A potential application for single-well acoustic imaging is the detection of an existing cased borehole in the vicinity of the well being drilled, which is important for drilling toward (when drilling a relief well) or away from (collision prevention) the existing borehole. To fulfill this application in the unconsolidated formation of shallow sediments, we have developed a detection method using low-frequency P-waves from dipole acoustic logging. For this application, we perform theoretical analyses on elastic wave scattering from the cased borehole and derive the analytical expressions for the scattered wavefield for the incidence of P- and S-waves from a borehole dipole source. The analytical solution, in conjunction with the elastic reciprocity theorem, provides a fast algorithm for modeling the whole process of wave radiation, scattering, and reception for the borehole acoustic detection problem. The analytical results agree well with those from 3D finite-difference simulations. The results indicate that P-waves, instead of S-waves as commonly used for dipole acoustic imaging, are particularly advantageous for the borehole detection in the unconsolidated formation. Field data examples are used to demonstrate the application in a shallow marine environment, where dipole-P-wave data in the measurement well successfully delineate a nearby cased borehole, validating our analysis results and application.

Over the last decade, distributed acoustic sensing (DAS) has received growing attention in the field of seismic acquisition and monitoring due to its potential high spatial sampling rate, low maintenance cost and high resistance to temperature and pressure. Despite its undeniable advantages, DAS faces some challenges, including a low signal-to-noise ratio, which partly results from the instrument-specific noise generated by DAS interrogators. We present a comparison between two deep learning approaches to address DAS hardware noise and enhance the quality of DAS data. These approaches have the advantage of including real instrumental noise in the neural network training dataset. For the supervised learning (SL) approach, real DAS instrumental noise measured on an acoustically isolated coil is added to synthetic data to generate training pairs of clean/noisy data. For the second method, the Noise2Noise (N2N) approach, the training is performed on noisy/noisy data pairs recorded simultaneously on the downgoing and upgoing parts of a downhole fiber-optic cable. Both approaches allow for the removal of unwanted noise that lies within the same frequency band of the useful signal, a result that cannot be achieved by conventional denoising techniques employing frequency filtering.

The downhole hydraulic fracturing process, besides fracturing formation rocks, generates small, secondary fractures around the borehole, allowing evaluation of the result of fracturing using borehole sonic measurements. We analyzed the near-borehole fracture network from an existing laboratory hydraulic fracturing experiment to study the fracture distribution around the borehole. The result indicates that the fracture distribution exhibits fractal characteristics. The fractal dimension is high in the near-borehole region and decreases away from borehole. Because the fractal dimension increases with fracture density, this indicates that fracturing produces a high fracture-density zone in the near-borehole region. The high concentration of the hydraulic fractures in turn can causes significant attenuation in the sonic-logging waveforms acquired after fracturing. The fracturing-induced sonic attenuation, averaged over the sonic frequency band, can be estimated using a median frequency shift method. Comparison of the attenuation of the fracturing interval with that of an unfractured interval, or with that of the same interval before fracturing allows for evaluating the result of fracturing and the fracture extension along the borehole. The application of the method is demonstrated with field-data examples and validated by comparing results from existing borehole techniques, thus offering a useful technique for evaluating the result of hydraulic fracturing using the borehole sonic-wave attenuation.

Single-well acoustic imaging using a dipole tool has been applied in anisotropic formations. For this application, the effect of anisotropy on the radiation and reception of acoustic waves in the borehole needs to be analyzed and a proper analysis method needs to be developed. We use the far-field asymptotic solution of the wavefield to calculate the radiation pattern of the dipole source in a borehole penetrating vertically transverse isotropic (VTI) formation. We model the spherical wave propagation characteristics of the radiated and reflected waves in the VTI formation and the receiving of the waves in the borehole for the on- and off-axis positions. Based on the developed analytical modeling methods, we study systematically the effects of the formation anisotropy on the radiation pattern and borehole response characteristics. Our modeling result demonstrates that due to the coupling between the longitudinal and the transverse particle motions, quasi-P-(qP-) and quasi-SV-(qSV-) wave radiation patterns contain two components. The result also indicates that the anisotropy causes the arrival time of the SH-reflection wave to differ from that of the qSV wave, and the misposition of the imaged targets would occur if the velocity measured along the borehole is used for migration. The azimuth sensitivity of the off-axis data can be used to help eliminate the 180° ambiguity for the dipole acoustic imaging in the VTI formation. Understanding the effects of anisotropy on borehole acoustic imaging can help improve the accuracy of the imaging result in anisotropic formations.