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Daniel Ahn+1 212 526 0706

[email protected]

Edward Morse

+1 212 526 3767

[email protected]

FIXED INCOME RESEARCH | COMMODITIES | WEDNESDAY, SEPTEMBER 17, 2008

Energy Special Report

PLEASE SEE ANALYST CERTIFICATIONS AND IMPORTANT DISCLOSURES STARTING AFTER PAGE 6

At what cost?Beginning in the fall of 2007, oil prices began overshooting beyond the levels predicted

by fundamental cost indicators. Despite the recent fall in oil prices and substantial cost

inflation, our analysis still indicates deferred prices should be in double-digits,

suggesting room for further declines in the price of oil.

Analysts of oil market fundamentals seeking to make a price forecast regularly study

global supply and demand balances to determine the appropriate price which would

equilibrate markets. However, this macroeconomic analysis can be challenging because

of difficulties of measuring price elasticities of global supply and demand, and

informational opacity over critical aspects of the global balance, such as Saudi Arabian

reserves and Chinese stockpiling. Indeed, despite looking at the identical information,

various analysts have returned wildly different forecasts for oil prices, with some ranging

as high as $150-200/bbl.

Analysts often turn to another powerful method in formulating a long-term price outlook:

analyzing the cost of producing oil. In an earlier report Stronger signals of weaker oil

prices, April 3, 2008, we argued that deferred oil prices had become de-linked from the

long-dated oil prices implied by cost indicators. In this report, we revisit the analysis indepth with updated numbers, as well as point out some potential pitfalls in cost analysis.

We argue that a study of cost indices makes it difficult to justify oil prices in the triple-

digits. This analysis forms a core component of our confidence in viewing $90/bbl as a

long-run benchmark in making forecasts.

Three approaches to studying oil production costs

Relating prices to cost indicators relies upon a microeconomic equilibrium relationship

where profit-maximizing firms equalizes marginal costs with marginal revenues. But

what measure of cost would be most appropriate to imply a price of oil? Perhaps the

simplest approach is to observe the price of oil which would make economical the

marginal basin, or the most expensive currently producing source of supply. Ideally,

one must pick the basin with sufficient spare capacity to remain the marginal basindespite additional demand. One notable and much-publicized source of supply is the

unconventional tar sands of Canada. Industry experts generally believe these tar sands

are economical at $85-95/bbl and the barrier should trend lower as technology improves

and economics of scale are exploited.

A second approach is to rely upon an industry rule-of-thumb relating the retail price of

crude oil and the finding and development (F&D) costs. The rule-of-thumb says one can

multiply the F&D cost per barrel of oil equivalent by a factor of 3 to 4 to produce a price

of oil that would deliver a sufficient internal rate of return (IRR) after lifting costs and

taxes to justify investment and production.

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Lehman Brothers | At what cost?

September 17, 2008 2

Surveys of the top 50 US integrated upstream oil companies by J.S. Herold found F&D

costs averaged$17.46 per barrel of oil equivalent (boe) over the past three years. Hence,applying the industry rule-of-thumb returns a supportable price of oil at most $70/bbl.

But perhaps the most rigorous method is to econometrically search for a relationship

between oil prices and a time series of production costs. However, this approach requirescareful and intelligent analysis. The box insert, A misleading approach to looking at

costs, below provides a cautionary tale on how naively relating cost measures to prices

can lead to misleading implications.

Measures of production costs

For our time series of costs, we rely mainly upon the Producer Price Indices (PPI)

provided by the US Bureau of Labor Statistics (BLS). The three PPI indices we use in

our econometric regression are:

Oil field and gas field machinery and equipment

Drilling oil and gas wells

Support activities for oil and gas operations.

Oil field and gas machinery is the broadest aggregate machinery index constructed by the

BLS, and includes sub-indices such as rotary and production machinery (Figure 1.) The

drilling index is fairly self-explanatory, and captures the costs of drilling the wells that

produce oil. The support activities index is important because it includes human costs

such as wages for engineers and oil workers.

Figure 1 shows a recent history of several US PPI indices. Note how there was

substantial cost inflation from about Jul-04 to Sep-06, followed by a pronounced

flattening. Only recently, since Jan-08, has there been an uptick in cost indices again

(though production machinery costs declined slightly in August). Figure 2 shows how

after accelerating even faster than the other PPI from 2002-06, drilling costs actually fell

before rising again recently. Unfortunately, the BLS only releases these PPI numberswith a substantial time lag, and only data to Aug-08 is available as of writing.

