Recent research has revealed that the two-point based traditional geostatistical simulation technique is ineffective in capturing complex geological structures while modeling subsurface heterogeneities. On the other hand, it has proven that the newly developed Multiple-Point Geostatistics (MPG) based simulation is a powerful technique to model such complex structures. This simulation is dependent on conceptual geological models called Training Images (TI). This study as its name implies, is aiming at quantifying and analyzing the uncertainties of reservoir models which are generated by MPG method. The uncertainties to be quantified are either model related or parameter related. The former which is related to the model structure (i.e. TI structure) will be modeled utilizing the Stanford Geostatistical Modeling Software (SGeMS). The latter is related to the input parameters such as porosity and permeability and will be modeled utilizing the “Advanced First Order Second Moment (AFOSM)” reliability method. Accordingly, this study is divided to three main parts. First of all, the impact of training images on predicted reservoir model will be investigated. Next, other sources of uncertainties in the model are going to be examined. Finally, a sensitivity analysis of the uncertainty sources found will be conducted.


Article metrics loading...

Loading full text...

Full text loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error