BUILDING AND RANKING OF GEOSTATISTICAL PETROLEUM RESERVOIR MODELS

BUILDING AND RANKING OF GEOSTATISTICAL PETROLEUM RESERVOIR MODELS

ABSTRACT

Techniques in Geostatistics are increasingly being used to generate reservoir models and quantify uncertainty in reservoir properties. This is achieved through the construction of multiple realizations to capture the physically significant features in the reservoir. However, only a limited number of these realizations are required for complex fluid flow simulation to predict reservoir future performance. Therefore, there is the need to adequately rank and select a few of the realizations for detailed flow simulation.

This thesis presents a methodology for building and ranking equiprobable realizations of the reservoir by both static and dynamic measures. Sequential Gaussian Simulation was used to build 30 realizations of the reservoir. The volume of oil originally in place, which is a static measure, was applied in ranking the realizations. Also, this study utilizes Geometric Average Permeability, Cumulative Recovery and Average Breakthrough times from streamline simulation as the dynamic measures to rank the realizations. A couple of realizations selected from both static and dynamic measures were used to conduct a successful history match of field water cut in a case study.

CHAPTER ONE – INTRODUCTION

1.1        PROBLEM DEFINITION

In Geostatistical reservoir characterization, it is a common practice to generate a large number of realizations of the reservoir model to assess the uncertainty in reservoir descriptions for performance predictions. However, only a limited fraction of these models can be considered for comprehensive fluid flow simulations because of the high computational costs. There is therefore the need to rank these equiprobable reservoir models based on an appropriate performance criterion that adequately reflects the interaction between reservoir heterogeneity and flow mechanisms.

Most techniques used in ranking of realizations are based on static properties such as highest pore volume, highest average permeability, and closest reproduction of input statistics. The drawback of these simple techniques is that they do not account for dynamic flow behavior which is very essential in predicting future reservoir performance.

BUILDING AND RANKING OF GEOSTATISTICAL PETROLEUM RESERVOIR MODELS