Exploiting new gas
reserves or increasing the throughput of existing liquefied
natural gas (LNG) operations involve a number of competing
technical, market and economic factors. For the business
decision maker, it is essential to be presented with key
options and tradeoffs as to which contracts to negotiate,
technologies to select, and capital investments to approve for
development. The separate environments in which analysts work
within an organization interfere with reaching the best
decisions quickly. Processes are screened with simulation
models and spreadsheet tools. Contractual, pricing and supply
chain information are analyzed through financial spreadsheets.
Capital costs are estimated with estimation systems while
resource and timing constraints are evaluated via planning and
project-management tools. Business
leaders are left with the results of these different analyses
that they need to weigh based on dueling PowerPoint
A better approach can be based on the interoperability of
software used during screening and front-end engineering and
design (FEED) studies that enable a better decision-making
framework. In particular, an innovative capability that has
been introduced to the market embeds accurate economic models
in the process modeling environment. This allows the process
modeler who is screening options to derive accurate and
comparable operating and capital costs during modeling studies.
These very early economics are particularly useful in the
comparison of alternatives. You can efficiently include
economics (capital and operating costs) in the technical,
energy and yield tradeoffs that you are considering.
LNG producers face numerous challenges to characterize the
capital and operating costs and risks early enough to be used
making investment decisions. Greenfield production facilities are increasingly in the
mega-project category, comprising gas processing facilities,
liquefaction and loading. In addition to new projects; in any
of these three asset areas, there could be opportunities to
leverage existing facilities that will involve debottlenecking
projects. Screening these projects involves complex interaction
between technical and facility cost parameters weighted against
commercial negotiation factors and logistical constraints, all
in the context of the business goals for a project. The
concepts discussed will focus on the LNG liquefaction end, but
they are applicable to all major capital projects in the value
If a stable process can be designed, will it be
cost-effective, make best use of capital, and achieve the
business and revenue objectives of the project? To answer these
questions confidently and rapidly, it is possible to use
powerful technical models, link them to economic ones, rapidly
screen alternatives, and further link them to Excel front
ends that can give broader access to the operation of
models beyond the realm of the model experts.
Early concept workflow.
Since early process screening usually involves small teams,
automating this workflow has not received the attention that
the detailed design workflow (usually involving large teams)
has. However, integrating this workflow, to remove the need for
data re-entry and copying, is valuable in enabling the process
screener to look at more alternatives in hopes of arriving at
the optimal choice. The typical workflow resembles the
simplified one shown in Fig. 1. In particular, the last three
steps are improved through the integration of simulation models and
economic evaluation systems.
1. Early screening
Process and energy optimization.
The chemical process simulation model is a key tool in
designing LNG facilities, on both the liquefaction and
regasification sides. While, frequently, process engineers only
model portions of a proposed process for schedule or effort
expediency, there are many advantages to building the complete
model. Rigorous models can be built much more quickly and
efficiently than organizations often realize.2 Some
recent advances in simulation modeling include energy integration analysis (that enable
system-wide balancing and optimization of energy sources and
uses), dynamics integration with steady-state models
to simplify the development and analysis of process dynamics,
and the addition of reporting tools to account for carbon emissions. All of these
developments mean that the process engineer can rapidly
evaluate several alternatives and optimize for yield, energy
cost and use, and carbon emissions. By incorporating
dynamics, the model becomes an invaluable tool for
understanding and improving startup conditions and avoiding
instabilities. As an example, Osaka Gas was able to apply
dynamic modeling to understand and solve LNG fractionation
tower instabilities, resulting in pre-construction design revamps that
increased process efficiency and reduced production costs by $3
million per year.3
By integrating heat-exchanger rating models with the general
process simulators, the heat-exchanger aspect of an LNG
facility, usually the dominant one in terms of the energy
balance of the process, can be analyzed with much greater
accuracy during screening studies. ConocoPhillips reports that
it has been able to achieve optimized design and improved
operations through its accurate modeling of brazed aluminum
heat exchangers within the simulation model and heat exchanger
model environment, using each tool to its
Model institutionalization at the business level.
