Through a multiple realizations approach based on experimental design and state of the art optimization algorithms, CougarFlow(R) delivers:
A thorough screening of uncertainties on a given range of input parameters and of their influence on key reservoir simulation outputs;
Rapid and robust Assisted History Match offering both a fast converging gradient method (local optimizer) and a thorough Bayesian approach (global optimizer);
Uncertainty analysis for quantifying parameter impact on modeled pressures, production rates and any other output variable; for better understanding the risks associated to a project, for the entire life-cycle of the reservoir;
Parameter optimization for a given field development scenario, to predict the most favorable well locations and perforations or for maximizing production rates. Input parameters from both the geological and the simulation model may be considered, bridging the gap between geomodeling and reservoir engineering teams.
Finally, by integrating the whole set of uncertainties related to a reservoir study - from geological to engineering or even economical parameters - into a complete risk analysis and optimization workflow, CougarFlow(R) provides you with a better grasp of the key influential parameters of a reservoir study and allows for safer decision making at every stage of the field development.
Seamlessly linked to Beicip-Franlab stratigraphic modeling software DionisosFlow® and basin modeling package TemisFlowTM, CougarFlow(R) features effective sensitivity and risk analysis. It encompasses the full range of intimately-coupled phenomena controlling hydrocarbon fluids occurrence in sedimentary basins, from the stratigraphic architecture and facies distribution up to the trapped hydrocarbon charge and quality.
Combining experimental design and response surface methodologies, CougarFlow(R) is one of the few affordable solutions to reliably express DionisosFlow® and TemisFlowTM results in a probabilistic manner. CougarFlow(R) eases uncertainty quantification and model optimization by offering a systematic and rigorous approach. It allows users to explore their models beyond the conventional best and worst case concept, for enlightened decision making.