Methods and systems described herein are directed to determining properties of a subterranean formation using an acoustic wave-equation with a novel formulation in terms of a velocity model and a reflectivity model of the subterranean formation. The acoustic wave equation may be used with full-waveform inversion to build high-resolution velocity and reflectivity models of a subterranean formation. The acoustic wave equation may be also used with least-squares reverse time migration in the image and space domains, to build a reflectivity model of the subterranean formation with enhanced resolution and amplitude fidelity. The velocity and reflectivity models of materials that form the subterranean formation reveal the structure and lithology of features of the subterranean formation and may reveal the presence of oil and natural gas reservoirs.

This specification describes workflows for, but is not limited to, performing full waveform inversion (FWI) to build high resolution velocity models to improve the accuracy of seismic imaging of a subterranean formation. This specification describes processes to automatically edit and enhance S/N quality of seismic data (such as land seismic data) to prepare the datasets for FWI. The methods for automatic corrections and pre-processing include: automatic iterative surface-consistent residual statics calculation, automatic rejection of anomalous traces (such as dead traces), and the automatic correction of surface-consistent amplitude anomalies (such as by scalar or deconvolution approaches). The operations include automatic “muting” of noise before first arrivals.

The present disclosure provides a method and a system for high-resolution seismic fault detection by means of an adversarial neural network, including following steps of: training a target adversarial neural network based on a preset training sample set, so as to obtain a trained target adversarial neural network, wherein the preset training sample set includes seismic data and fault labels, the target adversarial neural network includes: a segmentation module, a feature fusion module, and a discriminator module, the segmentation module is a module configured for obtaining a fault feature based on the preset training sample set, and the feature fusion module is a module configured for fusing the fault feature and the seismic data into a global feature map; and performing seismic fault detection on a target seismic image based on the trained target adversarial neural network.

The present invention belongs to the field of treatment for data identification and recording carriers, and specifically relates to a method and system for analyzing filling for a karst reservoir based on spectrum decomposition and machine learning, which aims to solve the problems that by adopting the existing petroleum exploration technology, the reservoir with fast lateral change cannot be predicted, and the development characteristics of a carbonate cave type reservoir in a large-scale complex basin cannot be identified. The method comprises: acquiring data of standardized logging curves; obtaining a high-precision 3D seismic amplitude data body by mixed-phase wavelet estimation and maximum posteriori deconvolution and enhancing diffusion filtering. According to the method and the system, the effect of identifying the development characteristics of the carbonate karst cave type reservoir in the large-scale complex basin can be achieved, and the characterization precision is improved.

Disclosed are a multi-scale photoacoustic detection method of geological structure around a borehole and related devices. The method includes: obtaining depth information and direction information of the borehole; generating trajectory data of the borehole according to the depth information and direction information; obtaining an optical image of the geological structure around the borehole; generating a first velocity model according to the optical image and the trajectory data; obtaining low-frequency acoustic wave data and high-frequency acoustic wave data of the geological structure around the borehole; performing a full waveform inversion on the first velocity model according to the low-frequency acoustic wave data and the high-frequency acoustic wave data to obtain a second velocity model; and determining the geological structure around the borehole according to the second velocity model.

The disclosed technology is generally directed to carbon capture and storage. In one example of the technology, a first neural network is trained with synthetic data that is associated with seismic images of synthetic simulated subsurfaces. The first neural network extracts features from multiple resolutions of the seismic images of the synthetic simulated subsurfaces. The ground truth includes synthetic labels that indicate probabilities of potential carbon dioxide leakage pathways of the synthetic simulated subsurfaces. A seismic image of a first subsurface is received. At least the trained first neutral network is used to generate output labels that indicate probabilities of potential leakage pathways of carbon dioxide storage of the first subsurface.

The disclosed technology is generally directed to carbon capture and storage. In one example of the technology, a first neural network is trained with synthetic data that is associated with seismic images of synthetic simulated subsurfaces. The first neural network extracts features from multiple resolutions of the seismic images of the synthetic simulated subsurfaces. The ground truth includes synthetic labels that indicate probabilities of potential carbon dioxide leakage pathways of the synthetic simulated subsurfaces. A seismic image of a first subsurface is received. At least the trained first neutral network is used to generate output labels that indicate probabilities of potential leakage pathways of carbon dioxide storage of the first subsurface.

Systems and methods are discussed to image lithological data within the strata beneath the earth surface, including a subterranean object detection system. The system may further comprise a pipeline operable to conduct a working fluid and an instrumented pig operable to travel within the pipeline and operable to image lithological strata and voids within the strata beneath and around the pipeline. The instrumented pig may comprise an outer case, a battery coupled to the outer case, a ground imaging unit operable to send a signal to image the lithological strata and voids within the strata beneath and around the pipeline and may be operable to receive a reflected signal indicating lithology data, wherein the ground imaging unit may be operably coupled to the battery.

A least one seismic attribute is determined for each voxel of the seismic volume. A first horizon is selected for mapping and a sparse global grid is generated which includes the horizon, at least one constraint point identifying the horizon, and a number of points having a depth in the seismic volume. A value of at least one seismic attribute is determined for each point and their depths are adjusted based on the value of the seismic attribute. A map of the horizon can be generated based on the adjusted depths. Multiple local grids can be generated based on the sparse global grid, and the depths of the local grid points adjusted to generate a map of the horizon at voxel level resolution. The seismic volume can be mapped into multiple horizons, where previously mapped horizons can function as constraints on the sparse global grid.

A method for generating a geophysical image of a subsurface region includes defining a computational sub-volume for the geophysical image including a predetermined number of seismic traces of a plurality of seismic traces and a predetermined number of samples per each one of the plurality of seismic traces, generating a data matrix corresponding to a first sub-volume of the subsurface region based on the defined computational sub-volume, the data matrix comprising the predetermined number of samples for the predetermined number of traces of a portion of a seismic dataset corresponding to the first sub-volume. The method also includes estimating a coherence between the predetermined number of traces of the data matrix by performing a sum of a variance of the predetermined number of samples of the data matrix, and assigning the estimated coherence to a location in the geophysical image.