A method for seismic processing includes extracting, using a first machine learning model, one or more seismic features from seismic data representing a subsurface domain, receiving one or more well logs representing one or more subsurface properties in the subsurface domain, and predicting, using a second machine learning model, the one or more subsurface properties in the subsurface domain at a location that does not correspond to an existing well based on the seismic data, the one or more well logs, and the one or more seismic features that were extracted from the seismic data.

Methods, systems, and computer-readable medium to perform operations including: clipping an average velocity grid of a seismic reference surface (SRSAV), in an oil and gas field, to remove average velocity data of a region containing high-angle, horizontal (HA/HZ) boreholes, wherein the seismic reference surface approximates a geological reference surface; based on (i) a depth grid of the geological reference surface (GRSD) generated using HA/HZ borehole data, and (ii) a time grid of the seismic reference surface (SRST), generating borehole average velocity grid (BAV) along the HA/HZ boreholes; gridding the BAV with the clipped SRSAV to generate a hybrid seismic borehole average velocity grid (HSBAV) of the oil and gas field; and based on the HSBAV and the SRST, generating a hybrid seismic geological depth grid (HSGD) of the oil and gas field.

Systems and methods are disclosed for hydrocarbon exploration using seismic imaging and, more specifically, measuring randomness in seismic data utilizing a vectorized disorder algorithm. The vectorized disorder algorithm is configured to measure the randomness level (e.g., noise) in seismic data to improve seismic data processing/imaging and the ability to expose subsurface geology. The vectorized disorder algorithm includes performing convolution of seismic data with a vectorized disorder operator having an extra dimension than the seismic data. A nonlinear reduction operation is performed on the vectorized output to generate a randomness distribution dataset having the same dimension as the input data. The randomness distribution dataset comprises data points representing the level of randomness for respective seismic data points. A more accurate seismic image is generated from the seismic data as a function of the measured randomness distribution.

A device may provide, to a user device, a first message instructing a technician to move fiber cables and may receive a first signal based on the technician moving the fiber cables and a rest signal based on the technician stopping movement of the fiber cables. The device may calculate a distance, an average peak signal, and a baseline signal based on the first signal and the rest signal and may calculate a data collection window based on the distance, the average peak signal, and the baseline signal. The device may provide, to the user device, a second message instructing the technician to move one fiber cable at a time and may receive second signals based on the technician moving one fiber cable at a time. The device may provide, for display to the user device, the data collection window and indications of the second signals.

A device may provide, to a user device, a first message instructing a technician to move fiber cables and may receive a first signal based on the technician moving the fiber cables and a rest signal based on the technician stopping movement of the fiber cables. The device may calculate a distance, an average peak signal, and a baseline signal based on the first signal and the rest signal and may calculate a data collection window based on the distance, the average peak signal, and the baseline signal. The device may provide, to the user device, a second message instructing the technician to move one fiber cable at a time and may receive second signals based on the technician moving one fiber cable at a time. The device may provide, for display to the user device, the data collection window and indications of the second signals.

Systems and methods are provided for determining a formation body wave slowness from an acoustic wave. Waveform data is determined by logging tool measuring the acoustic wave. Wave features are determined from the waveform data and a model is applied to the wave features to determine data-driven scale factors The data-driven scale factors can be used to determine a body wave slowness within a surrounding borehole environment and the body wave slowness can be used to determine formation characteristics of the borehole environment.

A method of exploring for hydrocarbons in a region, including the steps of obtaining seismic data for the region corresponding to two or more different times and analyzing the seismic data corresponding to the two or more different times to determine whether there are any changes in the seismic data.

Seismic data may provide valuable information with regard to the description such as the location and/or change of hydrocarbon deposits within a subsurface region of the Earth. The present disclosure generally discusses techniques that may be used by a computing system to interpolate or deblend data utilizing a projection on convex sets (POCS) interpolation algorithm. The utilized POCS interpolation algorithm operates in parallel for frequency of a set of frequencies of a seismic frequency spectrum.

Methods, computing systems, and computer-readable media for interpreting seismic data, of which the method includes receiving seismic data representing a subterranean volume, and determining a feature-likelihood attribute of at least a portion of a section of the seismic data. The feature-likelihood attribute comprises a value for elements of the section, the value being based on a likelihood that the element represents part of a subterranean feature. The method also includes identifying contours of the subterranean feature based in part on the feature-likelihood attribute of the section, and determining a polygonal line that approximates the subterranean feature.

Methods and systems for determining an interbed multiple attenuated pre-stack seismic dataset are disclosed. The methods include forming a post-stack seismic image composed of post-stack traces from the pre-stack seismic dataset and identifying a first, second, and third post-stack horizon on each of the post-stack traces. The methods further include for each pre-stack trace, generating a first, second, and third multiple-generator trace based on the first, second and third post-stack horizon and determining a correlation trace based, at least in part, on a correlation between the first multiple-generator trace and the second multiple-generator trace. The methods still further include predicting an interbed multiple trace by convolving the correlation trace and the third multiple-generator trace, determining an interbed multiple attenuated trace by subtracting the interbed multiple trace from a corresponding pre-stack seismic trace, and determining the interbed multiple attenuated pre-stack seismic dataset by combining the interbed multiple attenuated traces.