Dispersion correction for acoustic borehole logging data

Abstract

Compressional and shear formation velocities are key to the prediction of petrophysical properties from seismic attributes. In fast formations shear velocity may be obtained from monopole source, but in slow formations, it is commonly determined from the flexural mode associated with dipole excitation, which is a dispersive borehole-guided mode who’s low frequency and high frequency asymptote to the formation S-velocity, and to the Scholte-velocity, respectively. The shear slowness is commonly computed from well log dipole flexural mode data using Semblance Time Coherence (STC) processing. Dispersion is handled by restricting the waveforms spectral content to the low frequencies that travel close to the formation’s shear velocity. This restricting may not eliminate the need for a residual dispersion correction. Inversion addresses this difficulty by computing shear slowness directly from observed dispersion characteristics. In order to make the inversion efficient the iterative steps which compare observed and forward modeled dispersion curves are replaced with a neural net trained on a large number of pre-modeled curves generated with known formation and borehole properties. Automated mode frequency detection constrains the bandwidth over which dispersion curves are matched. Results from 127,000 modelled and field data points show improved accuracy and precision relative to STC processing.