1876-6102 © 2016 The Authors. Published by Elsevier Ltd. This .docx

1876-6102 © 2016 The Authors. Published by Elsevier Ltd. This
is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of the scientific committee of
the Applied Energy Symposium and Forum, CUE2016: Low
carbon cities
and urban energy systems.
doi: 10.1016/j.egypro.2016.12.040
Energy Procedia 104 ( 2016 ) 233 – 238
ScienceDirect
CUE2016-Applied Energy Symposium and Forum 2016: Low
carbon cities & urban
energy systems
The Selection and Promotion of Core Technology to China’s
Energy Goals
Mei Suna,*, Cuixia Gaoa, Changsheng Jiaa, Faye D.F.Nib,
Jijian Zhanga
aCenter for Energy Development and Environmental Protection,
Jiangsu University, Zhenjiang 212013, China
bDepartment of Building and Real Estate, Hong Kong
Polytechnic University, Kowloon, Hong Kong SAR, China
Abstract
Energy technology is perceived as the most powerful means to
improve energy efficiency and reduce emissions.

However, some technologies will not achieve the expected goals
due to uncertainties. It is necessary to identify the
most effective ones. After that, another issue is how to promote
and apply them successfully. This paper uses Kaya
equation to identify the core technologies for achieving China’s
energy goals: energy security and emission reduction.
Then, the external and internal environments for the promotion
of China’s core energy technologies are analyzed
using technology diffusion model. Lastly, conclusions and
certain policy suggestions are put forward.
© 2016 The Authors. Published by Elsevier Ltd.
Selection and/or peer-review under responsibility of CUE
Keywords: Energy technology; China’s energy goals; Kaya
equation; Technology diffusion model
Nomenclature
AFL Advanced fossil fuel liquids
TRAN Energy-efficient transportation
BIO Biomass energy
HYD Hydrogen energy
CCS Carbon capture and sequestration
BLDG Energy-efficient buildings
DG Deep geothermal engineering technology
NUC Nuclear energy
ELEC Efficient electricity generation and power safety
OCE Ocean energy
TNFF Non-fossil fuels ground transportation technology

SOL Solar energy
IND Energy-efficient industrial processes
WIND Wind energy
* Corresponding author. Tel.: +86 13775366657.
E-mail address: [email protected]
Available online at www.sciencedirect.com
© 2016 The Authors. Published by Elsevier Ltd. This is an open
access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of the scientific committee of
the Applied Energy Symposium and Forum, CUE2016: Low
carbon
cities and urban energy systems.
http://crossmark.crossref.org/dialog/?doi=10.1016/j.egypro.201
6.12.040&domain=pdf
6.12.040&domain=pdf
6.12.040&domain=pdf
234 Mei Sun et al. / Energy Procedia 104 ( 2016 ) 233 –
238
1. Introduction
Energy and environmental problem has become one of the
hottest issues attracting worldwide
attention today. Developing energy technology is the most
effective means to achieve energy
conservation and emission reduction [1,2]. As a major energy

consumer in the world, China faces great
challenges in balancing its goal of economic growth with
environmental sustainability. One of the most
important tasks is to develop renewables on a large scale and
focus on emission control to ensure China’s
energy security [3,4]. Much effort has been devoted to innovate
energy technologies. Additionally,
successful emission reduction can encourage more involvement
in innovative energy technologies [5].
Therefore, energy technology should become the first priority in
China. With an accurate estimation of
technologies’ development level, we can ensure the achievement
of the country's energy goals.
This paper identifies the importance of the selected 14 key
energy technologies that can reduce CO2
emissions and oil consumption in different combinations using
Kaya equation. To understand the
development of energy technology in different countries, we
compare the influence of energy technology
on national energy goals in China and the US. After identifying
the core energy technology, another
important issue is the promotion of the technology’s
application. Therefore, we use wind and solar energy
as examples for an empirical study of the influencing factors on
technology development. Lastly,
conclusions, and policy implications are summarized.
2. China's energy goals and potential core technologies
2.1. China’s energy goals
Both energy security and conservation with low emissions
should be considered simultaneously
when establishing national energy goals. China's energy goals
can be set as: (1) reducing the consumption

of imported oil within 1% of GDP in 2050; and (2) reducing
China’s CO2 emissions to 30-50% of the
level in 2005 by 2050.
2.2. Selection of key energy technologies and basic principles
Although each technology contains many specific sub-
technologies for achievement, we only
consider the ultimate energy effect that each technology can
achieve. After considerable discussion and
debate, a team of 30 scientists at Chinese Academy of Sciences
with expertise spanning a wide range of
energy science and technology decided in a list of energy
technology categories for mitigating emissions
and reducing oil dependence. According to the future
technologies development introduced in China
energy technology development roadmap to 2050 [6,7], the main
14 key technologies and their influences
are summarized.
Five basic principles are followed in the selection of 14 key
technologies. For technologies that
independent R&D but have been unable to keep up with demand
on time, the introduction, digestion,
absorption route is selected; For technologies that have a good
prospect but no large-scale application, a
key breakthrough route is selected; For forward-looking
technologies but still in the stage of scientific
research and exploration, a completely independent innovation
route is followed; For technologies that
have been mature or large-scale industrial application, such as
hydropower, are not consider; For various
factors, the priority rule of “Resource features-Contribution-
Environmental resistance-Technicality-
Achievable level-Economy” is implemented. As for
uncertainties including technological outcome of

R&D, market acceptance of a new technology, and the future
state of the world, probability of technology
success is the most uncertain and different to analyse. The
premise of this analysis is that valuable
insights can be gained by asking the question in reverse: What
must the probabilities of success for each
Mei Sun et al. / Energy Procedia 104 ( 2016 ) 233 – 238
235
technology be in order to be reasonably certain that society’s
energy goals will be achieved? Posing the
question in this way turns the goals into constraints rather than
objectives.
3. Methodology
The Kaya equation provides a framework to integrate impacts
across sectors and energy forms while
avoiding double counting. In general, CO2 emissions can be
calculated by Eq. (1).
1 1 1 1 1
(1 ) (1 ) (1 ) ( )
N N NU F
i iufC f i iufS uf i iufI uf uf
u f i i i
E C S I Q (1)
where is the total CO2 emissions in 2050. {agriculture,
buildings, industry, transportation and

electricity generation} is the set of sectors. {coal, natural gas,
oil, nuclear/renewable energy and
electricity} represents energy forms. i represents different
technologies. fC is the carbon intensity in
energy form f, and
ufS is the share of f in sector u . ufI is the corresponding
energy intensity, and ufQ is
the relative energy consumption.
iufC
represents the relative change of carbon intensity in u of f by
applying technology i . iufS represents the application
percentage of f in u by using i . iufI is the impact
on energy intensity.
The Kaya equation is also applicable to the calculation of
energy demand, but limited by liquid fuel
type, such as oil. Let {Oil}, and the equation for oil
consumption to 2050 can be calculated by Eq. (2).
1 1 1
(1 ) (1 ) ( )
N NU
i iuOS uO i iuOI uO uO u
u i i
O S I Q GDP
(2)
To measure the importance of various energy technologies, we

assume that if an energy technology
fails in each scenario, the remaining technologies will be
recombined. Consequently, the number of sets
that can achieve China’s energy goals will decline dramatically.
Therefore, the more the sets decline, the
more important the failed technology is to achieve the energy
goals. The importance of technology i to
realize China’s energy goals, represented by ip , can be
expressed by Eq. (3).
ies t echnologall
ihnology except t ec
goalsenergy sChina'meet which sets All
goalsenergy sChina'meet which sets All
Pi
(3)
4. Results: identification of China’s core energy technologies
Two assumptions: China’s economy retains its current growth
rate (approximately 7%), and the
structure of industry and energy consumption are invariant.
According to the two goals in section 2.1, we
perform statistical analysis on whether the goals can be
achieved by all possible energy technology sets.
Of the 16384 possible sets, 367 meet both the goals of -30%
CO2 and 150 million tons oil, in which no
sets that include fewer than 8 successful technologies can
achieve both energy objectives (see Fig. 1).
Similarly, there are 126 types of energy technology sets that can
meet the goals of -50% CO2 and 200
million tons oil. In this case, no set that includes fewer than 9

successful technologies can achieve both
China’s energy goals. After deleting one of the 14 energy
technologies in each scenario, we recombine
the remaining technologies and calculate the technology sets
that can achieve China’s energy goals, as
shown in Fig. 2.
According to Fig. 2, if BIO, CCS, NUC, SOL or WIND failed,
the probability to meet the second
type of energy goal is zero; this means that in the remaining
technologies, the goals to reduce carbon
emissions by 50% and oil consumption by 200 million tons by
2050 cannot be achieved. Thus, we must
ensure the usage level of the above five technologies to achieve
the intended goals. If China does not
develop AFL, TRAN and TNFF, the possibility of achieving the
energy goals will decrease to 7%, 13%
238
and 19%, respectively. The influences of BLDG, ELFC and IND
range from 40% to 60%. HYD, DG and
OCE have smaller influences on achieving the energy goals
because they are immature and constitute a
small portion of China's energy supply. Relaxing the energy
goals to -50% for CO2 and 200 million tons
oil makes it impossible to achieve both energy goals without
CCS. AFL, TRAN and TNFF are also
important energy technologies for achieving the higher energy
targets. In this paper, the five technologies
including BIO, CCS, NUC, SOL and WIND are defined as
China’s core energy technologies to achieve
the energy goals. Currently, solar and wind energy are the most

