Integrating multiscale static and dynamic reservoir data to predict well productivity for risk reduction

The deliverability of a hydrocarbon bearing reservoir is assessed in part by quantifying the reservoir permeability. This research investigates an alternative approach to improve the match between different permeability scales especially welltest and core and log permeabilities. Averaging techniques such as arithmetic and geometric can be unreliable. It is proposed that features observed in tidal heterolithics and turbidites is better assessed by simulating permeability anisotropy. This technique is not applicable to full field reservoir modelling.

The proposed technique is applied to case studies from tidal and turbidite environments. In a tidal heterolithics case study the arithmetic average log derived was 49 mD and the geometric average was 5 mD. The new workflow was 91.5 mD while the welltest reference permeability was 93 mD. The turbidites case study was 14 mD for the geometric average and 680 mD for the arithmetic average however the simulation was 193 mD and the welltest permeability was 217 mD. This comparison is much improved when permeability anisotropy is considered through integrating petrophysical and sedimentological observations. A sensitivity study also reveals a hierarchy of controls on permeability anisotropy which suggest the percentage of mud is the dominant factor.

Some key limitations and suggested recommendations for improvement are:

1. Lateral changes in reservoir quality cannot be accounted for. Utilize observations at the seismic scale.

2. Complexity of the near wellbore model and limits data availability. A database of analogs should be utilised in the absence of hard data.

3. Two phase flow has not been considered as part of this workflow due to availability. It is recommended that this be part of further research in this area.

4. Fractures and diagenetic changes cannot be simulated with this workflow

This work contributes a protocol for assessing and using permeability anisotropy to improve prediction of well productivity which should reduce the risk in the decision making.