The goal of this article is to follow a recommended machine learning workflow on how to perform a petrophysical interpretation using an ensemble technique (Supervised Learning model), which is widely ...

However, many geoscience applications are challenged with limited training data. Seismic petrophysical classification, mapping seismic data to litho-fluid classes, is one of these examples because the ...

Das, V. and T. Mukerji, 2019, Petrophysical properties prediction from pre-stack seismic data using convolutional neural networks: SEG Technical Program Expanded ...

Geophysics and Structural Geoscience Methodically Driving Future Growth Prospects Advancements Reveal a Significant Number of Untapped Regional ...

Grana D., Azevedo L., de Figueiredo L., Connolly P., and Mukerji T., Probabilistic inversion of seismic data for reservoir characterization, Geophysics, 1-47. Grana D ...

Al-Menhali, Ali S. Menke, Hannah P. Blunt, Martin J. and Krevor, Samuel C. 2016. Pore Scale Observations of Trapped CO2 in Mixed-Wet Carbonate Rock: Applications to ...

The rock groups are composed of mineral assemblages with variable contents of 15 major rock-forming minerals and porosities of 0–30 per cent. Petrophysical properties and their ...

Zhang, Kaiyi Du, Fengshuang and Nojabaei, Bahareh 2019. Effect of Pore Size Heterogeneity on Hydrocarbon Fluid Distribution and Transport in Nanometer-Sized Porous Media.

The estimation of petrophysical properties from seismic data can be formulated as a probabilistic inverse problem in which the goal is to predict the posterior probability distribution of the ...

Airborne Electromagnetic Survey Set to Launch Over New Denare West Property VANCOUVER, BC, Sept. 19, 2024 /CNW/ - Foran ...