ÇAĞRILI KONUŞMALAR / KEYNOTES Invited Speeches ... - TPJD

Quantification and Analysis of Uncertainties in Reservoir Modeling Using
Multiple-Point Geostatistics
Mohamed M. Fadlelmula F., Serhat Akın, H. Şebnem Düzgün
Middle East Technical University, Ankara
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.
Keywords : Multiple-point geostatistics, training image, reservoir modeling, uncertainty
quantification, SGeMS, AFOSM
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