Once a conceptual design is complete, the process model
itself should be a valuable asset that has lasting benefit,
both for the startup and operation of the facility, and perhaps
more subtly, for follow on capital investment decisions around
process improvement, commercial negotiations, debottlenecking
Using an Excel modeling executive is a proven
way to make technical models of LNG assets available for a
range of purposes. This involves running the model in the
background, while using the familiar Excel interface as the way
that the casual user can enter the scenario conditions and
otherwise interact with the model. In this way, business
analysts and process engineers can run scenarios involving
debottlenecking, energy use, pricing and other criteria.
BP is an example of an organization that has implemented
such an Excel interface layer to broaden the availability of
models of existing assets for decision-making, both at a
technical level for operating assets and at a business level
for operating strategies, enabling revenue
Typical debottlenecking project.
An existing LNG plant has usually been modeled fairly
completely by at least steady-state models during the design,
and, sometimes, dynamic models are added during startup stage.
When debottlenecking activities are studied, often a different
team is involved that may have a learning curve in reusing
these existing models or that may be unfamiliar with the model
details. This is where a spreadsheet interface can be
invaluable to enable a screening team to access a model and use
it for alternatives evaluation, without concerning themselves
with the details of model creation.
Incorporating relative economics in the decision-making
Estimators have long used unique rigorous
engineer-in-a-box class of estimating software
tools for the conceptual estimating of hydrocarbon facilities, both greenfield plant
sites, as well as brownfield upgrades and debottlenecking projects. These tools can be
calibrated to achieve better than 20% accuracy time after time.
For instance, ConocoPhillips reports moving to this approach
between 2004 and 2006 and, during that timeframe, reducing the
% variance of their estimates from actual at a starting point
of greater than 35% variance to less than 15%
variance.6 But these tools are too specialized and
complicated, in their native form, for the process engineer to
The innovation required to embed this powerful tool within
the process simulation environment is fourfold. First, some
of the power of the estimating tools (which enables the
estimators to calibrate the tools) must be hidden so that the
process engineers are not required to see that complexity.
Second, engineering rules need to be incorporated in the
interfacing activity to map the simulation blocks to equipment
types that can be estimated, and to size equipment and bulks
based on the models heat and material balance. Third,
operating cost itemssuch as feed costs, utility costs,
and product pricingneed to be captured from the model.
And finally, fourth, the tool is automated to run behind
the curtains so that, by simply pushing a button, the
process engineer accesses the estimation cost engine. All of
this workflow and engineering rules innovation has been
accomplished over the past three years by our organization.
This tool has been effectively adopted and used by several
enterprises to achieve economically superior process designs
and improved capital predictability. Kuwait Oil Company has
used this integrated economics approach to rapidly evaluate two
dramatically different options for a gas-dehydration
unit.7 Using this approach, the counter-intuitive
alternative, complete unit replacement, proved to be an
economically superior option, saving 50% of the total costs,
for a savings on that project of almost $20 million. The key to
achieving this was the ability to generate both capital and
operating costs so that lifecycle business impacts of design
alternatives could be measured fully.
Technip has used the integrated economics capability to
improve its ability to make bidding decisions and to study
tradeoffs in selecting designs.8 Technip reports an
ability to increase design flexibility, achieve maximum energy
efficiency and optimize designs from a cost point of view. It
employed this integrated approach on designs for Technip
proprietary technology for gas processing. It is
able to achieve economically superior designs and detailed
proposals in one-tenth of the former time. Technip now
incorporates training in using integrated economics during
early conceptual design as a core competency for its North
American process engineers.
Once the economics have been derived, the resulting capital
and operating forecasts can be easily brought into a master
spreadsheet, where the business factors such as product
transportation costs, contract values, royalty schedules,
reserves over time, and the like can be taken into account.
Several major LNG producers are currently considering this
approach to improve capital decision-making.
