Systems and method are claimed for forming an artifact attenuated seismic image. The method includes obtaining an input seismic image, selecting a seismic partition from the input seismic image and determining a seismic dip for the seismic partition. The method further includes determining flattened seismic partition from the seismic partition based, at least in part, on the seismic dip, determining a filtered seismic partition from the flattened seismic partition, and determining an unflattened seismic segment based on the filtered seismic partition. The method still further includes determining the artifact attenuated seismic image based on the unflattened seismic segment. The system includes a seismic source, a plurality of seismic receivers for detecting and recording an observed seismic dataset generated by the radiated seismic wave; and a seismic processor configured form the artifact attenuated seismic image.

Geologic modeling methods and systems disclosed herein employ an improved simulation gridding technique. For example, an illustrative geologic modeling method may comprise: obtaining a geologic model representing a faulted subsurface region in physical space; mapping the physical space geologic model to a design space model representing an unfaulted subsurface region; gridding the design space model to obtain a design space mesh; partitioning cells in the design space mesh with faults mapped from the physical space geologic model, thereby obtaining a partitioned design space mesh; mapping the partitioned design space mesh to the physical space to obtain a physical space simulation mesh; and outputting the physical space simulation mesh.

Aspects of the technology described herein identify geologic features within seismic data using modern computer analysis. An initial step is the development of training data for the machine classifier. The training data comprises an image of seismic data paired with a label identifying points of interest that the classifier should identify within raw data. Once the training data is generated, a classifier can be trained to identify areas of interest in unlabeled seismic images. The classifier can take the form of a deep neural network, such as a U-net. Aspects of the technology described herein utilize a deep neural network architecture that is optimized to detect broad and flat features in seismic images that may go undetected by typical neural networks in use. The architecture can include a group of layers that perform aspect ratio compression and simultaneous comparison of images across multiple aspect ratio scales.

Methods and systems, including computer programs encoded on a computer storage medium can be used for an integrated methodology that can be used by a computing system to automate processes for generating, and updating (e.g., in real-time), subsurface reservoir models. The methodology and automated approaches employ technologies relating to machine learning and artificial intelligence (AI) to process seismic data and information relating to seismic facies.

Methods and systems for determining an interbed multiple attenuated pre-stack seismic dataset are disclosed. The methods include forming a post-stack seismic image composed of post-stack traces from the pre-stack seismic dataset and identifying a first, second, and third post-stack horizon on each of the post-stack traces. The methods further include for each pre-stack trace, generating a first, second, and third multiple-generator trace based on the first, second and third post-stack horizon and determining a correlation trace based, at least in part, on a correlation between the first multiple-generator trace and the second multiple-generator trace. The methods still further include predicting an interbed multiple trace by convolving the correlation trace and the third multiple-generator trace, determining an interbed multiple attenuated trace by subtracting the interbed multiple trace from a corresponding pre-stack seismic trace, and determining the interbed multiple attenuated pre-stack seismic dataset by combining the interbed multiple attenuated traces.

There is disclosed in the present disclosure a seismic full horizon tracking method, a computer device and a computer-readable storage medium. The method includes: acquiring three-dimensional seismic data; extracting horizon extreme points from the three-dimensional seismic data to construct a sample space; equally dividing the sample space into a plurality of sub-spaces with overlapping portions, and performing a clustering process on the horizon extreme points in each sub-space to obtain horizon fragments corresponding to each horizon of the three-dimensional seismic data; establishing a topological consistency between the horizon fragments; and fusing the horizon fragments corresponding to each horizon of the three-dimensional seismic data based on the topological consistency, to obtain a full horizon tracking result of the three-dimensional seismic data. In the disclosure, a layer crossing phenomenon occurring in seismic full horizon tracking can be avoided, and a better full horizon tracking effect can be achieved.

A method for modeling a subsurface volume includes receiving a plurality of ordered seismic images including representations of objects in the subsurface volume, generating flow fields based on a difference between individual images of the plurality of ordered seismic images, and identifying the objects in the seismic images based on the flow fields and the plurality of ordered seismic images.

A lithology prediction that uses a geological age model as an input to a machine learning model. The geological age model is capable of separating and recoding different seismic packages derived from the horizon interpretation. Once the machine learning model has been trained, a validation may be performed to determine the quality of the machine learning model. The quality may be improved by refining the training of the machine learning model. The lithology prediction generated by the machine learning model that utilizes the geological age model provides an improved lithology prediction that more accurately reflects the subterranean formation of an area of interest.

Geologic modeling methods and systems disclosed herein employ an improved simulation meshing technique. One or more illustrative geologic modeling methods may comprise: obtaining a geologic model representing a faulted subsurface region in physical space; providing a set of background cells that encompass one or more partial faults within the subsurface region; defining a pseudo-extension from each unterminated edge of said one or more partial faults to a boundary of a corresponding background cell in said set; using the pseudo-extensions and the background cell boundaries to partition the subsurface region into sub-regions; deriving a simulation mesh in each sub-region based on the horizons in each sub-region; and outputting the simulation mesh.

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.