A Novel Methodology for Optimal Design of Compressor Plants Using Probabilistic Plant Design

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. Case studies are presented that highlight the methodology as applied to critical turbo-machinery.

Optimization of compressor stations

“…Since rarely all worst case conditions (highest ambient temperature, highest flow demand, highest engine degradation etc.) occur at the same time, significant size reduction, and fuel consumption reductions are possible (Kurz et al, 2013).…”

“…A more effective method is to assign probabilities to the input operating parameters such as pipe roughness, gas composition, ambient temperature, equipment degradation, or differences between compressor performance prediction and actual performance. Then, a Monte Carlo simulation ( Figure 6) can be performed (Kurz et al, 2013). 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. The referenced study (Kurz et al, 2013) 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. New approaches to the discussion of spare units are possible.…”

“…New approaches to the discussion of spare units are possible. Based on these considerations, significant cost reductions are possible in many cases (Figure 7, (Kurz et al, 2013)).…”

“…When comparing Figure 7. Fuel consumption and equipment size (CAPEX/OPEX), optimized using a Monte Carlo simulation (Kurz et al, 2013).…”