Systems and methods for simulating subterranean regions having multi-scale fracture geometries. Non-intrusive embedded discrete fracture modeling formulations are applied in conjunction with commercial simulators to efficiently and accurately model subsurface transient flow characteristics in regions having complex hydraulic fractures, complex natural fractures, or a combination of both, and geometries including corner point grids.

Methods and apparatus are described for using ambient acoustic energy as a passive source in marine seismic surveys. An example embodiment includes (a) accessing signals that were recorded by sensors in the presence of acoustic energy that was emitted by a passive source during a marine seismic survey; (b) identifying a point to represent a location from which the acoustic energy was emitted; (c) isolating, from the recorded signals, a direct wavefield arriving at the sensors from a direction of the identified point; and (d) generating an estimated passive source wavefield at the identified point by backpropagating the isolated direct wavefield to the identified point. The estimated passive source wavefield may be used, together with signals recorded by the sensors, to generate an image of a subsurface earth volume without the use of active seismic sources.

A method can include accessing a trained machine model as trained to analyze digital seismic data of a region with respect to a structural feature of a geologic region; analyzing at least a portion of the digital seismic data using the trained machine model to generate results; and outputting the results as indicators of spatial locations of the structural feature of the geologic region.

A seismic dataset and a task to be performed with the seismic dataset may be received. A representative seismic line representative of the seismic dataset may be generated. The representative seismic line may include pixel data representative of the seismic dataset. Based on the representative seismic line, the task may be performed. The task may include at least finding an analogous geological region by searching for an analogous seismic dataset existing in a seismic database by comparing the representative seismic line with the analogous seismic dataset's representative seismic line.

The present invention belongs to the field of treatment for data identification and recording carriers, and specifically relates to a method and system for evaluating the filling characteristics of a deep paleokarst reservoir through well-to-seismic integration, 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.

A method for seismic processing includes steps of seismic signal forward propagation and seismic data back propagation. The subsurface medium image is created after correlating and summarizing forward and backward propagation results. To address migration footprint and noise due to the incomplete data acquisition aperture and migration approximation in the migration operator, the iteration inversion strategy incorporates tensor flow calculated from seismic image. A regularization operator based on structure tensor of image is applied to seismic image inversion.

A computer-implemented method is provided for interpreting geophysical data utilising an Artificial Neural Network (ANN), performed by electronic operations executed by a computing device, comprising: performing a training processing step on at least one training-data set, comprising the steps of: (a) generating a first label-data by segmenting said at least one training-data set into at least a first region, representing a known first region having at least one identified geological feature, and/or a second region, representing a known second region having at least one unidentified geological feature, and a third region, representing an unknown region; (b) generating a first ANN model output for a dynamically adaptable Region of Interest (ROI) of said first label-data, said dynamically adaptable ROI including said first and/or second region; (c) generating an updated label-data by selecting at least a first portion of any one of said first, second and third region, and labelingly append at least said first portion to any one of said first, second and third region; (d) generating an updated ANN model output for an updated dynamically adaptable ROI of said updated label-data; (e) repeating steps (c) and (d) until a predetermined condition is met, providing a final ANN model output; and then applying said final ANN model output to a target-data set utilising said ANN, generating a desired output data.

Seismic image data acquired for a subsurface formation from a data acquisition system is input into a deep neural network to generate fault detection data for the subsurface formation comprising probability values at a grid of locations in the subsurface formation. The fault detection data is preprocessed via downsampling and distributed weighted factors and inputted into a generative adversarial network (GAN) upscaling generator to create high resolution fault detection data with minimized distortion and artifacts. The GAN upscaling generator is pre trained on synthetic fault data in a GAN training system using adversarial training against a GAN upscaling discriminator, and both the GAN upscaling generator and the GAN upscaling discriminator learn to approximate the distribution of the synthetic fault data.

Geologic modeling methods and systems disclosed herein employ an improved simulation gridding technique that optimizes simulation efficiency by balancing the computational burdens associated with remeshing against the performance benefits of doing so. One method embodiment includes: (a) obtaining a geologic model representing a subsurface region as a mesh of cells, at least some of the cells in the mesh having one or more interfaces representing boundaries of subsurface structures including at least one fracture; (b) determining a fracture extension to the at least one fracture; (c) evaluating whether the fracture extension is collocated with, or is proximate to, an existing cell interface, and using the existing cell interface if appropriate or creating a new cell interface if not; and (d) outputting the updated version of the geologic model.

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.