Abstract: Methods and systems for training a machine learning (ML) network to predict a likelihood of a presence of a geological fracture from an observed drill-bit seismic dataset are disclosed. The method may include obtaining, using a seismic processing system, a plurality of geophysical models, where each geophysical model includes a location of a drill bit. The method may further include simulating, for each geophysical model a corresponding simulated drill-bit seismic dataset for seismic waves emanating from the drill bit and recorded by at least one seismic receiver and forming a training dataset including a plurality of training pairs, with each training pair including a geophysical model from the plurality of geophysical models and the corresponding simulated drill-bit seismic dataset. The method may still further include training, using the training dataset, the ML network to predict the likelihood of the presence of the geological fracture from the observed drill-bit seismic dataset.

Abstract: In some examples, a system includes a stress field assessment engine, implemented by at least one processor, to determine stress distribution and yield state data based on a geomechanical model, and a natural fracture determination engine, implemented by the at least one processor, to generate a natural fracture network model based on the stress distribution and yield state data. The system may implement a method that includes generating a geomechanical model of a subsurface formation, where the geomechanical model includes a formation model and a wellbore model, simulating the geomechanical model to determine stress distributions and yield states in the subsurface formation, and generating a natural fracture network model using a machine learning technique on the stress distributions and yield states.

Abstract: This disclosure discusses methods and apparatus for generating vertical seismic profiles of a subsurface formation though deploying an untethered downhole tool into a wellbore, the untethered tool including at least one seismic sensor, a ballast, and a first electromagnet in the untethered downhole tool to attach the untethered downhole tool to a casing of the wellbore at a predetermined depth. The method further includes transmitting a seismic signal into the subsurface formation from a source located above the subsurface formation, receiving the seismic signal with the at least one seismic sensor, deactivating the first electromagnet to release the untethered downhole tool from the casing of the wellbore, releasing the ballast from the untethered downhole tool such that buoyancy effects move the tool uphole, and retrieving the untethered downhole tool from the wellbore.