One might argue that the US PPI indicators are solely for the US, and may not capture

the entire behaviour of global marginal costs in frontier basins. However, markets for

Industry rules-of-thumb imply

oil prices at $70/bbl

Our analysis relies heavily

on the US BLS Producer

Price Indices

The PPI indicators studied

cover many different oil-related

production costs

Cost inflation from 2002-04

was followed by flattening, with

only a recent uptick.

Figure 1. Selected oil-related PPI indices Figure 2. Drilling oil and gas well costs

90

100

110

120

130

140

150

160

2002 2003 2004 2005 2006 2007 2008

Oil & Gas Field Machinery

Support Activities for Oil & Gas

Rotary Oil & Gas Machinery

Oil & Gas Production Machinery

Index, Jan-02 = 100

90

110

130

150

170

190210

230

250

270

2002 2003 2004 2005 2006 2007 2008

Drilling oil and gas w ells

Index, Jan-02 = 100

Source: US Bureau of Labor Statistics Source: US Bureau of Labor Statistics

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rigs, oilfield machinery, and even geologists and engineers are globally integrated, and

thus prices in the US must reflect a global reality.

Nevertheless, we also include in the regression several monthly series provided by ODS

Petrodata of deepwater rig day-rates for semisubmersibles and dynamically positioned

drill-ships. These markets are highly globally integrated and also capture costs ofextreme deepwater marginal basins.

Figure 3 shows that their recent behaviour has largely mirrored the trajectory of the PPI

indices, with substantial cost inflation from 2004 to 2006 followed by stagnation since.

Despite a similar recent uptick, the global market for deepwater rigs is expected to loosen

substantially, with 97 new semisubmersibles and drill-ships expected to come online

from 2008 through 2012, as opposed to 10 for the past five years (see Figure 4).

Lastly, while economically the (nominal) price of oil must relate with the (nominal) costs

of production, the relationship between the dollar and oil has been getting increasing

attention. For instance, dollar weakness can increase global non-dollar denominated costs

relative to US-based costs. While an in-depth discussion on the myriad channels linking

the dollar and oil is beyond the scope of this report, we include a dollar index into the

regression for the sake of completeness.

Predicting oil prices with measures of production costs

We relate costs to prices with the classic Cobb-Douglas log-linear structure. See the

Technical Appendix for details. We use 5-year deferred WTI prices because they are

shielded from short-term market disruptions and are the best market expression of

expectations for the long-term price for oil, and govern capital commitment decisions to

upstream projects.

For later use, it will be helpful to divide the recent history of oil price deferred moves

into three phases. The first sustained bull-run, where prices rose from $30/bbl to $70/bbl,

ran from about Jul-04 to Jul-06; we shall call this Phase I. In Phase II, which went from

Aug-06 to Sep-07, oil prices were generally stable. And most recently, Phase III saw oilprices surge again and peaked at an average $132/bbl for Jul-08. See Figure 5.

We shall argue that while both Phase I and Phase II are well explained by the behaviour

of cost inflation, Phase III is not. This conclusion can be anticipated given our discussion

We also include deepwater rig

day-rates to capture marginal

basin costs

Rig markets are expected to

loosen with an expected surge

of deliveries

We include a weighted dollar

index to capture non-US or

non-physical costs

Figure 3. Deepwater rig day-rates from 2003 to 2008 Figure 4. Rig deliveries from 2002 to 2012 (estimated)

0

100

200

300

400

500

600

2003 2004 2005 2006 2007

US GOM semi 5000-7499-ftUS GOM semi 7500-ft+North Sea semi 3000-4999-ftDrillship GOM Dynam Positioned

$/day

42

03

0 1

12

18

118

5

5

8

12

12

6

0

5

10

15

20

25

30

02 03 04 05 06 07 08E 09E 10E 11E 12E

Semisubmersibles Drillships

# of rigs

Source: ODS Petrodata, Lehman Brothers Commodities Research Source: ODS Petrodata, Lehman Brothers Commodities Research

The recent history of oil prices

can be divided into three phases

Phase I and II well-explained

by costs; Phase III is not

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on costs above. The substantial cost inflation from 2004-06 neatly correlates with Phase I

and the price movement can be explained with a high degree of accuracy. The flattening

of costs from 2006 also matches well with Phase II of oil prices. But despite the recent

cost inflation, Phase IIIs bull run began too early and with too high a magnitude to be

explained solely by cost inflation. Ironically, the recent fall in oil prices is now

coinciding with the strongest cost inflation since 2006, an inflation compounded by the

declining value of the US dollar for much of this period. Nevertheless, our analysis

below suggests the hangover from Phase IIIs overshooting remains, and oil prices still

have further to fall before they reach levels implied by cost measures.

