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Many mature fields have a poor or disparate historical dataset, particularly with respect to core. Even where there has been diligent data collection, the varying vintages of core and log material, as well as a succession of different interpreters, alongside changing scientific paradigms might mean that there is a confusing, inconsistent opinion as to the origin and dimensions of geological parameters. Often there are perceptions as to why a field performs as it does which are not founded on systematic data collection and analysis. Consequently, prior to a full field re-evaluation of development strategy, a period of intense data collection and analysis is strongly recommended.

The aim of this phase of work is to ensure that the structural evolution of the carbonate platform is understood as completely as possible. This includes the style and timing of faulting, the relationships between faulting, burial and fracture distribution, and the timing of hydrocarbon charge

Normally during appraisal, reservoir architecture is established in the context of basin-scale sequence stratigraphy. This will have been driven largely by seismic data, with limited well calibration. With the addition of well data during appraisal and development drilling, this sedimentological interpretation can be revisited using the well and core interpretation workflows

Many reservoirs do not have a detailed conceptual model to explain the character and distribution of the diagenetic overprint and how it has influenced reservoir properties, even though most carbonate reservoirs have undergone sufficient diagenesis to significantly alter reservoir properties. Although sedimentary facies (associations) often form a template for diagenetic modification, porosity and permeability are typically also strongly influenced by dolomitization, cementation, post-depositional dissolution and/or fracturing. However, in the absence of clear workflows to link depositional rock properties to reservoir properties, and without guidelines for construction of diagenetic overprint within geocellular models, this phase of the workflow is often overlooked.

The aim of this phase of work is to group data into clusters of genetically associated rock types (i.e. mappable units which have evolved via the same depositional and diagenetic pathways) with consistent petrophysical properties. The process relies heavily on prior determination of a robust diagenetic framework and should build on data collected during Stages 1 and 2.