Uncertainty Analysis in Upscaling Well Log data By Markov Chain Monte Carlo Method

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Title: Uncertainty Analysis in Upscaling Well Log data By Markov Chain Monte Carlo Method
Author: Hwang, Kyubum
Abstract: 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 .
URI: http : / /hdl .handle .net /1969 .1 /ETD -TAMU -2009 -05 -680
Date: 2010-01-16

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Uncertainty Analysis in Upscaling Well Log data By Markov Chain Monte Carlo Method. Available electronically from http : / /hdl .handle .net /1969 .1 /ETD -TAMU -2009 -05 -680 .

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