Long term forecasts for oil prices: a review of oil supply, energy market

models, and the range of future trajectories for oil price.

Adrian R.H. Wiegman (awiegman@lsu.edu)

J.W. Day, C.F. D’Elia, C.A.S. Hall, D.J. Murphy, J.S. Rutherford, R.R. Lane

Louisiana State University

Biophysical Economics (BPE) Conference Washington D.C. June 28, 2016

Preview

Societal Tradeoffs in 21st CenturyReview

EROI Model: Cycles of EROI and Oil Prices Energy Market Model Assumptions

Demand: GDP and Population Growth Climate Policy & Producer Behavior

Long Term Forecasts for Oil PriceApplication of Forecast

Simulating Costs as a Function of Oil PricesConclusions and Future Work

Biophysical Model Capable of Simulating Price

Motivation: Decision Support & BPEAdapting to 21st century megatrends requires large investments and long term planning…• Mitigating harmful effects of climate change • Feeding and caring for a growing, aging population• Replacing an aging stock of infrastructure• Mitigating waste and pollution• Building a new renewable energy systemWe know that success/failure will be impacted by net energy and fossil fuels, But…• How do we use BPE to inform assessment of tradeoffs?• We need to speak in a language people understand…

($$) • Therefore we ought to model price.

Why Forecast the Price of Oil?

• Oil is a direct input to many processes, thus has measurable effect on the cost of an activity.

• Indirect effects on prices are evident in the relations to commodity prices and GDP growth.

• Energy Prices Follow Oil Prices (Tverberg, Shafie& Topal 2010)

• Commodity Prices Follow Energy Prices, Especially Oil (Ji & Fan 2012, Sadorski 2014)

Models ReviewedEN

ERG

Y M

AR

KET

MO

DEL

S U

se S

up

ply

Dem

and

Eq

uili

bri

um

Source: Heun & De Wit 2012, (Also see King & Hall 2011, King et al. 2015a)

Relation Between Price and EROI

Price Cycles Drive Investment and EROI (Murphy & Hall 2011)

0

50

100

150

Bre

nt

Pri

ce o

f C

rud

e O

il

($2010 b

l-1)

0

500

1000

1500

2000

U.S

. C

rud

e O

il

Ro

tary

Rig

s in

O

pera

tio

n

4000

5000

6000

7000

8000

9000

10000

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016

Cru

de

oil

P

rod

uc

tio

n

(kb

l d

ay-1

)

Year

A. Price Signal

B. Industry Response

C. Net Effect on Supply

Oil Price

Investment

Production

Time Delay

Demand

Conventional EMM

(EIA, IEA, McGlade)

(Data: EIA 2016)

Oil Price

Investment

Production

Demand

Biophysical(e.g. Murphy & Hall 2011)

Energy Market Models Calculate Price, Investment, and Production Based on Supply & Demand Equilibrium

McGlade 2014

Note: These prices are probably a bit inflated, given the strengthening of the U.S. dollar

Oil Price (SDEQ)

Demand at $

ENERGY MARKET MODEL

Supply at $

2:1 (Brandt et al. 2013)5:1

~1.5:1?

12-30:1

~8-30:1 (Hall et al. 2014)

(Brandt et al. 2015)

Supply Curve of the Types of Oil and EROI Estimates

5:1

>30:1

Supply Database

(see figure)

GDPPOP

Oil

Ind

ust

ry Investment at $

(NPV > 0)

Climate Policy (Demand) is an Important Driver

McGlade 2014, Model Outputs

• GDP & Pop. are forcings that grow exponentially…• Exp. grwth. outways negative feedback of high price…• Demand grows despite increasing prices, causing price

to sky rocket…such a result is not likely valid• Barring incredible efficiency gains, GDP must be

impacted by energy price!

Prices Diverge

Price Shock!

Oil Price (SDEQ)

Demand at $

ENERGY MARKET MODEL

Supply at $

Supply Database

(see figure)

Market Based Climate Policy

GDPPOP

Oil

Ind

ust

ry Investment at $

(NPV > 0)

The Industry behavior (and financing) fluctuates over time and exerts a major influence on price.

