Interpretation of elastic properties honoring fine heterogeneity has garnered recognition recently in petrophysical analysis, bedding failure prediction, and hydraulic fracture job design for unconventional reservoirs. Traditional sonic log processing assumes homogeneity of the formation over a specific sonic tool receiver aperture length, e.g., at least 2 ft. This assumption may not be appropriate for highly laminated reservoirs. Additionally, shear slownesses extracted from low- and high-frequency processing are associated with different wavelengths and different rock volumes. Shear slowness logs from a high- frequency monopole transmitter and a low-frequency dipole flexural mode can exhibit different axial resolutions even when using the same receiver aperture length.
We developed a new interpretation algorithm to improve the layer slowness contrast for thinly laminated formations in vertical wells using borehole sonic data from array-based logging tools equipped with either a monopole, dipole, or a quadrupole transmitter. This novel interpretation method can yield high-quality high- resolution sonic compressional and shear logs. It is based on a robust deconvolution technique that jointly combines all logs processed at different array resolutions. This method yields the sonic log with an optimal apparent resolution better than that estimated from the conventional 1-ft single-resolution subarray method. Finally, the residual is formulated to serve as a log quality-control flag and can be used to switch to more reliable low-resolution logs in depth intervals of poor-quality hole data.
The algorithm was validated with synthetic logs from finite-difference modeling and was then tested on a field dataset collected in a vertical well traversing a thinly laminated formation. The resolution of deconvolved compressional and shear logs from field measurements exceeds that from conventional processing, and is consistent with a higher resolution ultrasonic log from an ultrasonic imaging tool logged in the same well. The field- data application suggests that this deconvolution algorithm enhances the spatial resolution and more accurately captures the layer slowness contrast while removing outliers thereby improving the log quality.
The application of this method results in a superior characterization of the acoustic properties of thinly layered rocks relative to that from conventional processing. The estimated elastic moduli could improve stress profiling and rock-strength correlations for geomechanical modeling.