A Probabilistic Approach for Compressor Sizing and Plant Design
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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 take the unpredictability of component performance, as well as ambient conditions and demand, into account. 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.
Kurz, Rainer; Brun, Klaus; Thorp, J. Michael; Zentmyer, Erik G. (2013). A Probabilistic Approach for Compressor Sizing and Plant Design. Texas A&M University. Turbomachinery Laboratories. Available electronically from