McGlade 2014, Low Carbon Scenario

a. Producers hesitant to take on risk, increasediscount rate of NPV.

b. Dissolution of OPEC,members act as indvls.

Prior to 2008 a but not b, In 2015 we saw b but not a, In 2016, the world is seeing a and b

Oil Price (SDEQ)

Demand at $

ENERGY MARKET MODEL

Investment at $(NPV > 0)

Supply at $

Supply Database

(see figure)

Market Based Climate Policy

Dyn

amic

Oil

Ind

ust

ry

GDPPOP

0

50

100

150

200

250

300

350

400

2010 2015 2020 2025 2030 2035 2040 2045 2050

Re

al P

ric

e o

f C

rud

e O

il (

20

10

$/b

l)LCS_Institutions NPS_Libya NPS_OPEC NPS_InstitutionsEIA_REF LCS_Libya Heun & De Wit Fit1 EIA_LOWIEA_NPS IEA_CPS LCS_OPEC Heun & De Wit Fit2EIA_HIGH IEA_LowPrice IEA_450ppm

IEA and EIA Start

Projections in 2013

McGlade and

Heun & de Wit

Start Projections

in 2010

Summary of Models

• McGlade’s model has shorter time step & simulates price volatility, but lack of negative forcing on GDP leads to instability (exponential price increase).

• All assume GDP and Population growth rates are exogenous forcings… in other words there are no recessions.

• All assume high prices unlock new expensive substitutes, such as low EROI synfuels, to maintain supply indefinitely… likely a violation of first principles.

I Capped Prices @ $350

After 2035

Extapolation w. 5% decay in rate

0

50

100

150

200

250

300

350

400

450

500

2010 2020 2030 2040 2050

Real P

rice o

f C

rud

e O

il

(2010$/b

l)Central AVG

Low AVG

High_$350cap

GOEMETRIC MEAN

High_Unconstrained

Low Demand:Stringent Climate Policy (CO2 tax)

High Demand:No Climate Policy

There Will Be A Crash… But When??

Price (+/- 0.5 SD) and Estimated % GDP allocated to Energy Expenditure (Following King et al. 2015)

Low Central High

Year Avg. $/bl %GDP on E Avg. $/bl %GDP on E Avg. $/bl %GDP on E

2025 73(+/-7) 6-8% 99(+/-6) 7-10% 134(+/-20) 10-14%

2035 73(+/-6) 7-9% 124(+/-7) 7-12% 251(+/-44) 16-27%

Results

Producer BehaviorUp to +/- $30bl

Composite Forecast of Oil Prices Applied

$-

$10,000.00

$20,000.00

$30,000.00

$40,000.00

$50,000.00

$60,000.00

$70,000.00

$80,000.00

$90,000.00

No Change ($50/bl) LOW CENTRAL HIGH

Co

st o

f Su

stai

nin

g W

etla

nd

s w

ith

Dre

dgi

ng

(20

10

$/a

cre

)

(Wiegman et al. in prep)

Estimated Cost to Restore & Sustain 1 Acre of Louisiana Marsh Above Sea Level for 50 Years, with Sea Level Rise of 0.7m (Preliminary Results, Not an NPV, No Discount

Rate Applied, Dredging data from Lacoast.gov, email me for Methods)

Conclusions and Future Work• Oil prices will be impacted by industry behavior, and

climate policy.• Producers respond to price, this influences investment

and EROI, resulting in price cycles. • Current EMMs, do not Include net energy related

feedbacks to GDP… which is a problem.• Models of EROI (e.g. Dale 2012, Energies), should be

added to supply databases of EMM’s so that net energy and GDP related feedbacks can be included in global oil models.