promising technologies in China. Thus,
we will analyse their internal and external factors using the
technology diffusion model. And, the
development situations, problems and technical barriers for
BIO, CCS, and NUC are also discussed.
Besides, the differences and gaps in core energy technologies in
China and the US are compared
based on the results in Ref. [8]. The failures of NUC, SOL, and
WIND are less influential on the success
of the energy goals in the US. China needs to raise the overall
developed level of energy technologies and
reduce the risk that a single technology’s failure makes the
energy goals unattainable.
Fig. 1. Frequency distribution of technology combination. Fig.2.
Effects of losses of individual technology on achieving the
goals.
5. Promotion of China’s core energy technologies
5.1. Promotion of WIND and SOL
In this section, we conduct a macro-evaluation of the external
(such as media, policy, and investment
environment) and internal environment (such as feedback from
technology applications) of energy
technology promotion. China's wind energy and solar energy are
developing rapidly; however, certain
bottlenecks remain, such as wind curtailment and photovoltaic
products mainly for exportation. It is
significant to study how to promote them effectively. Therefore,
we analyse solar and wind energy
technology diffusion as an example. Similar analysis can be
conducted in the same manner for other core

technologies when data is available. The diffusion model is in
the form of a differential equation, as
shown in Eq. (4).
( )
[ ( )] ( )[ ( )]
dA t q
p m A t A t m A t
dt m
. (4)
where A(t) represents the total installed capacity, and dA(t)/dt
is the new added installed capacity in each
year, whereas m is the potential capacity. According to wind
and solar energy data (Table 1) of China, we
identify the parameters by the least square method; the results
are shown in Table 2 and Figs. 3-4.
First, Table 2 shows that both parameters p and q for wind and
solar are significantly positive. This
finding means that the effects of the external policy
environments and internal benefit of wind and solar
energy technologies are positive. Second, in the development of
wind energy, q is greater than p, which
means that the effects of technology use feedback and the
market demand are greater than the policy
incentive for wind energy development. This is because climatic
conditions and equipment constrain the
Mei Sun et al. / Energy Procedia 104 ( 2016 ) 233 – 238
237

scale of wind energy generation. In this circumstance, large-
scale development is more affected by
market factors. China's wind energy technology is progressing
rapidly in the positive conditions of a large
market with large demand and an excellent policy. As for solar,
q remains greater than p. It is worth
noting that China’s solar energy is exploited by the centralized
grid-connected mode, which is similar to
wind energy generation. China is currently the third largest
country in producing PV cells, after Japan and
Germany. However, the domestic solar technology development
lags because of the lack of core
technology, the lack of policy support and the exportation of
most of the products.
Table 1. Solar and wind energy resources in China. Table 2.
Parameter estimation results of wind and solar energy.
Energy Potential capacity
Wind
Total storage of wind energy resources is
23.80×105MW, in which, about 10×105MW
can be developed by technology.
Solar Potential resource is about 108 MW.
Energy Parameters Standardized Coefficients
Standard
Deviation Sig.
Adjust R
Square
Wind

p 6.760 0.315 0.000
0.994
q 7.510 0.338 0.000
Solar
p 3.299 0.280 0.000
0.999
q 4.295 0.304 0.000
Fig.3. Wind power installed capacity predicting outcomes.
Fig.4. Solar power installed capacity predicting outcomes.
5.2. Promotion of CCS, NUL and BIO
CCS. CCS is an essential energy technology for realizing
China’s energy goals. Meanwhile, the
situation of China’s energy development and geographical
conditions for carbon sequestration also
illustrates that CCS may become the largest single response in
China’s climate mitigation portfolio. The
chief barriers to implement CCS are poor economic feasibility
(cost) and lack of capital source, as well as
the policy environment. Therefore, development of CCS
technology in China should seize the opportunity.
For example, enhancing the technology of compression and
purification, improving the social public
acceptance of CCS, and promoting the technology transfer and
communication.
NUL. Nuclear is the most important decarbonisation electricity
supply second only to CCS during
the 14 key energy technologies. However, the development of

NUL has special industry disciplines.
Technologies barriers include new nuclear technology and
nuclear waste treatment technology.
Furthermore, other factors including continuous training of
skilled technical staff, and influences caused
by low flow rate are also should be take into consideration.
BIO. Regional distribution feature of biomass power generation
is obvious significantly which
resulting in the dependency on resources and production
characteristics for different areas. Technological
barriers include the development of distributed generation based
on direct-fired biomass power plant; the
need for new, specialized energy crops; and biomass collection
and transportation.
238
6. Conclusions and policy implications
This paper applies the enumeration method to analyse the
importance of 14 different key energy
technologies for achieving China's energy goals. We can
provide priority development to core
technologies that were identified as greatly influencing China’s
economy. Of course, other technologies
with weak profits should also be invested in. The results show
that CCS, NUC, BIO, SOL and WIND are
significant to guaranteeing the success of China’s energy goals.
Simultaneously, AFL, TRAN and TNFF
also have important impacts. By comparing with the situation in
the US, we conclude that the level of
China's energy technology development is less mature than the

US’s, and overall technology development
continues to lag. The lag is reflected in the critical impact of a
single technology failure on the energy
goals. Moreover, CCS technology is indispensable to achieve
the energy goals. However, CCS is costly
and creates a high energy consumption, which needs to be
addressed. The exploration of clean energy,
such as NUC, BIO, SOL and WIND, is the main tendency.
For the successful promotion of core energy technologies, we
study their internal and external
factors, and analyse the development of WIND and SOL in
China under the influence of external policy,
market environment and internal technique factors based on the
technology diffusion model. The current
development of WIND and SOL greatly relies on government
commitments because of their higher costs
in comparison to conventional fuels. Although policy support,
promotion and related external factors are
important at the early stage, the market should gradually begin
to regulate the development of technology,
depending on the internal factors and ultimately achieving
marketization. If the parameter p is relatively
large, the government can gradually reduce the policy subsidy.
Acknowledgments
This research was supported by the National Nature Science
Foundations of China (No. 71273119 and No.
71473108) and the Graduate Innovative Foundation of Jiangsu
Province KYLX15_1076.
References
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[3] J. Byrne, B. Shen. The challenge of sustainability:
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[5] J.A. Edmonds, M.A. Wise, J.J. Dooley, S.H. Kim, S.J.
Smith, L.E. Clarke, et al. Global energy technology strategy:
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Biography
Mei Sun is a Professor at Center for Energy Development and

Environmental Protection, Jiangsu
University. Her research interests are energy economy system
modeling, and complex system
analysis. She has published more than 40 papers in SCI/EI
indexed peer-reviewed English journals.
ORIGINAL PAPER
Study of the numerical simulation of tight sandstone gas
molecular
diffusion based on digital core technology
Hong-Lin Zhu1,2 • Shou-Feng Wang3 • Guo-Jun Yin3 • Qiao
Chen1,2 • Feng-Lin Xu2 • Wei Peng4 •
Yan-Hu Tan1 • Kuo Zhang1
Received: 9 June 2017 / Published online: 20 January 2018
� The Author(s) 2018. This article is an open access
publication
Abstract
Diffusion is an important mass transfer mode of tight sandstone
gas. Since nano-pores are extensively developed in the
interior of tight sandstone, a considerable body of research
indicates that the type of diffusion is mainly molecular
diffusion
based on Fick’s law. However, accurate modeling and
understanding the physics of gas transport phenomena in nano-

porous media is still a challenge for researchers and traditional
investigation (analytical and experimental methods) have
many limitations in studying the generic behavior. In this paper,
we used Nano-CT to observe the pore structures of
samples of the tight sandstone of western of Sichuan. Combined
with advanced image processing technology, three-
dimensional distributions of the nanometer-sized pores were
reconstructed and a tight sandstone digital core model was
built, as well the pore structure parameters were analyzed
quantitatively. Based on the digital core model, the diffusion
process of methane molecules from a higher concentration area
to a lower concentration area was simulated by a finite
volume method. Finally, the reservoir’s concentration evolution
was visualized and the intrinsic molecular diffusivity
tensor which reflects the diffusion capabilities of this rock was
calculated. Through comparisons, we found that our
calculated result was in good agreement with other empirical
results. This study provides a new research method for tight
sandstone digital rock physics. It is a foundation for future tight
sandstone gas percolation theory and numerical simulation
research.
Keywords Tight sandstone gas � Nano-CT � Digital core �
Molecular diffusion � Numerical simulation
1 Introduction

Tight sandstone gas (TSG) generated in tight reservoirs is
one of three major types of unconventional energy. TSG
has been found widely distributed worldwide and has great
potential for exploration. It has a tight matrix and in gen-
eral has a nanoscale pore system in which natural gas exists
and migrates with a mechanism different from conven-
tional gas reservoirs. Studies (Schloemer and Krooss 2004)
have demonstrated that natural gas diffuses under the
action of a concentration field and percolates under the
action of a pressure field among tight matrix pores and
throats, and that natural gas is released from micro- and
nano-pores through three major procedures: desorption,
diffusion, and percolation. Therefore, the study of desorp-
tion and diffusion mechanisms of natural gas in nanoscale
tight sandstone pores is significantly important to the
evaluation and development of natural gas. However, there
have been very few reports about quantitative studies of
Edited by Jie Hao