Running the regression from Dec-03 to Oct-07, we find that our measures of costs

explain deferred oil prices with an R2 between 94-95% depending on the regressors used.

However, since then, deferred prices have diverged from cost indices and attempts to

explain this rise with cost measures only weakens the fit of the regression. Using PPI

data alone, we find that cost measures predict oil prices for Aug-08 to be at $80.92.

Adding deepwater day-rate numbers to the regression unsurprisingly changes predicted

prices little, given their similar behaviour to PPI indices and the recent flattening of rates.

Adding the dollar does raise the predicted levels to $88.21, but still leaves much

divergence from deferred market prices unexplained. Note that all three predicted oil

price levels have begun to trend upward again, in line with the recent cost inflation, but

this fails to close the entire gap.

The recent fall in oil prices has done more to narrow the gap than the recent cost

inflation, but room remains for further correction. Taking into account a premium for

geopolitical and weather risk, and bolstered by our view of significant demand

destruction in the OECD, a slowing Chinese economy, a surge of supply from OPEC,

and new frontier basins such as tar sands and deep offshore fields remaining economical,

we reiterate our short-term price forecast for $93/bbl for 2009.

Our analysis of costs predicts

oil prices in double rather than

triple digits

We reiterate our forecast of

$93/bbl for 2009.

Figure 5. Phases of WTI bull runs since 2002 Figure 6. Predicted WTI based on cost analysis

0

20

4060

80

100

120

140

Jan-02

Jul-02

Jan-03

Jul-03

Jan-04

Jul-04

Jan-05

Jul-05

Jan-06

Jul-06

Jan-07

Jul-07

Jan-08

Jul-08

$/bbl

30

70 70

132

5-yr WTI bull run phases

I II III

115

10

20

30

40

50

6070

80

90

100

110

120

130

140

Jan-04 Oct-04 Jul-05 Apr-06 Jan-07 Oct-07 Jul-08

5-yr out WTI

Fitted WTI w PPI

Fitted WTI w PPI DW

Fitted WTI w PPI DW DXY

Aug-08

$114.90

$80.92

$79.80

$88.21

$/bbl

A recent convergence of prices

to levels implied by cost indicators.

- R-squared through Oct-07 is 95%

Source: Lehman Brothers Commodities Research, Bloomberg Source: Lehman Brothers Commodities Research, US BLS, Bloomberg

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AXIS-FITTING: A MISLEADING APPROACH TO LOOKING AT COSTS Daniel Ahn ([email protected])

Axis-fitting is a popular but fallacious approach to relate cost indicators to prices. By not relying upon a well-founded andrigorous econometric approach, the implied prices can be perilously misleading and subject to human error.

Axis-fitting is widespread but can be dangerously misleading

Axis-fitting involves rescaling the axes of times series plots of two economic variables so that their trajectories fit each

other, implying a causal relation. While common in visual presentations, this approach can lead to hazardously misleading

conclusions and should not replace sound statistical techniques in economic analysis.

First, axis-fitting by its nature relies upon the not-so-reliable human eye to fit the data instead of using a rigorous

estimation methodology. As we shall see, small variations in the fit can lead to wildly divergent implications, reflecting

poorly upon the robustness of the approach.

Second, axis-fitting imposes a level correlation between the two plotted time series. While the appropriateness of a level vs. a

percentage relationship depends on the sector studied and is ultimately on the judgment of the analyst, economists have

typically relied more upon a percentage (log-linear) rather than level (linear) relationship between prices and input costs. And

when an index is one of the time series being studied, the meaningfulness of level relationship becomes rather hollowbecause one does not know the unit of measurement used.

Third, axis-fitting can only incorporate a binary rather than multivariate causal relationship. By relying only upon a single

cost indicator, one may lose important information about the behaviour of other costs, when prices are typically subject to

multiple inputs.

Imprecise axis-fitting leads to divergent implications for oil prices

To take a concrete example, we again look at 5-year deferred WTI oil prices and the US Bureau of Labor Statistics PPI on

Oil and Gas Field Machinery. From Feb-98 to Jun-08, WTI prices rose from $20 to $130, a 6.5x fold or a 550% increase. In

the same time period, the Machinery PPI rose from 125.2 to 202, a 61% increase. In Figure 1, we see how the PPI index

seems to fit the trajectory of oil prices, with the numbers for Jul-08 implying an oil price of $95/bbl.

