| Popis: |
The petrophysical properties estimated from well data with sequential Gaussian simulation could not resolve the problem of identification of thin reservoir sands because of the lack of spatial variability control points between any two wells. The seismic attributes such as coherency, spectral decomposition, first and second order amplitude, phase and frequency attempted individually within interpreted seismic horizons could not detect these thin sands. The gross picture of the channel/bar system was evolved with simulation of net to gross of reservoir sands identified in 35 drilled wells for Upper sequences (4a,4b,4c) and Lower sequences (2a,2b), within Ratawi Shale and Ratawi Limestone of Cretaceous Age. The neural network technique was applied to estimate Vclay and effective porosity integrating well information and 3D multi-attributes between seismically mapped horizons to delineate this complex reservoir sand channels/bars. This method is more efficient than conventional estimation with the ability to build a non-linear relationship between seismic traces and target porosity and Vcl logs for interpolation. The sum of amplitude slice derived between sequence Top and base tracked on depth converted estimated Vclay along inlines and crosslines provided the envisaged pattern of distributary channels. |
