LAUSR

Tight sandstone reservoirs have complex petrophysical properties, which introduce difficulties to rock-physics modeling. Besides, weak reflection events appear with a high probability in the seismic profile for tight sandstones. By combining the soft-porosity model and Gassmann’s relation, weak reflection events are analyzed in detail, which can be contaminated by remaining internal multiples and the amplitudes may be lowered by the transmission loss. These pose challenges for the porosity prediction. To obtain the porosity estimate accurately of tight sandstone reservoirs, porosity prediction is performed in two steps. First, within the framework of Bayesian inversion, the elastic parameters are obtained with high accuracy by using the reflectivity method, which can effectively describe transmission loss and internal multiples. Second, the Bayes discriminant method is applied to predict porosity from the estimated elastic parameters. It avoids using deterministic rock-physics modeling because the difficulties in rock-physics modeling of tight sandstones make it hard to predict their petrophysical properties. To ensure the prediction accuracy, detailed lithology identification and sensitivity parameters analysis are performed. Different examples of well-logging data and seismic data demonstrate that our approach can well predict the porosity.