###### Abstract  
Reducing uncertainty in 3D carbonate rock type distribution is a critical factor that profoundly impacts field development for hydrocarbon or carbon capture and storage (CCS) projects. Miocene carbonate reservoirs in the Central Luconia offshore region are economically important global gas reservoirs. The nature of these carbonate rocks can be visually distinct in the core and the multiscale reservoir heterogeneity might vary in scale from the 100-m scale to the sub-millimeter scale. This work presents a series of steps workflow to obtain spatial information about the organization scheme of carbonate rock types, and capture the most important petrophysical and sedimentary controls on rock property distribution in the E11 field, a carbonate buildup, located in Central Luconia Province, offshore Malaysia. The spatial data were generated from a supervised neural Kohonen algorithm. The rock types predicted with this workflow were propagated using IPSOM probabilized self-organizing maps SOM. This tool is used for classifying multivariate data samples according to \"patterns\" or multivariate responses. The workflow includes several steps: A Step 1--Core data description, B Step 2--Thin section description, C Step 3--Well log interpretation, and D Step 4--IPSOM probabilized self-organizing maps for facies prediction SOM. The depth plots of the predicted rock type showed close correspondence to the core-based rock types in terms of the stratigraphic organization of tight and reservoir layers, proportions, and juxtaposition. This result is sufficient to merit the application of the rock type logs into a future porosity model of the E11 field, and to understand the lateral and vertical distribution of tight and reservoir rock types of distribution. The results can be used to build a future realistic digital twin of the subsurface, and in digital geological modeling. c ARTICIC 20222127688 20222127688 20222127688 2022127688 2022127688 20222127688 20222127688 20222127688 20222127688 20222127688 20222127688 20222127688 20222127688 20222127688 20222127688 20222127688 20222127688 2022212768 20222 The distribution of reservoir properties within these carbonate buildups (pore types, porosity, grain density, permeability) is probably an expression of the global or regional environmental factors, leading to exposure and flooding-related reservoir quality changes [11]. Thus, this highlights the need for a solid understanding of the original rock fabrics/facies, associated depositional environments, diagenetic modifications, and their petrophysical expression to consolidate lithofacies, petrophysical groups, and rock types that can be distributed in a geologically realistic manner during reservoir modeling. This distinction controls both the static and dynamic characteristics of a reservoir while linking geological attributes to spatial trends [12]. Primary depositional facies variability and stacking architecture, diagenetic overprint, and faults and fractures are the common controls on reservoir compartmentalization and high-permeability pathways and thus flow behavior in hydrocarbon reservoirs [13]. According to [14], rock properties are most commonly co-vary with porosity. Hence, there is a need for genetic rock typing in carbonates to improve the prediction of permeability and saturation behavior. In this work, rock types derived from core data are discussed in detail from one of these hydrocarbon-bearing buildups, the E11 field. The core data from two wells, wireline logs of 10 wells, and top markers are correlated in terms of flooding intervals, defining three main carbonate reservoir zones, divided by four tight layers corresponding to major flooding events. The E11 data are used to illustrate a workflow that makes use of a proxy of well log data for rock typing propagation in wells where the core is not available. The E11 field was chosen as a case study because the field was extensively studied in the past [1; 4; 5; 6; 7; 8; 9; 10; 15].