Intelligent Knowledge Management For
Identifying Excess Water Production In Oil Wells
Author(s)
M. Rabiei & R. Gupta
Abstract
In hydrocarbon production, certain amount of water production is inevitable and
sometimes even necessary. Problems arise when water rate exceeds the WOR
(water/oil ratio) economic level, producing no or little oil with it. A lot of
resources are set aside for implementing strategies to effectively manage the
production of the excessive water to minimize its environmental and economic
impact. Water shutoff technologies are available to effectively manage excess
water production; however, their use requires the knowledge of the underlying
cause. The conventional diagnostic techniques are only capable of identifying the
existence of excess water and cannot pinpoint the exact type and cause of the
water production mechanism (WPM). A common industrial practice is to
monitor the trend of changes in WOR against time to identify two types of
WPMs, namely coning and channelling. However, it has been demonstrated that
WOR plots are not general and there are deficiencies in the current usage of
these plots. In this paper we present a new technique for diagnosing WPMs. We
extracted predictive data points from plots of WOR against the oil recovery
factor and collect information on a range of basic reservoir characteristics. This
information is processed through tree-based ensemble classifiers. Next we
construct a new dataset smeared from the original dataset, and generate a
depictive tree for ensemble using a combination of the new and original datasets.
To generate the depictive tree we used a new class of tree classifiers called
logistic model tree (LMT). Our results show high prediction accuracy rates of at
least 93% and easy to implement workflow. Adoption of this methodology
would lead to accurate and timely management of water production saving oil
and gas companies considerable time and money.
Keywords: water production mechanisms, water/oil ratio, ensemble classifiers.
Keywords
water production mechanisms, water/oil ratio, ensemble classifiers.
Related Book
Other papers in this volume
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