  Reservoir Characterization (RC) can be defined as the act of building a
reservoir model that incorporates all the characteristics of the reservoir that
are pertinent to its ability to store hydrocarbons and also to produce them.It
is a difficult problem due to non-linear and heterogeneous subsurface
properties and associated with a number of complex tasks such as data fusion,
data mining, formulation of the knowledge base, and handling of the
uncertainty.This present work describes the development of algorithms to obtain
the functional relationships between predictor seismic attributes and target
lithological properties. Seismic attributes are available over a study area
with lower vertical resolution. Conversely, well logs and lithological
properties are available only at specific well locations in a study area with
high vertical resolution.Sand fraction, which represents per unit sand volume
within the rock, has a balanced distribution between zero to unity.The thesis
addresses the issues of handling the information content mismatch between
predictor and target variables and proposes regularization of target property
prior to building a prediction model.In this thesis, two Artificial Neural
Network (ANN) based frameworks are proposed to model sand fraction from
multiple seismic attributes without and with well tops information
respectively. The performances of the frameworks are quantified in terms of
Correlation Coefficient, Root Mean Square Error, Absolute Error Mean, etc.
