Uncertainty Analysis for GIS Estimates of the CO2 Storage Resource in the Oriskany Sandstone
This study focuses on the geologic CO2
sequestration resource in deep saline-filled formations, a class of
repositories believed to make up the bulk of the storage resource. The goals of
this research are (1) to better understand the sources of uncertainty in deep
saline-filled formation (DSF) resource estimates by developing a sequestration
resource model, (2) to employ kriging and cokriging to estimate input
parameters, and (3) using this model to probabilistically quantify the
sequestration resource for the Oriskany sandstone in Pennsylvania. The geologic
framework of the model is based on data provided by the various government
agencies. Due to the fact that this dataset includes information from wells
drilled by different operators there exists a need to properly estimate values
for parameters that aren't available for all wells.
The results of statistical studies of reservoir and
structural properties of the Oriskany sandstone suggest the best-fit
distribution for average formation depth. Regression models allow for the
prediction of (1) porosity as a function of depth and (2) formation temperature
and pressure as functions of depth. The equation of state developed by Span and
Wagner is used for calculation of density as a function of temperature and
pressure. Storage resource estimates are developed using a Monte Carlo
simulation. Four parameters are treated as uncertain: average formation depth
and regression model parameter estimates for porosity, pressure and
temperature. The results of the simulation for storage resource show that there
is a large variation in storage resource estimates and that a point estimate
using mean values of input parameters does not result in the same capacity as
the mean of the simulated storage resource distribution. Results indicate that
for the baseline storage scenario, which assumes an efficiency factor of 2%,
the Pennsylvania part of the Oriskany formation can hold 0.14 gigatonnes of CO2.
In this scenario mass of CO2 varies from a 5th percentile
of 0.05 gigatonnes to a 95th percentile of 0.5 gigatonnes.
Sensitivity analysis indicates that the two variables that contribute the most
to the uncertainty in estimates are porosity and temperature.
AAPG Search and Discovery Article #90142 © 2012 AAPG Annual Convention and Exhibition, April 22-25, 2012, Long Beach, California