A data acquisition program, which includes core, image log, microseismic, DAS, DTS, and pressure data, is described. This program can be used in conjunction with a variety of techniques to accurately monitor and conduct well stimulation.

An earthquake estimation method for more promptly estimating an earthquake on the basis of observation data. The earthquake estimation method includes, by a computer: generating an observation image showing a spatial distribution of seismic wave propagation on a basis of an observation result of seismic waves at a plurality of observation points on a ground; and estimating a parameter of an earthquake with respect to the observation image by using an earthquake estimation model in which a parameter of an earthquake including at least a position of a hypocenter and a magnitude is associated with a simulated observation image showing a spatial distribution of seismic wave propagation on a ground obtained from a result of a numerical simulation of the earthquake, performed with the parameter.

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.

A method for processing seismic data may include receiving, via a processor, the seismic data acquired via a seismic survey. The seismic survey may include seismic sources that emit seismic wavefields at different locations. Each of the seismic sources may change a directivity pattern of a respective seismic wavefield based on a respective location of the respective seismic source. The seismic survey may also include seismic receivers that may receive the seismic data. The method may also include generating one or more basis functions that correspond to measurements of the seismic data, modelling a signal component of the seismic data as a sum of the one or more basis functions, and storing the signal component in a storage component. The signal component may be used to acquire an image of a subsurface region of the earth for identifying a feature in the subsurface region of the earth.

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 method is described for seismic imaging including receiving a pre-stack seismic dataset and an earth model at one or more computer processors; performing least-squares reverse time migration of the pre-stack seismic dataset using the earth model to create a digital seismic image, wherein the least-squares reverse time migration includes wave-equation forward modeling based on an asymptotic expression for reflection in a subsurface Kirchhoff integral; and generating a display of the digital seismic image on a graphical user interface.

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.

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 can include receiving data that characterizes anisotropy of a formation; receiving a model that models one or more planes of weakness in an anisotropic formation; and, based at least in part on the model and the data, outputting information germane to stability of a bore in an anisotropic formation.