## 3 Materials and Methods
### Step 1  
Figure 4: The workflow used for this study. (**A**) Step 1: Core data description, (**B**) Step 2: Thin section description, (**C**) Step 3: Well log interpretation, (**D**) Step 4: Lithofacies and Rocktypes interpretation, and (**E**) Step 5: IPSOM probabilized self-organizing maps for facies prediction SOM. Step 1: Core Data Description  
A detailed sedimentological description was made for two cored wells of the Miocene E11 field at the Petronas Geo Sample Centre in 2017: E11-2 with a length of 339.55 m and EX-3 with 288.65 m. A detailed description of several attributes was made, as detailed in Table 2. Based on the description, 27 lithofacies were identified, as shown in Table 3, and they were upscaled to 6 groups of lithofacies Table 4.  
Step 2: Thin Section Description  
For the thin section analysis, 100 thin sections were described from plug samples from the E11-2 core and 68 from the E11-3 core. They were analyzed using a transmission light microscope Olympus BX43F with an attached Olympus DP72 camera to determine the mineral composition, sedimentary and diagenetic features, and component composition. Step 3: Well Log Harmonization, Normalization, and Interpretation  
The log data coverage comprises 10 wells, exploration/appraisal and development wells. A summary of the available logs is displayed in Table 1. The exploration wells are mainly vertical and provide full coverage, as observed during the core description. The development wells are named after 101; most of them have been directionally drilled as deviated in target zones of the reservoir. The wireline log data are considered reliable. The first stage is to harmonize the data. This stage implied renaming several curves to recognize the curve types before they could be interpreted. The second stage is to verify the consistency of the data as well as the depth match normalization of DEN, GR, NEU, and DT logs. The normalization initially identifies and removes systematic errors from well log data so that reliable results can be obtained. The normalization includes well-to-well comparisons using histograms, cross plots, depth plots, and statistical measurements to reduce the disparity between vintage petrophysics tools. Finally, an analysis of variance is made to verify that petrophysics logs can segregate between the rock types and to reduce the data redundancy. Step 4: Lithofacies and Rock Typing Analysis  
An initial rock type scheme of 27 lithofacies is presented Table 3. However, from a petrophysical and rock property perspective, the rocks are relatively simple, and the scheme can be simplified significantly for petrophysical evaluation purposes. Reducing the number of groups increased the predictability in IPSOM Table 4.  
The rock typing combined the information from cores, and contrasted against well log measurements within a stratigraphy framework for every interval. A scheme of 6 rock \begin{table}
\begin{tabular}{l l} \hline \hline Lithology & argillaceous and non-argillaceous limestone, \\  & dolo-limestone, dolostone, and shale \\ \hline Texture & mudstone, wackestones, packstone, grainstone, \\  & floatstone, rudstone, framestone, and bindstone \\ \hline Particle size & clay, silt; fine, medium, and coarse arenite and rudite \\ \hline Components & massive, branching, and platy corals, coralline red algae, \\  & benthic foraminifera, bivalves, brachiopods, bryozoans, \\  & gastropods, oncoids, skeletal debris, intra-clasts, and \\  & echinoids (0: absent, 1: rare, 2: common, 3: abundant). \\ \hline Sedimentary structures & bioturbation (0: absent, 1: rare, 2: common, 3: abundant), \\  & micro-karst, karst features, rootlets \\ \hline  & stylolite frequency per foot: 1 (1), 2 (2 to 3), 3 (4 to 10), \\  & 4 (11–30), 5 (>30); horsetails, solution seems (0: absent, 1: rare, 2: common, 3: abundant); mechanical compaction, \\  & dissolution/leaching, cementation, and recrystallization \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Attributes for sedimentological logging.