& Qiao Chen
[email protected]
& Yan-Hu Tan
[email protected]
1
Chongqing Institute of Green and Intelligent Technology,
Chinese Academy of Sciences, Chongqing 400714, China
2
State Key Laboratory of Oil and Gas Reservoir Geology and
Exploitation, Southwest Petroleum University,
Chengdu 610500, Sichuan, China
3
Oil and Gas Engineering Research Institute, Jilin Oilfield
Company, PetroChina, Jilin 138099, China
4
Shu’nan Gas Mine, Southwest Oil and Gas Field Branch
Company, PetroChina Co., Ltd, Luzhou 646300, Sichuan,
China
123
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0210-1&domain=pdf
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diffusion and mass transfer in gas reservoirs from a
microscale/nanoscale perspective although the molecular
diffusion effect is a key process of the migration of tight
sandstone gas.
The first diffusion equation was presented by Adolf Fick
as an empirical equation where a diffusion coefficient was
used to represent the speed of mass diffusion in diffusion
media. Today, a large number of studies on the determi-
nation of the diffusion coefficient of natural gas in cores
have been conducted by domestic and international schol-
ars (Bird et al. 2014; Wang et al. 2014; Liu et al. 2012; Jian
et al. 2012); diffusion equations have been optimized and
are capable of serving at high temperature and high pres-
sure; and diffusion coefficient values ranging from 10
-12
to

10
-5
m
2
/s have been obtained (Bera et al. 2011; Bing et al.
2013; Kelly et al. 2015; Nozawa et al. 2012). Ertekin et al.
(1986) described Knudsen flow under the action of a
concentration field using Fick’s Laws of Diffusion in
combination with the description of gas flow in tight pores
and deduced an empirical equation for the calculation of
gas diffusion coefficients through the replacement of Gra-
ham’s Law of Diffusion using the Jones and Owens
experimental data. Liu (2013) and Wu et al. (2012)
employed a molecular simulation method to study the
diffusion characteristics of CH4 and CO2. Curtis et al.
(2012) built a microscale two-dimensional finite element
model based on the diffusion studies of other porous
materials to study the diffusion mechanism of natural gas
in micro/nano-pores. Wu et al. (2015) deduced a mathe-

matical percolation model using the infinitesimal method
which is different from Darcy percolation and applies to
the micro-study of tight gas in matrix pores. Javadpour and
Moghanloo (2013) built a methane molecule migration
model and further discussed the contribution of molecular
diffusion to gas production.
Preceding studies have brought some results and pro-
gress, but these macrotheories and methods are insufficient
for the description and interpretation of a micro-mecha-
nism. There are still some unprecedented technical chal-
lenges to reproduce the migration of tight sandstone gas in
micro/nano-pores for studies on percolation mechanisms.
The nano-pore structure of tight sandstone reservoirs has
very critical influence on gas accumulation and mass
transfer, so the modeling of pores is directly related to the
accuracy of numerical simulation results. However, in
existing studies, models are just simplified ideal models
which do not represent the real pore structure of tight gas

reservoirs. In this study, a refined digital model of the pore
structure of tight sandstone reservoirs was built using
advanced digital core technology (Guo et al. 2016; Chen
et al. 2015; Liu et al. 2014, 2017; Ni et al. 2017; Yin et al.
2016), and a numerical simulation of molecular diffusion
was conducted on the basis of the model. This has provided
new ideas for the study of the micro-mechanisms of
molecular diffusion of tight gas.
2 TSG digital core modeling
2.1 Nano-CT experiment
Computed tomography (CT) is a technology used to non-
destructively detect the internal structure of an object. It is
today’s most practical and accurate method of building 3D
digital cores. CT identifies the pores and skeleton based on
the fact that components of different densities in the core
absorb different amount of X-rays. In this study, a ZEISS
Xradia 800 Ultra X-ray microscope (Fig. 1a) was used for
3D imaging. The experimental sample is u25 9 50 mm
cylindrical sample. Samples were dried before scanning.

For the u25 9 50 mm sample, to improve the sample
resolution as far as possible, the scanning area of the
experimental sample is u70 9 70 lm. The sampling res-
olution of this Nano-CT is down to 50 nm, which enables
the microscope to take 1022 two-dimensional section
images (Fig. 1b) with 1016 9 1024 pixels for one sample.
Each voxel has a spatial resolution of 65 nm. By super-
imposing these images in sequence, 3D grayscale images
can be obtained (Fig. 1c), representing the microstructure
of the tight sandstone samples.
2.2 Image processing
Various types of system noise exist in these grayscale
images of cores, decreasing image quality and negatively
affecting the subsequent quantitative analysis, so the first
step of image processing is to increase the signal–noise
ratio (SNR) using a filtering algorithm. For a 3D image, the
commonly used filtering algorithms include low-pass linear
filtering, Gaussian smoothing, and median filtering. A
comparison of the filtering effects among the three algo-

rithms has been made, and a median filter was employed in
this study. In a grayscale image filtered and processed with
the median filter, the transition between the pores and
skeleton is natural; the boundary is smooth; and important
characteristics are also retained in the image (as shown in
Fig. 2). For better identification and quantization of the
pores and skeleton, binary classification is required for the
grayscale image using the image segmentation method.
The segmentation threshold is crucial to binary classifica-
tion, so it must be selected carefully. In this paper, the
porosity of the core sample has already been determined
through Nano-CT scan, so the best segmentation threshold
obtained on the basis of the porosity can be used for image
segmentation. Based on the measured porosity, the
Petroleum Science (2018) 15:68–76 69
123
segmentation threshold k* is obtained through the follow-

ing equation:
f k�ð Þ ¼ min f kð Þ ¼ u �
Pk
i¼IMIN p ið Þ
PIMAX
i¼IMIN p ið Þ
( )
ð1Þ
where u is the porosity of the core, k is the gray threshold,
the maximum and minimum gray values of the image are
IMAX and IMIN, respectively, the number of voxels with a
gray value of i is p(i), the voxels with a gray value lower
than the gray threshold represent pores, the remaining
represents the skeleton. Based on the final value searched,
k* = 56, which is used as the segmentation threshold,
binary images are obtained after segmentation. As shown
in Fig. 3, the blue color represents the pores and the black-
colored background represents the skeleton. If necessary,
the mathematical morphological algorithm can be used for

further refined processing by removing the independent
voxels using open surface operations and filling the small
holes using closed surface operations to connect the
neighboring voxels.
2.3 3D surface reconstruction and pore structure
quantitative analysis
In this paper, the CT images measured 1016 9 1024 pixels. To
reflect macropore structures and macroscopic properties, and
taking into account the amount of reconstruction data generated
and the associated computational burden, in this paper, a com-
promise involved cutting the CT images. Selecting a represen-
tative elementary volume (REV, the smallest core unit that can
characterize the macroscopic physical properties of a core
effectively) is crucial to the follow-up study in this paper.
Fig. 1 The process of Nano-CT scanning technology. a Xradia
800 Ultra, b one of the CT slices, c 3D grayscale images
Fig. 2 The slice after median filtering Fig. 3 The result of
binarization (pores are blue)
70 Petroleum Science (2018) 15:68–76
123

Repeated experiments show that when the size of a digital core
model is 500 9 500 9 500 voxels, its porosity is almost
unaffected by size (Fig. 4), indicating that the REV size can
characterize the macroscopic physical properties of TSG.
The Marching Cube algorithm (Lorensen and Cline
1987) is used to obtain a triangle mesh set from the 3D data
cube of the image processing results and an illumination
model is used to render the triangle meshes. Then, a 3D
surface image of the core is formed. In this way, a 3D
digital model of tight sandstone cores is built (Fig. 5).
In the digital model built in the above step, most of the
pores are closely contacted. It is very hard to identify the
boundary of each pore, which negatively affects the subse-
quent quantitative statistics of pore size distribution.
Therefore, the boundary of each pore must be identified and
labeled. In this study, the fast watershed algorithm is used for
boundary detection. Using the image as the geo-scientific

topography, the gray value of each pixel on the image as the
sea level elevation of the pixel, each local minimum and its
affected areas as a catchment basin, and the boundary of the
catchment basin as the watershed, this algorithm has enabled
the identification of each pore and generated a spatially
labeled graph (as shown in Fig. 6) labeling each pore inde-
pendently; in this way a single pore boundary can be iden-
tified easily, which enables convenient collection of pore
data for quantitative analysis. As long as the volume of each
pore is determined, the porosity of the digital core can be
obtained through calculation. In the meantime, assuming
that the volume of a sphere is approximately equal to the
volume of pores at the corresponding location, the equivalent
pore size of each pore can be determined through Eq. (2) and
a pore size distribution histogram can be obtained on the
basis of the final statistics (Fig. 7).
Deq ¼ 3
ffiffiffiffiffiffiffiffiffiffiffiffi
6Vpore

p
r
ð2Þ
It is shown in Fig. 7 that the pore radius of this TSG
sample is mainly distributed in the range of 0.43–1
microns. At the current resolution, few 80-nm-sized pores
are captured. The widespread distribution of sub-micron
and nanoscale pores is the root cause of the difficulty of
developing such gas reservoirs. Table 1 shows that the
porosity obtained through calculation is slightly lower than
the measured porosity. This error is mainly caused by
image smoothing, because removing the small holes affects
the calculation of porosity to some extent.
3 Numerical simulation of molecular
diffusion
3.1 Theoretical foundation
3.1.1 Fick’s first law: definition of molecular diffusion
Molecular diffusion is a process whereby dissolved mass is

passively transported from a higher chemical energy state
to a lower chemical energy state through random molecular
0.10
0.09
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
0 100 200
Cube length, voxel
P
or
os
ity
300 400 500 600 700
Expand the cube length progressively The calculation porosity
change with cube length
(a) (b)
Fig. 4 The schematic diagram of REV analysis. a Expand the
cube length progressively, b the calculation porosity change
with cube length

