The Optimal Drawdown Policies for China’s Strategic Petroleum
Reserve based on a Dynamic Programming Model
Gang Wu a, b, *, Yi-Ming Wei b, c
a
Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190,
China
b
Center for Energy and Environmental Policy Research, Beijing Institute of Technology,
Beijing 100081, China
c
School of Management and Economics, Beijing Institute of Technology, Beijing
100081, China
Biographical notes
Dr. Gang Wu is an Associate Professor at the Institute of Policy and Management,
Chinese Academy of Sciences, China. Now he is a visiting scholar at the Harvard
school of engineering and applied sciences.
Dr. Yi-Ming Wei is a Professor at the Center for Energy and Environmental Policy
Research, Beijing Institute of Technology (BIT) and is Dean of the School of
Management and Economics, BIT. He was a visiting scholar at Harvard University,
USA.
Corresponding author
Dr. Gang WU
Institute of Policy and Management, Chinese Academy of Sciences
p.o. box 8712, Zhongguanccun East Road 55, Haidian District, Beijing 100190, China
Tel.: 86-10-62650865
E-mail: [email protected]
Grant Sponsors
National Natural Science Foundation of China under Grant Nos. 70733005 and
70701032.
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The Optimal Drawdown Policies for China’s Strategic Petroleum
Reserve based on a Dynamic Programming Model
1. Introduction
The cyclical fluctuation of international crude oil prices provides many opportunities to
accumulate strategic petroleum reserve (SPR) inventory when oil prices are low. In recent years,
with rapid and sustained economic development in China, oil consumption and oil import
dependency have rapidly increased and crude oil import dependency reached 51.75% in 2009. To
ensure oil supply security, in 2003 the Chinese government formally approved an SPR plan with a
total storage capacity of 68 million tons (~500 million barrels). It is estimated that the total
investment will be 100 billion Chinese yuan and the project will take 15 years to complete in three
phases with a reserve capacity of 12.0, 28.0 and 28.0 million tons, respectively. The first SPR
project with four bases located in coastal areas (Zhenhai, Daishan, Huangdao and Xingang) was
started in March 2004. The Zhenhai base was finished in 2008 and has a total inventory of 16.4
million cubic meters. The second SPR project is mainly located in hinterland areas, including
Lanzhou, Shanshan and Jinzhou, and the Shanshan and Binhai bases were started in 2009.
Therefore, the development of stockpile and drawdown strategies to guarantee the security of the
national oil supply has become an important issue for the Chinese government.
This paper introduces a new model for analysis of Chinese optimal SPR drawdown strategies
under different scenarios. The objective of our analysis is to determine an SPR policy that will
minimize the expected averaged insecurity cost to the Chinese economy arising from uncertainty
in the supply of imported oil.
2. Dynamic Programming Model
In this paper, we develop a dynamic programming model for analyzing an optimal SPR
strategy for China. The basic elements of the model include an oil market insecurity cost function
and GDP loss, which depend on time t, the state of the oil market and the SPR scale in period t.
The insecurity cost function for the oil market consists of four components: (1) net losses in
consumer welfare due to price changes induced by oil supply interruption; (2) SPR stockpiling or
drawdown; (3) the reserve holding cost; and (4) loss of GDP induced by oil supply interruption.
The insecurity cost for oil consumers can be summarized as the loss of their surplus. This
comprises three elements. The first is the net surplus reserve flow. Let A be the cost when it is
positive and the surplus (profit) when negative. The second is the GDP loss caused by oil supply
shortage or interruption. The third is the cost of the reserve. We assume that the monthly average


reserve cost is v . Therefore, the cost of the whole reserve cost will be v E (t )  A( t ) . Finally, we
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consider the additional surplus arising from SPR stockpiling or drawdown.
