rock property prediction

Acoustic deterministic inversion

  • Borehole-seismostratigraphic data correlation


  • 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