One of the characteristics of LNG processing plants is the
repeatable nature of the designs. Large-scale LNG liquefaction
plants usually involve multiple identical process trains, and
LNG facilities bear many similarities
from a process point of view. This can be taken advantage of to
create libraries of reusable design elements, both from the
process viewpoint and from the economic modeling viewpoint.
This general approach has been descri-bed quite clearly by
one organization, DSM, which gained a significant competitive
advantage in reducing time to market for new
processes.2 DSM broke down commonly reused processes
into libraries of design fragments that were built
up into simulation models and the associated economic
Samsung Heavy Industries proposed such a library approach
for the rapid FEED design of LNG floating production storage
offshore (FPSO) topsides.9 Its goal, during
pre-FEED, is to rapidly estimate the total cost, weight and
layout of an LNG FPSO facility. In their analysis of the
repeatable design problem, Samsung concluded that the process
units could be divided into those that are common across all
LNG projects and those that vary with the type of source gas
being processed. In the case of Samsung, a benefit of this
approach is to enable the company to begin to penetrate the
FEED phase of these projects, from its traditional strengths in
the areas of fabrication and detailed design.
The innovations described provide tremendous opportunity to
rethink the way that early process design is conducted. The
next areas of innovation will most likely involve applying the
new usability paradigms, common to mobile devices and the web,
to the technical engineering modeling domain. Social media
tools will present additional opportunities for sharing of
best-practice modeling ideas within organizations and, with the
appropriate intellectual property protections, across
2. Economic analysis with a
With the fast pace and dynamic nature of the LNG
marketplace, the pressure to make capital decisions better and
faster is increasing. The technical groups supporting these
decision-making processes are hard-pressed to keep up. One of
the reasons is the highly manual process by which information
is distributed between groups and the fragmented way in which
the different engineering and economics aspects of the problem
are often tackled. Fig. 3 indicates the typical, traditional
approach that is taken, highlighting the ad hoc nature of the
communications and data handoffs between the groups. What we
have described in this article are a number of innovations that
change the game in terms of the ability to make these decisions
better and faster. By incorporating equipment sizing, energy
analysis and rigorous economic modeling within the world of the
process modeler, the technical organizations can respond more
quickly and with better choices and financially superior
designs. Fig. 4 provides a simplified summary view of the
approach that we have been discussing. Measureable benefits as
described by Kuwait Oil, Osaka Gas and others in the examples
above are just the tip of the iceberg. The potential payoff of
adopting of these new approaches is high.
3. Traditional approach.
4. Integrated, innovative approach.
1 Eijkenboom, M.,
Developing Scope for Proposed Processes by Integrating
Aspen Economics with Aspen Plus, May 2011.
2 Eijkenboom, M., Achieve Better Process
Designs with Integrated Economics, Public Webinar
Broadcast, October 2010, http://
3 Emi, H., Solving Unit Instability Problems
in LNG Separation Through Use of Aspen HYSYS Dynamics,
Aspen Japan User Meeting, Tokyo, June 2008.
4 Evans, M. and M. Gentry, Optimizing LNG
Plant Design and Operations with Aspen HYSYS and Aspen
MUSE, AspenTech User Conference, May 2009.
5 Stewart, Ramidial and Hudson, Asset
Optimization at BP Trinidad, AspenTech User Conference,
6 Whiteside, J., Use of Historical Data to
improve Conceptual estimates with Aspen ACCE Estimating
System, AspenTech User Conference, June 2006.
7 Madhusudana, V., Project Optimization at Conceptual
Level by Using Aspen HYSYS Tools, AspenTech Global
Conference, May 2011.
8 Tipton, E., Best Practices for Process and
Collaborative Engineering, Aspen Engineering Public
Seminar, Puerto la Cruz, Venezuela, April 2010.
9 Hwang, J., et. al., Application of an
Integrated FEED Process Engineering Solution to Generic LNG
FPSO Topside, ISOPE, 2009.
Ron Beck is a member of the
engineering products group at Aspen Technology in Burlington,
Massachusetts. His experience is in introduction and
implementation of systems for design and management of
process plants globally. Mr. Beck holds a BA degree in
science from Princeton University.