A method and system for picking first-break times for a seismic dataset are disclosed. The method includes generating a pre-processed seismic dataset and an initial refraction velocity model from the pre-stack seismic dataset and generating a first-break energy-enhanced seismic dataset using nonlinear beamforming applied to the pre-processed seismic dataset and the initial refraction velocity model. The methods further include estimating a refined refraction velocity model from the first-break energy-enhanced seismic dataset, and generating a post-processed seismic dataset from the refined refraction velocity model and first-break energy-enhanced seismic dataset. The methods still further include, for each pre-stack trace, determining a first-break time from the post-processed seismic dataset and the refined refraction velocity model. The methods also include generating a seismic image based on the first-break time for each pre-stack trace and determining a location of a hydrocarbon reservoir based on the seismic image.

A method can include receiving realizations of a model of a reservoir that includes at least one well where the realizations represent uncertainty in a multidimensional space; selecting a portion of the realizations in a reduced dimensional space to preserve an amount of the uncertainty; optimizing an objective function based at least in part on the selected portion of the realizations; outputting parameter values for the optimized objective function; and generating at least a portion of a field operations plan based at least in part on at least a portion of the parameter values.

A process for generating a velocity model for a sediment-basement interface of a subsurface region includes receiving seismic data representing acoustic signals that are reflected from regions of the subsurface. The process includes receiving potential fields data comprising potential field values that are mapped to locations in the subsurface. The process includes generating weighted time-depth data pairs. The process includes selecting a velocity model that relates a velocity value to a depth value in a time-depth relationship. The process includes optimizing velocity coefficients of the velocity model by determining, for each velocity model of a set, a set of depth estimates for corresponding time values and comparing the set of depth estimates to depth values of the weighted time-depth data pairs. The process includes adjusting the velocity coefficients of the velocity model. The process includes generating a seismic image of the sediment-basement interface.

In some examples, a system may receive log data including a depth-series of data for a sensed parameter. The system may determine a parameter value of the depth series data for individual subunits of depth corresponding to a larger unit of depth. The system may further determine a scale of graphic effects corresponding to parameter values for the depth-series data. The system may present, on a display, a visualization of the depth-series data. For instance, the visualization may include a plurality of cells arranged in a plurality of rows, with each cell corresponding to the larger unit of depth and including a plurality of subcells corresponding to the subunits of depth. Additionally, each subcell may be presented with a respective graphic effect corresponding to the parameter value determined at a corresponding depth, and the graphic effect may correspond to the parameter value on the scale of graphic effects.

The present disclosure relates to a method of processing seismic signals comprising: receiving a set of seismic signals, applying a wavelet transformation to the set of signals and generating transformed signals across a plurality of scales. Then for each scale determining coherence information indicative of the transformed signals and generating a comparison matrix comparing the transformed signals, then outputting seismic attribute information based on combined coherence information.

A method of evaluating processing imprint on seismic signals includes receiving a first and a second seismic dataset of a reservoir. A first and a second synthetic dataset are generated, where the second synthetic dataset is generated by multiplying at least a portion of data in the first synthetic dataset by a scaling factor. A first and a second combined dataset are generated by adding the respective seismic dataset and the respective synthetic dataset. A first and a second processed dataset are generated by applying a seismic processing step on the first and the second combined dataset, respectively. A difference factor between the first and the second processed dataset is calculated. Based on the difference factor and the scaling factor, it is determined whether the seismic processing step is able to preserve signal amplitude changes between the first and the second seismic dataset.

A method may include determining various migrated azimuthal dip-angle gathers based on a migration function and seismic data from a seismic survey regarding a geological region of interest. The method may further include determining various partial dip-angle images using the migrated azimuthal dip-angle gathers. The method may further include determining various azimuthal bins that include the partial dip-angle images. The method may further include determining various wavefield components using an independent component analysis (ICA) function and the partial dip-angle images among the azimuthal bins. The method may further include determining a geological feature within the geological region of interest using the wavefield components.

Method for source localization for cable cut prevention using distributed fiber optic sensing (DFOS)/distributed acoustic sensing (DAS) is described that is robust/immune to underground propagation speed uncertainty. The method estimates the location of a vibration source while considering any uncertainty of vibration propagation speed and formulates the localization as an optimization problem, and both location of the sources and the propagation speed are treated as unknown. This advantageously enables our method to adapt to variances of the velocity and produce a better generalized performance with respect to environmental changes experienced in the field. Our method operates using a DFOS system and AI techniques as an integrated solution for vibration source localization along an entire optical sensor fiber cable route and process real-time DFOS data and extract features that are related to a location of a source of vibrations that may threaten optical fiber facilities.

A method can include receiving realizations of a model of a reservoir that includes at least one well where the realizations represent uncertainty in a multidimensional space; selecting a portion of the realizations in a reduced dimensional space to preserve an amount of the uncertainty; optimizing an objective function based at least in part on the selected portion of the realizations; outputting parameter values for the optimized objective function; and generating at least a portion of a field operations plan based at least in part on at least a portion of the parameter values.

A cloud-based, time-limited, confidentiality-rated data management system for survey ancillary data associated with acquisition of seismic data during at least one seismic survey. The system includes a database module configured to store in a first region the survey ancillary data, a data application configured to interact with the database module and upload or download the survey ancillary data, a time module configured to count a lifetime associated with the survey ancillary data, and an erase module configured to erase the survey ancillary data from the database module at the end of the lifetime counted by the time module. The lifetime of the survey ancillary data is shorter than a duration of the seismic survey.