--> Abstract: Carbonate Reservoir Rock Typing - From Integrated Case Study, by Hani Al-Sahn, Amaud Mayer, Ibrahim Al-Ali, Habeeba Al-Housani, and Fathy El-Wazeer; #90105 (2010)

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AAPG GEO 2010 Middle East
Geoscience Conference & Exhibition
Innovative Geoscience Solutions – Meeting Hydrocarbon Demand in Changing Times
March 7-10, 2010 – Manama, Bahrain

Carbonate Reservoir Rock Typing - From Integrated Case Study

Hani Al-Sahn1; Amaud Mayer2; Ibrahim Al-Ali1; Habeeba Al-Housani1; Fathy El-Wazeer1

(1) ADCO, Abu Dhabi, United Arab Emirates.

(2) Total, Paris, France.

Reservoir rock typing is a process by which the reservoir is abstracted into discrete units characterized by certain static properties and dynamic behavior. The challenges of this process are to find the correspondence between rock fabrics with their diagenetic alterations and their petrophysical properties then to distribute these discrete entities in the reservoir. This paper presents a real case study completed for a major complex carbonate reservoir onshore Abu Dhabi. The study included 3 main elements. The first element is a detailed facies analysis where facies has been described using all available cores. The second element is a detailed petrographic analysis where diagenetic overprints were described using 3000 thin sections. The third element is a petrophysical data grouping using static and dynamic properties.

The main challenge was to establish a systematic relation between the three elements (facies, diagenesis and petrophysical groups) because, apart from diagenetic modifications, similar carbonate facies deposited under similar conditions would exhibit different petrophysical properties due to other factors (e.g. compaction, micrite content and dominant grain types). The study resulted in establishing a manageable number of Reservoir Rock Types with distinct capillary pressure properties applied to the cored wells. For non-cored wells, an artificial neural network back-propagation algorithm was applied to estimate permeability. We achieved permeability prediction with more than 90% correlation coefficient and then used it with log porosity to assign petrophysical groups using calibrated MICP driven Winland’s R35 cutoffs. The workflow followed and the techniques applied are presented in this paper.