le of Contents
Managing Dynamics in the Global Oil & Gas Industry:
Past Applications & Emerging Trends
Peter J. Genta and Craig A. Stephens
PA Consulting Group, Inc.
One Memorial Drive, Cambridge, Massachusetts 02172 USA
Voice: 617-225-2700, Fax: 617-225-2631
craig.stephens@pa-consulting.com
Abstract
The global oil and gas industry is among the world’s largest and most complex, and billions of
consumers around the world experience the consequences of its dynamics every day as
inventories wax and wane and prices fall and rise. The industry has a long history of System
Dynamics applications in three main areas: 1) market dynamics, the interactive movements of
capacity, supply, demand, and prices; 2) business dynamics, the performance-driving
interactions of corporations and their business units with suppliers, customers, competitors and
other stakeholder groups; and 3) project dynamics, the interactions driving cost and schedule
performance on the complex projects that develop new reserves and production/distribution
capacity.
System Dynamics has a bright future in the oil & gas industry, being in a unique position to
contribute to more systematic management that will drive faster and more consistent growth of
shareholder value. The future will see a blending of market, business and project dynamics,
reflected in models that integrate the commoditized marketplace and asset portfolio
management. These models will be the analysis engines for management systems that are fully
integrated into the strategy-forming, planning and decision-making processes of the major oil
and gas companies.
Keywords: Project dynamics, market dynamics, business dynamics, oil & gas, commodities,
supply & demand.
Introduction
The global oil and gas industry is among the world’s largest and most complex, and billions of
consumers around the world experience the consequences of its dynamics every day as
inventories wax and wane and prices fall and rise. The industry has a long history of System
Dynamics applications in three main areas: 1) market dynamics, the interactive movements of
capacity, supply, demand and prices; 2) business dynamics, the performance-driving interactions
of corporations and their business units with suppliers, customers and competitors; and 3) project
dynamics, the interactions driving cost and schedule performance on the complex exploration
and development projects.
This paper is a summary view of business dynamics issues and System Dynamics applications in
the global oil and gas industry from the vantage point of PA Consulting Group and its clients. It
is not an exhaustive survey of such applications, although we have cited relevant journal articles
for those who are interested.
1) Market Dynamics
In California, weather conditions in the Summer of 2000 coupled with decades of little capacity
growth caused wholesale electricity prices to soar well over $100 per megawatt-hour. Yet just
two years later long-term supply contracts are being signed below $20 per megawatt-hour. The
electricity market did not move alone — oil prices sank below $18 per barrel from $34 in 2000
and in May of this year rose again to over $30, and natural gas prices hover below $2.00 per mcf
down from over $5.00 last year.
The deregulated markets for energy are driven by the same laws of economics that operate in
other agricultural and mineral commodities. Most commodities follow a recurring demand/price
pattern of boom succeeded by bust. Prices are high when the supplies are tight, and prices are
low when capacity is abundant and demand is relatively scarce. Many markets have excess
capacity, and substantial fixed costs in these strongly interlinked industries make for volatile
pricing.
Although it is common knowledge that oil, gas and power are volatile commodities, big bets are
made every day and many of them go wrong. Enron (for example) apparently thought they were
adequately hedged, yet falling energy prices contributed significantly to their downfall. The
State of California “solved” its electricity crisis by entering into long term supply contracts at
$40-$50 per megawatt-hour, well above current wholesale prices.
For centuries economists have been trying to reduce business risk by understanding and
predicting commodity capacity/supply/demand/price movements. The advent of the digital
computer initiated a steady stream commodity price forecasting models of various types,
including some System Dynamics models. Yet it is widely acknowledged that the models in
common usage do a poor job of predicting commodity capacity/supply/ demand/price
movements beyond the very short term, and require significant judgmental inputs even for short-
term forecasting. As a result, in the oil and gas business (as in other parts of the energy industry)
it is widely believed that reliable price forecasting is a pipe dream, and investment and other
management decisions are often made based on forward price curves.
