An MCMC algorithm for sampling from the posterior conditioned
to production history
An MCMC algorithm for sampling from the posterior conditioned
to production history
by Håkon Tjelmeland (Norwegian University of Science and Technology,
Norway), Statistics No
2/2000, Department of Mathematical Sciences, Norwegian
University of Science and Technology, to be
presented at
Geostats
2000 - South Africa.
In evaluation of petroleum reservoirs, observed production history
contains valuable information. A natural approach to integrate this
type of data with other sources of observations is to adopt a
Bayesian model. To sample from the resulting posterior
distribution, MCMC algorithms have to be applied.
Even if MCMC provides a very flexible framework in which to
construct simulation algorithms,
so far only standard MCMC algorithms have
been employed for sampling from this type of posterior. As
a result, a large number of iterations has been necessary to
obtain convergence. In this paper, we first discuss the
characteristics of the type of distribution in question
and thereafter define an MCMC algorithm specifically designed
to cope with these features.