Application Of Xu-White And Critical Porosity Models To A Carbonate Reservoir Using An Optimization Algorithm

Petro-elastic modeling plays a crucial role in closing the loop between the reservoir model and seismic data by means of relating elastic properties to the reservoir model properties. Accordingly, precise estimation of elastic parameters leads to a more reliable petro-elastic model and helps to reduce uncertainties related to reservoir model construction. One of the pitfalls in petro-elastic modeling is the values of mineral elastic properties, which are conventionally considered constant for most of the reservoirs. Disregarding the lithological characteristics may increase uncertainty in the estimation of parameters and can be misleading especially in 4D studies on saturation effects. Carbonate rocks as the most predominant rocks in the reservoirs show a more complex behavior in comparison to sandstone reservoirs; hence, the effects of physical properties such as aspect ratio and critical porosity should be considered with more care. In this paper, solving the multivariate optimization problem is addressed via very fast simulated annealing algorithm. The optimum values of elastic moduli are determined considering two rock physics models, Nurs’ critical porosity, and the simplified Xu-White model. Depending on dry rock physics relation, the correspondent physical parameter (critical porosity or aspect ratio) is optimised as well. The case study is a carbonate reservoir located in the southwest of Iran. The variation of lithological characteristics with depth for each rock type necessitates constraining the physical parameters of each rock physics model to lithology during the optimization workflow. The output of the optimization workflow is The output of the workflow is the optimised elastic moduli of the mineral components and the regression coefficient of the fitting parameters to effective porosity. In addition, the optimised fitting parameters provide some insights into pores shape and diagenesis processes of the rock in the target zone. Comparison of the modeled and observed elastic logs confirms the accuracy of the proposed workflow.