3. Data Sources
Historical data for international crude oil prices and forecasting data during the period 2010–
2020 are from the US EIA/DOE. Oil prices forecast by the EIA are annual data, whereas oil prices
in our model are monthly data, so we divided the prices between 2010 and 2020 into two segments,
2010–2015 and 2016–2020. We used wavelet pattern matching theory to find the period in which
forecast oil prices match historical oil price fluctuations. Then we deduced forecasted monthly
international crude oil prices for 2010–2015 and 2016–2020 using the trends and patterns for the
historical data. Chinese crude oil consumption and world oil consumption data from 2010 to 2020
are from the IEA (2008). We also assume that the Chinese GDP between 2010 and 2020 will have
an annual growth rate of 7%, which is the same level as in 2008.
4. Results and Discussion
As the aim of an SPR is not to stabilize oil prices, large-scale SPR drawdown is generally
only implemented when an oil supply shortage or interruption occurs. Taking several large-scale
drawdowns of the US SPR as a reference, we considered three emergency scenarios: a natural
disaster, a financial crisis and an armed conflict.
250
Natural disaster
Financial crisis
Armed conflict
Reference
Crude oil price (US$/bbl)
200
150
100
50
Jul-2020
Jan-2020
Jul-2019
Jan-2019
Jul-2018
Jan-2018
Jul-2017
Jan-2017
Jul-2016
Jan-2016
Jul-2015
Jan-2015
Jul-2014
Jan-2014
Jul-2013
Jan-2013
Jul-2012
Jan-2012
Jul-2011
Jan-2011
Jul-2010
Jan-2010
0
Fig. 1. Fluctuation of oil prices for three emergency scenarios
We assume that the three scenarios occur in January 2015 in our analysis. As the impact on
oil prices and the duration are different, we use historical data to adjust the monthly oil price (i.e.
the deviation of the monthly price) after January 2015. The results show that: (1) a local armed
conflict has the greatest impact on oil prices, but the duration is not long; (2) a sudden natural
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disaster has the least impact on oil prices and the shortest duration; (3) a financial crisis has the
longest impact on oil prices with relatively moderate impact (Figure 1).
Our model results show that the optimal SPR drawdown strategies obviously differ when
different oil-supply shortages occurs. In the natural disaster scenario, the optimal SPR drawdown
strategy for China is to rapidly release approximately 10% of the SPR to stabilize oil prices and
ease the oil supply shortage when it occurs, then to take appropriate strategies according to oil
prices and supply shortages; the total release could be up to 30%. For the armed conflict scenario,
approximately 20% of the reserve should be released when war breaks out to ease demand
pressures. Then appropriate strategies should be taken according to oil prices and supply shortages;
the total release could be up to 35%. The specific strategies for different emergency scenarios are
discussed below (as shown in Figure 2).
400
Natural disaster
Financial crisis
Armed conflict
350
SPR stocks (Million bbls)
310.0
300
300.0
272.0
266.0
267.0
250
238.0
200
175.0
143.0
150
100
171.0
159.0
155.0
141.0
95.0
251.0
163.0
122.0
129.0
153.0
113.0
Jul-2020
Jan-2020
Jul-2019
Jan-2019
Jul-2018
Jan-2018
Jul-2017
Jan-2017
Jul-2016
Jan-2016
Jul-2015
Jan-2015
Jul-2014
Jan-2014
Jul-2013
Jan-2013
Jul-2012
Jan-2012
Jul-2011
Jan-2011
Jul-2010
0
Jan-2010
50
Fig. 2. Optimal SPR strategies for different emergency scenarios
5. Conclusions
In the event of an oil supply shortage, the optimal SPR stockpiling and drawdown strategies
differ for different scenarios. In the natural disaster scenario, the optimal strategy is to rapidly
draw down 10–30% of the SPR onto the market to stabilize oil prices and ease oil supply
shortages. In the financial crisis scenario, the optimal strategy is to rapidly increase the reserve by
~77% at low prices to reduce the total reserve cost. In the armed conflict scenario, the optimal
strategy is to rapidly release 20–35% of the reserve onto the market to protect oil supply security.
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