--> Back to the Rocks: Integrated Approach of Rock-Typing from Core Scale to Log Scale—Example of Upper Thamama from Onshore Abu D

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Back to the Rocks: Integrated Approach of Rock-Typing from Core Scale to Log Scale—Example of Upper Thamama from Onshore Abu Dhabi

 

Rebelle, Michel1, Mohamed Al Nuaimi1, Maria Teresa Ribeiro1, Stephanie Gottlib­Zeh2, Bertrand Valsardieu2, Brian Moss2 (1) ADCO, Abu Dhabi, United Arab Emirates (2) TECHSIA, Montpellier, France

 

Rock-Typing is an important issue for the Geological Model and Dynamic simulation. In many Abu Dhabi onshore carbonate reservoirs studies, Rock-Typing is not supported by a High Resolution Sequence Stratigraphy and is essentially borne by arbitrary cut-offs of poro-perm values. Relationships between Lithofacies and Petrophysics should be defined using objective and quantified statistical approach. Distribution of petrophysics properties should rely on detailed sedimentological and diagenesis studies, supporting a “back to the rocks” philosophy.

Such an approach has been used to define static Rock-Types for an Upper Thamama reservoir of the Field A (onshore Abu Dhabi).

A detailed core description and petrographic analysis leads to identification of 11 Lithofacies. A High Resolution Sequence Stratigraphy framework could be defined, evidenc­ing 5 High Frequency Sequences (HFS) within a 3rd order sequence.

A quantitative statistical approach incorporating available core petrophysical data (Phi, K, Rho, Pc curves) is achieved within Techlog© software. Direct integration of Pc and rou­tine core data allows development of 6 Petrophysical Groups at core scale (PGc). A strong relationship between Lithofacies (ordered by half-HFS) and PGc was found.

After log data QC and normalization, an unsupervised log data partitioning was indexed against depth-by-depth core descriptions to apply physical meaning to log clusters. This indexation strategy permits robust prediction of lithofacies from log data. Permeability mod­elling was also performed. Similarly, propagation of PGc in uncored wells was achieved through indexation of log data partitions to establish 6 Petrophysical Groups (Rock Types) at log scale (PGL). Correspondence between PGC and PGL was quantified.