Sonic data are commonly acquired in exploration, appraisal, and development wells using wireline, logging-while-drilling, or through-the-bit conveyance for applications within petrophysics, geophysics, geomechanics, and geology disciplines. The measurement data require processing to obtain elastic wave slownesses (inverse of velocity) and associated attributes before the results can be used in petrotechnical workflows. The objective of the digital transformation is to streamline and automate the processing workflow to reduce user intervention and turnaround time while increasing the accuracy of results and possibly extracting more answers by fully utilizing all waveform attributes, which consequently benefits downstream applications.
There are four workflows that are the foundation of the transformation. They support the overall goals of reducing user interactions and providing robust results in a timely manner for continuous slowness logs. First, data-driven inversions done during acquisition with automatic quality control and interpretation flags immediately provide assurance about the data quality and identify formation intervals that require further evaluation. Second, automatic dipole-flexural shear extraction is done using physics-based machine learning (ML) where purely data-driven models are inadequate due to borehole or geological conditions. The physics-based ML utilizes cloud-based computing that is needed for large volume synthetic data generation and neural network training. Third, a multiresolution analysis of the monopole waves for the compressional slowness uses automatic peak detection on multiple receiver levels removing any subjective manual labeling after the semblance processing. Finally, the multimode (flexural and Stoneley) inversion determines anisotropic constants and accounts for mud-speed variations in the borehole, including detailed uncertainties.
The new methods address underlying concerns most users and waveform processing experts already observe in their sonic deliverables. Enabling wellsite algorithms to be more automatic and data driven improves the robustness of the field deliverables and provides insight into the quality of the data. For the shortcomings with regards to borehole or geological conditions such as laminations, sharp lithological transitions, or the presence of anisotropy, the physics-based ML is shown to honor the physics of the dipole flexural mode, while the multiresolution for the monopole provides physics-based reasoning for discrepancies between the geological layering and receiver aperture. By incorporating the range of results derived from the inversions with advanced interpretations such as transversely isotropic constants, these uncertainties can be further used in stochastic models in downstream workflows. All these methods are fully automated and can be done in a short timeframe to be used without doubt in operations.
