Reservoir prediction modelling conventionally involves complex statistical models that aim to integrate feature on multiple scales. These features are sourced from various types of data and often have a significant impact on flow performance. Conventional geostatistical algorithms provide a framework to integrate data from different scales, such as: geological interpretation of depositional structure based on analogues (e.g. by using conceptual training images); spatial correlation of geological bodies, their variety and geometrical relations (e.g. with imbedded geometrical shapes or elicited relations from analogues); high resolution seismic can be a source of multi-scale model features that can be integrated into stochastic model by means of soft conditioning.
|Number of pages
|Published - Jun 2014
|76th EAGE Conference and Exhibition 2014 - Amsterdam, Netherlands
Duration: 16 Jun 2014 → 19 Jun 2014
|76th EAGE Conference and Exhibition 2014
|16/06/14 → 19/06/14