| dc.description.abstract | Currently, effective reservoir management systems play a very important part in
exploiting reservoirs. Fully exploiting all the possible events for a petroleum reservoir is a
challenge because of the infinite combinations of reservoir parameters. There is much
unknown about the underlying reservoir model, which has many uncertain parameters.
MCMC (Markov Chain Monte Carlo) is a more statistically rigorous sampling method,
with a stronger theoretical base than other methods. The performance of the MCMC
method on a high dimensional problem is a timely topic in the statistics field.
This thesis suggests a way to quantify uncertainty for high dimensional problems by
using the MCMC sampling process under the Bayesian frame. Based on the improved
method, this thesis reports a new approach in the use of the continuous MCMC method
for automatic history matching. The assimilation of the data in a continuous process is
done sequentially rather than simultaneously. In addition, by doing a continuous process,
the MCMC method becomes more applicable for the industry. Long periods of time to
run just one realization will no longer be a big problem during the sampling process. In addition, newly observed data will be considered once it is available, leading to a better
estimate.
The PUNQ-S3 reservoir model is used to test two methods in this thesis. The methods are:
STATIC (traditional) SIMULATION PROCESS and CONTINUOUS SIMULATION
PROCESS. The continuous process provides continuously updated probabilistic forecasts
of well and reservoir performance, accessible at any time. It can be used to optimize
long-term reservoir performance at field scale. | en |