• For questions and potential collaboration email me: awiegman@lsu.edu

Oil Price (SDEQ)

Demand at $

ConventionalENERGY MARKET MODEL

Investment at $

Supply at $

Supply Database

Market Based Climate Policy

Oil Price (SDEQ)

Demand at $

Proposed BPE BasedENERGY MARKET MODEL

Investment at $

Supply at $

Climate Policy

Supply Database w/

EROI Data

Dyn

amic

Oil

Ind

ust

ry

Dyn

amic

Oil

Ind

ust

ry

Net EnergyModule

GDP functionOr EnergyConsumingCapital Stocks

GDPPOP

Co

nsu

me

r Econ

om

y

LogicalAssumptions

MarketSignals

PolicyDriver

Energy &Material

4. Consumer Economy (Population, GDP and End

Use Consumption)

5. E&M Production (Well Level Drilling Activity

and Production)

6. Market (Transactions

& Price SettingSDEQ)

2. Reserves & Resource

Stock

3. Policy Drivers

New Market Price (t)

1. Capital Stock &

Technology

Price (EROI) GDP Feedback: If Price*Quantity > %5.5 of GDP, then debt cycle slows, capital investment decreases

Rational Producer Behavior: If NPV(t) of Field is > 0, Then Drill

Discretionary & Maintenance Investments

Energy Investments

Products(Efficiency)

Raw Energy

Incentives

Commands

Commands

$$

$$

$$

Dat

abas

eD

atab

ase

Global Biophysical Model of Energy Markets withHypotheses for future of the energy transition

a. Central Price Scenario –Moderate Transition

b. Low Price Scenario -Successful Climate Policy Rapid Transition

c. High Price Scenario -Unsuccessful Climate Policy Slow Transition

1. Seasonal & IntermittantSolar Flux

2. Total and Discretionary Portion of GDP

3. Fossil Fuel (or Oil) Supply

4. Price of Energy $ EJ

5. Biophysical Systems –Provide Food and Assimilate Wastes

6. WASP – Wind and Solar Power

References• Energy Information Administration. 2015. Annual Energy Outlook.

• Hamilton, J. D. (2012). Oil prices, exhaustible resources, and economic growth (No. w17759). National Bureau of Economic Research.

• IEA (International Energy Agency). 2015. World Energy Outlook 2015. Paris: International Energy Agency

• Heun, M. K., & de Wit, M. 2012. Energy return on (energy) invested (EROI), oil prices, and energy transitions. Energy Policy, 40, 147-158.

• Ji, Q. and Fan, Y., 2012. How does oil price volatility affect non-energy commodity markets?. Applied Energy, 89(1), pp.273-280.

• King, C. W., & Hall, C. A. (2011). Relating financial and energy return on investment. Sustainability, 3(10), 1810-1832.

• King, C. W., Maxwell, J. P., & Donovan, A. (2015a). Comparing world economic and net energy metrics, Part 1: Single Technology and Commodity Perspective. Energies, 8(11), 12949-12974.

• King, C. W., Maxwell, J. P., & Donovan, A. (2015b). Comparing World Economic and Net Energy Metrics, Part 2: Total Economy Expenditure Perspective. Energies, 8(11), 12975-12996.

• Lacoast. 2015. Projects Lists. Coastal Wetlands Planning Protection Restoration Act. Available online: http://lacoast.gov/new/Projects/List.aspx accessed: Nov, 1, 2015

• Loulou, R. and Labriet, M. (2008). ETSAP-TIAM: the TIMES integrated assessment model. Part I: Model Structure. Computational Management Science, Special issue "Managing Energy and the Environment", Vol. 5, Issue 1, pp.7-40.

• Loulou, R. (2008). ETSAP-TIAM: the TIMES integrated assessment model. Part II: Mathematical formulation. Computational Management Science, Special issue "Managing Energy and the Environment", Vol. 5, Issue 1, pp.41-66.

• McGlade, C. E. 2014. Uncertainties in the outlook for oil and gas (Doctoral dissertation, UCL (University College London)).

• McGlade, C., & Ekins, P. (2015). The geographical distribution of fossil fuels unused when limiting global warming to 2 [deg] C. Nature, 517(7533), 187-190.

• Murphy, D.J., and Hall C.A.S. 2011. Adjusting the economy to the new energy realities of the second half of the age of oil. Ecological Modeling. DOI:10.1016/j.ecolmodel.2011.06.022

• World Bank. 2015. Commodities Price Forecast. Released October 20, 2015.