Uncertainty Quantification in 3D Reservoir Modeling by Probabilistic Forecasting Approach

Reservoir studies examine the importance of geologic, engineering, and economic parameters to formulate and optimize production plans. Ifthere are many factors, these studies are high-priced unless simulation runs are chosen and analyzed efficiently. Reservoir modeling requires integration reservoir data and economic parameters. Integration is complex, because parameters such as permeabillity drive mechanism, structural framework, and fluid saturation distributions are uncertain. Uncertainty in permeability, for example, could be caused by prediction at unknown locations from inexact seismic data, poorly distributed of precise well data and imprecise seismic data. Therefore the impact of uncertainty levels in key geologic and production parameters such as NTG, permeability, porosity, vertical transmissibility, skin factor that must be assessed. Uncertainty quantification is attained in the development of a method that can model and quantify uncertainty in reservoir simulation in anefficient and practical way. In this study variety of approaches are investigated to estimate the uncertainty in a recovery prediction. The methodology employed in thisstudy uses Monte Carlo simulation approach (probabilistic forecasting) and efficient selective simulation runs with simultaneous, multi variableinput modifications. The results indicate that in terms of reserves points of view the main uncertainties are represented by NTG andpermeability distribution. This can be attributed to indigenous heterogeneous carbonate reservoir deposition and dissolution. The uncertainity to the OOIP is also affected mainly by the reservoir structural framework; reservoir top depth variations.

Keywords:

Integtated uncertainty , Probabilistic forecasting , Uncertainty