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Tapan Mukerji

Stanford University

United States

Research Interests

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Professors in United States

Articles (21)

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2023

Collaborators (5)

SanLinn I Kaka

Associate Professor / Head, CPG Student Affairs Office

King Fahd University of Petroleum and Minerals

SAUDI ARABIA

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Per Avseth

NORWAY

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Divakar Vashisth

Stanford University

UNITED STATES

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Dario Grana

Professor

University of Wyoming

UNITED STATES

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Klaus Mosegaard

Københavns Universitet

DENMARK

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Tapan Mukerji

Research Interests

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Articles (21)

Pix2Geomodel: A next-generation reservoir geomodeling with property-to-property translation

Bayesian Inference for Subsurface Geophysical Inverse Problems

Physics-Informed KAN PointNet: Deep Learning for Simultaneous Solutions to Inverse Problems in Incompressible Flow on Numerous Irregular Geometries

Prestack and poststack seismic inversion using quantum computing

Seismic organic facies constrained geostatistical inversion for petrophysical properties in shale reservoirs

Assessing feasibility and information gain of geophysical facies classification in Brazilian presalt carbonates: Atapu field case study

Joint rock physics inversion and basin modeling for comprehensive source rock characterization

Generative Geomodelling: Deep Learning Versus Geostatistics with Insights from Sedimentary Systems

Assessing uncertainty in source rock properties using Monte Carlo basin modeling: Application to the Canning Basin, Australia

Enhanced anomaly detection in well log data through the application of ensemble GANs

Geomodelling of multi-scenario non-stationary reservoirs with enhanced GANSim

Reservoir geophysics' contributions to the energy transition and climate change challenge - Introduction

Statistical rock physics inversion for assessing source rock properties from seismic signatures: An application to the Canning Basin, Australia

Symmetric Fourier Neural Operators With Translation, Rotation, and Scaling Equivariances for Wave Propagation Modeling

Vision Mamba for permeability prediction of porous media

Pushing the Boundaries of Porous Material Characterization: Universal Stochastic Data Fusion With Deep Learning

Techno-economic analysis of natural and stimulated geological hydrogen

The concept of a Blind Hydrogen System

Deep learning forecasts the spatiotemporal evolution of fluid-induced microearthquakes

Algorithms for extraction of reliable density ratios from pre-stack seismic data-Part 1: Theory

Bayesian geophysical basin modeling with seismic kinematic metrics to quantify uncertainty for pore pressure prediction

Collaborators (5)

SanLinn I Kaka

Per Avseth

Divakar Vashisth

Dario Grana

Klaus Mosegaard

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