CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a national stage entry of PCT/US2018/056253 filed Oct. 17, 2018, said application is expressly incorporated herein in its entirety.

TECHNICAL FIELD

The present technology pertains to the processing of acoustic logging data acquired in a well bore, and more specifically to formation slowness estimation.

BACKGROUND

Various acoustic properties of a borehole are logged and processed in order to monitor and predict borehole characteristics throughout drilling. Generally, acoustic phenomena, such as borehole dispersion, are analyzed in order to extract other properties related to the borehole and formation. For example, a body wave slowness can be extracted from borehole dispersion curves in order to gain insight into petrophysical and geomechanical properties of the formation.

Conventional processing often utilizes either the most energetic part of waveform data (e.g., Airy-Phase components) or the most coherent part of the data (e.g., data with the highest signal-to-noise ratio or “SNR”) in order to determine a body wave slowness log from dispersive waves. However, this might require separation of tool modes from a target mode of interest, which may be challenging for certain target modes due to borehole-tool mode coupling (e.g., due to proximity of the tool to the borehole interior and the like). Furthermore, some target modes may require a dispersion correction in order to relate a measured slowness along a dispersion curve to a low-frequency asymptote characterizing formation slowness. The resulting dispersion-corrected slowness is often inaccurate due to a low signal-to-noise ratio (SNR), interference from other modes, or incorrect assumptions about the dispersion model. For example, when conventionally real-time processing for compressional slowness (DTC) in a slow formation, a picked slowness at the most coherent part may be taken as a final value due to a lack of information visible above a noise floor and thus an inability to characterize a low frequency asymptote as a result.

In other examples, such as for shear slowness (DTS) picking from dipole or quadrupole data, conventional processing may apply a frequency-domain and model-based dispersion correction on extracted time-domain slowness peaks. However, time-domain slowness peaks do not necessarily represent the phase slowness that is assumed in the subsequent dispersion correction schemes, and so the resultant formation slowness measurement may be of limited or low accuracy. Furthermore, a dispersion correction on a time-domain slowness pick often requires a frequency associated with the pick. Typically, either an instantaneous frequency at the time of the pick or a central frequency computed from averaging magnitude spectra of all receivers is used for the dispersion correction. In either case, the chosen frequency will be prone to significant error, and so it too can impact the dispersion correction, potentially leading to a biased formation slowness estimation. Additionally, unknown factors such as formation anisotropy and alteration may limit the accuracy of real-time processing results and require an interpretation for estimating true poroelastic and/or geomechanic rock properties.

In yet other cases, frequency-domain and data-driven processing can be used to determine DTC and DTS when data is of a high quality and the borehole environment is simple. However, frequency-based processing often uses a Fourier transform to convert time-domain data to frequency-domain data and includes noise signals with different arrival times, thus impacting all processed frequencies. Consequently, complex borehole environments may cause low SNRs and so require preprocessing to either isolate target modes from interfering modes or suppress the noise level.

