A computer-implemented method can include the following. Seismic vibratory waves through the Earth along a selected vector path are received. An initial value is selected for a first arrival for each of the seismic vibratory waves. Initial values are determined for travel times and velocities of the seismic vibratory waves. Reversed signs of amplitudes of the seismic vibratory waves are determined and corrected. Time intervals are determined based on the initial values of the travel times. Time windowing and filtering in a frequency domain are performed. Final values are determined for first arrivals and travel times for each of the seismic vibratory waves based on the time windowing and filtering. Final values are determined for velocities of the seismic vibratory waves.

The present disclosure describes methods and systems, including computer-implemented methods, computer program products, and computer systems for direct gas reservoir detection using frequency amplitude. One computer-implemented method includes spectrally decomposing seismic data associated with a target area into a plurality of mono-frequency volumes. Further, the method includes based on a low-frequency volume of the plurality of volumes, generating a low frequency map of the target area. Yet further, the method includes based on a high-frequency volume of the plurality of volumes, generating a high frequency map of the target area. Additionally, the method includes dividing the low frequency map by the high frequency map to generate a frequency ratio map. The method also includes using the frequency ratio map to identify a subsurface gas reservoir in the target area.

A method of predicting parameters of an earthquake uses an array of ionosondes to scan an observed volume of an ionosphere located above a seismically active zone. The method includes monitoring ionograms provided by the array of ionosondes; detecting the presence of at least one seismic-induced irregularity (SII); determining a first predicted parameter corresponding to an epicenter location; and determining one or more predicted parameters selected from a group consisting of a magnitude, a time of occurrence, and a hypocenter depth. Algorithms for calculating the predicted parameters are presented in detail.

The present disclosure discloses a method for identifying a boundary of a sedimentary facies, a computer device and a computer readable storage medium. The method comprises: acquiring a preliminary marked result of the sedimentary facies in a seismic attribute map; acquiring a color-based K-means classification result of the seismic attribute map by using a maximal between-cluster variance and a K-means clustering; acquiring a super-pixel classification result of the seismic attribute map according to a SLIC super-pixel segmentation; and performing a region growing fusion on the super-pixel classification result by taking the preliminary marked result and the K-means classification result as constraints, to determine an identification result of the boundary of the sedimentary facies in the seismic attribute map.

Techniques for calibration of a simulation of a subterranean region having complex fracture geometries. Calibration of indeterministic subsurface discrete fracture network models is performed via non-intrusive embedded discrete fracture modeling formulations applied in conjunction with well testing interpretation and numerical simulation. Subterranean fracture networks are characterized dynamically by embedded discrete fracture modeling to accurately and efficiently determine an optimal fracture model.

Methods, apparatus, and systems for mapping surface and near surface features and processing artifacts from stacked and processed seismic data are disclosed. In some implementations, a computer system receives a three-dimensional (3D) seismic cube including seismic reflectivity data obtained at a geographical location. The computer system generates a vertical analysis window of the 3D seismic cube. The computer system extracts a second 3D seismic cube from the seismic reflectivity data based on the vertical analysis window. The second 3D seismic cube has multiple vertical amplitude traces associated with the seismic reflectivity data. The computer system generates 3D data comprising multiple frequency traces from the multiple vertical amplitude traces using a domain transform. The computer system generates a two-dimensional (2D) map from the 3D data. The 2D map represents geographical features of the geographical location.

An automatic seismic facies identification method based on combination of Self-Attention mechanism and U-shape network architecture, including: obtaining and preprocessing post-stack seismic data to construct a sample training and validation dataset; building an encoder through an overlapped patch merging module with down-sampling function and a self-attention transformer module with global modeling function; building a decoder through a patch expanding module with linear upsampling function, the self-attention transformer module, and a skip connection module with multilayer feature fusion function; building a seismic facies identification model using the encoder, the decoder, and a Hypercolumn module, where the seismic facies identification model includes a Hypercolumns-U-Segformer (HUSeg); and building a hybrid loss function; iteratively training the seismic facies identification model with a training and validation set; and inputting test data into a trained identification model to obtain seismic facies corresponding to the test data.

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.

A system includes a processor and a memory. The memory includes instructions that are executable by the processor to access a plurality of seismic images of a subterranean formation in a first geological area. The instructions are also executable to generate a plurality of fault estimates from each of the plurality of seismic images. Further, the instructions are executable to generate a processed seismic image of the first geological area by normalizing and merging the plurality of seismic images and the plurality of fault estimates. Additionally, the instructions are executable to generate a statistical fault uncertainty volume of the first geological area using the processed seismic image. Furthermore, the instructions are executable to control a drilling operation in the first geological area using the statistical fault uncertainty volume of the first geological area.

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.