... is often carried out manually through visual inspection on a depth-by-depth basis. This is mainly because the dispersion data are represented by scattered and noisy points. Consequently, such a process is time-consuming and requires knowledge on sonic dispersion signatures. To automate sonic data...

... well logging frequency neural network model log analysis upstream oil & gas dispersion data machine learning neural network spwla 62 artificial intelligence mode-search solver mud slowness dispersion curve slowness nd annual logging symposium spwla-2021-0059 flexural...

... model is used as a reference to compensate for the dispersion effect, or a model-independent method, which optimizes nonphysical parameters to fit a simplified model to the field dispersion data extracted in the slowness-frequency domain. Many methods often require inputs such as mud slowness, frequency...

... and its effect on log response. In such situations, using acoustic dipole shear and flexural wave dispersion data coupled with induction logs in the petrophysical workflow provides an alternative method for evaluating porosity and water saturation. Shear and flexural wave data are less influenced by fluid...

... sensitive to the near-borehole velocity change. This allows for developing an inversion method to estimate the radial change from the dispersion data. We also discuss the nonuniqueness issue of the inversion and ways to solve it. The dispersion data and the inversion results clearly demonstrate...

... that the above procedure yields reliable compressional wave slowness for the unconsolidated slow formations. Upstream Oil & Gas dispersion data frequency range frequency waveform data unconsolidated slow formation dispersion effect spwla 46 well logging modeling slow formation dispersion...