Categories
Finance & Economics Transfer

Legendary Investor Peter Lynch Shares His Bullish Oil Views

Photo: Chris Liverani, Unsplash

During his 13 years managing Fidelity’s Magellan Fund, Peter Lynch navigated the fund to returns double that of the S&P 500.  From 1977 to 1990, the Magellan Fund’s annual returns averaged more than 29%, with assets under management growing from $18 million to $14 billion.

Mr. Lynch has been credited with bringing a variety of investment frameworks to the masses using common-sense terminology: “Invest in What You Know”, “Growth at a Reasonable Price”, and my personal favorite, “Ten Bagger”— a stock that increases in value by at least 10 times its purchase price.

Victims of Their Own Success

As energy specialists can tell you from experience, “investing in what they know” has been painful over the last decade.  OFS bellwether Schlumberger is priced below 2008 crisis levels, as are several global E&Ps.  If nothing else, these producers have proven their ability to grow over the past decade, flooding the market with excess capacity.  “Growth at a Reasonable Price” is a perfect description of the current backdrop: prices flirting with all-time lows as a result of remarkable production growth.

Back to School

Like any commodity product, oil prices can be simply described as a function of supply and demand. From Econ 101: reduced demand at the same level of supply reduces the market clearing price, as does increasing supply with a constant level of demand.

Currently, public energy equity investors believe that both are happening simultaneously: supply is increasing (ample US shale) and demand is falling (trade wars, coronavirus).  The result is rapid declines in commodity prices, and the enterprise values of the firms that produce them.

Peter Lynch On Demand

“Everybody’s assuming the world’s going to not use oil for the next 20 years, or next year.  China might sell five million electric vehicles next year, but they might also sell 17 million internal combustion engines. … Near term, liquid natural gas and liquid petroleum gas might replace diesel fuel for trucks.”

Peter Lynch, Barron’s

Peter Lynch On Supply

“The difference between a glut and a short is 1 million barrels per day.  The world consumes 100 million barrels per day … and shale’s going to slow down.  We’ve gone from [producing] 5 million barrels per day in the US, to 12.5.  People think that’s going to continue; I don’t think it will.”

Peter Lynch, Fox Business

A “Ten Bagger” In the Making?

Historically, Peter Lynch has shown an uncanny ability identify investment opportunities using simple frameworks.  After examining both supply and demand assumptions, Mr. Lynch is of the opinion that supply assumptions are overly bullish, while demand assumptions are too bearish. Put these two things together, and energy equities may be poised for a “ten bagger”.

Categories
Data Science & Analytics Production & Operations Transfer

JPT Reflection: Tearing Down the Walls Among Disciplines

Happy New Year to everyone. I took some time over the last couple weeks to reflect on the decade behind us, as well as the decade ahead of us.  As all of us found out on January 1st, our friends at the JPT took some time to do exactly that!  In case you missed it, here’s a link to the article

A Changing Industry 

It is obvious that data science and analytics are front-of-mind for the SPE. The group has gone so far as to redefine the role of Management and Information Director as Data Science and Engineering Analytics Director. There cannot be a clearer signal that data science and analytics are critical to the next chapter of oil and gas.  At Petro.ai, we are proud to have been at the forefront of delivering Data Science and Petroleum Analytics to the industry since 2013. 

Common Vision 

Each member of the technical leadership of SPE shares a vision for the future of petroleum technology.  The technical directors unanimously declared that our “traditionally fragmented industry must become more integrated and collaborative.  A primary solution to breaking down those barriers: the continued evolution and adoption of digital technologies.” 

Photo by Mitchell Luo on Unsplash

While there are many great quotes in the article (you’ll find several below), this is the most striking.  The group is acknowledging that the status quo isn’t good enough and is issuing a call to action to the industry.  All of us have experienced the pain of the last 5 years; we’ve made great strides to streamline and improve our processes, but the work isn’t done yet.  I am convinced that Petro.ai will help the industry achieve this goal. 

Data Science and Engineering 

“Work flows will be more consolidated and integrated, a departure from the current status quo according to discipline, de-facto norms dictated by software, or the way things have always been done … Organizations will have to break down traditional work flow-deadline mandated “compartments” through a fundamental change in their culture …”

—Birol Dindoruk, Data Science and Engineering Analytics Director

This quote is incredibly exciting for me to read, since the team at Petro.ai shares the same view.  Today, data is stored and curated according to the OFS service line that collected the data: drilling data in a drilling database, completions data in a completions database, and so on.  In order to perform any meaningful data science or analytics at the well level (much less the reservoir level), a great deal of data cleansing, engineering, and normalization must be done.  Petro.ai eliminates these repetitive tasks and empowers engineers by delivering high-caliber data and analytics tools. 

Completions 

“Ultimately, the industry will need a better understanding of the production mechanism of unconventional wells. It’s not the same as in a conventional well where it’s just plain Darcy flow through a matrix [and the industry is] not going to solve these completions challenges with just completions engineers. This is a cross-discipline issue, and our biggest companion in this is reservoir engineers.” 

—Terry Palisch, Completions Director

The gap between completions engineers and reservoir engineers remains wide, even within single asset teams.  During a recent Petro.ai training course, we asked completion engineers and reservoir engineers to list the 5 most important factors in delivering a highly productive well.  The two groups did not share a single common factor within the top five. At Petro.ai, we believe that geomechanics is critical to bridging the gap between completions engineers and reservoir engineers.  We have partnered with Dr. Mark Zoback to incorporate his expertise into Petro.ai, delivering powerful geomechanical insights to engineers of all disciplines. 

Reservoir 

“When it comes to reservoir technologies, the industry has neglected [unconventionals] for quite some time because it was always about drilling and completions. Now that cash flow has shrunk and the treadmill of drilling and completing wells has slowed, the reservoir discipline is getting more attention. More emphasis is being placed on recovery factors as companies try to squeeze more out of each existing well … For this approach to be successful … the industry needs to further improve its understanding of the unconventional reservoir.”

Erdal Ozkan, Reservoir Director

I absolutely agree with this quote; economically increasing recovery factor is the ultimate challenge in unconventionals.  One of my colleagues calls this the Shale Operator’s Dual Mandate: increase production while decreasing spend.  More simply: do more with less.  Engineers are learning every day what levers they can (and cannot) pull to achieve this goal.  The challenge is disentangling the multiplicity of factors that can impact a well’s productivity: lateral length, completion intensity, fluid system, landing zone, parent/child (horizontal spacing), parent/cousin (vertical spacing), etc.  There are simply too many factors for a human brain to internalize and reason about.  The good news? Machine learning is the perfect tool to solve interconnected, large-scale problems like this.  Petro.ai delivers pre-made machine learning models that allow operators to identify which AFE dollars matter the most, allowing engineers to spend time (and money) on things that matter and eliminate things that don’t.