A method for modeling a subterranean volume includes receiving seismic data comprising a signal, generating a semblance in the frequency-wavenumber domain for the seismic data, wherein the semblance represents a coherence of the signal in the frequency-wavenumber domain, extracting one or more wave energy modes in the semblance using a machine learning model trained to identify dispersion curves in the semblance based on a visible characteristic of the dispersion curves, and generating a model representing surface wave propagation based at least in part on the identified one or more wave energy modes.

The present disclosure relates to the field of geophysical processing methods for oil and gas exploration, and more particularly, to a method for gas detection using multiple quantum neural networks. A plurality of stratigraphic and structural seismic attributes are extracted from the seismic data of a target horizon, and input seismic characteristic parameters are divided into different classes by using an unsupervised learning and supervised learning combined quantum self-organizing feature map network. Gas detection is then performed using a particle swarm optimization based quantum gate node neural network with clustering results of various seismic characteristic parameters output by the quantum self-organizing feature map network as inputs. The present method uses the unsupervised learning and supervised learning combined quantum self-organizing feature map network for a plurality of stratigraphic and structural seismic attributes of the seismic data and thus has improved accuracy and uniqueness of clustering.

The present disclosure relates to the field of geophysical processing methods for oil and gas exploration, and more particularly, to a method for gas detection using multiple quantum neural networks. A plurality of stratigraphic and structural seismic attributes are extracted from the seismic data of a target horizon, and input seismic characteristic parameters are divided into different classes by using an unsupervised learning and supervised learning combined quantum self-organizing feature map network. Gas detection is then performed using a particle swarm optimization based quantum gate node neural network with clustering results of various seismic characteristic parameters output by the quantum self-organizing feature map network as inputs. The present method uses the unsupervised learning and supervised learning combined quantum self-organizing feature map network for a plurality of stratigraphic and structural seismic attributes of the seismic data and thus has improved accuracy and uniqueness of clustering.

Disclosed are a multi-scale photoacoustic detection method of geological structure around a borehole and related devices. The method includes: obtaining depth information and direction information of the borehole; generating trajectory data of the borehole according to the depth information and direction information; obtaining an optical image of the geological structure around the borehole; generating a first velocity model according to the optical image and the trajectory data; obtaining low-frequency acoustic wave data and high-frequency acoustic wave data of the geological structure around the borehole; performing a full waveform inversion on the first velocity model according to the low-frequency acoustic wave data and the high-frequency acoustic wave data to obtain a second velocity model; and determining the geological structure around the borehole according to the second velocity model.

Disclosed are a multi-scale photoacoustic detection method of geological structure around a borehole and related devices. The method includes: obtaining depth information and direction information of the borehole; generating trajectory data of the borehole according to the depth information and direction information; obtaining an optical image of the geological structure around the borehole; generating a first velocity model according to the optical image and the trajectory data; obtaining low-frequency acoustic wave data and high-frequency acoustic wave data of the geological structure around the borehole; performing a full waveform inversion on the first velocity model according to the low-frequency acoustic wave data and the high-frequency acoustic wave data to obtain a second velocity model; and determining the geological structure around the borehole according to the second velocity model.

A well tool can be used in a wellbore that can measure characteristics of an object in the wellbore. The well tool includes an ultrasonic transducer for generating an ultrasonic wave in a medium of the wellbore. The ultrasonic transducer includes a front layer, a rear layer, backing material coupled to the rear layer, and piezoelectric material coupled to the front layer and to the backing material. The rear layer can improve signal-to-noise ratio of the transducer in applications such as imaging and caliper applications.

A well tool can be used in a wellbore that can measure characteristics of an object in the wellbore. The well tool includes an ultrasonic transducer for generating an ultrasonic wave in a medium of the wellbore. The ultrasonic transducer includes a front layer, a rear layer, backing material coupled to the rear layer, and piezoelectric material coupled to the front layer and to the backing material. The rear layer can improve signal-to-noise ratio of the transducer in applications such as imaging and caliper applications.

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.

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.

A method is provided for identifying and characterizing structures of interest in a formation traversed by a wellbore, which involves obtaining waveform data associated with received acoustic signals as a function of measured depth in the wellbore. A set of arrival events and corresponding time picks is identified by automatic and/or manual methods that analyze the waveform data. A ray tracing inversion is carried out for each arrival event (and corresponding time pick) over a number of possible raypath types to determine i) two-dimensional reflector positions corresponding to the arrival event for the number of possible raypath types and ii) predicted inclination angles of the reflected wavefield for the number of possible raypath types. The waveform data associated with each time pick (and corresponding arrival event) is processed to determine a three-dimensional slowness-time coherence representations of the waveform data for the number of possible raypath types, which is evaluated to determine azimuth position and orientation of a corresponding reflector, and determine the ray path type of the reflected wavefield. The method outputs a three-dimensional position and/or orientation for at least one reflector, wherein the three-dimensional position of the reflector is based on the two-dimensional position of the reflector determined from the ray tracing inversion and the azimuth position of the reflector determined from the three-dimensional slowness-time coherence representation. The information derived from the method can be conveyed in various displays and plots and structured formats for reservoir understanding and also output for use in reservoir analysis and other applications.