IDENTIFICATION OF MIXED ACOUSTIC MODES IN THE DIPOLE FULL WAVEFORM DATA USING INSTANTANEOUS FREQUENCY-SLOWNESS METHOD

摘要

Dipole full waveform acoustic tools are used to estimate shear wave velocities, especially in soft and poorly consolidated formations. Under ideal conditions dipole source employed by those tools excites only borehole flexural wave that is propagating along fluidsolid interface This frequency dispersed flexural wave is used to estimate the velocity of the formation shear wave. In very soft formations, the dipole source may also excite a phase reversed compressional mode, sometimes referred to as a slow compressional wave (primarily due to its dispersed character). The above scenario is frequently complicated by the presence of other acoustic modes: e.g. Stoneley waves, tool mode flexural waves, and multiple flexural modes due to shear wave anisotropy. Stoneley waves are generated either due to the tool decentralization, borehole ovality, or due to the dipole source malfunction. Tool mode flexural waves are observed when acoustic isolator underperforms and frequently in highly deviated holes. The Stoneley wave is particularly difficult to identify and suppress during data processing. Like the flexural wave, it propagates along the fluid-solid interface, albeit with the velocity that is affected by formation shear wave slowness and borehole parameters. Very often both waves overlay each other in time and frequency domain (especially at near receiver levels) thus making it difficult to compute flexural wave slowness using conventional processing methods. Instantaneous Frequency-Slowness Method, derived from complex waveform analysis, is particularly well suited for processing contaminated dipole data sets. The absence of mixed acoustic modes in a dipole excitation creates unique signatures of instantaneous frequency and slowness curves that are characterized by non-linear increases of frequency and slowness as a function of travel time due to dispersive effects. On the other hand, the presence of multiple modes within a processing window modifies the instantaneous frequency and slowness curves in such a way that the presence of competing modes can be detected and under certain conditions identified. Therefore, by analyzing instantaneous frequency and slowness signatures, it is possible to avoid many processing errors resulting from the improper identification of acoustic modes, thus avoiding a mistake frequently made when processing these datasets with other methods. The Instantaneous Frequency-Slowness Method is presented and discussed. Corresponding examples of field data further validates proposed processing methodology.