A method may include calculating a first set of hash values for a set of well log channels, extracting a well log channel snippet from an unknown well log channel, calculating a second set of hash values for the well log channel snippet, identifying, for the unknown well log channel, a matching well log channel by searching the first set of hash values with the second set of hash values, and storing, for the unknown well log channel, a channel context corresponding to the matching well log channel.

Methods, computing systems, and computer-readable media for interpreting seismic data, of which the method includes receiving seismic data representing a subterranean volume, and determining a feature-likelihood attribute of at least a portion of a section of the seismic data. The feature-likelihood attribute comprises a value for elements of the section, the value being based on a likelihood that the element represents part of a subterranean feature. The method also includes identifying contours of the subterranean feature based in part on the feature-likelihood attribute of the section, and determining a polygonal line that approximates the subterranean feature.

A detection system includes a drill bit where electro-acoustic transducers operate as a transmitter and/or receiver, are integrated; electronic circuits; a control unit associated with a data storage unit and is powered by an electrical supply system, the processing and control unit for generating driving signals sent to the electro-acoustic transducer acting as a transmitter by the analogue driving electronic circuits, for acquiring signals received from the transducer and for processing the received signals to determine discontinuity interfaces and/or anomalies in pore pressures in geological formations; wherein each of the electro-acoustic transducers is in contact with a pressurised fluid and includes: a tubular body with two end portions opposed to each other longitudinally, internally a first chamber with the first end portion and a second chamber on one side adjacent and in fluid communication with the first chamber and, on the other side ending with the second end portion.

A method for evaluating a sealing material positioned between a casing of a wellbore and a subsurface formation in which the wellbore is formed includes emitting an acoustic waveform outward from a position within the casing and detecting a return waveform that is generated in response to the acoustic waveform interacting with a region of interest that includes at least a portion of the sealing material. The method includes determining a first time window of the return waveform associated with the region of interest and trimming the return waveform based on the first time window. The method further includes determining a first spectral power density for the first time window of the trimmed return waveform and determining a composition ratio for the region of interest based on the first spectral power density.

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 computer-implemented method is provided for interpreting geophysical data utilising an Artificial Neural Network (ANN), performed by electronic operations executed by a computing device, comprising: performing a training processing step on at least one training-data set, comprising the steps of: (a) generating a first label-data by segmenting said at least one training-data set into at least a first region, representing a known first region having at least one identified geological feature, and/or a second region, representing a known second region having at least one unidentified geological feature, and a third region, representing an unknown region; (b) generating a first ANN model output for a dynamically adaptable Region of Interest (ROI) of said first label-data, said dynamically adaptable ROI including said first and/or second region; (c) generating an updated label-data by selecting at least a first portion of any one of said first, second and third region, and labelingly append at least said first portion to any one of said first, second and third region; (d) generating an updated ANN model output for an updated dynamically adaptable ROI of said updated label-data; (e) repeating steps (c) and (d) until a predetermined condition is met, providing a final ANN model output; and then applying said final ANN model output to a target-data set utilising said ANN, generating a desired output data.

Techniques for generating a geological model from 3D seismic data and rock property data are disclosed. Rock property data and 3D seismic data are received. Based on the rock property data and the 3D seismic data, an adaptive geological model is generated. The adaptive geological model includes a characteristic geological property. Synthetic seismic data is generated from a first region of interest of the adaptive geological model. The synthetic seismic data is adapted to facilitate a comparison between the first region of interest and a corresponding region of interest of the received 3D seismic data. The characteristic geological property is adjusted until the comparison indicates a result that is within a predetermined threshold region of the corresponding value from the rock properties. A validated geologic model is then generated.

The disclosed embodiments include devices and methods to perform inversion processing of well log data. In one embodiment, a method to perform inversion processing of well log data includes obtaining an initial model of an earth formation based on a plurality of modeling parameters that includes formation parameters of the earth formation and calibration factors associated with orientations of antennas of a logging tool utilized to measure raw measurements of the earth formation. The method also includes performing a forward modeling of the modeling parameters to obtain a modeling response, and performing a joint cost function of the first modeling response and raw measurements obtained by the logging tool. The method further includes readjusting the initial model if a result of the joint cost function is not below a threshold, and providing the modeling response if the result of the joint cost function is below the threshold.

A method comprises positioning a receiver in a borehole and determining an offset acoustic waveform at a target point. The method includes generating a reverse time sequence waveform of the determined offset acoustic waveform and generating, by a transmitter, an acoustic pulse based on the reverse time sequence waveform. The method includes detecting, by the receiver, an acoustic response to the acoustic pulse.

A method and system for estimating a full elastic tensor. The method may comprise taking a measurement for compressional wave sonic data and cross-dipole shear data with a sonic logging tool at a first location as cross-dipole data, processing the compressional wave sonic data to produce a compressional wave slowness (P), and processing the cross-dipole shear data to produce a fast horizontal polarized shear wave slowness (SH) and a slow quazi-vertical shear wave slowness (qSV) as a function of depth. The method may further comprise setting an initial guess for at least five constants of the full elastic tensor for Vertical Transversely Isotropy (VTI) symmetry, determining a modeled slowness surfaces from the full elastic tensor, and comparing the modeled slowness surfaces with measured values of the P, the SH, and the qSV. The method may be performed by a system comprising a sonic logging tool and an information handling system.