A fully integrated approach for the development of rock type characterization, in a middle east giant carbonate reservoir

Abstract

Rock typing in modelling and simulation studies is usually based on two techniques; routinely defined rock types and those defined by special core analysis (SCAL). The challenge in these techniques is that it is frequently assumed that these two are the same to assign, but in practice static rock-types (routinely defined) are not always representative of dynamic rock-types (SCAL defined) in the reservoir. There is also no significant link between these two. To fill this gap, we propose a method for the combination of core description data with well log data for identification of the optimal number of static rock-types, and SCAL data with its high interpretive potential for identifying effective dynamic rock-types within determined static rock-types in a given reservoir zonation. In this paper, the proposed method was applied for two new exploration wells in one of the southern carbonate reservoirs in Iran with the lowest number of available data. First, with the integration of well logs and core description data using multivariate statistical methods, seven static rock-types were identified. Next, in these static rock-types the values of the permeability and reservoir quality index were predicted using neural networks with matching of core and log data. Then, based on the correlation of static rock-types and reservoir quality index a reservoir zonation consisting of nine zones was determined. Finally, with interpretation of SCAL data in the different positions of static rock-types for each zone, six different dynamic rock-types with distinct flow behaviour above the oil water contact were identified. This method can be used for the development of rock-type characterization in any heterogonous reservoir and it is a workable solution as input for saturation functions to the simulation model.