However, we have no knowledge of the goodness of fit of the relationship or the significance of the input variable.Furthermore, note how we can fiddle with the axes in Figure 7 and 8, by shifting the maximum and minimum cut-offs and

scale, to get a seemingly different conclusion. Now the exact same data seem to imply an oil price of $115 instead of $95!

Figure 7. Axis-fitting implies $95/bbl Figure 8. Readjustment of axis implies $115/bbl

15

25

35

45

55

6575

85

95

105

115

125

135

98 99 00 01 02 03 04 05 06 07 08

125

135

145

155

165

175185

195

205

215

225

2355-yr out WTI prices (lhs)

Oil and Gas Field Machinery (rhs)

$/bbl$/bbl Jan-82=100

01020

3040506070

8090

100110

120130140

98 99 00 01 02 03 04 05 06 07 08

115

125

135

145

155

165

175

185

195

205

215

2255-yr out WTI prices (lhs)

Oil and Gas Field Machinery (rhs)

$/bbl Jan-82=100

Source: US Bureau of Labor Statistics, Lehman Brothers Research Source: US Bureau of Labor Statistics, Lehman Brothers Research

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TECHNICAL APPENDIX

We use the classic Cobb-Douglas cost structure to relate prices to costs:

...***

21

21

CCP =

where P = price; = markup; C1, C2, = cost indicators, 1, 2, = weightings

The popularity of the Cobb-Douglas model stems in part from its easy translation into a

log-linear equation for regression analysis:

...loglogloglog 2211 +++= CCP

Once we have an estimate for the coefficients , 1, 2, we can predict prices using out-of-

sample data on costs C1, C2, .

Observation of the cost measures shows they are highly correlated, thus potentially

causing multi-collinearity problems to the regression. We attempt to mitigate this by

selecting only a handful of the broadest indices that minimizes the Akaike information

criterion (AIC), instead of using all available PPI and day-rates in the regression.

The regressors used are:

Oil field and gas field machinery and equipment

Drilling oil and gas wells

Support activities for oil and gas operations

US Gulf of Mexico dynamic positioning drill-ship day rates

The DXY dollar index (available on Bloomberg)

-This index makes a basket of 6 major world currencies: the euro, the

Japanese yen, the British pound, the Canadian dollar, the Swiss franc,

and the Swedish krona

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Analyst CertificationThe views expressed in this report accurately reflect the personal views of Daniel Ahn and Edward Morse, the primary analystsresponsible for this report, about the subject securities or issuers referred to herein, and no part of such analysts' compensationwas, is or will be directly or indirectly related to the specific recommendations or views expressed herein.

Important DisclosuresLehman Brothers Inc. and/or an affiliate thereof (the "firm") regularly trades, generally deals as principal and generally providesliquidity (as market maker or otherwise) in the debt securities that are the subject of this research report (and related derivativesthereof). The firm's proprietary trading accounts may have either a long and / or short position in such securities and / or

derivative instruments, which may pose a conflict with the interests of investing customers.Where permitted and subject to appropriate information barrier restrictions, the firm's fixed income research analysts regularlyinteract with its trading desk personnel to determine current prices of fixed income securities.The firm's fixed income research analyst(s) receive compensation based on various factors including, but not limited to, thequality of their work, the overall performance of the firm (including the profitability of the investment banking department), theprofitability and revenues of the Fixed Income Division and the outstanding principal amount and trading value of, the profitabilityof, and the potential interest of the firms investing clients in research with respect to, the asset class covered by the analyst.Lehman Brothers generally does and seeks to do investment banking and other business with the companies discussed in itsresearch reports. As a result, investors should be aware that the firm may have a conflict of interest.To the extent that any historical pricing information was obtained from Lehman Brothers trading desks, the firm makes norepresentation that it is accurate or complete. All levels, prices and spreads are historical and do not represent current marketlevels, prices or spreads, some or all of which may have changed since the publication of this document.Lehman Brothers' global policy for managing conflicts of interest in connection with investment research is available atwww.lehman.com/researchconflictspolicy .To obtain copies of fixed income research reports published by Lehman Brothers please contact Valerie Monchi([email protected] ; 212-526-3173) or clients may go to https://live.lehman.com/.

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