The presumption in this trading-based approach is that forward curves represent the best
available information. But forward curves embody two substantial and well-known
shortcomings as decision-support tools: 1) they are poor predictors of price movements in
volatile markets; and 2) many management decisions involve risk-bearing commitments that
extend well beyond the liquidity tenor of the commodities involved — that is, beyond the point
where markets become illiquid and forward curves cease to be available. The inadequacy of
conventional oil and gas models is likely to increase as markets are increasingly connected
dynamically — the oil and gas markets are more tightly coupled now than in the past, and gas and
electric power markets are rapidly becoming more coupled as well.
Given the industry’s huge cash flows, risky high-stakes decisions and history of price volatility,
a more reliable capacity/supply/demand/price forecasting capability would be worth a great deal.
In our experience there are two fundamental reasons why most oil-industry price forecasting is
chronically unreliable beyond the very short term. The first is that most forecasting efforts rely
on static, open-loop models that, in effect, seek to replace missing feedback loops with
exogenous data inputs. Models with a useful breadth of industry detail will involve far too many
such inputs for those inputs ever to be internally consistent over time, and this inconsistency is a
built-in source of forecast unreliability. System Dynamics modeling helps to reduce this source
of forecasting unreliability — by making it possible to replicate real-world feedback loops in the
computer, it dramatically reduces the number of required data inputs and thus the potential for
significant inconsistencies between them.
The second reason for forecast unreliability is what we term The Scenario Problem, a forecasting
by-product of model boundary issues. In a nutshell, a forecast is only as good as the business
scenario on which it is based. That’s true even for a theoretical perfect model, but in assessing
forecast reliability most skeptics do not differentiate between the reliability of the model and that
of the modeled scenario. System Dynamics helps to reduce this source of forecasting
unreliability as well because of its speed advantage against static, open-loop models. All other
things being equal, a dynamic model will require only about one-tenth of the input data needed
for an open-loop model covering equivalent organizational or industry scope — this gives a
roughly proportionate reduction in the turn-around time for scenario analyses. It makes it
possible to simulate and analyze many more scenarios, including Fit-Constrained Monte Carlo
analyses requiring hundreds or thousands of simulation runs. If approximate probabilities can be
attached across a range of such scenarios, the entire scenario spectrum can serve as the basis for
multiple Monte Carlo simulations and for the design and testing of management decisions and
policies. In this way System Dynamics modeling can provide a solution to The Scenario
Problem and its effects on the reliability of oil & gas market forecasts.
Some in the oil and gas industry will wonder why we do not include the policies and actions of
the OPEC cartel as a perhaps insurmountable barrier to oil & gas market forecasting reliability.
That is because, like other organizations, cartels have dynamics that can be reliably simulated —
OPEC is no exception. Those dynamics have a great deal to do with the internal politics and
shifting balance of power between OPEC members and with the balance of capacity, supply and
demand between OPEC and non-OPEC nations. These tend to shift systemically in normal times
based on supply, demand and price movements in the marketplace. That they can be reliably
simulated is evidenced by a model of global oil market dynamics PA built and operated for a
major oil company. The consequences of non-systemic OPEC actions (another Arab oil
embargo, for example) are readily simulated using scenarios inputs to the model, inputs which
can be the basis for Monte Carlo simulation.
Today the main barrier to development and application of such market models in the oil & gas
industry is not technical, rather, it is the skepticism of many in the industry that such models are
feasible or that they can be reliable. But given that it has been done already, and that
comparably complex models of global markets are in use in other industries, such skepticism is
likely to influence the rate at which dynamic market models are employed in the oil and gas
industry and not whether they are so employed.
PA Models Description Client engagements
Natural gas Simulates the supply, demand and Several with major oil, gas
upstream market pricing dynamics of a regional natural | and gas transmisiOn firms in
model gas market North America
Crude Oil Supply _ | Simulates Global upstream With two major oil and gas
and Demand production and Refinery Capacity; firms
Model Shows the drivers of the cyclical price
differential between heavy and light
crude oil.
References:
Armstrong, J. (1985) Long Range Forecasting: From Crystal Ball to Computer. New York: John
Wiley.
Davidsen, P., J. Sterman, and G. Richardson (1990) A petroleum life cycle model for the United
States with endogenous technology, exploration, recovery, and demand, System Dynamics
Review 6(1), 66-93.
Ford, A. (1997) System dynamics and the electric power industry. System Dynamics Review
13(1), 57-85.
