According certain aspects, embodiments of the invention consider the problem of microseismic event localization from a parameter estimation perspective, and include a method and system for computing and displaying characteristics of event localization errors. According to certain other aspects, embodiments of the invention include techniques for deriving aggregate statistics from a set of event location estimates, including methods for computing and displaying the probability that an event occurred in any given volume, and methods for describing and displaying the smallest volume that contains a specified percentage of the event probability or expected to contain the specified percentage of the events.

A method is described for seismic depth uncertainty analysis including receiving wavelet basis functions and cutoff thresholds and randomly perturbing wavelet coefficients in reduced wavelet space based on the wavelet basis functions and the cutoff thresholds to generate a plurality of random wavelet fields; receiving a reference model in a depth domain; transforming the plurality of random wavelet fields to the depth domain and combining them with the reference model to form candidate models; performing a hierarchical Bayesian modeling with Markov Chain Monte Carlo (MCMC) sampling methods using the candidate models as input to generate a plurality of realizations; and computing statistics of the plurality of realizations to estimate depth uncertainty. The method may be executed by a computer system.

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 is described for seismic depth uncertainty analysis including receiving wavelet basis functions and cutoff thresholds and randomly perturbing wavelet coefficients in reduced wavelet space based on the wavelet basis functions and the cutoff thresholds to generate a plurality of random wavelet fields; receiving a reference model in a depth domain; transforming the plurality of random wavelet fields to the depth domain and combining them with the reference model to form candidate models; performing a hierarchical Bayesian modeling with Markov Chain Monte Carlo (MCMC) sampling methods using the candidate models as input to generate a plurality of realizations; and computing statistics of the plurality of realizations to estimate depth uncertainty. The method may be executed by a computer system.

A method for imaging a downhole formation. The method includes combining the captured images to generate a partial image of the formation, wherein the partial image includes captured images separated by gaps representing portions of the formation not captured with sensors what were disposed downhole. The method includes locating dips in the formation within the partial image and interpolating the partial image using the located dips within the partial image.

Systems and methods of detecting marine seismic survey parameters are provided. A data processing system can obtain seismic data from seismic data acquisition units disposed on a seabed responsive to an acoustic signal propagated from an acoustic source through a water column. The data processing system can determine from the seismic data, a direct arrival time for the acoustic signal at each of the plurality of seismic data acquisition units, and can obtain an estimated depth value of each of the plurality of seismic data acquisition units and an estimated water column transit velocity of the acoustic signal. The data processing system can apply a depth model and a water column transit velocity model to the estimated depth value and to the estimated water column transit velocity determine an updated depth value and an updated water column transit velocity for each of the plurality of seismic data acquisition units.

Embodiments herein relate to a computer-implemented technique that includes generating, in a first portion of a graphical user interface (GUI), a first graphical element related to reflection seismic data of an area of interest. The technique further includes generating, in a second portion of the GUI, a second graphical element related to well structural data of the area of interest. The technique further includes generating, in a third portion of the GUI, a third graphical element that is based on the reflection seismic data and the well structural data. In embodiments, an alteration of the first graphical element or the second graphical element results in a concurrent alteration of the third graphical element. Other embodiments may be described or claimed.

A method is described for localized seismic imaging around a wellbore. The method uses at least one non-conventional seismic source such as an electrical submersible pump to generate seismic signals which are reflected by the surrounding wellbore and rock formation and recorded by a fiber optic cable or downhole geophones. The seismic data is then processed and imaged to generate an image of the volume around the wellbore. The method may be executed by a computer system.

A method may include obtaining seismic data regarding a geological region of interest. The seismic data may include various pre-processed gathers. The method may further include obtaining a machine-learning model that is pre-trained to predict migrated seismic data. The method may further include selecting various training gathers based on a portion of the pre-processed gathers, a migration function, and a velocity model. The method may further include generating a trained model using the training gathers, the machine-learning model, and a machine-learning algorithm. The method may further include generating a seismic image of the geological region of interest using the trained model and a remaining portion of the seismic data.

A method for estimating a thickness of a deep reservoir may include obtaining seismic data relating to the deep reservoir. The method may include performing spectral decomposition to obtain one or more frequency components from the seismic data. The method may include identifying a number of mono-frequency horizons corresponding to high frequencies in the seismic data, determining whether the deep reservoir is a thin reservoir based on the number of mono-frequency horizons, and estimating the thickness of the deep reservoir when the deep reservoir is determined to be the thin reservoir.