Abstract: Waves from cement bond logging with a sonic logging-while-drilling tool (LWD-CBL) are often contaminated with tool waves and may yield biased CBL amplitudes. The disclosed LWD-CBL wave processing corrects the first echo amplitudes of LWD-CBL before calculating the BI. The LWD-CBL wave processing calculates a tool wave amplitude and a phase angle difference as the difference of the phases between the tool waves and casing waves. The tool waves are then used to correct the LWD-CBL casing wave amplitude and remove errors introduced from tool waves. In conjunction with the sets of operations described, the LWD-CBL wave processing also include array preprocessing operations. Array preprocessing may employ variation of bandpass filtering and frequency-wavenumber (F-K) filtering operations to suppress tool wave.

Abstract: A method and system for producing a Quasi-Static Stoneley Slowness log. The method for producing a Quasi-Static Stoneley Slowness log may comprise recording a pressure wave at a receiver; determining a slowness-frequency range with an information handling system from the pressure wave, processing a frequency-domain semblance, extracting a Stoneley Dispersion, minimizing a misfit between theoretical and the Stoneley Dispersion, and identifying Quasi-Static Stoneley slowness from the Stoneley Dispersion. The well measurement system for producing an Quasi-Static Stoneley Slowness log and shear slowness anisotropy may comprise a downhole tool, a vehicle, and an information handling system.

Abstract: An acoustic logging system determines slowness picks using acoustic waveform phase and amplitude data. An amplitude-based first-arrival-picking (“FAP”) technique is applied to acquired waveforms to derive a first set of slowness picks, and a waveform phase coherence technique is also applied to derive a second set of slowness picks. The first and second slowness pick sets are then compared in a variety of ways to determine a final set of slowness picks.

Abstract: An acoustic logging system identifies hydrocarbon formation types by a real-time model-constrained mud wave slowness determination method using borehole guided waves. The system also combines data processing from different acoustic waveform processing techniques using an information sharing procedure, for example, using monopole source data and dipole source data, to further improve the processing results and to achieve more stable and reliable real-time shear slowness answers.

Abstract: A method of obtaining an equivalent tool model includes obtaining a set of known well data, in which the known well data includes sensor data measured by a logging tool and an actual dispersion response, and the logging tool has an actual tool size. The method also includes obtaining one or more well parameters from the known well data, and inputting the one or more well parameters and a model tool size into a rigid tool model. The method further includes obtaining an estimated dispersion response from the rigid tool model, and fitting the estimated dispersion response to the actual dispersion response by adjusting the model tool size.

Abstract: Techniques for estimating and visually presenting formation slowness are disclosed herein. The techniques include receiving acoustic signal responses from adjacent formations at a plurality of depths in a borehole environment, mapping a distribution of the acoustic signal responses at each depth according to slowness and a frequency values, determining at least one confidence interval to define a coherence threshold for the distribution of the acoustic signal responses at each depth, generating a variable density log for each depth based on the distribution of acoustic signals responses that satisfy the confidence interval for one or more frequency ranges, determining a formation slowness value for each depth based on the variable density log for the each depth, and presenting a semblance map that includes a slowness axis, a depth axis, the formation slowness value for each depth, and at least a portion of the distribution of acoustic signal responses at each depth.

Abstract: Disclosed are systems and methods for processing acoustic logging information with automatic dispersion matching. The method comprises obtaining, from an acoustic logging tool, acoustic waveforms from within a borehole, applying dispersion analysis to the acoustic data to extract a slowness-frequency response, calculating a slowness density log of the slowness-frequency response, obtaining an initial shear slowness estimate based on the slowness density, generating a dispersion model comprising model parameters, and performing a dispersion response auto match inversion to minimize the difference between the slowness-frequency response and the dispersion model in order to obtain a final shear slowness estimate.

Abstract: Disclosed are systems and methods for high precision acoustic logging processing for compressional and shear slowness. The method comprises measuring, by a sonic logging tool, sonic data associated with a formation within a borehole, attempting a detection of a first arrival within the sonic data determining whether the attempted detection of the first arrival is accurate, and in response to an accurate detection of the first arrival determining a travel time of the first arrival, generating a coherence map including the first arrival, and determining, based on the coherence map, a characteristic of the formation.

Abstract: Disclosed are systems and methods for selecting modes and frequencies of interest in a slowness-frequency map of sonic logging information. These include measuring, by a sonic logging tool, sonic data within a borehole, determining a frequency range for a selected mode of the sonic data, determining a slowness range for the selected mode of the sonic data, applying the frequency range and the slowness range to the sonic data to select a subset of data from the sonic data, processing the selected subset of data.

Abstract: A portion of an anisotropy formation through which a wellbore is formed can be identified. An estimate of an elastic anisotropy parameter for the portion can be adjusted based on a first quality control analysis using the elastic anisotropy parameter for the portion. The first signal representing the elastic anisotropy parameter for the portion. The estimate of the elastic anisotropy parameter for the portion can be adjusted based on a second quality control analysis using estimates for the elastic anisotropy parameters for two or more portions of the anisotropy formation.

Abstract: A method of obtaining an equivalent tool model includes obtaining a set of known well data, in which the known well data includes sensor data measured by a logging tool and an actual dispersion response, and the logging tool has an actual tool size. The method also includes obtaining one or more well parameters from the known well data, and inputting the one or more well parameters and a model tool size into a rigid tool model. The method further includes obtaining an estimated dispersion response from the rigid tool model, and fitting the estimated dispersion response to the actual dispersion response by adjusting the model tool size.

Abstract: A method and system of performing quality control for a downhole tool. An acoustic source is employed to generate a Stoneley wave, and acoustic receivers generate signals indicative of the Stoneley wave. A reference value is calculated from the signals to assess the quality of the receivers. The reference value may be for a selected receiver or a selected receiver ring. The reference value is compared to a threshold deviation to determine if the reference value is outside the threshold deviation. If the reference value is outside of the threshold deviation, the deviation for one of the selected receiver or the selected receiver ring is corrected.