Due to reservoir description uncertainties, the building of multiple geologic realizations has become an acceptable industry standard for geo-modelling. This paper discusses an approach to rank these multiple realizations, and to determine the geological realization(s) which is/are the closest to reality. It then describes how to further calibrate this best ranked geo-realization(s), especially in situations where there are no production and static pressure data to be used for history matching.
The proposed approach allows us to screen multiple geologic realizations using pressure transient data, and to improve the distribution of permeability away from well control points. The objective is to Rank the several geo-realizations on the basis of global best fit between the simulated pressure transient derivative and the observed pressure transient derivative, then perform a history Match of the pressure derivative for the best geo-model using permeability multipliers, and finally, create a permeability multiplier map, and Spread this permeability multiplier map over the entire best fit geo-model.
Sometimes, the observed pressure transient data may indicate a boundary away from the well, which the original static geo-model may not capture. More so, information from 3D-siesmic may not also indicate any barrier near this well. Using this proposed approach, we can reproduce the well's pressure transient by introducing stratigraphic features in the static model. In the conventional approach, static modeling is solely based on core data measured at well control points, but in this new approach, a dynamic correction factor based on average drainage area permeability is also applied, resulting in a better characterization of the permeability and boundary features.
This approach adds an additional layer of reservoir characterization to geo-modeling by ensuring that the geo-model contains as much dynamic features as possible. In the case of non-producing fields, where there is no further history matching to be done, this new approach guarantees a more reliable static geo-model.
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