Machine-Learning-Enabled Joint Interpretation of Dipole Sonic and Borehole Image Data
Authors:   SLB, Keretchashvili Gurami , Lei Ting ,   SLB, Loviken Pontus ,   SLB, Kherroubi Josselin ,   SLB, Liang Lin ,   SLB, Donald Adam ,   SLB, Prioul Romain ,   SLB
Published: November 2024

Borehole sonic and formation image logs represent two widely used measurements for formation evaluation. Many important geomechanical and geological features, such as thin layers, breakouts, natural fractures, drilling fractures, stress effects, and intrinsic anisotropic effects, can be detected by either or both measurements. These features have noticeable impacts on geomechanical and petrophysical applications. Therefore, it is important to label and classify them as accurately as possible. On the one hand, the borehole sonic measurement, which is usually used to provide valuable inputs for rock elastic properties, is now regularly used to classify anisotropy mechanisms with the development of a wideband dipole dispersion extraction algorithm. However, due to its long wavelength, the dipole sonic measurements do not probe borehole walls at high resolutions or with high confidence. The borehole image measurement, such as microresistivity and ultrasonic data, on the other hand, can provide rich information around borehole surfaces at a much higher resolution. An efficient way to integrate the two measurements yields a better understanding of geomechanical and geological features. In this study, we present a new machine-learning (ML)-enabled workflow that integrates the interpretation of borehole dipole sonic data and microresistivity borehole image data. The first step is to label sonic dispersion features under three main categories based on cross-dipole dispersion signatures. The first category is about borehole and tool conditions, where dipole dispersion showing various levels of frequency shifting signature are labeled. The second category, extrinsic anisotropy, is to label logging intervals with the existence of strong or weak azimuthal anisotropy dispersions. This type of azimuthal anisotropy can be caused by several factors, including fractures, stresses, or dipping layers. The third category, intrinsic anisotropy, labels intervals into various degrees of vertical transverse isotropy (VTI) types based on a physical-driven ML classifier. In the second step, cross-dipole dispersion splitting labels are further compared against borehole image data using analytical and ML methods. The comparison is carried out to resolve the non-uniqueness issue found in the first step, where different physical factors might demonstrate similar dispersion signatures. Consequently, the new comparison workflow allows us to distinguish ambiguities efficiently and accurately between tool decentralization and breakout and between layer-induced and stress-induced azimuthal anisotropy. Finally, modeling studies are performed to verify the labeling accuracy. We apply the developed workflow to a field case. The study first demonstrates that it can be difficult to label all features relying on either measurement independently. However, joint interpretation can resolve ambiguity and improve classification significantly. Labeled data were then used for a geomechanical study to build a mechanical earth model (MEM). It was found that an increased level of accuracy in the classification can enable a better estimation of formation in-situ stresses in the MEM workflow. The labels enable a good understanding of the in-situ elastic mechanism. The ambiguity issues resolved using this ML workflow help to de-risk geomechanical analysis in a way that is automatic.