| dc.description.abstract | Equipment sizing decisions in the Oil and Gas Industry often
have to be made based on incomplete data. Often, the exact
process conditions are based on numerous assumptions about
well performance, market conditions, environmental
conditions and others. Since the ultimate goal is to meet
production commitments, the traditional way of addressing
this is, to use worst case conditions, and often adding margins
onto these. This will invariably lead to plants that are
oversized, in some instances by large margins. In reality, the
operating conditions are very rarely the assumed worst case
conditions, but they are usually more benign most of the time.
Plants designed based on worst case conditions, once in
operation, will therefore usually not operate under optimum
conditions, have reduced flexibility, and therefore cause both
higher capital expenses and operating expenses.
The authors outline a new probabilistic methodology that
provides a framework for more intelligent process-machine
designs . A standardized framework using Monte Carlo
simulation and risk analysis is presented that more accurately
defines process uncertainty and its impact on machine
performance .
This paper describes a new method for the design of efficient
plants. The use of statistical and probabilistic tools allows to
better account for the unpredictability of component
performance, as well as for ambient conditions and demand.
Using the methodology allows to design plants that perform
best under the most likely scenarios, as opposed to traditional
designs that tend to work best under unlikely worst case
scenarios. A study was performed for a relatively simple
scenario, but the method is not limited, and can easily be
adapted to scenarios involving entire pipeline systems,
complete plants, or platform operations. Based on these
considerations, significant cost reductions are possible in
many cases. | en |