Fig. 5 The digital core model (3D reconstruction of pores)
123
motion. Steady state diffusion of a chemical species in free
solution can be described empirically using Fick’s first law:
j~¼ �D � r~c ð3Þ
where j~ is the solute mass flux (in mol m-2 s-1); D is the
diffusion coefficient of the solute in the solvent (in
m
2
s
-1
); c is the concentration of the solute in the solvent
(in mol m
-3
).
3.1.2 Fick’s second law
The partial differential equation describing transient dif-

fusion in a homogeneous (only one solid phase), saturated
(the void space of the material is filled by the solvent),
porous medium can be developed from the Fick’s first law
and conservation of mass. This equation is called Fick’s
second law and is written as follows:
oc
ot
¼ �D � r~c ð4Þ
To simulate a molecular diffusivity laboratory mea-
surement, a classical experiment is suggested, which is
based on the double reservoir test. Two reservoirs having
the same volume VR are positioned on each side of the
sample in a chosen direction. The other directions are
closed with impervious planes, so that no diffusion occurs.
The initial concentrations of the reservoirs are different:
Cin (t) and Cout (t). The sample is initially filled with the
solution at Cin (t0), t0 being the instant when the experiment
starts. At time t = t0, the reservoirs are connected to the
sample and the diffusion process starts. The influence of

gravity is neglected, only passive diffusion is considered,
not advection.
Considering these boundary conditions, Fick’s second
law governs the diffusion and defines the concentration
field in the sample. The concentration of the reservoirs also
evolves since they have a finite volume VR. By default, VR
is supposed to be 100 times higher than the void space
volume in the sample. Let us note b =
Vvoidspace
VR
, the ratio of
the void space volume and the reservoir volume. The fol-
lowing equations govern the concentration in the
reservoirs:
VR
oCinðtÞ
ot
¼ D
Z
Sin

r~c � n~dS ð5Þ
VR
oCoutðtÞ
ot
¼ �D
Z
Sout
r~c � n~dS ð6Þ
where Sin and Sout are the faces of the sample where the
reservoirs are connected.
Once the diffusion process starts, the concentration in
the sample quickly evolves and the exchanges with the
reservoirs are asymmetrical. This transient state is then
replaced by an established state, when the exchanges with
the reservoirs are equal.
This established state is characterized by the fact that
oCinðtÞ
ot
= -
oCoutðtÞ
ot

. Once this state is attained, the concen-
tration in the reservoirs will continue to vary until they
reach an equilibrium concentration c!. The difference of
these concentrations, Cout(t) - Cin(t), follows an expo-
nential law:
CoutðtÞ � CinðtÞ ¼ p � expð�k2tÞ ð7Þ
where p and k2 are constant coefficients to be determined.
An analytic solution to this problem is suggested:
Fig. 6 The quantification and characterization of pore structure
in
digital coal model label image of pores
0%
20%
40%
60%
80%
100%
0
50

100
150
200
250
0.
08
0.
43
0.
78
1.
13
1.
48
1.
83
2.
17
2.
52
2.
87
3.

22
3.
57
3.
92
4.
27
4.
62
F
re
qu
en
cy
Pore radius, μm
Frequency
Cumulative %
Fig. 7 The pore size distribution histogram
Table 1 The comparison of porosity results
The calculated porosity, % The measured porosity, %
9.37 9.64

123
cðX; tÞ ¼ A
cos kffiffiffiffiffiffiffi
Dapp
p � 1
� �
sin kffiffiffiffiffiffiffi
Dapp
p
� �
2
6
6
4
3
7
7
5 cos
k
ffiffiffiffiffiffiffiffiffi
Dapp
p X

!
2
6
6
4
þ sin
k
Dapp
p X
!#
expð�k2tÞ þ c1
ð8Þ
where c(X, t) is the local concentration at position X and
time t, A is a constant coefficient.
Knowing this solution must verify the previous
hypothesis of flux equality (
oCinðtÞ
ot
= -
oCoutðtÞ
ot

), the follow-
ing equation is derived:
�k2
cos kffiffiffiffiffiffiffi
Dapp
p � 1
� �
sin kffiffiffiffiffiffiffi
Dapp
p
� � ¼ Dappb
k
Dapp
p ð9Þ
which links the k2 coefficient to the apparent diffusivity
Dapp of the sample.
To sum up:
1. A first transient state during which the diffusion
process starts must be achieved before an established
state appears.
2. Once the established state has begun, the difference of

the reservoir concentrations follows an exponential
law. Therefore, the slope of the linear curve followed
by ln(Cout (t) - Cin (t)) can be estimated easily.
3. This slope is k2, the exponential coefficient, which is
related to the apparent diffusivity Dapp.
3.1.3 Volume averaged form of Fick’s law
The effective molecular diffusivity tensor gives global
information about the diffusion capabilities of the material.
A change of scale to get equations valid for the entire
volume is necessary. The method of volume averaging is a
technique that accomplishes a change of scale. Its main
goal is to spatially smooth equations by averaging them
over a volume.
This theory develops a closure problem that transforms
the Fick equations to a vectorial problem; closure variable
b
!
is used to state the concentration perturbation in a new
problem:

r2 b
!
¼ 0 ð10Þ
When the problem is solved, it is possible to compute
the dimensionless diffusivity tensor defined as:
/
D
!!
Dsolution
¼ / I!
!
þ
1
Vf
Z
Sfs
nfs
�! b
!
dS
� �
ð11Þ

where / is the porosity; Dsolution is the bulk solution dif-
fusivity; Vf is the volume of fluid; Sfs is the area of the
fluid–solid interface; nfs
�! is the normal to the fluid–solid
interface directed from the fluid to the solid phase.
3.1.4 Boundary conditions
In the molecular diffusivity experiment simulation based
on the solution of Fick’s equations, the rate of reaction of
the solid is assumed to be zero: there is no reaction
occurring at the fluid–solid interface. Then the boundary
condition at fluid–solid interface is:
� nfs�! � rc
�!
¼ 0 ð12Þ
where nfs
�! is the normal to the fluid–solid interface
directed from the fluid to the solid phase.
Besides this fluid–solid interface condition, a one-voxel-
wide plane of solid is added on the faces of the image that
are not perpendicular to the main diffusion direction. This

allows isolation of the sample from the outside.
Boundary conditions at inlet and outlet require knowl-
edge of the concentrations in the reservoirs. These con-
centrations evolve over time:
Vr
oCin
ot
¼
Z
Sin
nSin
��! � r
!
cdS ð13Þ
Vr
oCout
ot
¼ �
Z
Sout
nSout
��! � r

!
cdS ð14Þ
when Sin and Sout are, respectively, the input and output
face of the sample; nSin
��!
and nSout
��!
are, respectively, the
normal to the input and output face.
In the molecular diffusivity tensor calculation by vol-
ume averaging, the vectorial problem that is solved in this
case is closed by imposing periodic boundary conditions to
b
!
and the geometry. The fluid–solid interface condition
has the following similar form:
� nfs�! � r
!
b
!
¼ nfs�! ð15Þ

Then, we used a finite volume method to solve the
equation systems. The finite volume method (FVM) is a
method for representing and evaluating partial differential
equations in the form of algebraic equations. Similar to the
finite difference method or finite element method, values
are calculated at discrete places on a meshed geometry.
‘‘Finite volume’’ refers to the small volume surrounding
each node point on a mesh. In the finite volume method,
volume integrals in a partial differential equation that
contain a divergence term are converted to surface inte-
grals, using the divergence theorem. These terms are then
evaluated as fluxes at the surfaces of each finite volume.
Because the flux entering a given volume is identical to
123
that leaving the adjacent volume, these methods are con-
servative. Another advantage of the finite volume method

is that it is easily formulated to allow for unstructured
meshes. The method is used in many computational fluid
dynamics packages. In this paper, the discretization
scheme assumes that the voxel is isotropic (cubic). Once
discretized, the closure equation system can be written as
Ax = b, A being a sparse, symmetric matrix. The equation
system is solved using a fully implicit method (matrix
inversion). The PETSc (Portable, Extensible Toolkit for
Scientific Computation) library is used for the direct res-
olution of the linear system. An iterative resolution with a
conjugate gradient and ILU (Incomplete lower and upper
triangular factorization method) preconditioner is
Fig. 8 Visualization of the concentration field in an experiment
simulation with molecular diffusion
Table 2 The computed results
of molecular diffusivity
X direction, 10
-3
m
2

s
-1
Y direction, 10
-3
m
2
s
-1
Z direction, 10
-3
m
2
s
-1
0.042 0.056 0.051
0
0.05
0.10
0.15
0.20
0 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20

M
ol
ec
ul
ar
d
iff
us
iv
ity
Porosity
Maxwell
Weissberg
Numerical simulation
Analytic solution
Fig. 9 Comparison of empirical laws and numerical simulations
for
the determination of molecular diffusivity with respect to the
porosity
123

performed. The convergence criterion used is the relative
decrease of the residual l2-norm.
3.2 The result of molecular diffusivity simulation
The default parameters simulate an experiment along the Z axis
with a concentration in the input reservoir at initial time of
1711 mol m
-3
(Cin (t) = 1711 mol m
-3
) and the concentra-
tion in the output reservoir at initial time being null (Cout
(t) = 0). The default solution bulk diffusivity is 1 m
2
s
-1
.
Then, the direction of the molecular diffusion can be adjusted to
X, Y, or Z direction. If several directions are selected, the
computations will be done successively. The concentration
values used as boundary conditions of the experiment can also
be modified. Modifying these values will not change the

molecular diffusivity, which is intrinsic to the porous medium.
It will only modify the output concentration field.
The resulting visualization in X, Y, and Z direction,
respectively, should look like Fig. 8. We can observe the
decrease of the concentration from the input reservoir (in
yellow), to the output reservoir (in light blue).
With these parameters, we compute the full intrinsic
diffusivity tensor. A full tensor computation requires three
computations, each equivalent in time and memory con-
sumption to one experiment simulation. Experiment sim-
ulation in each direction gives the following results (shown
in Table 2).
3.3 Comparison results
We base our validation on several studies reporting the
results of molecular diffusivity experimental measurements
and empirical laws aimed at computing the molecular
diffusivity of a material. Glemser (2008) reported the fol-
lowing analytical estimations of molecular diffusivity with

respect to the porosity /:
Maxwell 1881ð Þ : /
D
Dsolution
¼
2/
3 � /
ð16Þ
Weissberg 1963ð Þ : /
D
Dsolution
¼
/
1 � lnð/Þ=2
ð17Þ
We compare our values to the values computed with
empirical laws [Eqs. 16, 17)] and the analytic solution. All
these results are displayed in Fig. 9. It can be seen from
Fig. 9 that our computed result is in good agreement with
the other results as well as the analytic solution.
4 Conclusions

