companies to the brink of bankruptcy. According to Standard & Poor’s, worldwide corporate defaults reached about $50 billion in the first three-and-a-half months of 2016. Almost half of the 46 companies that defaulted came from two industries: oil and gas, and mining.

But the financial distress has also triggered interest in mergers and acquisitions. Writing for Wood Mackenzie, Corporate Analysis Research Director Luke Parker pointed out that balance sheets will become ever more stretched without asset sales to balance the books. He predicted financing options will become more limited, and debt and equity investors are unlikely to be as welcoming as they were in the first half of 2015 when there were still expectations for a quick rebound in the oil price.

Irrespective of whether oil prices go up or down, Wood Mackenzie predicts the industry will see more M&A activity in 2016 than in 2015, which was the slowest year for M&A in a decade.

Breaking down complexities

Many of the firms defaulting are not being acquired outright but are instead being broken up and sold in smaller parts. “This is making it critically important for the financial-services firms funding the oil companies to be able to understand production potential and profitability potential depending on where the oil price is,” Birdzell said.

Banks lending money to investors who want to buy or invest in oil companies are interested not only in the companies’ reserves; they also want to know about well-discounted cash-flow base valuations, and valuations of specific assets within these companies.

“We had requests to evaluate not only the value of the actual company but for numerous configurations of how the company’s assets can be divided,” Birdzell said.

Potential buyers are not always other oil producers but frequently also private-equity firms, hedge funds and investment banks holding the debts of oil companies.

Private-equity investors had anywhere between $40 billion and $100 billion of funds earmarked for investment in oil and gas in 2015, according to Wood Mackenzie. Much of the capital available was raised specifically to take advantage of the opportunities that would arise in the low oil-price environment. However, there were fewer M&A opportunities in 2015 than anticipated, which has left private-equity firms well-capitalized and well-positioned to invest this year.

“Private-equity firms are on the preferred-buyer list considering buying certain oil companies, and they have access to the companies’ data. Once we establish mutual confidentiality we analyze the data and build predictive models using machine-learning technology,” said Birdzell.

Long reach of predictive technology

Founded in 2013, OAG has worked with several leading US oil companies to develop software that analyzes large volumes of existing data to provide new insights that were previously inaccessible.

The models referred to by Birdzell can predict production not only from individual wells but even from wells that have not yet been drilled. OAG Analytics uses the short-term production data to calculate estimated ultimate recovery (EUR), net recovery over the life of the well, the cost of future drilling, and ongoing operating costs.

The final step in the analysis is to run the models through the various oil-price scenarios and calculate the net-asset value and internal rate of return for each well after one or two years.

OAG estimates production after two years using machine learning from existing data, Birdzell explained. It calculates EUR, overlays this with costs and then aggregates this information about each individual well and whatever area of interest has been specified. This gives it a production and economic profile for each well at different prices.

Although low oil prices were the trigger for this trend, a price recovery is unlikely to stymie the use of predictive analytics in company valuations. Wood Mackenzie expects oil prices to rally to $65 per barrel in the fourth quarter and forecasts that in that scenario companies will move quickly to catch the next up-cycle and re-focus from survival to growth. If anything, there is likely to be more demand for very-precise valuations in future.

By Vanya Dragomanovich