Tobin, Rick C.¹ 
(1) BP America, Inc, Houston, TX

ABSTRACT: Reducing Uncertainty in Reservoir Quality Modeling by Avoiding Common Quality Control Pitfalls

A critical element of exploration risk analysis is the prediction of pore system quality in sandstone reservoirs. Reservoir quality modeling software provide a means of quantitative prediction, but the degree of model uncertainty must be identified, quantified, and mitigated. Risk mitigation is focused in five areas of model vulnerability: proper analogue selection, data quality, burial history accuracy, suitability of model parameters, and accuracy of model calibration and degree of predictive modeling diligence. 
Common pitfalls that increase model uncertainty and inaccuracy can be avoided through project planning and data integration. For example, geologic analogues selected for the model should be representative of the same or similar provenance, environment, stratigraphic age and diagenesis as that expected at the prospect location. Once an appropriate analogue is selected, the sample data used as modeling inputs (core analysis, petrography, etc.) must be complete, accurate and statistically representative of the reservoir. The burial history used to drive the model (time, temperature, depth, overpressure and effective stress) must be calibrated with analogue well data. The model parameters chosen (i.e., computational variables such as cement activation energy, grain ductilities, etc.) should be geologically reasonable, and should be in agreement with petrographic observations. Finally, reservoir quality simulations at analogue well locations should be successfully calibrated with sample data before they are applied to predictive prospect locations. 
The uncertainty inherent in reservoir quality models may be numerically quantified. A method for quantifying uncertainty along with case histories of modeling success and failure are presented for a variety of exploration prospects.

AAPG Search and Discovery Article #90026©2004 AAPG Annual Meeting, Dallas, Texas, April 18-21, 2004.