1. Nano-CT scanning can be used to photograph the true
pore structure of tight sandstone. The image
segmentation method based on experiment-measured
porosity is established to binarize the digital image of
tight sandstone.
2. The pore size distribution is obtained based on the
digital core model of tight sandstone. It has been found
that the radii of pores of this sample are mainly
distributed in the range of 0.43–1 microns, and few
80-nm-sized pores are captured at the current
resolution.
3. By the use of finite volume method, the molecular
diffusion process of gas in real pore space can be
simulated visually based on the digital core model of
tight sandstone, and the diffusion coefficient can also
be calculated, which is in good agreement with the
others’ empirical laws results.
With the development of computer technology, digital

rock physics will become an important technical means to
participate in the exploration and development of tight oil
and gas. Our study provides a new research method for
digital rock physics of tight sandstone. It is a foundation
work for the future tight gas percolation theory and
numerical simulation research.
Acknowledgements This study is supported by Open Fund
(PLN1506) of State Key Laboratory of Oil and Gas Reservoir
Geology and Exploitation, Chinese National Natural Science
Foun-
dation (41502287), Chongqing Basic and Frontier Research
Projects
(CSTC2015JCYJBX0120), Chongqing City Social Undertakings
and
Livelihood Protection Science and Technology Innovation
Special
Project (CSTC2017SHMSA120001), Chongqing Land Bureau
Sci-
ence and Technology Planning Project (CQGT-KJ-
2017026,CQGT-
KJ-2015044,CQGT-KJ-2015018, CQGT-KJ-2014040).

Open Access This article is distributed under the terms of the
Creative Commons Attribution 4.0 International License
(http://crea
tivecommons.org/licenses/by/4.0/), which permits unrestricted
use,
distribution, and reproduction in any medium, provided you
give
appropriate credit to the original author(s) and the source,
provide a
link to the Creative Commons license, and indicate if changes
were
made.
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https://doi.org/10.6023/A12110858
https://doi.org/10.6038/cjg20161031
https://doi.org/10.6038/cjg20161031Study of the numerical
simulation of tight sandstone gas molecular diffusion based on
digital core technologyAbstractIntroductionTSG digital core
modelingNano-CT experimentImage processing3D surface
reconstruction and pore structure quantitative
analysisNumerical simulation of molecular diffusionTheoretical
foundationFick’s first law: definition of molecular
diffusionFick’s second lawVolume averaged form of Fick’s
lawBoundary conditionsThe result of molecular diffusivity
simulationComparison
resultsConclusionsAcknowledgementsReferences
Comparative Economic Research, Volume 21, Number 3, 2018
10.2478/cer-2018-0018
JANINA WITKOWSKA*
Corporate Social Responsibility (CSR) of Innovative
Pharmaceutical
Corporations. The Case of BIOGEN
Abstract
The aim of this paper is to discuss the common features and
specificity of Corpo-
rate Social Responsibility (CSR) practices of innovative
transnational corpora-
tions (TNCs) acting in the pharmaceutical industry. The
innovativeness of phar-
maceutical firms is understood here as their ability to make a

breakthrough
in the treatment of rare, incurable diseases. The examination of
the issue leads to
the conclusion that the specificity of CSR in this industry is
related to the contra-
diction between the economic and social/ethical aspects of
innovation processes
in this field. A key issue of CSR in the innovative
pharmaceutical industry seems
to be the pricing of drugs, especially orphan and ultra‑orphan
drugs, resulting
in patients from less developed countries having limited access
to life‑saving med-
icines or those that improve the quality of life. Corporations use
their monopolis-
tic position to set extremely high prices. However, without the
market/marketing
exclusivity offered to pharmaceutical firms by the law, orphan
drugs would prob-
ably not be developed, produced and commercialized.
Traditional CSR practices
(corporate philanthropy, community and neighborhood
programs, volunteerism
etc.) cannot be treated as sufficient ‘compensation’ for the high
prices of medi-
cines. Real, true CSR in the innovative pharmaceutical industry
requires either
abandoning or reducing extreme monopolistic privileges and
offering medicines
for rare diseases at lower prices.
Keywords: Corporate Social Responsibility (CSR), innovative
pharmaceutical
corporations, orphan drugs, access to drugs, BIOGEN
JEL: M14, L65

Janina Witkowska
* Ph.D., Full Professor at the University of Lodz, Institute of
Economics, Faculty of Econom-
ics and Sociology, Lodz, Poland, e-mail: [email protected]
http://doi.org/10.2478/cer-2018-0018
mailto:e-mail:[email protected]
46 Janina Witkowska
1. Introduction
The aim of this paper is to discuss the common features and
specificity of CSR
practices of innovative transnational corporations (TNCs) acting
in the pharma-
ceutical industry and to evaluate some CSR practices in this
field. The detailed
research tasks are as follows: to discuss theoretical approaches
towards CSR that
might constitute the most promising explanation of the behavior
of innovative
TNCs in the pharmaceutical industry; to define some economic
and ethical di-
lemmas of CSR activities in the pharmaceutical industry; to
discuss some lim-
itations in access to innovative medical treatment for patients
from less devel-
oped countries in the context of CSR practices of
pharmaceutical firms; to present
a case study of BIOGEN and compare the theoretical findings
with the practices
of this TNC.

Corporate Social Responsibility (CSR) is defined as the
voluntary integration
of social and environmental issues into business activities and
relations with stake-
holders combined with the readiness to sacrifice profit for the
sake of certain social
interests (Carroll, Shabana 2010, pp. 85–100; Benabou, Tirole
2010, pp. 1–19). In its
Europe 2020 Strategy, the EU proposes a new definition of
CSR: “the responsibil-
ity of enterprises for their impacts on society” (EU 2011, p. 6).
All these aspects
will be taken into consideration while discussing the main issue
in the paper.
The innovativeness of pharmaceutical firms is understood here
as their abili-
ty to make a breakthrough in the treatment of rare, incurable
diseases. One of the
links between the CSR of the pharmaceutical industry and its
innovativeness is its
attitude to so-called orphan drug development and the marketing
strategy in this
field. These drugs are called “orphan drugs” because no one
wants to “adopt”
or manufacture them because of weak economic incentives and
their lack of com-
mercial value (Bruyaka, Zeitzmann, Chalamon, Wokutch,
Thakur 2013, p. 117).
The concept of an ‘orphan disease’ implies a lack of
stewardship; rare diseases
have been neglected by society for a long time (Berman 2014, p.
4).
The main theoretical and practical issues that appear here are

the contradiction
between the economic and social aspects of innovation
processes in the pharma-
ceutical industry and ways to smooth it over. Long-term, costly
and risky innova-
tion processes (economic aspect) encounter high expectations of
patients relat-
ed to the accessibility of innovative, life‑saving treatments
(social aspect). Some
questions arise in this context: How could the contradiction
between the economic
and social aspects of innovation in the pharmaceutical industry
be solved in less
developed countries? What is the essence of real Corporate
Social Responsibili-
ty in such a difficult case? What are the drivers of CSR in the
field of the orphan
drugs market?
Even quite recently, intense disputes went on between CSR
proponents and
opponents (Friedman 1970; Henderson 2001; Porter, Kramer
2006; Carroll, Shaba-
47Corporate Social Responsibility (CSR)…
na 2010). Nowadays, the question of “whether to do this or not”
has been replaced
rather by “why-, how- and which- questions” in the course of
theoretical and em-
pirical analyses. Business practices in different industries
reflect the CSR concept
more and more. Tradition and respected ethical norms are
decisive for whether

enterprises treat social responsibility as a value deeply rooted in
their practice
or whether they use it mainly for marketing purposes.
2. CSR and the pharmaceutical industry – theoretical
background
The literature on CSR identifies six key characteristics around
which there is a wide
consensus. These are:
1. CSR is voluntary and goes beyond activities prescribed by
the law.
2. It focuses on integrating or managing externalities which
arise when products
or services are delivered/rendered by companies.
3. CSR targets various stakeholder groups such as consumers,
employees, sup-
pliers, and local communities. The company not only has
responsibilities to its
shareholders, but it also caters to groups other than businesses.
4. There is a need to integrate social, environmental and
economic responsibility
with everyday business operations and decision making. It
should not, how-
ever, conflict with the profitability of the company.
5. CSR must be integrated into normal business practice and in
a company’s
system of values.
6. CSR goes beyond philanthropy and focuses on “real CSR.”
The company

