Multicomponent data are acquired using a downhole acoustic tool having transmitters and receiver stations distributed azimuthally in a plane perpendicular to the axis of the tool. The receiver stations are located at several receiving stations along the axis of the tool. At each acquisition depth, waveforms are processed through a multi-dimensional fast Fourier transform, extrapolation and inverse multi-dimensional fast Fourier transform. At each receiver station, waveforms are combined to produce the standard monopole waveforms and the inline and crossline dipole waveforms along fixed azimuths. These oriented waveforms produce a finer azimuthal sampling of the surrounding formation, and can then be used for imaging geological features within the surrounding formation.

Methods of characterizing acoustic output from a hydrocarbon well and hydrocarbon wells that include controllers that perform the methods are disclosed herein. The methods include receiving the acoustic output, determining a plurality of acoustic fingerprints, and electronically clustering the plurality of acoustic fingerprints. The acoustic output includes information regarding a plurality of sound events, and each sound event of the plurality of sound events includes at least one corresponding sound detected at the hydrocarbon well. The plurality of acoustic fingerprints includes a corresponding acoustic fingerprint for each sound event of the plurality of sound events. The electronically clustering includes utilizing a clustering algorithm to generate a plurality of acoustic event clusters. Each acoustic event cluster of the plurality of acoustic event clusters includes a corresponding fingerprint subset of the plurality of acoustic fingerprints, and each acoustic fingerprint in the corresponding fingerprint subset includes at least one similar acoustic property.

The present invention discloses a physical embedded deep learning formation pressure prediction method, device, medium and equipment, the present invention characterizes seismic attenuation by logging impedance quality factor Q, based on the Q value and rock physics model of formation pressure, the physical mechanism of this kind of certainty replace Caianiello convolution neurons of the nonlinear activation function, using the convolution neurons, build deep learning convolution neural networks (CCNNs), can greatly increase the stress inversion precision and learning efficiency, get accurate formation pressure prediction results. Compared with the prior art, the present invention uses acoustic attenuation instead of the traditional acoustic velocity to characterize formation pressure, and solves the problem that the traditional pressure prediction method based on velocity has strong multiple solutions due to high gas content and complex structure.

A seismic attenuation quality factor Q is determined for seismic signals at intervals of subsurface formations between a seismic source at a marine level surface and one or more receivers of a well. Hydrophone and geophone data are obtained. A reference trace is generated from the hydrophone and geophone data. Vertical seismic profile (VSP) traces are received. First break picking of the VSP traces is performed. VSP data representing particle motion measured by a receiver of the well are generated. The reference trace is injected into the VSP data. A ratio of spectral amplitudes of a direct arrival event of the VSP data and the reference trace is determined. From the ratio, a quality factor Q is generated representing a time and depth compensated attenuation value of seismic signals between the seismic source at the marine level surface and the first receiver.

The present disclosure provides a physics-based and data-driven integrated method for rock burst hazard assessment, including the following steps: determining an initial stress concentration coefficient by conducting grid discretization on an assessment region, and assigning a value to each of grid nodes using a Weibull distribution function; obtaining a stress concentration coefficient value of each grid node under physics-based models; introducing seismic wave CT detection data to obtain stress concentration coefficient distribution in the assessment region under the integration of a seismic wave CT detection and its derived characterization stress model; introducing microseismic data to obtain stress concentration coefficient distribution in the assessment region under the integration of a microseismic damage reconstruction stress model; and assessing the degree of rock burst hazard according to the size of the stress concentration coefficient value.

A method and system for analyzing a seismically active field based on expansion of an empirical orthogonal function is provided. The research region of the seismic active field is gridded at equal intervals for the preset research region of a seismic active field; a seismic active field function matrix correlated with the research region of the seismic active field spatially and temporally is constructed according to the gridding of the research region of the seismic active field; and the seismic active field function matrix is expanded with an empirical orthogonal function to obtain a main typical field and a temporal factor thereof, and an anomaly on the temporal factor of the seismic active field is analyzed with a method index, a parameter index and an anomaly index.

A method can include acquiring imagery of an exposed surface of the Earth; generating a multi-dimensional model based at least in part on the imagery; generating synthetic seismic data utilizing the multi-dimensional model; acquiring seismic data of a subsurface region of the Earth; performing a search that matches a portion of the acquired seismic data and a portion of the synthetic seismic data; and characterizing the subsurface region of the Earth based at least in part on the portion of the synthetic seismic data.

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 comprises conveying a downhole tool in a production tubing within a casing that is around a wall of a wellbore formed in a subsurface formation, wherein cement is placed in an annulus defined between the casing and the wall of the wellbore. The downhole tool includes at least one unipole receiver and a transmitter that comprises at least one of a unipole transmitter and a monopole transmitter. The transmitter and receiver are mounted on a rotatable portion of the downhole tool. The method includes performing the following operations at at least two azimuthal positions, emitting an acoustic transmission outward toward the cement and detecting an acoustic response that is in response to the acoustic transmission propagating through the production tubing and the casing and into the cement. The acoustic response includes casing extensional waves, casing non-extensional waves, and tubing waves. The method includes evaluating the cement based on the casing extensional waves.

A method for determining an internal multiple attenuated seismic image is disclosed. The method includes obtaining a seismic dataset composed of a plurality of seismic traces and for each seismic trace determining an internal multiple trace based, at least in part, on a nested truncated correlation and a bounded convolution of the seismic trace with itself. The method further includes determining an internal multiple attenuated seismic trace based, at least in part, on subtracting the internal multiple trace from the seismic trace and combining the internal multiple attenuated seismic trace to form the internal multiple attenuated seismic image. A system including a seismic source, a plurality of seismic receivers, and a seismic processor for executing the method is disclosed.