ECONOMICS OF GEOLOGICAL SEQUESTRATION OPTIONS FOR
THE SHENHUA COAL LIQUEFACTION PLANT
Hui Su, Ph.D., Natural Resource Economics,
West Virginia University, Phone+1 304 216 1628, E-mail: Shuinju@gmail.com
Jerald J. Fletcher, Professor and Director, US-China Energy Center,
West Virginia University, Phone+1 304 293 5499, E-mail: JJFletcher@mail.wvu.edu
Overview
China is currently the largest coal consuming and greenhouse gas (GHG) emitting nation in the world. The
fact that coal is the dominant component of China’s energy mix, contributing to approximately 70 % of the
total primary energy supply, will not significantly change in the near future. Since China plans to enhance
energy security through the use of domestic coal, GHG emission abatement will grow difficult. Therefore,
successful large-scale applications of carbon capture and storage (CCS) could reduce CO2 emissions and
allow for the continued use of coal.
The Shenhua’s direct coal liquefaction (DCL) was developed in the Inner Mongolia region of China as the
partial plan for improving energy security. When fully operational, the DCL plant is expected to produce
nearly 1 Mt of oil products per year, equivalent to approximately 25,000 barrels of oil per day. The plant
will also release an estimated 3.6 Mt of CO2 per year, but approximate 90% of these emissions will be pure
enough to capture without significant additional costs. CCS is particularly well suited for the CTL plant
since the cost of capturing CO2 is the main contributor to the total cost of CCS.
The purpose of this study is to evaluate the economic viability of Shenhua’s decision to deploy CCS
technologies and to assess the impacts of China’s potential climate change policies on the firm’s CCS
decision. In addition, as a case study of a large-scale CCS project, this study aims to improve the
understanding of the potential to incorporate CCS as one of GHG mitigation options in China.
Methods
This paper presents a profit-maximizing programming model representing the CCS process from CO 2 point
sources to various geological storage reservoirs within the Ordos Basin in China. The cost simulation is
based on published technical-economic models for the three stages of CCS: compression, transmission, and
storage. Within this study, eleven reservoirs within Ordos Basin including four oil fields, five gas fields,
and two saline aquifers are considered as sites for the potential geological sequestration. The simulation of
the Mixed Integer Nonlinear Programming (MINLP) model was performed within GAMS using solvers of
dicopt, cplex and conopt.
Results
The output of the simulation is an optimal allocation of carbon storage within the different geological
storage reservoirs from the various carbon sources during each period at a given carbon price. The
relationship between the carbon price and the quantities of carbon sequestered can be described as a
positively related, step-function CO2 sequestration supply curve. The selection of injection sites that are
commercially viable and the associate sequestration levels are directly dependent on their geological
characteristics and the carbon price.
When the enhanced oil recovery (EOR) benefits are considered, the additional income could be used to
offset part of the CCS cost. In this particular application, without the benefit of EOR oil fields will not be
economically viable, so gas fields and one saline aquifer were selected due to their favorable location.
Conclusions
If CCS projects are accepted under the Clean Development Mechanism (CDM) framework, it could be
economically viable for Shenhua to perform CCS within its coal conversion project especially with the
additional benefits from EOR.
The economic viability of Shenhua’s CCS project demonstrates the potential for successful CO2
sequestration related to coal liquefaction and gasification production and could facilitate a continued
growth of the coal gasification and liquefaction industry in China.
In terms of policy implications for macro-level carbon management, both incentive and restrictive policies
from the government in the future would greatly affect the deployment and development of CCS projects.
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