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GeoSymphony - Porosity Prediction Using Hybrid Rock Physics and Statistical Neural Network
In Geosymphony, porosity distribution can be effectively predicted by integrating core, well log, and seismic data, incorporating geological, petrophysical, and geophysical parameters to account for lithological heterogeneity. One of the key indicators of reservoir quality is porosity, which reflects the rock’s capacity to store fluids. Accurate porosity prediction requires a detailed understanding of the lithology distribution, as different rock types exhibit varying porosity-permeability relationships due to differences in grain size, compaction, cementation, and depositional environment.
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Matrix Porosity Prediction
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Fracture/Secondary Porosity Prediction
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In reservoir characterization, accurately distinguishing and predicting matrix porosity and fracture porosity is essential, especially in heterogeneous or naturally fractured reservoirs such as carbonates, tight sandstones, or volcanic rocks.
In fractured reservoirs, dual-porosity models are commonly used, where both porosity types are represented separately but interactively in simulation. Distinguishing between matrix and fracture porosity is crucial because the accurate separation improves reservoir quality assessment, production forecasting, and development planning.
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Matrix Porosity Prediction Map
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Fracture Porosity Prediction map
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Core Observation for Fracture Porosity Prediction
Core analysis provides a direct, high-resolution means of identifying and quantifying fracture porosity in reservoir rocks. Through detailed visual and microscopic examination, core observations help distinguish natural fractures from drilling-induced features and characterize their geometry, spacing, orientation, and aperture.
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