Establishing Rapport Throughout Carbonate Reservoirs: A Rock Typing Networking Based on Pore Throat

Abstract

Carbonates reservoir has an elevated level of heterogeneity than clastic reservoir, which is relatively controlled only by depositional facies. It is because of the facies variation vertically and laterally which is more intensive, as well as intensive diagenesis. Therefore, an accurate method is required to ensure hydrocarbon development is effective and efficient. Challenges in the characterization of the carbonate are related to rock type and porosity. The permeability of rocks cannot to determined only by porosity. The method that can be used to determine rock type and rock permeability estimation is through rock typing method. This method is aptly applied for carbonate reservoir which is dynamically change due to diagenesis. It is believed to predict and optimize carbonate reservoir better. Core data can be used to determine rock type based on geology named litho-facies or petrophysics named electro-facies characterization There are many rock typing methods, which are Pore throat group based on shape and trend, PGS - Pore geometry structure, Lucia, FZI – flow zone indicator, Winland R35. Those methods use different principles in classifying rock type. Main objective to merge core results between geological statement information based with digital engineering data. By combining these two pieces of information and data, the more precise rock type and able to achieve in solving more finer on carbonate reservoir characterization. Furthermore, the analysis has been conducted over multiple carbonates environments including platform carbonate, pinnacle carbonate and complex carbonate lithology. This paper presents the rock typing classification in carbonate environments which consider geological, and engineering elements mainly through Pore Throat based Rock typing. The main rock typing group can be derived from either stratigraphy or the distribution shape of the pore throat. This will produce the porosity-permeability relationship for all the samples. Geological inputs are then used to describe more refined and detailed characteristics of the relationship. These variety sets of data will help to populate the geological features of the reservoir in bulk and each individual layer in depths. The process includes developing the correlation between pore throat size and pore throat connectivity networking. Defined from core plug pore throat pattern and tie to well logs respond. Consequently, to be propagated in the non-cored intervals through correlation between multiple well logs respond. Some of the key petrophysical measurements will be discussed and how to interpret the borehole images associated with carbonates. As well as looking at different methods of rock typing and best practices to build a static carbonate model. This approach is using pore throat group to classify the rock typing of the carbonate reservoirs. The main rock typing group can be derived from either stratigraphy or the distribution shape of the pore throat. The methodology must be tested first in cored intervals. This is to ensure that sufficient data has been incorporated considering the complexity of the carbonate structure. This will produce the porosity-permeability relationship for all the samples. Geological inputs are then used to describe more refined and detailed characteristics of the relationship. Post drill analysis of the core plugs usually come from the sedimentology analysis, thin section, SEM, XRD and even the core photos. These variety sets of data will help to populate the geological features of the reservoir in bulk and each individual layer in depths. These will be the steps that will aid in re-clustering the porosity-permeability relationship. After these steps have been implemented, the outputs will be calibrated before the methodology will be adopted and regressed to the un-cored intervals. The permeability prediction based on pore throat group by using this methodology matches with measured core permeability with capture the complex respond of permeability variation. The result shows rock typing can be generated by using the pore throat distribution of the reservoirs. This is because permeability populated by this method captures the complexity of the reservoir. Results are more detailed by creating rock typing based on the pore throat. This is furthermore supported and incorporated with all available geological data. There is a significant difference that can be seen between platform, pinnacle, and complex carbonate. The workflow integrates critical information to further capture the complex carbonate reservoir system. This kind of approach is novel and should be adopted to the other carbonate reservoirs in the world for us to understand more on complicated carbonate reservoir structures or network. This study is robust and able to capture multiple carbonate environments and in comparison, with several basins from various parts of the world.