Capturing channelized reservoir connectivity uncertainty with amalgamation curves

Abstract

During reservoir characterization all the geological uncertainties affecting the quantity and distribution of hydrocarbons should be captured to assess the risks affecting final recovery.In a typical modeling workflow the geological uncertainties are accounted for through the construction of a sufficiently large set of 3-D static models. Out of this set, a few representative models are selected and dynamically simulated so as to correlate the geological characteristics of the reservoir with its dynamic performance and to propagate the uncertainty onto the final recovery factors, yet maintaining the computational run time acceptable. In channelized depositional environments, which are strongly heterogeneous, the selection approach must also account for channel connectivity, which plays a key role in the possibility of efficiently draining the reservoir for a reasonable number of wells.This study can be seen as a step forward in the assessment of the risks associated to the development of channelized reservoirs under the assumption that a way to express the concept of channel connectivity is channel amalgamation. Channel amalgamation is here defined through amalgamation curves which are numerically described using a set of indexes whose combination provide spatial information of channel intersections. These indexes were calculated for a full set of 3-D geological models and used to steer the selection of a representative model sub-set for subsequent fluid flow simulations.The validity of the index-based selection was verified on different sets of synthetic reservoir models through the evaluation of the representativeness of the model sub-set in reproducing the uncertainty of the original dataset. Eventually, the existence of a strong correlation between channel amalgamation and production performance was proved. From a practical perspective, the possibility to include channel amalgamation in the assessment of the geological models can considerably improve the representativeness of the selected models for uncertainty propagation thus reducing significantly the number of geological models to be considered.