Publications

It is well known that the resistivity is one of the best tools for hydrocarbon identification. In well domain, whether the reservoir containing gas, oil or water, can be distinguished easily by using resistivity especially in non-low resistivity pay zone. However, away from the well, resistivity model in three dimensions is still in the research project. In this paper, we test the possibility of resistivity prediction from the elastic properties derived from the seismic data. The success of this work will create a breakthrough in resistivity prediction which is useful for advanced reservoir characterization including reservoir properties prediction and hydrocarbon delineation. Not only for reservoir characterization purposes, the advantages of this work can also provide a 3-D resistivity model that can be used as a bridge between all electrical method including Electromagnetic (EM) with Seismic. We introduce two new attributes called SQp (Scaled inverse quality factor of P-wave) and SQs (Scaled inverse quality factor of S-wave) attributes which are derived from elastic properties. To show the ability of these attributes in term of lithology and fluid changes, a soft sediment model is used in Rock Physics model to construct the response of SQp and SQs attributes, then comparison are conducted to see the effectiveness compared with the Vp/Vs and AI cross plot. In well domain, we show the calculated SQp and SQs attributes are able to discriminate the lithology and fluid effectively. In comparison with Vp/Vs and AI cross plot, the SQp and SQs cross plot gives an optimum separation where the effect of fluid and lithology are separated orthogonally. An example from Malaysian offshore data showed that the prediction of resistivity from SQs using exponential relation shows that the coefficient correlation between actual and predicted deep resistivity is high enough (R=0.83). Application of these attributes on 3D seismic data successfully delineate the hydrocarbon based on 3D resistivity model derived from seismic data which is confirmed with consisting existing resistivity logs.