should consider how its entire operations, i.e., its core business
functions,
impact society (Crane, Matten, Spence 2014, pp. 9–12; Bondy,
Moon, Mat-
ten 2012, p. 283).
The hitherto attempts to explain the specificity of CSR in the
pharmaceuti-
cal industry are based on general models of CSR or their
modifications. The gen-
eral attitude boils down to the acceptance of the fact that the
pharmaceutical in-
dustry “provides cure to a life‑threatening disease, but is
incapable of providing
cure to everyone at affordable prices” (Nussbaum 2008, p. 67).
Nevertheless,
the establishment of the relationship between actions and
business practices in
the pharmaceutical industry and CSR seems to be possible.
Some specific positive
effects of CSR actions for pharmaceutical companies are
distinguished among the
general effects of CSR, e.g., building a strong corporate
reputation, attracting and
retaining a motivated workforce, and reducing regulatory
oversight (Nussbaum
2008, pp. 67–76).
According to Freeman’s stakeholder theory, pharmaceutical
companies pro-
actively engage in stakeholder management. The list of the
major stakeholders
48 Janina Witkowska

of these companies, apart from typical groups of stakeholders,
e.g., stockholders
and investors, employees, communities, competitors and the
media, also includes
special stakeholders, such as patients (consumers),
physicians/prescribers (cus-
tomers), regulatory agencies, legislators, and scientific and
patient associations.
The following are perceived as the main CSR goals of
pharmaceutical companies:
reducing their environmental footprint, employee safety, the
safe handling of un-
used medicines, supplier management, material reduction,
sustainable workforce,
employee and community involvement, and access to medicines
(Min, Desmoul-
ins‑Lebeault, Esposito 2016, pp. 58–69).
Schwartz and Carroll’s three‑domain approach to CSR
(economic, legal and
ethical domains existing simultaneously) combined with the
concept of strategic
CSR could be used in order to discuss CSR in the
pharmaceutical industry (Bruy-
aka, Zeitzmann, Chalamon, Wokutch, and Thakur 2013, pp. 45–
65). These two
theoretical approaches are perceived as complementary in
explaining CSR ac-
tivities in orphan drug development. Schwartz and Carroll’s
model concentrates
mainly on a firm’s motives for acting in the field of CSR.
Strategic CSR is defined
as “… any ‘responsible’ activity regardless of motive that
potentially allows a firm
to achieve a competitive advantage” (Bruyaka, Zeitzmann,

Chalamon, Wokutch,
Thakur 2013, p. 46). In terms of Schwartz and Carroll’s model,
the attitude of phar-
maceutical companies to orphan drug development shows that:
• economic motivations are important for these firms, but they
are not the
only ones,
• incentives provided by orphan drug legislation also create
important encour-
agement for their activities in this field,
• the specificity of doing business in rare diseases requires
ethical responsi-
bilities.
The motives of biopharmaceutical firms to develop orphan
drugs range from
economic reasons (“develop and commercialize breakthrough
innovations”)
to dominant ethical motives (“to save people”). However, there
is also a wide‑spread
view presented by smaller firms that established pharmaceutical
companies are
driven primarily by economic interest and opportunism created
by orphan drug
legislation (Bruyaka, Zeitzmann, Chalamon, Wokutch, Thakur
2013, p. 56).
3. The specificity of CSR of the pharmaceutical industry
– ethical and economic dilemmas
The pharmaceutical industry is often criticised for unethical
behaviour such as in-
dustry-funded ghostwriting, publication bias, prescription data

mining, gifts to doc-
tors. It is also criticized for sanctioning excessive prices for
life-saving medicines
for those in the developing world (Lὅrinczy, Formankova 2015,
pp. 2011–2012;
Lee, Kohler 2010, p. 642). The empirical questionnaire research
results – scarce
as they are – also show that ‘…pharmaceutical companies do
not have clear-
ly set procedures for ethical and CSR activities’ (Lὅrinczy,
Formankova 2015,
p. 2014). Such an attitude was spotted in some of the new EU
Member States, i.e.,
in the Czech Republic and Hungary. Moreover, the differences
in ethical behavior
between so-called original and generic pharmaceutical
companies are observed.
While the original companies implement ethical issues to a
greater extent, the ge-
neric companies “…still do not have strict rules which would
help the employees
not to be misled in their work” (Lὅrinczy, Formankova 2015, p.
2014). Although
the generic companies usually have Codes of Ethics, they are
not respected. At the
same time, public pressure on pharmaceutical companies occurs,
in general, to im-
plement ethical rules in their strategies and practices.
Areas for CSR in the pharmaceutical industry are perceived to
include pricing,

patents, research and development (R&D), joint public‑private
initiatives (JPPIs),
and the appropriate use of medicine (Nussbaum 2008, p. 71).
The focus of this pa-
per is on the pricing of drugs as a controversial issue connected
with accessibility
to medicines, especially in the case of orphan and ultra-orphan
diseases. Discuss-
ing this problem requires that social expectations, economic
determinants (costs
of R&D and medical trials, risks, market failure) and
international trade repercus-
sions be taken into account.
Regarding social expectations, pharmaceutical corporations are
expected
to provide societies with medicines of good quality at fair
prices. The pharma-
ceutical industry is criticised, even blamed, for the fact that
prices for life-saving
drugs are much too high when considering the poverty of
individuals and whole
nations. Critics point out that companies put corporate profits
before human life.
Such views negatively influence the public image of
pharmaceutical corporations,
which causes serious reputation problems (Leisinger 2005, pp.
577–594). In this
context, the question arises if CSR practices, such as corporate
philanthropy, com-
munity and neighborhood programmes, volunteerism, and
donations could be so-
cially accepted as sufficient compensation for the high prices of
medicines?
The reactions of pharmaceutical corporations to the above

mentioned social
expectations could range from a readiness to help out with
donations of medicines
in cases of acute emergency (for example, Novartis provides
free treatment for all
leprosy patients in the world) to differential pharmaceutical
pricing for patients
from developing countries on a case-by-case basis. Some
corporations are also
involved in strengthening the drug infrastructure, mHealth
initiatives and target-
ed R&D in developing countries (Leisinger 2005, pp. 577–594;
Droppert, Bennett
2015, pp. 1–8).
As for the economic determinants of pricing of medicines, it is
worth men-
tioning that drugs for typical diseases in developing countries,
such as tuberculo-
sis, diarrheal diseases, pneumonia, malaria, and measles, are
relatively cheap, ef-
50 Janina Witkowska
fective and off‑patent. However, they are not available where
they are needed (for
example, the lack of access to medicines in the rural regions of
sub‑Saharan Afri-
ca, Leisinger 2005, p. 590). Nevertheless, approximately
one‑third of the world’s
population suffers from a lack of access to medicines or
vaccines for treatable dis-
eases. This number is higher in Africa and South East Asia,
reaching 50%. These

data, together with the information that 15% of the world’s
population consume
over 90% of the pharmaceuticals, confirm the existing
inequality in access to med-
icines between developed and developing countries (Lee, Kohler
2010, p. 641).
The situation in the market of innovative drugs – orphan or
ultra‑orphan
drugs – is different as far as the pricing of medicine is
concerned. The rare fre-
quency of some diseases, which is defined by law, determines
the pricing policies
of pharmaceutical companies. In the USA, according to the
Orphan Drug Act
(Public Law 97–414, as amended), the term “rare disease or
condition” means any
disease or conditions which affect (A) fewer than 200,000
persons in the country
or (B) affect more than 200,000 in the USA and for which there
is no reasonable
expectation that the cost of developing and making available in
the USA a drug
for such disease or condition will be recovered from sales in the
USA of such drug
(Orphan Drug Act, https://www.fda.gov;). In the EU, a common
definition of rare
diseases has been accepted in the official documents for the
purposes of Com-
munity‑level policy work. The EU considers diseases to be rare
when they affect
not more than 5 per 10,000 persons in the integration grouping,
i.e., fewer than
1 in 2000 persons (EC 2000, pp. 2–3; EC 2008, p. 2). In the
case of a life‑threaten-
ing, seriously debilitating or serious and chronic condition, a

status of rare disease
is eligible even when its prevalence is higher than 5 per 10,000
(EC 2000, pp. 2–3).
These two definitions above show the similarities in the
attitudes of both the USA
and the EU towards numerical criteria for rare diseases.
It is estimated that there are about 7000–8000 rare diseases and
these num-
bers might still be underestimated. Rare diseases affect, in
aggregate, 25–30 mil-
lion people in the USA and 6–8% of the population in the EU,
i.e., between 27 and
36 million people (Public Health, https://ec.europa.eu, Berman
2014, p. 3). There
is a lack of information about the situation in this field in
developing countries.
Long‑term and costly R&D processes, high risks and
uncertainty, the costs
of medical trials, and the narrow markets have a serious impact
on the attitude
of pharmaceutical corporations towards orphan drugs and their
pricing. The prices
of orphan drugs are extremely high. They are less likely to face
competition, and
they provide a high return on investment: orphan drugs without
competition are
2.6 times more expensive than those with competition
(MarketLine 2013, p. 12).
As illustrations of the problem of the pricing of orphan drugs,
data related to the
annual costs of the treatment per patient can be used in the case
of:
• Cinryze; in this case, the annual cost per patient amounts to

USD 487,000.
• Soliris, with an annual cost of USD 486,000 respectively
(MarketLine 2013,
p. 12).
The costs of orphan drugs have been growing consistently. One
decade ago,
the company GENZYME sold some of the most expensive drugs
in the world,
costing up to $200,000 per patient per year for disorders often
requiring life‑long
treatment, and usually, the same price was charged all over the
world (Nussbaum
2008, p. 72).
It is obvious that such high costs of the treatment with orphan
drugs cannot
be covered by patients on their own. They are usually covered
by insurance com-
panies (sometimes with a co‑payment by patients) or by
governments from pub-
lic budgets. Both forms of financing could be available in
developed countries.
However, in a crisis, even these countries have negotiated
prices or demanded that
companies cut prices. It is estimated that around one-third of
EU patients have
difficulty accessing drugs or do not have access to the drugs
they need (Market-
Line 2013, pp. 12–13).

