Sampling and
Data Collection Strategies for Minimizing Inaccuracy in Predictive Pre-Drill
Reservoir Quality Model Simulations
Tobin, Rick1 (1) BP America,
Inc,
Predictive diagenetic
models of sandstone reservoirs are increasingly being relied on to help
estimate total reserves and fluid deliverability (flow rate) ahead of the drill
bit. However, model simulations of key rock properties (porosity and
permeability) may ultimately fail to accurately predict observed rock
properties in the wellbore because of
unrepresentative sample selection and/or incomplete or inaccurate data
collection used to build the model. The good news is that highly accurate
pre-drill model predictions can be achieved given a logical sampling strategy
coupled with generation of a complete and accurate data set.
Sampling strategy should be fit for
purpose, and must ensure that petrophysically defined
rock facies being analyzed and used in the model
statistically represent the net thickness (“net h”) of the defined pay being
used in resource calculations (i.e., reserves and flow rates. Sample selection
should also avoid the inclusion of petrofacies that
are not being used in the net h calculations, or petrofacies
that are beyond the current capability of the modeling software being used.
Petrographic and core analysis data
collected for modeling input must be as complete and accurate as possible. Data
collection protocol should be designed to avoid the following potential
pitfalls that can lead to poor model performance: sample handling and damage
avoidance, sample preparation technique, sample preparation and analysis under
the correct net confining stress, quality of equipment being used, quality of petrographic description and diagenetic
interpretations, and thin-section point count analysis accuracy and technique.
Detailed examples of strategy issues and subsequent model performance are
compared.
AAPG Search and Discover Article #90063©2007 AAPG Annual Convention, Long Beach, California