Common sedimentary rocks are sandstone, limestone, and shale.
The model is successfully tested on sandstone and carbonate samples.
Here, a SVR-based porosity prediction model is developed for a heterogeneous sandstone reservoir.
The numerical model is considered for North-Sea sandstone, a general anisotropic porous reservoir rock of density ρ = 2216 kg/m3 and porosity (Rasolofosaon and Zinszner, 2002).
The model is validated in the conventional Bentheimer sandstone and then applied to shale matrix with the consideration of variable wettability.
Combined with advanced image processing technology, three-dimensional distributions of the nanometer-sized pores were reconstructed and a tight sandstone digital core model was built, as well the pore structure parameters were analyzed quantitatively.
Sandstone reservoirs prediction models are based on Darcy's theories of porosity and permeability.
For synthetic rock and Berea sandstone, the computational models are created through direct mapping of their CT-scanned images, and the numerical simulation results are checked with laboratory experimental data.
Two general regression neural network (GRNN) models were developed to predict Vs of sandstones, shaly sands, and carbonate rocks as a function of the compressional velocity (Vp), grain density (ρg), clay content, porosity, permeability (k), and the cementation exponent (m) at a fixed effective stress and frequency.
Our porosity modelling is based on data from the Bunter Sandstone and Gassum formations.
