KRACQS™ Metadatabase

This is a proprietary module available on license from K2V Limited

For more information follow this Link

or see the summary below:

Once we know who knows what where and when using the Knowledge PinMap™ tool, responders are invited to contribute their knowledge of an area by completing an online questionnaire of drop-down options at a basin, play, venture or opportunity level** using the new enhanced KRACQS™ tool (Knowledge Ranking Assessment from Calibrated Quantitative Screening).

Kracqed segments roll up value at a basin level so that a contiguous heat map of global basins can be visually represented, in effect creating a crustal mosaic expressed as a combination of geo-technical and geo-commercial value drivers. Ultimately, the granularity of the visual representation and breadth of metrics is customisable for your organisation.

The KRACQS™ will score the results of the questionnaire as a single record collaboratively or as independent views later stacked to collapse cognitive bias. Knowledge sharing or stacking in this way is the most crucial step in the journey to value creation from the knowledge lying dormant in your organisation. The choice of sharing or stacking depends on the lateral permeability of your organisational structure*.

The key to success for Kracqing:

• Achieving the correct balance of granularity in the metrics (all basins are unique but uniqueness is the enemy of predictability)
• Maximising the contributions of your knowledge holders (one person's view is dogma - two peoples' views are already a conversation)

** Selecting the appropriate granularity of segment is a business choice. The activity is called "Basin Kracqing" because all assessment segments should be rolled up to a basin level to take advantage of basin evolution perceptions, which is of particular importance to polyphase petroleum systems.
*it is recognised that some organisations have legally constrained boundaries within their organisations that inhibit the cross-flow of knowledge between internal entities. In these cases, knowledge can be stacked mathematically to reduce cognitive bias rather than shared.