Ranking and Upscaling of Geostatistical Reservoir Models Using Streamline Simulation: A Field Case Study

Abstract

In this paper, we present a field example where a streamline simulator was used to rank multi-million cell geostatistical reservoir descriptions and to find the optimum level of vertical upscaling for finite-difference simulation. During geostatitstical 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 and performance predictions. However, only a small fraction of these models can be considered for comprehensive flow simulations because of the high computational costs. A viable alternative is to rank these multiple ‘plausible’ reservoir models based on an appropriate performance criterion that adequately reflects the interaction between heterogeneity and the reservoir flow mechanisms. In this study, we explore the use of ranking based on streamline time of flight connectivity derived from a streamline simulator. The time of flight reflects fluid front propagation at various times and its connectivity at a given time provides us with a direct measure of volumetric sweep efficiency for arbitrary heterogeneity and well configuration. The volumetric sweep efficiency is the simplest measure that reflects the interaction between heterogeneity and the flow field. It is a dynamic measure that can be easily updated to account for changing injection/production conditions. We show that the proposed connectivity criterion can also be used to evaluate the effects of vertical upscaling in the dynamic performance and to determine the optimal level of upscaling for numerical simulation purposes. Our field study involves a Middle Eastern carbonate reservoir under a moderate to strong aquifer influx. The reservoir is on primary depletion and has no injectors. Multiple geostatistical reservoir descriptions were generated using a hierarchical approach whereby the larger level of uncertainty is defined first followed by smaller levels. The aquifer is modeled with a constant pressure boundary and for each time update, the location of the boundary was modified to account for the water encroachment. Using the field-wide sweep efficiency as a performance measure, the realizations were ranked, and used for flow simulation to assess risks associated with various development strategies. Subsequently, three selected realizations were upscaled for the purpose of comprehensive history matching and performance prediction. Background With the wide-spread use of geostatistics, it has now become a common practice to generate a large number of realizations of the reservoir model to assess the uncertainty in reservoir descriptions and performance predictions. Most commonly, these multiple realizations account for spatial variations in petrophysical properties within the reservoir and thus, represent a very limited aspect of uncertainty. For reliable risk assessment, we need to generate realizations that capture a much wider domain of uncertainty such as structural, stratigraphic, as well as petrophysical variations. From a practical point of view, we want to quantify the uncertainty and at the same time keep the number of realizations manageable. In this study, we will adopt an approach that is based on hierarchical principles. Thus, the uncertainty having the most potential impact is identified first. For example, with limited well control, the structural uncertainty derived from the seismic interpretations can have the most impact on the flow performance. Or, for faulted reservoirs, the uncertainty with respect to locations of faults can have the most impact. Then, the next level of uncertainty is identified and so on. The last level of uncertainty is the multiple geostatistical realizations of reservoir properties for a given set of input parameters. The petrophysical uncertainties generally tend to have a much lower impact on the reservoir performance compared to factors affecting large-scale fluid movements.

Fundamental controls on fluid flow in carbonates: current workflows to emerging technologies

“…The validity of the upscaling should, therefore, always be confirmed (e.g. Li & Beckner 2000;Ates et al 2003;Elsaid et al 2006;King et al 2006;. The challenges for upscaling carbonate reservoir properties are exacerbated if the formation contains fractures because fracture networks are normally not stationary and, hence, do not possess an REV (Berkowitz 2002).…”

“…Li & Beckner 2000;Pickup et al 2000;Christie 2001;Christie & Blunt 2001;Farmer 2002;King et al 2006;Ringrose 2007;Zhang et al 2008;Hui et al 2013) that can be validated with streamline simulation (e.g. Samier et al 2002;Ates et al 2003;Elsaid et al 2006). If the property distribution in a carbonate reservoir is not stationary, a well-defined representative elementary volume (REV) cannot be defined for the property, which renders upscaling difficult.…”

Fining up the seismic stochastically for reservoir characterization, offshore North West Australia

“…This work uses the pore-volume ranking approach that has been used in other fields (Ates et al 2005).…”

Selecting an Appropriate Upscaled Reservoir Model Based on Connectivity Analysis

“…A recent comprehensive review of the influence of connectivity on fluid flow is available in [7]. There are several works in the literature, which use streamlines for upscaling and its evaluation [8][9][10]. Even though faster than classical volume finite simulation, streamline simulation still calls for the solution of a transport equation.…”