A method is disclosed that includes: obtaining a seismic data volume for a subterranean region of interest; transforming, by a computer processor using a non-stationary series analysis, the seismic data volume into a seismic spectral volume where the seismic spectral volume includes a seismic spectrum for each of a plurality of voxels; and determining a seismic attribute volume composed of a seismic attribute for each of the plurality of voxels. The seismic attribute for a voxel of the plurality of voxels is based, at least in part, on an integral of the seismic spectrum for the voxel over a range bounded by a first frequency and a second frequency. The method further includes determining a presence of hydrocarbon in the subterranean region of interest based on the seismic attribute volume. A system for performing the method is also disclosed and described.

The invention is a method applicable to oil and gas exploration and exploitation for automatically detecting geological objects belonging to a given type of geological object in a seismic image, on a basis of a priori probabilities of belonging to a type of geological object assigned to each of samples of the image to be interpreted. The image is transformed into seismic attributes applied beforehand, followed by a classification method. For each of the classes, an a posteriori probability of belonging to a type of geological object is determined for each of the samples of the class according to the a priori probabilities, of the class, of belonging, and according to a parameter α describing a confidence in the a priori probabilities of belonging. Based on the class of the sample, the determined a posteriori probability of belonging to a type of geological object is assigned for the samples of the class. The geological objects belonging to the type of geological object are detected based on determined of the a posteriori probabilities of belonging to the type of geological object for each of the samples of the image to be interpreted.

Rock reservoir structure characterization method comprises: acquiring a three-dimensional seismic data volume of a rock reservoir to be characterized; performing a transformation on all the intrinsic mode function components obtained through decomposition to obtain time-frequency spectrum of each intrinsic mode function component, and adding the time-frequency spectrums of all the components to obtain the time-frequency spectrums of the seismic data; performing cross-correlation between each of the time-frequency components of the near-well seismic traces and the synthetic seismic trace obtained by logging data of the same well, and screening out a sensitive component with the highest correlation degree as an input feature, and performing fuzzy C-means clustering and spatial smoothing on the sensitive component with the highest correlation degree to obtain a seismic facies with set standard division; and depicting the rock reservoir according to the seismic facies divided by the set standard.

A method for building a three dimensional (3D) model of a subsurface formation includes selecting, from a set of seismic shots, a plurality of first arrival signals representing the seismic shots. The method includes applying a quality control function to the plurality of first arrival signals to obtain a set of remaining first arrival signals. For each remaining first arrival signals, the method includes applying a velocity inversion function to obtain a depth velocity value at a common-midpoint (CMP) location in a shot gather including the seismic shot associated with that remaining first arrival signal, the CMP location representing a lateral variation of the shot gather including that seismic shot. The method includes, based on the depth velocity value for the seismic shot associated with each remaining first arrival signal, generating a velocity model representing the 3D model of the subsurface formation.

A method is disclosed and includes receiving a seismic cube. The seismic cube includes a three-dimensional image of a portion of a subsurface area. The method further includes providing the seismic cube to a machine learning process. The machine learning process includes one or more neural networks used for predicting a location of a subsurface seismic layer in the received seismic cube. The method also includes receiving, from the machine learning process, the prediction of the location of the subsurface seismic layer in the seismic cube.

Methods of identifying a subterranean tunnel using digital imaging that may include: obtaining data of a propagating wavefield through a propagating volume that includes a portion of the earth's subsurface; obtaining a reference digital image of the propagating volume; selecting a holographic computational method of wavefield imaging; selecting a wavefield based on one or more parameters; calculating a sampling ratio by dividing a number of data samples in the data subset by a number of image samples in the data subset; decimating the data subset; generating a new digital image based on the selected holographic computational method of imaging, the decimated data subset, and parameters corresponding to the data subset; determining a quantitative difference measure between the reference digital image and the new digital image, and image quality; and identifying the subterranean tunnel.

Computer vision systems and methods for detecting anomalous building models are provided. The systems and methods can detect anomalies in building models using one or more of an independent univariate Gaussian algorithm, a multivariate Gaussian algorithm, a combination of a multivariate Gaussian algorithm for continuous features and a frequency histogram algorithm for discrete features, and/or a bin frequency model. The system automatically processes computerized models to determine anomalies, and indicates whether the models are accurate and whether correction is required.

Methods and systems for forming a three-dimensional (“3D”) seismic image of a subterranean region of interest is disclosed. The method includes obtaining a seismic dataset a seismic trace for each of a plurality of pairs of one source and one receiver location and obtaining a 3D travel-time cube for each source location and each receiver location. The method further includes dividing the seismic dataset into a plurality of seismic subsets composed of set of source locations, set of receiver locations a seismic trace for each pair of source and receiver location and the 3D travel-time cube for each source for each receiver location. The method still further includes transmitting, to a random-access memory block of a computer processing unit the seismic subset, and forming a seismic partial image based on the seismic subset, and determining the 3D seismic image based on a combination of the seismic partial images.

A fracture mapping system for use in hydraulic fracturing operations utilizing non-directionally sensitive fiber optic cable, based on distributed acoustic sensing, deployed in an observation well to detect microseismic events and to determine microseismic event locations in 3D space during the hydraulic fracturing operation. The system may include a weighted probability density function to improve the resolution of the microseismic event on the fiber optic cable.

Methods of identifying a subterranean tunnel using digital imaging that may include: obtaining data of a propagating wavefield through a propagating volume that includes a portion of the earth's subsurface; obtaining a reference digital image of the propagating volume; selecting a holographic computational method of wavefield imaging; selecting a wavefield based on one or more parameters; calculating a sampling ratio by dividing a number of data samples in the data subset by a number of image samples in the data subset; decimating the data subset; generating a new digital image based on the selected holographic computational method of imaging, the decimated data subset, and parameters corresponding to the data subset; determining a quantitative difference measure between the reference digital image and the new digital image, and image quality; and identifying the subterranean tunnel.