  In this paper, we illustrate the modeling of a reservoir property (sand
fraction) from seismic attributes namely seismic impedance, seismic amplitude,
and instantaneous frequency using Neuro-Fuzzy (NF) approach. Input dataset
includes 3D post-stacked seismic attributes and six well logs acquired from a
hydrocarbon field located in the western coast of India. Presence of thin sand
and shale layers in the basin area makes the modeling of reservoir
characteristic a challenging task. Though seismic data is helpful in
extrapolation of reservoir properties away from boreholes; yet, it could be
challenging to delineate thin sand and shale reservoirs using seismic data due
to its limited resolvability. Therefore, it is important to develop
state-of-art intelligent methods for calibrating a nonlinear mapping between
seismic data and target reservoir variables. Neural networks have shown its
potential to model such nonlinear mappings; however, uncertainties associated
with the model and datasets are still a concern. Hence, introduction of Fuzzy
Logic (FL) is beneficial for handling these uncertainties. More specifically,
hybrid variants of Artificial Neural Network (ANN) and fuzzy logic, i.e., NF
methods, are capable for the modeling reservoir characteristics by integrating
the explicit knowledge representation power of FL with the learning ability of
neural networks. The documented results in this study demonstrate acceptable
resemblance between target and predicted variables, and hence, encourage the
application of integrated machine learning approaches such as Neuro-Fuzzy in
reservoir characterization domain. Furthermore, visualization of the variation
of sand probability in the study area would assist in identifying placement of
potential wells for future drilling operations.
