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.

Implementations described and claimed herein provide systems and methods for reservoir modeling. In one implementation, an input dataset comprising seismic data is received for a particular subsurface reservoir. Based on the input dataset and utilizing a deep learning computing technique, a plurality of trained reservoir models may be generated based on training data and/or validation information to model the particular subsurface reservoir. From the plurality of trained reservoir models, an optimized reservoir model may be selected based on a comparison of each of the plurality of reservoir models to a dataset of measured subsurface characteristics.

A method for seismic imaging includes receiving a multi-shot seismic data set that was collected using one or more streamers having recorders configured to detect seismic waves that propagate through a subterranean domain. The method also includes partitioning the multi-shot seismic data set into windows including a source dimension. The method also includes defining one or more first basis functions that describe the windows of the multi-shot seismic data set. The method also includes generating a model that describes a decomposition of the multi-shot seismic data set using the one or more first basis functions. The method also includes defining one or more second basis functions that describe a selected output data. The method also includes combining the one or more second basis functions with the model to produce a result for a source side wavefield and a receiver side wavefield.

Systems and methods for picking seismic stacking velocity based on structures in a subterranean formation include: receiving seismic data representing a subterranean formation; generating semblance spectrums from the seismic data representing the subterranean formation; smoothing the semblance spectrums; and picking stacking velocities based on the smoothed semblance spectrums.

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.

In seismic imaging, accurate velocity functions (velocity model) defining seismic velocity as a function of depth in the earth are required. The velocity model is obtained as a result of seismic surveying. Delayed travel times in near surface refraction seismic surveys, an effect known as shingling, can result from an anomalous condition, seismic velocity decreasing with depth. Inclusion of such delayed travel times in a tomographic process for seismic imaging would otherwise cause large errors in determination of a seismic velocity model for seismic imaging of subsurface features. At locations (source-receiver offset) in the survey where the shingling occurs, the velocity inversions are identified. The undesirable effects the delayed travel times caused by the velocity inversions are removed from the survey dataset.

The present embodiments describe a system and method for generating one or more predictive models to reduce the static interference present in seismic reflection studies. The system can include a user device and a server. The method proceeds with gathering historical data, generating synthetic data, generating a predictive model based on those data sets, and applying that model to a current set of a data to calculate a seismic reflection of a geological space.

Systems and methods to estimate a pore pressure of source rock include a pore pressure estimation processor, an executable, or both, and are operable to (i) calculate an estimate pore pressure based on overburden gradient data, a compaction velocity profile, hydrocarbon maturity, and an unloading velocity profile, (ii) determine a total organic content (TOC) estimate of the source rock based on a bulk density at a vertical depth measured using the density logging tool, (iii) determine a correction factor based on (a) the TOC estimate and (b) vitrinite ratio R.sub.o data, and (iv) update the estimated pore pressure in real-time based on the correction factor.

A method for a method for identifying a subsurface pore-filling fluid and/or lithology. A training set of field-acquired geophysical data and/or simulated geophysical data is provided to train a backpropagation-enabled process. The trained process is used on a field-acquired data set that is not part of the training set to infer presence of a subsurface pore-filling fluid and/or lithology.

A method for a method for identifying a subsurface pore-filling fluid and/or lithology. A training set of field-acquired geophysical data and/or simulated geophysical data is provided to train a backpropagation-enabled process. The trained process is used on a field-acquired data set that is not part of the training set to infer presence of a subsurface pore-filling fluid and/or lithology.