A method of predicting pore pressure based on seismic data can include obtaining seismic inversion data based in part on seismic data collected from a formation. The method also includes calculating a pore-pressure transform, wherein the pore-pressure transform comprises parameters derived using measured pore pressure data, upscaled sonic logs, and density logs, wherein the pore-pressure transform comprises an objective function to reduce unphysical variations in predicted pore pressure corresponding to depth. Additionally, the method can include adjusting the pore-pressure transform for sampling bias caused by pore pressure measurements being restricted to a plurality of lithologies by accounting for a difference between upscaled seismic velocities and average sonic velocities within each of the lithologies. Furthermore, the method can include generating pore pressure prediction values based on the pore-pressure transform for the lithologies and the seismic inversion data, and modifying a seismic model based on the generated pore pressure prediction values.

A method, and system to implement the process, of selecting a plurality of sets of source and receiver locations over a survey area, modeling on a subsurface attribute model of a subterranean region each source and receiver pair of the plurality of sets of source and receiver locations to generate low frequency seismic data, performing a reverse time migration on the low frequency seismic data to reposition diving wave energy of each source and receiver pair of the plurality of sets of source and receiver locations to generate a diving wave illumination image, extracting seismic amplitudes from the diving wave illumination image at a region of interest, and computing a contribution of a respective diving wave from each source and receiver pair of the plurality of sets of source and receiver locations to diving waves passing through the region of interest.

Methods and seismic shot generation and data collection systems configured to determine a monotonically increased velocity v*(z) from a monotonically increased velocity model by requiring the monotonically increased velocity v*(z) to be nearest to a refraction velocity v(z) determined for an estimated depth z and to be characterized by a positive slope such that the refraction velocity v(z) increases with depth, and to generate a subsurface image based on the estimated depth z and the determined monotonically increased velocity v*(z).

A method may include obtaining core image data regarding a geological region of interest. The method may further include obtaining well log data regarding the geological region of interest from one or more wells. The method may further include determining a sliding window that corresponds to a predetermined window size. The method may further include determining various quantitative image attributes using the core image data, the well log data, and the sliding window. The quantitative image attributes may be determined in a continuous manner by moving the sliding window along the core image data. The method may further include generating predicted rock data for the geological region of interest using the quantitative image attributes, a machine-learning algorithm, and a machine-learning model.

A computer-implemented method for seismic event localization includes: generating, with at least one processor, a vectorized snapshot matrix representing wave propagation data at a series of snapshots in time for a subterranean formation; computing a reduced orthonormal column basis matrix based on the vectorized snapshot matrix; constructing a reduced order wave propagation model based on the reduced orthonormal column basis matrix; receiving seismic data collected from a plurality of receivers at the subterranean formation; generating a time-domain coefficient matrix based on back propagation of the received seismic data and the reduced order wave propagation model; reconstructing time-reversed wavefield data based on the time-domain coefficient vector; and generating signals for outputting wavefield or seismic event location information based on the time-reversed wavefield data.

A method for interpreting seismic data includes receiving seismic data that represents a subterranean volume, and generating inline probability values and crossline probability values using a first machine learning technique. The first machine learning technique is trained to identify one or more vertical fault lines in a seismic volume based on the seismic data. The method includes generating a merged data set by combining the inline probability values and the crossline probability values, training a second machine learning technique based on a subset of labeled horizontal planes from the merged data set, the second machine learning technique trained to identify horizontal fault lines from the seismic volume, and generating a representation of the seismic volume based on the second machine learning technique, the representation including an indication of a three-dimensional fault structure within the seismic volume.

Systems and methods for updating a reservoir model are disclosed. In one embodiment, a method of updating a computer model includes receiving actual well data from a plurality of wells, and accessing model well data from the computer model for a plurality of modeled wells, wherein the plurality of modeled wells correspond to the plurality of wells. The method further includes comparing, by a computing device, the actual well data to the model well data according to a grid model vertical mismatch metric. When the grid model vertical mismatch metric is satisfied, the method includes globally updating the computer model. When the grid model vertical mismatch metric is not satisfied, the method includes comparing the plurality of wells of the actual well data to a cluster metric, when the cluster metric is satisfied, locally updating the computer model, and when the cluster metric is not satisfied, globally updating the computer model.

Methods of identifying a subterranean tunnel using digital imaging that may include: obtaining data of a propagating wavefield through a propagating volume that includes a portion of the earth's subsurface; obtaining a reference digital image of the propagating volume; selecting a holographic computational method of wavefield imaging; selecting a wavefield based on one or more parameters; calculating a sampling ratio by dividing a number of data samples in the data subset by a number of image samples in the data subset; decimating the data subset; generating a new digital image based on the selected holographic computational method of imaging, the decimated data subset, and parameters corresponding to the data subset; determining a quantitative difference measure between the reference digital image and the new digital image, and image quality; and identifying the subterranean tunnel.

The claimed invention relates to means for analysis of a mineral deposit under development using noise logging. The aim of invention consists in increasing accuracy of sound source position determining at surveying in wells with complicated multi-barrier design. The method for locating an acoustic noise source in a well comprises the stages of: computer simulation of acoustic field generated by one or more sources of acoustic signal in the well; simultaneous recording of acoustic signals inside the wellbore using a device for recording acoustic signals comprising at least two acoustic sensors; locating the sought acoustic signal sources in the well by means of co-processing of computer simulation data and data on acoustic signals inside the wellbore recorded using the aforementioned device.

Method for characterizing subterranean formation is described. One method involves injecting a fluid into an active well of the subterranean formation at a pressure sufficient to induce one or more hydraulic fractures. Measuring, via a pressure sensor, a poroelastic pressure response caused by inducing of the one or more hydraulic fractures. The pressure sensor is in at least partial hydraulic isolation with the one or more hydraulic fractures.