Flexural-dipole sonic logging has been widely used as the primary method to measure formation shear slowness because it can be applied in both fast and slow formations and can resolve azimuthal anisotropy. The flexural-dipole waveforms are dispersive borehole-guided waves that are sensitive to borehole geometry, mud, and formation properties, and therefore the processing techniques need to honor the physical dispersive signatures to obtain an accurate estimation of shear slowness. Traditional processing techniques are based on either a model-dependent method, in which an isotropic model is used as a reference to compensate for the dispersion effect, or a model-independent method, which optimizes nonphysical parameters to fit a simplified model to the field dispersion data extracted in the slowness-frequency domain. Many methods often require inputs, such as mud slowness, frequency bandpass filter, or an initial guess of formation shear. Consequently, these methods often fail to interpret the dispersion signature properly when those inputs are inaccurate or when the waveform quality is poor due to downhole logging noises. The users must manually tune the processing parameters and/or choose different methods as a workaround, which causes extra time and effort to obtain the result, hence imposes a significant challenge for automating sonic shear processing.
We developed a physics-driven, machine-learning-based method for enhancing the interpretation of borehole sonic dipole data for wireline logging in an openhole scenario. A synthetic database is generated from an anisotropic root-finding, mode-search routine and used to train a neural network model as an accurate and efficient proxy. This neural network model can be used for real-time sensitivity analysis and performing inversion to the measured sonic dipole dispersion data to estimate relevant model parameters with associated uncertainties. We introduce how this trained model can be used to enhance the labeling and extraction of the dipole dispersion mode. We developed a new method that outperforms previous model-dependent and model-independent approaches because the new method introduces a mechanism to constrain the solution with physics that also has the capability to incorporate more complicated physical dispersion signatures. This new method does not rely on a good initial guess on mud slowness and formation shear slowness, nor any tuning parameter. This leads to significant progress toward fully automated sonic interpretation. The algorithm has been tested on field data for challenging borehole and geological conditions.