Reducing uncertainty in geologic interpretation of petroleum reservoir containing thin layers requires increasing vertical resolution via appropriate advanced resolution enhancement methods. This problem was resolved here by introducing an alternative approach in resolution enhancement. Our method uses Gabor deconvolution (GD) combined with wavelet scaling. First, the seismic trace is transformed in the time-frequency domain using the Gabor transform. Subsequently, the Gabor magnitude spectrum of the seismic trace is smoothed to estimate the wavelet magnitude, which is then divided by the original value on the Gabor magnitude spectrum along the frequency axis to design a scale transformation filter. Finally, the filtered Gabor magnitude spectrum of the seismic trace is transformed back to the time domain using an inverse Gabor transformation. The result of using this technique is an increase of the dominant frequency, which produces a higher resolution of the seismic trace compared with using only the original GD. This method was applied to two synthetic and one field seismic data sets and compared with using only a GD. After applying the new approach, all three data sets indicate an extension in bandwidth (BW) and an enhancement in the resolution adequate for thin-layer seismic interpretation. When compared with the data sets using only a GD, the new approach produced comparable extension in the effective BW, while it pushing the dominant frequency to higher values. This allowed the imaging of many thin layers and geologic intervals in the field data example that could not be interpreted by the GD method.

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This research aims at improving the methods of prediction of shear wave velocity in underground layers. We propose and showcase our methodology using a case study on the Mashhad plain in north eastern part of Iran. Geotechnical investigations had previously reported nine measurements of the SASW (Spectral Analysis of Surface Waves) method over this field and above wells which have DHT (Down Hole Test) result. Since SASW utilizes an analytical formula (which suffers from some simplicities and noise) for evaluating shear wave velocity, we use the results of SASW in a trained artificial neural network (ANN) to estimate the unknown nonlinear relationships between SASW results and those obtained by the method of DHT (treated here as real values). Our results show that an appropriately trained neural network can reliably predict the shear wave velocity between wells accurately.

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