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• W4313019200 abstract "Identifying depositional facies and diagenetic modifications from BHI logs in sections without core is challenging due to heterogeneities in fabric of the carbonaceous rock, which are below the resolution of the BHI logs in these complex reservoirs. This is mainly due to different diagenetic processes resulting in different types of reservoir rocks (RRTs) with different petrophysical properties in the same geological facies. This work describes an innovative new workflow that uses a deep learning model to identify heterogeneities of fabric (textures) in complex carbonate reservoirs in sections without core; as input we use conventional (gamma ray, density, neutron, sonic), NMR, BHI (acoustic imaging) logs, core CT images and physical porosity and permeability measurements from plugs. This new workflow combines supervised deep learning and unsupervised machine learning methods, consisting of four steps: (1) Identification of textures in Core CT (2) Calibration of features of BHI logs in core section (3) Training a deep learning model with cropped images from BHI logs to extract texture curves (4) train a machine learning regression model with texture curves, WL logs and plug porosity and permeability values to propagate prediction of textures, porosity and permeability along the well. The texture curves generated by the ML model are in good match with the recognized core section textures (CT Images). The results show that the NMR Total Porosity has a good correlation with the porosity predicted by the regression model. In addition, the petrophysical data from plugs measured in the laboratory show a good match with the porosity and permeability predicted in the core zone. In the non-core zone, the predicted textures by the ML model relate to the heterogeneities of the carbonaceous rock fabric and the propagation of the petrophysical properties have good correlation with the predicted textures. In some cases, vuggy porosity combined with fractures do not allow correct porosity measurement with NMR or density tools due to the sensitivity of these tools to poor borehole conditions. In these cases, the NMR presents a fast relaxation of the T2 distribution due to the heterogeneity of these carbonaceous fabric and therefore the porosity and permeability measurements will be affected. The ML model uses CT Textures to calibrate BHI Crops in the core section zone and NMR and Basic logs as input, improving the recognition of the textural heterogeneities and refine the traditional Rock typing of these complex Pre-Salt carbonate reservoirs." @default.
• W4313019200 created "2023-01-05" @default.
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• W4313019200 date "2022-06-11" @default.
• W4313019200 modified "2023-10-18" @default.
• W4313019200 title "BHI Logs and ML Automated Pre-Salt Carbonate: Texture Recognition and Petrophysical Properties Propagation Using Image Log, Core Images and Porosity-Permeability From Plugs" @default.
• W4313019200 doi "https://doi.org/10.30632/spwla-2022-0095" @default.
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