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.

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.

Disclosed are methods of marine 3D seismic data acquisition that do not require compensation for winds and currents.

A computer implemented method for analyzing a reservoir grid modeling a reservoir geological formation is provided in which the reservoir grid corresponds to a 3D grid of cells associated to respective values of at least one geological property. The method includes obtaining a 4D seismic image of the reservoir geological formation. A skeleton of the 4D seismic image is calculated, and the skeleton extends between at least one origin and a plurality of extremities. Each point of the skeleton is associated to a value of the at least one geological property of the reservoir grid. Flow time values are calculated for a fluid flowing from the origin to the extremities along the skeleton, based on the at least one geological property values associated to the points of the skeleton. The reservoir grid is calculated based on the flow time values.

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.

Systems and a method are disclosed. The method includes obtaining a plurality of raw seismic datasets for a subterranean region of interest, wherein each raw seismic dataset is generated by a high-speed train traversing a train track at a unique speed. The method further includes determining a plurality of processed seismic datasets by processing each of the plurality of raw seismic datasets and determining a final seismic dataset by combining the plurality of processed seismic datasets. The method still further includes identifying subterranean features within the subterranean region of interest using the final seismic dataset.

There is disclosed in the present disclosure a seismic full horizon tracking method, a computer device and a computer-readable storage medium. The method includes: acquiring three-dimensional seismic data; extracting horizon extreme points from the three-dimensional seismic data to construct a sample space; equally dividing the sample space into a plurality of sub-spaces with overlapping portions, and performing a clustering process on the horizon extreme points in each sub-space to obtain horizon fragments corresponding to each horizon of the three-dimensional seismic data; establishing a topological consistency between the horizon fragments; and fusing the horizon fragments corresponding to each horizon of the three-dimensional seismic data based on the topological consistency, to obtain a full horizon tracking result of the three-dimensional seismic data. In the disclosure, a layer crossing phenomenon occurring in seismic full horizon tracking can be avoided, and a better full horizon tracking effect can be achieved.

A method for modeling a subsurface volume includes receiving a plurality of ordered seismic images including representations of objects in the subsurface volume, generating flow fields based on a difference between individual images of the plurality of ordered seismic images, and identifying the objects in the seismic images based on the flow fields and the plurality of ordered seismic images.