30 WW W.OGFJ. COM | OIL & GAS FINANCIAL JOURNAL NOVEMBER 2017
F3: SWEEP OF CAPITAL-MARKET LINES ANCHORED BY
VARYING INTERES T RATES
Portfolio Monte Carlo simulations of 2500 Oil $50
0.0 0.2 0.4 0.6 0.8 1.0 1. 2 1. 4
Note: Sweep of capital-market lines anchored by interest rates varying from 2.5% to 8% (i.e., solid lines with positive intercepts). Strike zone (dashed box) associated with interest rate of 2.5%, shrinks with Fed rate hike and expands with cut (imagine base moving up and down).
F4: WEIGHTED AVERAGE COS T OF CAPI TAL S WEEP WITH
INTEREST RATE AND COST OF EQUITY
Note: Weighted average cost of capital sweep with reference interest rates ( 2.5%, 5.25%, 8%) and cost of equity
( 17.5%, 27.5%, 38.1%) for selected portfolios along the efficient frontier (Figure 3, green, blue, and red stars).
Relative WACC (vertical axis) simply means it can be calibrated to operator-specific cost of equity.
Will oil be $50 or $80 by 2020? Investing in new high-impact projects is about making
probabilistic inference with imperfect information about unknowable events in future.
With the advent of data analytics and machine-learning algorithms, we will get
past using averages for major investment decisions, better exploit the spread, and
explore the “data” more fully and efficiently. As continuous improvement we can only
get better with data, reasons being:
• Financial technology – efficient frontier, debt-equity calculus, faster what-if and
visual risk-return dynamic, constitute the first line of defense against irrational
• Deeper learning – at the junction of
first principle and learning algorithm,
the hybrid approach bodes well to
incorporate oil and gas production
data at the well and asset level, develop actionable insights and a robust
strategy to thrive in the new normal
“lower for longer” world.
When we have access to more data
and train machines to learn, it will be
possible to minimize the avoidable bias,
i.e., closing the gap between how well an
experienced practitioner can do versus
a machine. Our ultimate goal is to digitize
prudent decision-making by capitalizing
on the advances in algorithm and computing, come up with a better way to
quantify and manage risk, as well as optimize debt-equity funding. Connect
strategy to execution and move the needle.
We thank Rystad Energy for providing
the production data for portfolio simulation and analysis.
ABOUT THE AUTHORS
Patrick Ng is a partner at
Real Core Energy in Houston. Ng has a focus on acquisitions and divestitures.
He held operations and
technology leadership positions at WesternGeco and Fugro, developing and bringing solutions to market
at the intersection of data and technology. He earned his MS in geology and
geophysics from Yale University and has
an MBA from the University of
Geoffrey Wong, CFA is a
portfolio manager at IAM
Legacy, a family office in
Hong Kong. He has worked
previously as an analyst and
a portfolio manager for several major financial institutions and boutique investment banks in New York,
London, and Hong Kong. He received a
BA from the University of California,
Berkeley and an MEng from Cornell