Meadows, D.L. (1970) Dynamics of Commodity Production Cycles. Waltham, MA: Pegasus
Communications.
Paich, M. and J. Sterman (1993) Boom, bust, and failures to learn in experimental markets,
Management Science 39(12), 1439-1458.
Sterman, J. (1998b) Modeling the formation of expectations: The history of energy demand
forecasts, International Journal of Forecasting 4, 243-259.
Weymar, H. (1968) Dynamics of the World Cocoa Market. Cambridge, MA: MIT Press.
2) Business Dynamics
The upstream oil and gas industry is highly capital intensive even when overall capacity is not
being expanded. Production volumes from a portfolio of producing assets will decline rapidly
without significant new capital investment each year. Worldwide the industry spends over $100
Billion annually on exploration and production capital projects, and this rises significantly in
periodic waves of capacity expansion.
Oil & gas producers face hard investment decisions because it is difficult to know whether a
given project will ever pay off. They place big bets on capital projects with a thirty-year lifespan
in a market where prices fluctuate widely and frequently. System Dynamics has helped
managers understand the interconnections and tradeoffs between exploration and development
projects, reserve additions, production profiles, expected revenue and cash flows.
Recently there has been a strong move towards an “asset-light” business model in the energy
industry. Enron’s spectacular demise masks the fact that more conservative trading firms seem
to succeeding with this new business model. Older capital-intensive energy firms are
establishing trading arms and trying to decide on the right mix of asset-heavy and asset-light
businesses. There are many unanswered questions about how to manage these new businesses
both alone and alongside their more capital-intensive cousins.
The future of energy business simulation is being driven by this new and broader view of the
assets on which the industry is built. Many new risks are associated with financial rather than
physical assets, in the form of new types of supply contracts between players at different points
in the industry supply chain. Awareness is growing that physical and financial assets cannot be
adequately managed as stand-alone entities, that risks and management decisions alike ripple
through each company’s whole portfolio of assets and must be understood and managed at the
portfolio level.
This represents a significant challenge for oil and gas companies, because the data, information
systems and models to quantify and manage portfolio risk have not yet been integrated. The
large number of active business elements (both physical and financial), the many connections
between such elements, and the pronounced effects of market volatility on the performance of
those elements make this a dynamically complex problem. We are convinced that System
Dynamics is an essential element of solutions that will emerge during the next decade. We
expect that application of System Dynamics will bring about a significant change in how the oil
& gas industry defines, measures and manages risk. At present risk management is fragmentary
and incomplete: System Dynamics will help to make it comprehensive and integrated.
PA Models Description Client engagements
Upstream Oil & Business Unit Model of E&P sector Multiple oil & gas companies
Gas Exploration evaluates strategies for increasing
and Production production and cash flow
Business Simulates the consequences of Multiple oil & gas companies
Dynamics of the various strategies for entering a new
Exploration area to explore for oil and gas.
Process
Gas Pipeline Simulates the monopoly utility Gas transmission company
Utility De- provider and evaluates the
Regulation Model | consequences of strategies for moving
toward a deregulated environment.
Reputation Simulates impact of company’s Multinational oil & gas
Strategy “green” investment (including company.
interactions with stakeholder &
special interest groups) on reputation
and shareholder value
References:
Bunn, D. and E. Larsen (eds.) (1997) Systems Modelling for Energy Policy. Chichester,
England: John Wiley and Sons.
Forrester, J.W. (1964) Modeling the dynamic processes of corporate growth. Proceedings of the
IBM Scientific Computing Symposium on Simulation Models and Gaming. Reprinted in
Forrester, J.W. (1975a) Collected Papers of Jay W. Forrester. Waltham, MA: Pegasus
Communications.
Genta P, et al (1994) How to Use System Dynamics to Create Your Own Future: A Case Study
of a Worldwide Oil and Gas Exploration Group, proceedings from the International
System Dynamics Conference.
Genta, P. and N. Sokol (1993) Applying a Systems Thinking Approach to Business Process Re-
Engineering: A Case Study of a Canadian Oil and Gas Producer, proceedings from the
International System Dynamics Conference.
Lyneis, J. (1980) Corporate Planning and Policy Design. Waltham, MA: Pegasus
Communications.
