M Hasanabadi, M Nadaripari, T Behroz,
Volume 12, Issue 2 (11-2012)
Abstract
Production optimization is an important and challenging task in oil industry. In the present paper, we look For an acceptable setting of Intelligent Control Valves (ICVs) which leads to optimum production respect To reservoir conditions and operational restrictions. The Design of Experiment (DoE) as a structured, Organized method is used to determine the relationship between different settings affecting petroleum Production. In this context, the Taguchi method and Response Surface Method are employed and tested on a horizontal well with few ICVs, in order to maximize oil production while minimizing produced Water. The summary of approach and computational results is reported.
Atefe Mokhtari Hasanabadi, Manouchehr Kheradmandnia,
Volume 13, Issue 3 (11-2013)
Abstract
Monitoring process mean and variance simultaneously in a single control chart simplifies
the process monitoring. If in addition, a simultaneous control chart is capable of
recognizing the source of contamination, this capability leads to additional simplicity.
These are the reasons why simultaneous control charts have attracted many researchers and
manufacturers.
Recently, in the statistical process control literature some control charts have been
introduced which are based on the idea of Bayesian predictive density. This type of control
charts, not only brings into account the uncertainty concerning the estimation of unknown
parameters, but also do not need extensive simulations for computation of control limits.
These control charts have been introduced for mean and variance in both univariate and
multivariate situations.
Up to now, no simultaneous control chart has been introduced based on Bayesian predictive
density. In this paper, using the idea of Bayesian predictive density, we introduce a new
simultaneous control chart for monitoring univariate mean and variance. We illustrate the
important capabilities of this new chart through simulated data.
This new chart is applicable when parameters are unknown. In other words, it brings into
account the uncertainty concerning the unknown parameters. This chart is able to recognize
the source of contamination and is sensitive to small changes in the mean and variance. In
this chart the control limits, needless of simulation, can simply be obtained from normal
table.
