Geostatistical Modeling Integral to Effective Design and Evaluation of SAGD Processes of an Athabasca Oil-Sands Reservoir: A Case Study

Abstract

fax 01-972-952-9435. AbstractThe design and implementation of a successful thermal recovery project starts with a representative static geological model. SAGD operating strategies of the steam chamber for optimum economical oil recovery with minimum SOR are largely controlled by the target reservoir geological and pressure environment. This paper describes how geostatistical modeling techniques were used to build a static reservoir model over a 32-section area of a Fort McMurray oil sand reservoir. The model was constructed by using Roxar's Irap RMS modeling software. Irap RMS provided a platform that facilitated geostatistical mapping of horizons and populating the petrophysical properties over a 3.46 million cell static geological model. This paper describes how the petrophysical logs and core data from 33 wells in the study area were integrated into a coherent geostatistically based geological model from which any number of equiprobable realizations of the geological environment may be drawn.The large scale geological model was built so that the impact of regional variability of reservoir quality on SAGD process strategies could be evaluated, optimized and readily assessed by using dynamic simulation techniques and by extracting and appropriately upscaling portions of the static reservoir model. This modeling strategy also allows risk analysis of geological uncertainty at a given location on SAGD performance to be evaluated by conducting and analyzing multiple dynamic simulation runs generated from equiprobable realizations of the static geological model. The use of geostatistical techniques to assess data quality and impact of geological uncertainty on the geological description are reviewed. The impact of this uncertainty on quantification of bitumen in place and its impact on establishing and prioritizing the placement of well pairs are also presented.A review of the geology and the workflow followed by integrating petrophysics, core data, mapping of horizons, and rock types are discussed. The extraction and upscaling process used to create the dynamic simulation is described. The workflow strategy that was adopted allowed the definition and evaluation of SAGD operating strategies across the field to be evaluated concurrently and quickly with representative dynamic simulation models of a reasonable size. CANADIAN HEAVY OIL ASSOCIATION SPE/PS-CIM/CHOA 97743

Comparative Simulation Study and Economic Analysis of Thermal Recovery Processes in Athabasca Reservoirs

“…Consequently, the starting injection pressure is the formation pressure. In fact, it has been shown categorically that injecting steam at high pressure increases oil production (Kisman and Yeung, 1995;Robinson et al, 2005). However, one other complexity of the injection process is the pressure schemes maintained throughout the lifetime of the wells.…”

A Critical Review of the Status of SAGD: Where Are We and What Is Next?

“…They concluded that high pressure SAGD induced higher porosity and permeability with higher oil production. In order to observe the effects of geomechanics variation and hence permeability enhancement due to shear dilation, steam chambers should be operated at or near the minimum total stress, which implies HP-SAGD ( Another study showing favourability of HP-SAGD was conducted by Robinson et al (2005), where they also reported an increase in oil production. Kisman and Yeung (1995) also showed that operating at low pressure decreased oil production and improved OSR in a simulation model for the Burnt Lake oilsands conditions.…”

“…Thus, one has to pay attention to use representative reservoir models in SAGD performance analyses using numerical simulators as the reservoir heterogeneity may have considerable effects on the accuracy of the process. A good demonstration for such approach is shown by Robinson et al (2005). McLennan and Deutsch (2005) stated that flow modeling is a transfer function converting the geological uncertainty to production uncertainty.…”