Online Journal for E&P Geoscientists

Al Shemsi, Abdullah H.^*1; Khemissa, Hocine ¹; Elsaid, Mohamed E.¹; Amer, Mohamed ¹; Jacolin, Jean-Etienne ²; Looser, Mirko ²
(1) ADMA-OPCO, Abu Dhabi, United Arab Emirates. (2) Fugro-Techsia, Paris, France.

Rock Types is a rock or group of rocks that have similar rock properties. It is one of the principal parameters in reservoir characterisation and modeling. Many temptations from subsurface disciplines (Petrophysics, reservoir geology, reservoir engineer) try to use part of the rock properties to create Petrophysical Groups (PGs). The appropriate method is to combine all rock properties (from Sedimentology, Petrography, CCA, SCAL and Logs) to define a robust Rock Type. This study summarizes a methodology and results of the applications of statistical law on core measurements (MICP, Pore Throat Size, Porosity, Permeability, and Grain Density) combined with sedimentological and petrographic analysis to derive Rock Types, and then propagate to log domain in order to predict Rock Types in a high heterogeneous reservoir in the entire field.

The reservoir is a complex reservoir, corresponding to alternate of high heterogeneous dense and porous layers. The lithology is mainly Limestone and Dolomite, deposited in a wide environment (upper marine to sabkha).

The integration of all data available (core Sedimentology description, thin section description, CCA, SCAL and Logs) is the back bone of the process. The applied workflow was as follows:

Petrophysical groups (PGs) were derived based on capillary pressure (+300 MICP) curves to capture significantly different pore geometries. The grouping has been performed statistically by a Self-Organized-Map “SOM”, using Techlog Software.

The integration of sedimentological analysis of each sample, such as thin section description, diagenetic analysis and depositional environment interpretation, resulted in refining the initial 19 PG’s.

The derived model of Rock Type was propagated into larger conventional core analysis (+7000 CCA plugs) data set, such as porosity, permeability and grain density to maximize reservoir heterogeneity coverage.

The quality of the propagation was enhanced by associating grain density measurements. It enabled distinguishing lithology between limestone and dolomite. The quality of the propagation was also enhanced by creation of different IPSOM maps for each geological unit.