%0 Journal Article
%@ 20763417
%A Jiménez Soto, G.
%A Abdul Latiff, A.H.
%A Ben Habel, W.
%A Bing Bing, S.
%A Poppelreiter, M.
%D 2022
%F scholars:16518
%I MDPI
%J Applied Sciences (Switzerland)
%N 15
%R 10.3390/app12157688
%T Integrated Carbonate Rock Type Prediction Using Self-Organizing Maps in E11 Field, Central Luconia Province, Malaysia
%U https://khub.utp.edu.my/scholars/16518/
%V 12
%X 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. © 2022 by the authors.
%Z cited By 1