Methods for separating the unknown contributions of two or more sources from a commonly acquired set of wavefield signals based on varying parameters at the firing time, location and/or depth of the individual sources in a lateral 2D plane.

Disclosed herein is a method of stripping a strong reflection layer based on deep learning. The method establishes a direct mapping relationship between a strong reflection signal and seismic data of a target work area through a nonlinear mapping function of the deep neural network, and strips a strong reflection layer after the strong layer is accurately predicted. A mapping relationship between the seismic data containing the strong reflection layer and an event of the strong reflection layer is directedly found through training parameters. In addition, this method does not require an empirical parameter adjustment, and only needs to prepare a training sample that meets the actual conditions of the target work area according to the described rules.

A system and method for analyzing geologic features including fluid estimation and lithology discrimination may include the steps of identifying areas of interest on a seismic horizon, computing statistical data ranges for the seismic amplitudes within the areas of interest, and analyzing the geologic features based on the amplitude variation with offset (AVO) or angle (AVA) curves including the statistical data ranges.

Limitations in accuracy and computing power requirements impeding conventional Kirchhoff migration and reverse time migration are overcome by using the wave-equation Kirchhoff, WEK, technique with Kirchhoff migration. WEK technique includes forward-propagating a low-frequency wavefield from a shot location among pre-defined source locations, calculating an arrival traveltime of a maximum amplitude of the low-frequency wavefield, and applying Kirchhoff migration using the arrival traveltime and the maximum amplitude.

A method for generating seismic attribute gathers, the method including: computing, with a computer, seismic images with a field dataset; generating, with a computer, synthetic data corresponding to the seismic images; computing, with a computer, an attribute volume by applying an expectation method to the synthetic data; mapping, with a computer, the attribute volume to the seismic images; and generating, with a computer, seismic attribute gathers by stacking the seismic images mapped to the attribute volume.

Computing device, computer instructions and method for estimating a broadband wavelet associated with a given seismic data set. The method includes receiving broadband seismic data; constructing and populating a misfit function; calculating the broadband wavelet based on the misfit function and the broadband seismic data; and estimating physical reservoir properties of a surveyed subsurface based on the broadband wavelet. The broadband wavelet is constrained, through the misfit function, by (1) an amplitude only long wavelet, and (2) an amplitude and phase short wavelet. The amplitude and phase short wavelet is shorter in time than the amplitude only long wavelet.

A system and method for analyzing geologic features including fluid estimation and lithology discrimination may include the steps of identifying areas of interest on a seismic horizon, computing statistical data ranges for the seismic amplitudes within the areas of interest, and analyzing the geologic features based on the amplitude variation with offset (AVO) or angle (AVA) curves including the statistical data ranges.

A method includes generating a seismic shot by a seismic source, the seismic shot directed at a geological subsurface, and receiving, by one or more receivers, a plurality of reflected seismic traces from the seismic shot. The method further includes generating a correlogram of each reflected seismic trace to generate a plurality of correlograms, isolating a coda wave train of each correlogram of the plurality of correlograms, and computing an energy ratio between an energy of the coda wave train of each correlogram and a total energy of a corresponding correlogram of the plurality of correlograms to generate a plurality of energy ratios. The method further includes determining an average of the plurality of energy ratios to generate an average energy ratio of the seismic shot and determining a likelihood of striking a subsurface geohazard when drilling into the geological subsurface based on the average energy ratio.

Various embodiments described herein provide methods of hydrocarbon management and associated systems and/or computer readable media including executable instructions. Such methods (and by extension their associated systems and/or computer readable media for implementing such methods) may include obtaining geophysical data (e.g., seismic or other geophysical data) from a prospective subsurface formation (that is, a potential formation or other subsurface region of interest for any of various reasons, but in particular due to potential for production of hydrocarbons) and using a trained machine learning (ML) system for direct hydrocarbon indicators (DHI) analysis of the obtained geophysical data. Hydrocarbon management decisions may be guided by the DHI analysis.

A method of identifying inflow locations along a wellbore comprises obtaining an acoustic signal from a sensor within the wellbore, determining a plurality of frequency domain features from the acoustic signal, and identifying, using a plurality of fluid flow models, a presence of at least one of a gas phase inflow, an aqueous phase inflow, or a hydrocarbon liquid phase inflow at one or more fluid flow locations. The acoustic signal comprises acoustic samples across a portion of a depth of the wellbore, and the plurality of frequency domain features are obtained across a plurality of depth intervals within the portion of the depth of the wellbore. Each fluid flow model of the plurality of fluid inflow models uses one or more frequency domain features of the plurality of the frequency domain features, and at least two of the plurality of fluid flow models are different.