The pricing of medicines could also have international trade
repercussions
if drugs at lower prices – although they are conventional or
orphan drugs – are of-
fered to patients from developing countries. The trade in such
medicines should
be controlled to prevent re-exportation or leakage of low-priced
drugs to the mar-
ket of developed countries (Leisinger 2005, p. 587).
4. Regulatory policies towards the promotion of development of
innovative
orphan drugs
Taking into account that the development, production and
commercialization of or-
phan drugs encounter serious economic barriers, developed
countries introduce
specific legislative guarantees/incentives for any company that
obtains an orphan
drug designation. The above‑quoted regulations implemented by
the USA and the
EU not only define the notion of a rare disease but also
guarantee the market/
marketing exclusivity for producers of medicines for rare
diseases. Firms be-
come monopoly providers able to charge monopoly or near-
monopoly prices.
In the USA, exclusivity means exclusive marketing rights
granted by the FDA
upon approval of a drug and they can run concurrently with a
patent or not. Or-
phan Drug Exclusivity lasts seven years. It prevents the FDA
from approving any
other application for the same drug for the same orphan disease

or condition in the
7‑year‑period (FDA/CEDER SIBA 2015).
In the EU, market exclusivity means that the Community and
the Member
States shall not accept any application for marketing
authorization, or grant such
an application for similar medicines for a period of 10 years.
This should protect
producers from the market competition of similar medicines
with similar indica-
tions. Nevertheless, this period could be reduced to 6 years if,
at the end of the
52 Janina Witkowska
fifth year, there is evidence available that the medicine is
sufficiently profitable not
to justify maintaining market exclusivity (EC 2000, pp. 7–8). It
is worth noting
that marketing authorization is carried out centrally in the EU,
which means that
a single decision of the European Commission is valid in all EU
Member States
(EMA 2018).
The market/marketing exclusivity is generally regarded as the
most signifi-
cant incentive offered to develop orphan drugs (IOM Institute of
Medicine 2010,
pp. 88–87). Other incentives for orphan drug development,
production and com-
mercialization include:

• fee reductions or exemption from user fees,
• tax reductions or tax credits,
• grants for clinical trials,
• consultation with staff on acceptable research designs.
The above‑quoted instruments are treated as “push” incentives
which are in-
tended to subsidize or lower research and other development-
related costs. The mar-
ket/marketing exclusivity and the mechanisms to speed and
facilitate the review
of drugs are called “pull” incentives (IOM Institute of Medicine
2010, p. 86).
According to the regulations in the USA, 50% of the qualified
clinical testing ex-
penses for drugs for rare diseases or conditions could be treated
as a credit against the
tax imposed for the taxable year. Grants and contracts for the
development of drugs for
rare diseases and conditions are also foreseen by law (Public
Law 97–414, 1983). The
FDA provides grants for clinical studies on the safety and/or
effectiveness of products
for rare diseases that will either result in, or substantially
contribute to market approv-
al of these products. Grant funding lasts for 3 to 4 years. At any
one time, financial
sources are used for 60 to 85 ongoing projects (FDA 2018).
The list of incentives offered by the EU embraces protocol
assistance – a form
of scientific advice – at a reduced charge for designated orphan
medicines, and fee
reductions for different regulatory activities related to
designated orphan medi-

cines. Companies classified as small and medium enterprises
(SMEs) benefit from
further incentives which include administrative and procedural
assistance. The
European Medicines Agency (EMA) does not offer research
grants for sponsors
of orphan medicines, but funding is available for these purposes
from the Euro-
pean Commission and under Horizon 2020, the Framework
Programme for Re-
search and Innovation (the theme Personalising Health and Care
which covers
New therapies for rare diseases) as well as under the
transnational programme
for rare diseases E‑Rare. Member States also offer some
incentives for designated
orphan medicines (EMA 2018).
All these instruments have played an important role in the
development
of R&D in the field of rare diseases and the production of
drugs. Some data seem
to confirm this observation. Regarding the USA, before 1983,
only 38 orphan drugs
were developed, while after introducing supporting policy
instruments on the ba-
sis of the Orphan Drug Act of 1983 more than 220 new orphan
drugs were ap-
proved and marketed in the USA, and more than 800 additional
drugs were in the
research pipeline (Rare Disease Act of 2002, 116, STAT. 1988–

1989). In the years
2000–2008, orphan drugs accounted for 22% of the innovative
drugs approved
by FDA and 31% of the innovative biologics. It is worth noting
that 55% of 108 or-
phan drugs approved from 1984 to 1999 in the USA, and which
were still avail-
able in 2010, had generic equivalents on the market
manufactured by competing
companies (IOM Institute of Medicine 2010, pp. 92–93). These
data indicate that
the processes of distributing innovative medicines occur.
Some data on orphan drug sales also confirm the growing
activity of pharma-
ceutical firms in this field and show promising prospects for the
development of the
orphan drug market. For example, global orphan drug sales
grew to USD 83 billion
in 2012, showing an annual growth rate of 7.1% compared to
the previous year.
In 2012, around 35% of the pharmaceutical industry’s new drug
offerings were
orphan drugs. Orphan drugs are expected to bring in revenues of
USD 127 billion
by 2018 and will account for almost 16% of total prescription
drug sales, compared
to 12.9% in 2012 (MarketLine 2013, p. 14).
5. A case study – BIOGEN – some facts
BIOGEN, formerly known as Biogen Idec, was founded in 1978
by a group of sci-
entists and three venture capitalists in Geneva/Switzerland.
Now it is a transna-
tional pharmaceutical corporation introducing onto the market

the most innovative
drugs for rare diseases (A biotech pioneer, www.biogen.com).
BIOGEN focuses
on developing, manufacturing and delivering therapies for
neurological, autoim-
mune and hematologic disorders. BIOGEN has introduced
leading marketing prod-
ucts for rare diseases, among them AVONEX (interferon beta–
1a) approved for
the treatment of relapsing forms of multiple sclerosis (MS).
AVONEX was among
the Top 10 Orphan Drugs in 2012 and is predicted to remain in
this group until
2018 (MarketLine 2013, p. 10). In 2016, BIOGEN registered in
the USA the first
and only one approved drug for the treatment of a rare genetic
disease, SMA (Spi-
nal Muscular Atrophy), i.e., SPINRAZA. In 2017, its
registration was accomplished
in the EU. The company continues its innovation efforts, which
are confirmed by
14 drug candidates in clinical trials (BIOGEN 2017a).
The company operates in the US, Canada, Australia, New
Zealand, Japan, Eu-
rope, and Central and South America. It has manufacturing
facilities located in Re-
search Triangle Park, North Carolina and Cambridge,
Massachusetts and Hillerød,
Denmark. The major competitors of BIOGEN, Inc. are Abbott
Laboratories, Am-
gen, Inc., Bristol‑Myers Squibb, GlaxoSmithKline Plc, Pfizer
Inc., Sanofi Genzyme,
and Teva Pharmaceutical Industries Limited (MarketLine 2015;
p. 29, 34).

54 Janina Witkowska
Graphs 1 and 2 present key financial information on the
economic activities
of BIOGEN, including some data on total revenue, gross profit,
R&D expenditure,
net income, profit margin, and profit per employee.
Graph 1. BIOGEN – Financial overview, 2006–2016, USD,
Million
Source: MarketLine and own elaboration.
Graph 2. BIOGEN – Profit margin, 2011–2015, %
Source: MarketLine and own elaboration.
An analysis of the financial data related to BIOGEN allows us
to summarize
the following:
• Total revenue of the corporation grew twofold between 2012–
2016, reaching
USD 11.5 Billion.
• R&D expenditures amounted to almost USD 2 Billion in 2016
(about 20% of an-
nual revenues has been reinvested back into R&D over the past
decade).
• Net income grew to the level of USD 3.7 Billion in 2016.
• The profit margin increased from 24.5% in 2011 to 33.0% in
2015.
• Total employment amounted to 7400 people worldwide in
2016.

• Profit per employee almost doubled in the same period.
• Payouts to members of the Executive Board ranged from USD
4 to 18 million
annually (MarketLine 2015; BIOGEN 2016, 2017a).
The above‑quoted data confirm the good financial condition of
the company
and indicate its prosperous future. Although it is difficult to
judge what the impact
of incentives offered by the state has been on its
financial/market situation, one
can suppose that the state policy towards orphan drug
development has given the
company strong and positive motives.
As for revenues by geography, developed countries remain the
largest geograph-
ical market for BIOGEN’s products. The USA was the leading
market for products
offered by BIOGEN in 2015. It accounted for 73.9% of its total
revenues. Europe
took second place with 14.2% (excluding Germany), then
Germany with 6.2%, Asia
– 1.9% and others – 3.7% respectively. The high geographical
concentration of rev-
enues is treated as a weakness in BIOGEN’s SWOT analysis
(MarketLine 2015,
pp. 28–29). However, a widening of the market for
innovative/orphan drugs in other
parts of the world economy seems to be limited by the high
prices of the drugs.

