Disclosed is a method for detecting a fluid, including at least one step of: measuring by at least one sensor of a wave propagating in an environment of the wave, in order to obtain at least one measured signal, the wave being particularly a mechanical wave; splitting the measured signal over a plurality of split time intervals with a predefined duration in order to obtain samples of the measured signal; computing the temporal coherence of the samples; and determination of the presence of the fluid using the computed temporal coherence.

In at least one embodiment, a well inspection method and system is disclosed. The method includes transmitting an acoustic signal from a well inspection tool into a casing structure, receiving return signals from the casing structure to the well inspection tool, performing, using at least one processor associated with the well inspection tool, a Fast Fourier Transform (FFT) on a plurality of frequencies of the return signals to generate spectrogram data, determining entropy spectra from the spectrogram data, and determining casing loadings associated with the casing structure based at least in part on the entropy spectra.

A method and system to be used in well inspection. An acoustic signal is transmitted from a well inspection tool into a well structure and one or more return signals is detected using at least one receiver. At least one processor is used to generate variable density log (VDL) data that includes multiple waveforms in a time domain from the one or more return signals. A number of independent components to be used based on variances in the VDL data is determined and the multiple waveforms are decomposed into multiple components associated with one or more local structure variances of the well structure using independent component analysis (ICA) and the number of independent components. Characteristics of the well structure is determined based in part on patterns or features associated with one or more independent components from the multiple components.

A velocity model is generated based upon seismic waveforms via any seismic model building method, such as full waveform inversion or tomography. Data representative of a measurement of a physical attribute of an area surrounding a well is received and an attribute model is generated based upon the velocity model and the data. An image is rendered based upon the attribute model for use with seismic exploration above a region of a subsurface comprising a hydrocarbon reservoir and containing structural or stratigraphic features conducive to a presence, migration, or accumulation of hydrocarbons.

Method for locating fault lines or surfaces in 2-D or 3-D seismic data based on the fact that fault discontinuities in the space domain span a wide range in a local slowness (slope) domain, whereas other dipping events in the space domain data, such as noise, tend to be coherent, and hence will appear focused in the slowness dimension. Therefore, the method comprises decomposing the seismic data (102) by a transformation to the local slowness domain, preferably using Gaussian slowness period packets as the local slowness or slope decomposition technique, thereby avoiding problems with the data stationary assumption. In the local slowness domain, faults may be identified (104) using the principle mentioned above, i.e. that faults are represented as a truncation in the space domain data, hence they will appear broadband in the slowness dimension.

A method of characterizing a subsurface of a region includes preparing a plurality of spectra illustrating a spectral density of passive seismic signals obtained in a vicinity of a surface of the region at one or more points of the region where recordings are made of the passive seismic signals. Each spectrum is prepared from an associated signal representative of a movement. The method also includes determining at least one spectral attribute for each frequency appearing in each spectrum so as to obtain a set of spectral attributes associated with the recordings and with the frequencies. The method further includes organizing the set of spectral attributes in a matrix in which each row is associated with one of the recordings. In addition, the method includes applying a principal component analysis method to the matrix in order to determine principal components and deduce therefrom one or more characteristics of the subsurface.

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 of estimating a position of a well path within a subsurface formation of the Earth includes determining a well path estimate using navigation measurements from a downhole tool and a position estimate derived from seismic data. A method of geosteering includes estimating a position of the well path and controlling a drill bit in response to the estimated position of the well path to follow a desired well trajectory.

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 may include determining an energy factor based on scratch test data and ultrasonic wave data regarding a geological region of interest. The method may further include determining an amount of inelastic energy regarding the geological region of interest using triaxial compression data and rock property data. The method may further include determining a tensile strength regarding the geological region of interest using Brazilian test data. The method may further include generating a geomechanical model regarding the geological region of interest using the energy factor and the amount of inelastic energy. The geomechanical model may include various brittleness values for the geological region of interest. The method may further include determining an injection fluid pressure to induce a hydraulic fracture at a predetermined location in the geological region of interest using the geomechanical model, the tensile strength, and fracture plane roughness data.