Prediction ofP‐ andS‐ wave logs for seismic modeling

Abstract

The Xu-White clay-sand mixture model simulates the effects of porosity, clay content, water saturation and invasion fluids on sonic wave velocities. Its predictions compare well with observed P- and S-wave sonic logs. P-wave logs can be predicted from a suite of logs including at least {gamma}-ray, density and deep resistivity logs. The model provides three ways of predicting S-wave velocity: (1) from porosity and shale volume, (2) from the P-wave sonic log and shale volume, and (3) from the P-wave sonic log and porosity. Comparison of the results from these methods not only helps improve the prediction but also provides a method of quality control. This paper illustrates application of the model in improving the tie between a well-log synthetic seismogram and seismic data at a well. It is well known that the S-wave log from a full waveform tool is unreliable over soft-formation intervals where the S-wave velocity is lower than the P-wave velocity of the drilling mud. Comparison between the predicted and measured S-wave logs demonstrates that the full-waveform S-wave transit time is severely underestimated in soft shale formations. The V{sub p}/V{sub s} is then underestimated, which was found to significantly affect seismic modelling. The effects of watermore » saturation and mud filtrate invasion were investigated. Comparisons between the measurements and our predictions show that the fit between measured P-wave sonic log and the prediction assuming a 100% mud filtrate saturation is better than that when water saturation has been considered, implying that the propagation paths of the measured P- and S-wave velocities are largely in the flushed zone. The well tie was considerably improved after editing of the sonic log guided by the predicted P-wave sonic and model-based correction of the log for the effect of fluid content. Use of the predicted S-wave sonic for the soft shale formation gave more diagnostic modelling of the pre-stack seismic data.« less