Systems and methods for modeling dispersion curves are disclosed. The method includes obtaining an acoustic dataset along a well that accesses a hydrocarbon reservoir. The method further includes determining a set of depth windows along the well and determining a first subset of dispersion curves for a first subset of depth windows using a dispersion model. The method still further includes initializing a second subset of dispersion curves for a second subset of depth windows using a nearest neighbor search of the first subset of dispersion curves. The method still further includes determining slowness-frequency pairs for the second subset of depth windows using the acoustic dataset and updating the second subset of dispersion curves using a recursive scanning method. The method still further includes characterizing rock properties near the well based, at least in part, on the first subset of dispersion curves and the second subset of dispersion curves.

The present disclosure belongs to the technical field of seismic exploration imaging, and relates to an interpretive-guided velocity modeling seismic imaging method and system, a medium and a device. The method comprises the following steps: S1. performing first imaging on a given initial velocity model to obtain a first imaging result; S2. performing relative wave impedance inversion on the first imaging result to obtain a relative wave impedance profile; S3. performing Curvelet filtering on the relative wave impedance profile to obtain a first interpretation scheme; S4. superposing the first interpretation scheme and the initial velocity model to obtain a new migration velocity field; S5. performing second imaging on a new migration velocity field to obtain a second imaging result; and S6. repeating steps S2-S4 for the obtained second imaging result until a final seismic imaging result is obtained.

This disclosure describes a system and method for generating a subsurface model representing lithological characteristics and attributes of the subsurface of a celestial body or planet. By automatically ingesting data from many sources, a machine learning system can infer information about the characteristics of regions of the subsurface and build a model representing the subsurface rock properties. In some cases, this can provide information about a region using inferred data, where no direct measurements have been taken. Remote sensing data, such as aerial or satellite imagery, gravimetric data, magnetic field data, electromagnetic data, and other information can be readily collected or is already available at scale. Lithological attributes and characteristics present in available geoscience data can be correlated with related remote sensing data using a machine learning model, which can then infer lithological attributes and characteristics for regions where remote sensing data is available, but geoscience data is not.

A neural network is utilized to improve the resolution of subsurface inversion. The neural network leverages posterior distribution of samples and adds high frequency components to the inversion by utilizing the data in both the time domain and the frequency domain. The improved resolution of the subsurface inversion enables more accurate prediction of subsurface characteristics (e.g., reservoir architecture).

System and methods for automated seismic processing are provided. Historical seismic project data associated with one or more historical seismic projects is obtained from a data store. The historical seismic project data is transformed into seismic workflow model data. At least one seismic workflow model is generated using the seismic workflow model data. Responsive to receiving seismic data for a new seismic project, an optimized workflow for processing the received seismic data is determined based on the at least one generated seismic workflow model. Geophysical parameters for processing the seismic data with the optimized workflow are selected. The seismic data for the new seismic project is processed using the optimized workflow and the selected geophysical parameters.

Aspects of the present disclosure describe distributed fiber optic sensing systems, methods, and structures that advantageously are employed to determine the location and depth of underground fiber-optic facilities that may be carrying telecommunications traffic.

In some embodiments, an apparatus and a system, as well as a method and an article, may operate to generate a parent population, wherein each member of the parent population includes a set of model parameters describing a layer model of the geological formation; to execute a perturbation algorithm to generate subsequent child populations, from the parent population, until a termination criterion is met; to provide a plurality of solutions based on at least one member of the parent population and on at least one member of each child population; and to control a drilling operation based on a revised layer model that has been generated based on a selected one of the plurality of solutions. Additional apparatus, systems, and methods are disclosed.

A measure of structural growth of subsurface geological formations layers or intervals is obtained to investigate and analyze layer structure development history. The measure or indication can be considered a structural growth index (SGI) and is used to evaluate for possible presence of hydrocarbons in the layers or strata in a structure of concern. The structural growth measure or index for a given geologic interval of interest is defined as the ratio of the layer thickness difference measured at two selected locations, one at a structural high and the other at a structural low of layer or interval, over the larger number of the paired-thickness measurements. The structural growth measure is determined for a series of sequentially layered geologic formations of interest and displays of determined measures are formed as functions of geologic time for the formations. Based on the structural growth measure, the geologic growth history of the given oil field structure is revealed quantitatively.

A backpropagation enabled model is trained for estimating time-lapse property changes of a subsurface volume. Synthetic models of the subsurface volume are generated, with pre-determined property changes before and after a time lapse. These models are used to compute baseline-monitor pairs of synthetic seismic traces before and after the time lapse, wherein the baseline synthetic traces are computed from the synthetic model before the time lapse and the monitor synthetic traces are computed from the synthetic model after the time lapse. A ground truth 4D attribute characterizing the time-lapse property changes in the synthetic models is defined, and a backpropagation enabled model is trained by feeding the baseline-monitor pairs of synthetic seismic traces and the corresponding ground truth 4D attribute. The thus obtained trained backpropagation enabled model can be used to estimate time-lapse property changes of the actual subsurface Earth volume from actual baseline-monitor pairs of seismic traces.

A method for seismic surveying comprises deploying a plurality of seismic receivers proximate an area of subsurface to be surveyed. At least one seismic energy source moves in a path that circumscribes a center, wherein positions of the plurality of seismic receivers remain fixed. At least one of a distance between the path and the center changes monotonically as seismic energy source traverses the path, or the center moves in a selected direction as the seismic energy source traverses the path. The source is actuated at selected times as the at least one seismic energy source traverses the path, such that a spacing between positions of the source along the source path and transverse to the source path varies between successive actuations of the source. Seismic energy is detected at the plurality of seismic receivers resulting from actuating the at least one seismic energy source.