Reservoir Characterization Solutions: geoDataMiner*

Gross-Sand and Net Pay Mapping

  The sand thickness can be determined from well data

   The seismic is poor vertical, but to combine logs and seismic attributes is possible for sand thickness prediction.

  The sliced seismic petrophysical inversion property is re-scaled and re-interpolated into regular gridding property.

  Sand thickness from target stratigraphic formation of wells

   Support Vector Regression (SVR) will be used to build the bridges between sand thickness of target layer and sliced-seismic petrophysical inversion property

   The training phase of SVR will generate a prediction function from both sand thickness of borehole and sliced-property

  The mapping phase of SVR will make the sand thickness prediction based on the function

  Sand thickness display and contour mapping