A sonic tool is activated in a well having multiple casings and annuli surrounding the casing. Detected data is preprocessed using slowness time coherence (STC) processing to obtain STC data. The STC data is provided to a machine learning module which has been trained on labeled STC data. The machine learning module provides an answer product regarding the states of the borehole annuli which may be used to make decision regarding remedial action with respect to the borehole casings. The machine learning module may implement a convolutional neural network (CNN), a support vector machine (SVM), or an auto-encoder.

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 method is provided of inspecting a nested multi-layer structure including a first and second electrically conductive layer and a third layer disposed behind the second conductive layer. The method includes deploying a sensor device including an electromagnetic acoustic transducer to a borehole location proximate to the structure, generating a drive signal including a plurality of frequencies, applying an electrical current signal to the sensor device based on the drive signal and inducing currents in the first conductive layer that induce currents generating acoustic signals having the plurality of frequencies, detecting a first set of resonant frequencies based on received electromagnetic signals, detecting a second set of resonant frequencies based on received acoustic signals, estimating a property of the first and/or the second conductive layer based on the first set of resonant frequencies, and estimating a property of the third layer based on the second set of resonant frequencies.

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.

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.

Using microseismic analysis to identify and quantify the hydraulic fracture interaction in the Earth formation. Identification of the interaction is based on the magnitude of the events and therefore independent of the location uncertainty. Quantification of the interaction is location based.

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.

Methods including determining a measurement plan, having acoustic measurements, and lowering in a borehole penetrating a subsurface formation a toolstring having phased array modules. Each phased array module includes acoustic transducers operable to emit an acoustic excitation signal and receive an echo signal, as well as a programmable circuit for setting one or more variables of the phased array module. The phased array modules are configured, including programming the programmable circuit to set variables of the phased array modules according to the measurement plan. The acoustic measurements of the measurement plan are performed using the configured phased array modules. One or more of the formation, a casing disposed in the borehole, and/or an annulus between the casing and the formation are characterized using results of the performed acoustic measurements.

The present invention belongs to the field of geophysical exploration, and discloses physical seismic simulation test apparatus and method based on reflected wave field for a hydrate formation. The apparatus comprises a hydrate preparation device and a reflected acoustic wave test device, wherein the hydrate preparation device is configured to generate simulated hydrates, and the reflected acoustic wave test device is configured to continuously emit ultrasonic waves to the simulated hydrates in the generation process, process the received reflected waves to identify and extract reflection characteristic information and acoustic wave velocity change information, and continuously monitor the degree of saturation of the simulated hydrates at the same time to obtain a corresponding relationship between the reflection characteristic information and acoustic wave velocity change information and the degree of saturation of the simulated hydrates. The apparatus and method provided by the present invention obtain the reflection characteristics of a hydrate formation and acoustic wave velocity change by acquiring the reflected wave field of the hydrate formation, thereby obtain the relationship with the degree of saturation of the hydrates, and have important guiding significance for interpretation of the offshore seismic exploration data of natural gas hydrates and estimation of the degree of saturation of natural gas hydrates.

A reference curve may be used as the goal for alignment when depth shifting one or more target well logs. Traditionally the reference curve has been measured data, and is usually of the same measurement type as the well log for shifting when performed algorithmically. The reference curve may be generated by a weak learner machine learning model. The weak learner machine learning model may preserve shape characteristics and depth information of one or more input curves in the reference curve. Depth shifting of a target well log may be performed by iteratively using sliding correlation windows of differing sizes.