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Description:
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More difficulties are now expected in exploring economically valuable reservoirs
because most reservoirs have been already developed since beginning seismic exploration
of the subsurface . In order to efficiently analyze heterogeneous fine -scale properties
in subsurface layers , one ongoing challenge is accurately upscaling fine -scale
(high frequency ) logging measurements to coarse -scale data , such as surface seismic
images . In addition , numerically efficient modeling cannot use models defined on the
scale of log data . At this point , we need an upscaling method replaces the small scale
data with simple large scale models . However , numerous unavoidable uncertainties
still exist in the upscaling process , and these problems have been an important emphasis
in geophysics for years . Regarding upscaling problems , there are predictable
or unpredictable uncertainties in upscaling processes ; such as , an averaging method ,
an upscaling algorithm , analysis of results , and so forth .
To minimize the uncertainties , a Bayesian framework could be a useful tool for
providing the posterior information to give a better estimate for a chosen model
with a conditional probability . In addition , the likelihood of a Bayesian framework
plays an important role in quantifying misfits between the measured data and the
calculated parameters . Therefore , Bayesian methodology can provide a good solution
for quantification of uncertainties in upscaling .
When analyzing many uncertainties in porosities , wave velocities , densities , and
thicknesses of rocks through upscaling well log data , the Markov Chain Monte Carlo
(MCMC ) method is a potentially beneficial tool that uses randomly generated parameters
with a Bayesian framework producing the posterior information . In addition ,
the method provides reliable model parameters to estimate economic values of hydrocarbon
reservoirs , even though log data include numerous unknown factors due to
geological heterogeneity . In this thesis , fine layered well log data from the North Sea
were selected with a depth range of 1600m to 1740m for upscaling using an MCMC implementation . The results allow us to automatically identify important depths
where interfaces should be located , along with quantitative estimates of uncertainty
in data . Specifically , interfaces in the example are required near depths of 1 ,650m ,
1 ,695m , 1 ,710m , and 1 ,725m . Therefore , the number and location of blocked layers
can be effectively quantified in spite of uncertainties in upscaling log data . |