Abstract
In an era of automated workflow-assisted dynamic modelling, Special Core Analysis (SCAL) parameters require updating for each static realisation and evaluation at a quantifiable, probabilistic level-of-certainty. Additionally, SCAL data gaps combined with limited reliable SCAL data drive the need to establish trends and correlations from analogues.
SCAL parameters from analogue fields were selected and filtered by depositional environment and laboratory experiment type (centrifuge versus displacement). These analogue SCAL parameters were allocated to statistical bins defined by absolute permeability ranges. Statistical analysis of each SCAL parameter allocated to each permeability bin produced a probability distribution discretised by percentile. Multi-variable linear regression (MVLR) was then implemented to correlate each SCAL parameter, as the response variable, to input variables absolute permeability and percentile. SCAL correlations of reasonable to excellent quality were obtained.
The depositional environment was of second order influence in establishing these SCAL correlations. This was due to the selection of core plugs for laboratory analysis from layers of similar quality irrespective of the depositional environment, highlighting the need to select samples characterising a range of lithology and reservoir quality. Centrifuge experiments of water displacing gas were discarded as unreliable due to the compression of the gas phase by the experimental technique.
The multi-variable linear regression methodology enabled SCAL parameters to be determined as a function of both absolute permeability and probability. This approach should enable an automated implementation of SCAL parameters within each dynamic model realisation.
