Carbonate rock typing

Lateral and vertical heterogeneities in carbonate units are substantial, and relate to the complex variability of depositional environments. Carbonate platforms composition, in par-ticular, results from combined influences of environmental, ecological, geotectonic and eu-static factors on sedimentation (Pomar and Kendall, 2008), simultaneously or subsequently overprinted by variable diagenetic processes. In carbonate reservoirs, these heterogeneities are expressed through various and composite petrophysical properties and are responsible for large uncertainties in predicting reservoir quality, distribution of productive zones and resources recovery volumes (Burchette, 2012). Rock typing is the process of combining multi-disciplinary investigations on reservoir rock and fluid-rock interactions in an integrated ap-proach, which provides clues to the spatial distribution of reservoir properties, and to solve

1.4. State-of-the-Art 31 or reduce such uncertainties.

The goal of rock typing is to provide associations of petrophysical properties consider-ing geological attributes, which characterize optimally the dynamic behaviour of a reservoir (Gomes et al., 2008 ; Skalinski and Kenter, 2015). Rock types are currently defined by the lat-ter authors as categories of rock, which formed under specific sedimentary/geological condi-tions and underwent similar diagenetic processes, leading to unique petrophysical properties characterizing the rock fabric (pore network, porosity-permeability relationship), and fluid-rock interactions (wettability, relative permeability and capillary pressure) (Gomes et al., 2008). Predominantly based on well data, rock types should contribute to the prediction of reservoir behaviour in the inter-well area, in accordance with established local and regional geological trends (Hollis et al., 2010). Their propagation allows the proper characterisation of reservoirs in quantifying and describing the spatial distribution of petrophysical properties, rooted in a coherent geological framework. Thus, rock typing helps to assess most accurately resource volumes and their productive capacity.

Methods to define carbonate rock types have evolved over time with the improvement of technology also driven by enhanced research interests in this topic. Pore type for instance, is related to the rock fabric and is not fluid-dependent like relative permeability and cap-illary pressure. However, it is a critical parameter to investigate as it influences fluid flow behaviour. Pore types are defined by static geometrical factors, and they greatly affects more intricate dynamic parameters. For this reason, pore types are typically considered very important in petrophysical classifications. Because the understanding of factors influ-encing porogenesis has been enhanced through time, the criteria for determining pore types have evolved from simple morphological description to relations including depositional and diagenetic processes. Depending on reservoir conditions and aim of rock typing, additional dynamic parameters such as wettability has become important to define the rock types in the case of two phase flow for instance. Overall, innovative logging and laboratory technologies, as well as the development of new concepts in reservoir studies, have promoted the integra-tion of different approaches and the elaboraintegra-tion of rock typing workflows. Their guidelines and limits greatly depend on data type, availability and quality. The best case rock types will honour core, log and field data, integrating information at different scales, and therefore helping to constrain reservoir rock upscaling and property propagation through reservoir bodies.

Different rock typing schemes have been defined according to the methodology used.

Skalinski and Kenter (2015) provided a review of these current schemes, and distinguished seven main categories depending on the methods used and the dataset investigated:

1. Depositional Rock Type (DRT), using core description and observations (Dunham, 1962 ; Lucia, 1995, 2007)

2. Pore type, using core observations, detailed petrography on thin sections and additional NMR logs or MICP measurements when available (Choquette and Pray, 1970 ; Lønøy, 2006 ; Ahr, 2008)

3. Integrated approach, using core observations, MICP measurements and detailed

pet-32 Chapter 1. Introduction rography on thin section allowing the definition of DRT and pore types, and the iden-tification of diagenetic modifications (Archie, 1952 ; Hollis et al., 2010 ; Salman and Bellah, 2009)

4. Partitioning-flow units, using core material to define flow zone indicator (FZI), addi-tional dynamic producing well information and logs to acquire generalized hydaulic elements (GHE), routine core analysis (RCA) and porosity/permeability-logs (Amae-fule et al., 1993 ; Cortez and Corbett, 2005 ; Gunter et al., 1997 ; Wibowo and Permadi, 2013)

5. Partitioning-log clustering or electrofacies, using well logs (Serra and Abbott, 1982) 6. Dynamic rock types, using detailed petrography on thin sections allowing the

identifi-cation of depositional rock types, pore types and diagenetic modifiidentifi-cations, and special core analysis (SCAL) (Ghedan, 2007 ; Gomes et al., 2008)

7. Petrophysical rock types, using core description, detailed petrography on thin sections allowing the identification of depositional rock types, pore types, diagenetic modifica-tions and permeability barriers, integrated to the log domain and further 3D Earth models (Skalinski and Kenter, 2015)

These different schemes show specific strengths and weaknesses depending on their primary goal. The first two schemes describe only static properties, whereas the others integrate at different levels dynamic, fluid-dependent parameters by imposing fluid models or adding upscaled SCAL measurements (performed on plug). Since a major source of uncertainty is related to representativity of samples considered, upscaling procedure should be constrained step by step using multi-dimension data, whose increasing scales allow the control and un-derstanding of any potential bias. However, the lack of prediction in the log domain is one of the recurring weakness in the schemes presented above, as well as implementation of geo-logical trends in spatial distribution of rock types. Overall, the complexity of methods used is in accordance with the need of extended core and log dataset, and the higher the quality and number of data supplied, the more accurate the models produced. Hence, a preliminary evaluation of the dataset and its quality is required to design an appropriate rock typing workflow, in order to balance the method consistently with the data scenario, whilst it can evolve through time according to the field development.

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