A Diffusion Filter Based Scheme to Denoise Seismic Attributes and Improve Predicted Porosity Volume

This paper proposes a diffusion filter based scheme to denoise seismic attributes and to improve the porosity volume, which is predicted from seismic attributes. We compare the performances of multiple diffusion [such as Perona-Malik diffusion filter, complex diffusion filter, improved complex adaptive diffusion filter (ICADF)] and nondiffusion (such as two-dimensional (2-D) median, 3-D median, smoothing, and bilateral filter) based filters in terms of four metrics such as root mean square error (RMSE), normalized RMSE, signal to noise ratio (SNR), and peak SNR (PSNR). In our earlier publication, we used an artificial neural network (ANN) to predict a lithological property (sand fraction) over a study area. We trained the ANN using an integrated dataset of low-resolution seismic attributes and a limited number of high-resolution well logs. In this paper, we generate the porosity volume from the seismic attributes using an ANN. The predicted porosity logs contain irregularities and artifacts due to the nonlinear mapping of the learning algorithm (e.g., ANN). We apply a set of filters to the output of the ANN to regularize the predicted porosity volume. The filtered porosity logs are compared with the generated log. The ICADF has been found to be most suitable for denoising the seismic data and the porosity volume. Generation of porosity maps from seismic inputs would be helpful to petroleum engineers for reservoir characterization.