The Athabasca oil sands contain more than a trillion barrels of oil within the Cretaceous McMurray Formation of northeastern Alberta. In most of the oil sands area, the McMurray Formation is generally considered to be a compound estuarine valley system characterised by multiple cuts and fills. It is bounded below by Devonian rocks at the pre-Cretaceous unconformity and above by the widespread transgressive marine shales and sands of the Wabiscaw Formation. In the Long Lake area, it is 60 to 100m thick, with net pays of greater than 40m. Still, its complexity is legendary. Stacked channel deposition exhibits a high degree of reservoir variability both vertically and laterally making lithological predictability difficult.

Traditionally, at least 8 and often many more vertical wells per square mile are drilled and cored to obtain enough data to be confident in defining a SAGD1 project area. Even then, significant variations occur between wells. 3D seismic data has been used successfully in the past mainly to define the base of the zone of interest (there is a strong reflector at the Cretaceous-Devonian boundary), and the gross thickness of the interval. Various attempts have been made to decipher the internal composition of the channelled interval with limited success.


In this paper, I describe the method, application and results of a technique of quantitatively extracting and classifying elastic rock properties from seismic data. The extraction process uses AVO (amplitude vs offset) analysis to separate the compressional (P-wave) and shear (S-wave) components of the seismic data. The resulting components are then used to calculate physical rock properties2 such as shear rigidity (mu) and incompressibility (lambda). It is common knowledge among oil sands geoscientists that the density log through the McMurray Formation shows a strong correlation to the gamma ray log and is therefore a good lithology indicator. Recent work directed at determining facies from seismic relies on an estimate of density from the seismic data. In this process I incorporate an estimate of density obtained from seismic using a neural-network approach3.


Wireline logs directly (or indirectly) measure P-wave velocity, S-wave velocity and density. Integrating this data with core and log analysis, the lambda and mu properties are calculated and assigned lithologies and fluid properties. Detailed quality assessment and cross-plot analysis is carried out to assign empirical limits and guidelines for lithology and fluid discrimination based on the measured rock physics properties ( figure 1). The determined relationships are then used to calibrate and classify the seismically-derived properties. The result is a seismic volume transformed to a detailed lithological characterization of the reservoir within the zone of interest. Drilling results are shown to validate and quantify the success of the method (figure 2).

Fig. 01
Figure 1. Cross plots of computed well logs from 85 wells with dipole sonic logs, separated by core facies. The curve shows the empirical limit of shale facies which when plotted on the sand facies plot shows the extent of facies overlap. The number of sand facies points that plot on the shale side of the line is less than 20% of the total.

Applying this technique over a project area allows more confident mapping of the channels and the reservoir quality and continuity within the channels. A few of the potential benefits in oil sands areas include fewer vertical wells required to define the resource area, and more confidently placed horizontal wells for optimal production.

Fig. 02
Figure 2. Comparison of conventional seismic profile (bottom) with derived facies profile (top). Black represents non-reservoir (shale or bottom water), yellow areas are bitumen reservoir. Gamma ray logs with 0 to 70 (at baseline) api range are displayed on the profiles. 13-15 was the only well on this profile used in the derivation of facies shown above, the rest were drilled after the facies volume was completed. The numbers shown below the well bores are the percentage match on a meter-by-meter basis of the predicted facies from seismic with the actual facies from logs within the zone of interest.



About the Author(s)

Laurie Bellman has over 20 years experience in geophysics for the oil business. A physicist by training, she has applied her knowledge and skills to oil and gas exploration and production in many areas in the world.

Laurie started her career with Shell Canada doing seismic processing and interpretation in the central plains area of Alberta. Seeking adventure and travel, she took a position with LASMO plc in London, to work on various European, North African and Middle East projects. After joining Wascana Energy in London, she was transferred back to Canada to work domestic cold-flow heavy oil projects. Following a short sabbatical to focus on family, she began her consulting career with Alberta Energy (AEC) in 1999. She has contracted for a number of other companies and projects, but since 2000, her professional interest has been the Canadian oil sands. The unique challenges in that geological environment have provided her the opportunity to apply and develop state-of-the-art geophysical processes and workflows. She formed Oil Sands Imaging in 2007 to optimize seismic value for clients in this fascinating area.


Steam Assisted Gravity Drainage – visit to find more details on this process.

Goodway, W., Chen, T., and Downton, J., 1997, Improved AVO fluid detection and lithology discrimination using Lame petrophysical parameters; “Lambda*rho ” , “mu*rho” and “lambda/mu fluid stack”, from P and S inversions: 1997, 67th Annual International Meeting., SEG Expanded Abstracts, p183-186.

Dumitrescu, C., Weston Bellman, L., and Williams, A., 2005, Delineating Productive Reservoir in the Canadian Oil Sands using Neural Networks Approach, CSEG Technical Abstracts.


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