The following G&G data is used to perform thick-layer modeling of interval velocities: averaged acoustic
logging, stacking velocities (migration) and structural model of object
Convolution wavelet determination
Iterative process of acoustic inversion
Lithology prediction
Neural network technologies. Fast predictor method
Methods of Bayesian statistics. One of the advantages of this method is a presentation of the forecast
results in the form of probability characteristics of presence of one kind or another lithotype in the interwell space. This method
requires a usage of pre-stack seismic data as well as S-wave data velocity log, or synthetic log, that are needed to create cube of
Poisson's coefficients and determined by empirical methods
Prediction of porosity distribution. Based on the results of acoustic inversion (acoustic impedance) and
lithology prediction (in the form of discrete characteristics of certain lithotypes or definition of «reservoir - non-reservoir»),
the porosity prediction is conducted by the geostatistics methods within zones of individual lithotypes or reservoir, where the
relationship between the acoustic impedance and porosity is linear
Solid reservoir modeling
Prediction of the type of reservoir fluid saturation
Fluid saturation prediction, lithology prediction, AVO classification. In case of favorable geological
conditions it is possible to evaluate the type of prospective object's fluid saturation, as well as to determine the lithological
component of the geological section using methods of AVO/AVA-analysis