6. The CSR practices of BIOGEN
BIOGEN is a socially responsible transnational corporation
engaged in all
general and specific areas of CSR which are also typical of
other firms’ activities
in this field, i.e.:
• Protection of the environment, including driving responsibility
across the val-
ue chain; (BIOGEN has been a carbon neutral company since
2014 and intro-
duced a Zero waste to landfill strategy).
• Stakeholder engagement (including investors, patients, patient
groups, health-
care professionals, employees).
• Social performance (i.e. community giving in the form of
grants, the Match-
ing Gifts Programme, Volunteer Hours).
• Diversity & Inclusion (Women and Minorities).
• Employee development; Health & Safety at Work.
• The establishment of the BIOGEN Foundation (providing
access to hands-on
science education, teacher development in science, college
readiness and sup-
port, and basic social needs – combatting child hunger, poverty
and social mo-
bility) (BIOGEN 2016, 2017a, 2017b.).
It is worth pointing out that in 2014, BIOGEN (at that time
BIOGEN Idec) was

named the Global Biotechnology Industry Leader on the Dow
Jones Sustainability
World Index (DJSI World), (Regional Business News
09/23/2014).
56 Janina Witkowska
All these CSR activities of BIOGEN confirm that the idea of
traditional social
responsibility is a value deeply rooted in the practice of the
company. This TNC
carries out all conventional activities which are treated as a core
of CSR in the
light of theoretical findings, as well as some industry‑specific
activities. Table 1
presents a comparison between the main theoretical
characteristics of CSR, phar-
maceutical industry‑specific CSR and BIOGEN’s CSR practices.
Nevertheless,
the high pricing of medicines for rare diseases remains a
controversial issue. This
situation is common in the pharmaceutical industry. The case of
BIOGEN illus-
trates this problem.
Regarding its pricing policy, BIOGEN informs that the company
strives
to achieve an appropriate balance among three key principles:
• The clinical value of the product.
• The impact of the therapy on the health care system, including
the financial
implications on payers and patients.

• Stakeholder returns.
The company is aware of the need to remove barriers to access
the medicines.
A justification for its pricing policy could be, among others, the
fact it spent about
20% of its revenues on R&D in the last decade, and provided
over USD 1.1 Billion
in patient financial assistance in 2016 (BIOGEN 2017a).
Nevertheless, some ethical dilemmas related to the selectivity of
this assis-
tance have not disappeared. Someone decides indirectly “Who
will live who will
not have a chance?” Also, the argument that high prices enable
them to earn mon-
ey for further R&D cannot be convincing for patients and
organizations from less
developed countries. Future new cutting-edge solutions would
be not available for
them either. In this context, some doubt arises that perhaps real,
true CSR requires
abandoning or at least reducing monopolistic privileges and
offering medicines for
rare diseases at lower prices.
These dilemmas could be illustrated by the first‑ever medicines
for SMA
(Spinal Muscular Atrophy) – the newly registered SPINRAZA
(nusinersen). SMA
is a rare disease, affecting about 35,000 patients worldwide,
mainly children,
so it is treated as a small addressable market size. The drug will
cost USD 125,000
per injection, amounting to USD 750,000 for the first year and
USD 375,000 each

year after that (Weintraub 2017). After the registration of
SPINRAZA in the Eu-
ropean Union, the issue of the price was intensively discussed,
and it is expected
that the price of the medicine will be up to EUR 270,000 for the
three‑dose‑main-
tenance per individual per year (SMA Europe 2017). It is worth
noting that this
therapy is lifelong. This extremely costly therapy arouses
heated discussions, even
in the USA. The drug is out of the reach of individual patients,
not only those
from less developed countries, unless governments pay for it.
Predictions related
to worldwide sales of SPINRAZA are promising. It is estimated
that it will amount
to over USD 2.3 billion by the early 2020s (Weintraub 2017).
Intensive marketing
and BIOGEN’s involvement in multiple community engagement
initiatives may
boost future revenues for SPINRAZA (Market Realist 2017).
Finally, the role of other organizations in the breakthrough in
the treatment
of SMA should be pointed out. Cure SMA is a non‑profit
organization dedicated
to the treatment and cure of SMA, funding groundbreaking
research in this field
and providing support to families that suffer from SMA. Since
its founding in 1984,
Cure SMA has invested USD 70 Million in research on the

treatment of SMA.
In 2003–2006, Cure SMA provided over USD 500,000 in seed
grants to found the
therapeutic approach that led to SPINRAZA. The critical
intellectual property was
generated by Cold Spring Harbor Laboratory (CSHL) and the
University of Mas-
sachusetts Medical School at the preclinical phase of
development of SPINRAZA.
In 2010, IONIS (then ISIS Pharmaceuticals) licensed the
intellectual property to be-
gin the development of SPINRAZA (Cure SMA 2018).
In January 2012, IONIS entered into a collaborative agreement
with BIO-
GEN for the development and commercialization of the drug.
BIOGEN received
worldwide rights for commercialization of the drug in August
2016. IONIS has re-
ceived a payment of USD 320 Million from BIOGEN for the
development of SPIN-
RAZA, including a USD 60 Million milestone payment and a
further USD 90
Million based on regulatory approvals in Europe and Japan.
IONIS will also re-
ceive tiered royalties on the drug’s sales up to 1%
(Drugdevelopment–technolo-
gy.com 2017). This means that financial obligations of BIOGEN
towards the in-
novator will decrease future profits of the company coming
from the global sale
of SPINRAZA.
However, the question whether the extremely high price of
SPINRAZA is eco-
nomically and ethically justified remains unanswerable.

7. Conclusions
1. The innovative pharmaceutical industry is involved in CSR
practices which
could be discussed on the grounds of main CSR models or a
combination
of them.
2. The specificity of CSR in this industry is related to the
contradiction and the
conflict between the economic and social/ethical aspects of
innovation pro-
cesses in this field. The essence of this contradiction is the
limited access
of patients from less developed countries to life-saving
medicines or those
that improve the quality of life.
3. A key issue of CSR in the innovative pharmaceutical industry
seems to be the
pricing of drugs, especially orphan and ultra‑orphan drugs.
Corporations
use their monopolistic position to set extremely high prices.
However, with-
58 Janina Witkowska
out the market/marketing exclusivity offered to pharmaceutical
firms by the
law, orphan drugs would not be probably developed, produced
and commer-
cialized.

4. Traditional CSR practices in the pharmaceutical industry
(corporate philan-
thropy, community and neighborhood programmes, volunteerism
etc.) do not
seem to be sufficient “compensation” for the high prices of the
medicines.
5. The case study of the pharmaceutical company BIOGEN
shows that the phar-
maceutical company is aware of the need to remove barriers to
access the
medicines. Nevertheless, financial assistance for patients and
free drug pro-
grammes offered to some patients are inevitably connected with
their se-
lectivity.
6. Real, true CSR in the innovative pharmaceutical industry
requires either aban-
doning or at least reducing monopolistic privileges and offering
medicines for
rare diseases at lower prices. Pharmaceutical corporations
should take into
consideration the differences in GDP per capita between
developed and de-
veloping countries.
7. Stronger co‑operation between different groups of
stakeholders in different
countries would be necessary in order to use financial resources
in a more ef-
ficient way; societies and individuals would be able to offer to
support patients
with rare diseases and their families.

Table 1. Main theoretical characteristics of CSR,
pharmaceutical industry‑specific CSR
and BIOGEN’s CSR practices
Main characteristics
of CSR – theoretical
findings
Pharmaceutical
industry‑specific
CSR areas and
practices
The CSR practices of BIOGEN
Conventional
practices
Industry‑specific
practices
• Voluntary character
of CSR practices.
• Focus on integrat-
ing or managing the
externalities which
arise when prod-
ucts or services are
delivered/rendered
by companies.

• Orientation of CSR
practices on stake-
holders and other
social groups.
• Integration of so-
cial, environmen-
tal and economic
responsibility with
everyday business
operations and deci-
sion making.
• Integration of CSR
into normal busi-
ness practices and
a company’s system
of values.
• Concentration
on “real CSR”‑ go-
ing beyond phi-
lanthropy.
• Pricing
of medicines.
• Patenting of new
medicines.
• Research and devel-
opment (R&D).
• Joint public-private
initiatives (JPPIs),

• Appropriate use
of medicines.
• Proactive engage-
ment in stakehold-
er management
(including special
stakeholders such
as patients (con-
sumers), physicians/
prescribers (cus-
tomers), regulatory
agencies, legisla-
tors, scientific and
patient associations.
• Safe handling of un-
used medicines.
• Increased access
to medicines for dif-
ferent social groups.
• Donations of medi-
cines in emergency
situations.
• Protection of the en-
vironment, includ-
ing driving respon-
sibility across the
value chain.
• Stakeholder en-
gagement.

• Social activi-
ties (communi-
ty giving – grants,
Matching Gifts
Programme, Volun-
teer Hours)
• Activities in the
field of “Diversity
& Inclusion”.
• Employee de-
velopment.
• Health &Safety
at work
• Proactive engage-
ment in stakehold-
er management
(patients, patient
groups, healthcare
professionals).
• Investigational ac-
cess to medicines
for patients (clini-
cal trials, expand-
ed access programs
(EAPs) to pa-
tients who are un-
able to participate
in clinical trials,
single-patient ac-
cess or emergen-

cy use).
• Affordability access
(financial assistance
to patients who are
otherwise unable
to access BIOGEN’s
medications).
• Education of physi-
cians and demon-
stration of product
efficacy and value.
• R&D expenditure
in the field of rare
diseases.
• Grants for middle
and high school stu-
dents made through
the BIOGEN Foun-
dation and the Com-
munity Lab.
Source: Own elaboration on the basis of the references used in
the paper.
60 Janina Witkowska
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