A method of identifying regions in a subsurface that may be a hydrocarbon reservoir, the method including: extracting features from cross-attribute clusters; assigning a distance metric and linkage criterion in feature space; calculating, with a computer, a degree of anomaly for the cross-attribute clusters in the feature space; ranking the cross-attribute clusters in accordance with the degree of anomaly; and prospecting for hydrocarbons by investigating a subsurface region in accordance with the rankings.

A system and methods are disclosed. The method includes obtaining a seismic dataset including a plurality of recorded multiple events, generating a predicted multiple model using a multiple prediction method and the seismic dataset, and estimating a set of initial matching filters using a matching method, to match the plurality of estimated and recorded multiple events. The method further includes generating a tensor field based on the predicted multiple model, determining a set of structure-oriented matching filters based on the set of initial matching filters and the tensor field, generating a filtered multiple model based on the predicted multiple model and the set of structure-oriented matching filters, and generating a multiple-attenuated seismic dataset based on the filtered multiple model and the seismic dataset, forming a seismic image based, at least in part, on the multiple-attenuated seismic dataset, and determining a location of a hydrocarbon reservoir based on the seismic image.

Methods, devices and computer-readable media for predicting hydrocarbon production rates for a subterranean formation are described. A method includes: receiving or generating, by at least one processor, well logs from data collected from at least one well in the subterranean formation; generating from the well logs a predicted production rate log for the at least one well; receiving, by the at least one processor, a field dataset for the subterranean formation, the field dataset including field data at locations in 3-dimensions of a volume of the subterranean formation; identifying the predicted production rate log for the at least one well as one or more targets, determining a transform relating the field data and the predicted rate log for the at least one well; and using the transform, generating a predicted production rate for each location of the volume of the subterranean formation.

A system for enhancing traveltime information in a seismic dataset and determining a velocity model. The system includes a first initial velocity model, a forward modelling procedure, a machine-learned model, a drilling system with a wellbore planning system, and a computer. The computer is configured to: receive a non-synthetic seismic data set for a subsurface region of interest; perturb the first initial velocity model forming a first plurality of velocity models; simulate, with the forward modelling procedure, a first plurality of seismic data sets; form a first plurality of transformed seismic data sets with enhanced traveltime; train the machine-learned model using the first plurality of velocity models and the first plurality of transformed seismic data sets; transform the non-synthetic seismic data set to a non-synthetic transformed seismic data set; and process the non-synthetic seismic data set with the trained machine-learned model to predict a velocity model for the subsurface region.

Methods may include applying a correlation between the probability of stress variations in a first well and variations in drilling data obtained from the first well to drilling data obtained from one or more additional wells; calculating a proportion of high stress and low stress contrast stages within the one or more additional wells; and creating one or more synthetic stress profiles for various completion techniques based on the calculated proportion of high stress and low stress contrast stages within the one or more additional wells; and using the one or more synthetic stress profiles to create a synthetic stress log for a geometric completion (GC) and an engineered completion (EC). Methods may also include obtaining drilling data from one or more wells; calculating a proportion of high stress and low stress contrast stages within the one or more additional wells; and creating one or more synthetic stress profiles for various completion techniques based on the calculated proportion of high stress and low stress contrast stages within the one or more wells.