Prediction of Shale Plugs between Wells in Heavy Oil Sands using Seismic Attributes

Abstract

A fundamental geologic problem in the Steam-Assisted Gravity Drainage (SAGD) heavy oil developments in the McMurray Formation of Northern Alberta is to determine the location of shales in the reservoirs that may interfere with the steaming or recovery process. Petrophysical analysis shows that a key acoustic indicator of the presence of shale is bulk density. In theory, density can be derived from seismic data using Amplitude Versus Offset (AVO) analysis of conventional or multicomponent seismic data, but this is not widely accepted in practice. However, with billions of dollars slated for SAGD developments in the upcoming years, this technology warrants further investigation. In addition, many attributes can be investigated using modern tools like neural networks; so, the density extracted from seismic using AVO can be compared and combined with more conventional attributes in solving this problem. Density AVO attributes are extracted and correlated with “density synthetics” created from the logs just as the seismic stack correlates to conventional synthetics. However, multiattribute tests show that more than density is required to best predict the volume proportion of shale (Vsh). Vsh estimates are generated by passing seismic attributes derived from conventional PP, and multicomponent PS seismic, AVO and inversion from an arbitrary line following the pilot SAGD wells through a neural network. This estimate shows good correlation to shale proportions estimated from core. The results have encouraged the application of the method to the entire 3D.