Building and ranking of geostatistical petroleum reservoir models

Thesis Overview

<p> </p><p>&nbsp;<strong>INTRODUCTION</strong></p><p><strong>1.1</strong>&nbsp; &nbsp; &nbsp; &nbsp; <strong>PROBLEM DEFINITION</strong></p><p>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.</p><p>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.</p><p>This thesis work seeks to build and rank equally probable representations of the reservoir using petrophysical properties such as porosity, water saturation, and permeability. The multiple reservoir descriptions are ranked using both static (Stock tank oil originally in place) and dynamic (geometric average permeability, connected hydrocarbon pore volume, average breakthrough times and cumulative recovery) measures.</p><p><strong>1.2</strong>&nbsp; &nbsp; &nbsp; &nbsp; <strong>OBJECTIVES</strong></p><p>The main purpose of reservoir characterization is to generate a more representative geologic model of the reservoir properties. The objectives of this study are as follows.</p><p>In building a static representation of what a reservoir is most likely to be it is necessary to adequately capture the uncertainty associated with not knowing its exact picture. In the effort of capturing the uncertainty, this study focuses on generating multiple equiprobable realizations of the reservoir with each having unique static and dynamic properties.</p><p>Only a few number of the realizations generated could be carried on for complex flow simulation due to the computational cost. Usually ranking of the realizations to select a limited number are done by static means which do not capture the dynamic mechanisms essential to reservoir performance. Only few dynamic ranking measures are currently in use. This research work seeks to define new dynamic ranking criteria and compare them with existing criteria.</p> <br><p></p>