Application of Bayesian in determining productive zones by well log data in oil wells
Abstract
Exploration specialists conventionally utilize a cut-off-based method to
find productive zones inside the oil wells. Using conventional method, pay
zones are separated crisply from non-pay zones by applying cut-off values on
some petrophysical features.
In this paper, a Bayesian technique is developed to find productive
zones (net pays), and Bayesian Network is used to select the most appropriate
input features for this newly developed method. So, two Bayesian methods
were developed: the first one with conventional pay determination inputs
(shale percent, porosity and water saturation), the other with two inputs,
selected by Bayesian Network (porosity and water saturation). Two
developed Bayesian methods are applied on well log dataset of two wells:
one well is dedicated for training and testing Bayesian methods, the other for
checking generalization ability of the proposed methods. Outputs of two
presented methods were compared with the results of conventional cut-offbased
method and production test results (i.e. a direct procedure to check
validation of proposed methods).
Results show that the most prominent advantage of developed
Bayesian method is determination of net pays fuzzily with no need to identify
cut-offs, in addition to higher precision of classification: nearly 30%
improvement in precision of determining net pays of first well (training well),
and about 50% improvement in precision of determining productive zones
through the generalizing well.
Domains
Applied geology
Origin : Files produced by the author(s)
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