A New Method for Porosity Estimation Based on Adaptive Critical-porosity Pride Model Using Seismic Data

Abstract

Porosity is a significant parameter for characterizing oil/gas reservoirs, and porosity estimation using seismic data is of great practical value. In the process, the accuracy of rock physics model and the inverted density are the two most important aspects. This paper proposes a new rock physics model (adaptive critical-porosity Pride model), which can be used to calculate dry rock bulk modulus with more precision. The new model dynamically adjusts solidification factor, and critical porosity, which means it fully takes the influence of pore shapes into consideration. Then, based on Gassmann equation, combined with the new rock physics model and density data from pre-stack noise-resistant density inversion, a new nonlinear chaotic quantum particle swarm optimization algorithm is used to obtain porosity, which can greatly enhance the uniqueness of solutions. Lastly, the seismic inversion porosity method is applied to ZG8 area in Tarim Basin in China. The results show that the inverted porosity from seismic data is in good agreement with the logging data, which proves that this method can be feasible and applicable in carbonate reservoirs.