Naill, R. (1973) The discovery life cycle of a finite resource : A case study of U.S. natural gas, in
Meadows, D.L. and D.H. Meadows (eds.), Toward Global Equilibrium: Collected Papers.
Waltham, MA: Pegasus Communications, 213-256.
Packer, D. (1964) Resource Acquisition in Corporate Growth. Cambridge, MA: MIT Press.
Vennix, J. (1996) Group Model Building: Facilitating Team Learning Using System Dynamics.
Chichester, England: John Wiley and Sons.
Project Dynamics
Include project portfolios both for exploration and for development.
Because the oil and gas industry is so capital intensive, growth of shareholder value depends
greatly on the return it can secure on that capital. The industry record is not good — it routinely
averages 8-9% ROE, well under the overall market average ROE of 12-13% for publicly held
companies. This underperformance is a result of the high complexity and capital cost of
individual projects in the oil and gas industry, and of the great difficulty the industry has in
avoiding cost and schedule overruns on those projects. While projects have been growing
steadily in complexity and risk, the project analysis tools and management methods in regular
use have not advanced significantly.
A deepwater production platform usually costs between $200 million and $1 Billion and take
several years to design and build. A recent study by the Norwegian government of 13
deepwater projects costing a total of $ 9.5 Billion found that development costs averaged 27%
over budget. Cost overruns of that magnitude make the difference between a project generating
12% ROE and one returning 9%. In addition, significant delays in first oil or gas production and
revenues are also commonplace on such projects. In the next few years the stock price of more
than one major oil company will be sharply affected by the rate at which new oil and gas
production come on line from deepwater fields. For such companies, project performance will
directly affect shareholder value.
It is the dynamics of complex projects and project portfolios that make them so prone to cost and
schedule overruns. Since the late 1970s System Dynamics models have been a highly effective
means of improving performance of complex projects in the aerospace, shipbuilding, computer
software, civil construction and automotive industries. The oil & gas industry has lagged behind
these other industries in making use of System Dynamics to facilitate the management of
complex-project management. For these other industries complex projects are the primary
source of revenue, which probably explains why they are ahead of the oil industry in employing
System Dynamics as a project management tool. But the oil industry in beginning to realize that,
although complex projects are not themselves a source of revenue, without new management
methods they have the potential to destroy increasing amounts of shareholder value.
Increasingly the oil & gas industry is launching mega-projects that consist of several large,
interdependent field development projects. As a result, there is an increasing need for tools that
support the management of entire portfolios of multiple complex projects with strong
interdependencies. The automotive industry is the leader in applying System Dynamics to better
manage portfolios of projects, and this technology is expected to spread into the oil and gas
industry.
PA Models Description Client engagements
The Project Simulates the dynamics of complex Multiple firms managing
Management projects and portfolios of such onshore & offshore
Modeling System | projects development projects
(PMMS)
References:
Cooper, K. (1980) Naval ship production: A claim settled and a framework built, /nterfaces
10(6), 20-36.
Cooper, K. (1993a) The rework cycle: Why projects are mismanaged, PM Network (Feb), Project
Management Institute, Newtown Square, PA 19073, 5-7.
Cooper, K. (1993b) The rework cycle: How it really works...and reworks..., PM Network (Feb),
Project Management Institute, Newtown Square, PA 19073, 25-28.
Cooper, K. (1993c) The rework cycle: Benchmarks for the project manager, Project Management
Journal 24(1), 17-21.
Cooper, K. (1994) The $2,000 hour: How managers influence project performance through the
rework cycle, Project Management Journal 25(1), 11-24.
Cooper, K. and T. Mullen (1993) Swords and plowshares: The rework cycles of defense and
commercial software development projects, American Programmer, 6(5), 41-51.
Conclusions
System Dynamics has a bright future in the oil & gas industry, being in a unique position to
contribute to more systematic management that will drive faster and more consistent growth of
shareholder value. The future will see a blending of market, business and project dynamics,
reflected in models that integrate the commoditized marketplace and asset portfolio
management. We expect that these models will be the analysis engines for management systems
that are fully integrated into the strategy-forming, planning and decision-making processes of the
major oil and gas companies.
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