287437 A Monte Carlo Approach for Estimating and Validating Contingencies for CCS

Tuesday, October 30, 2012: 10:35 AM
302 (Convention Center )
Paul Worhach, Nexant, Inc., San Francisco, CA, Mark Woods, NETL, Booze Allen, Pittsburgh, PA and John Wimer, Department of Energy, National Energy Technology Laboratory, Morgantown, WV

The AACE International recommended practice for estimating process and project contingencies provides a set of contingency factors to be applied to total process capital cost, as a function of process technology development and class of estimate.  Process contingency ranges from 0 to 10 percent for commercial technologies to 40 percent plus for new concepts with limited data, and project contingency ranges from 15 to 30 percent, with a recommended factor of 25 percent.

Monte Carlo simulation can be a useful technique to validate and to quantify key sensitivities in the application of these factors.   A Monte Carlo simulation model was developed based upon a cost estimation case for an IGCC plant from the NETL draft report “Supplemental Analysis for Baseline Studies – GEE IGCC and SP PC Zero Liquid Discharge Plants.”   Probability ranges of P10/P90 and P1/P99 were developed for project account costs, and a correlation factor matrix was constructed based upon a set of key risk drivers.  Triangular, Double Triangular, and PERT distributions were specified for the cost ranges.  The simulation calculates the total contingency required to cover 95% of the simulated cost range, and additional cases were run to compare the alternative distributions and probability ranges, confidence targets, and the use of and sensitivity to the correlation matrix factors.

While the Monte Carlo simulation approach for contingency estimation has limitations, it can be a useful technique to validate factor-based methodologies by building up cost account-based probability ranges, specifying correlations, and quantifying sensitivities.  The simulations can be used as an indication of where the factor-based contingencies might be adjusted to better estimate total project costs.

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