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From fluid flow to coupled processes in fractured rock: Recent advances and new frontiers
Quantitative predictions of natural and induced phenomena in fractured rock is one of the
great challenges in the Earth and Energy Sciences with far‐reaching economic and …
great challenges in the Earth and Energy Sciences with far‐reaching economic and …
Stacked ensemble machine learning for porosity and absolute permeability prediction of carbonate rock plugs
This study employs a stacked ensemble machine learning approach to predict carbonate
rocks' porosity and absolute permeability with various pore-throat distributions and …
rocks' porosity and absolute permeability with various pore-throat distributions and …
A machine learning framework for rapid forecasting and history matching in unconventional reservoirs
We present a novel workflow for forecasting production in unconventional reservoirs using
reduced-order models and machine-learning. Our physics-informed machine-learning …
reduced-order models and machine-learning. Our physics-informed machine-learning …
Machine-learning predictions of the shale wells' performance
The ultra-low permeability nature of shale reservoirs leads to an extended linear flow and
necessitates horizontal wells with multi-stage engineered fractures to efficiently extract …
necessitates horizontal wells with multi-stage engineered fractures to efficiently extract …
A multi-dimensional parametric study of variability in multi-phase flow dynamics during geologic CO2 sequestration accelerated with machine learning
Successful geologic CO 2 storage projects depend on numerical simulations to predict
reservoir performance during site selection, injection verification, and post-injection …
reservoir performance during site selection, injection verification, and post-injection …
Design and performance analysis of dry gas fishbone wells for lower carbon footprint
Multilateral well drilling technology has recently assisted the drilling industry in improving
borehole contact area and reducing operation time, while maintaining a competitive cost …
borehole contact area and reducing operation time, while maintaining a competitive cost …
Unsupervised time series clustering, class-based ensemble machine learning, and petrophysical modeling for predicting shear sonic wave slowness in …
Shear sonic logs are critical for formation evaluation, rock physics, quantitative reservoir
characterization, and geomechanical studies. Although empirical and conventional machine …
characterization, and geomechanical studies. Although empirical and conventional machine …
Computationally efficient and error aware surrogate construction for numerical solutions of subsurface flow through porous media
Limiting the injection rate to restrict the pressure below a threshold at a critical location can
be an important goal of simulations that model the subsurface pressure between injection …
be an important goal of simulations that model the subsurface pressure between injection …
Deep learning to estimate permeability using geophysical data
Time-lapse electrical resistivity tomography (ERT) is a popular geophysical method to
estimate three-dimensional (3D) permeability fields from electrical potential difference …
estimate three-dimensional (3D) permeability fields from electrical potential difference …
[HTML][HTML] Coupling Upscaled Discrete Fracture Matrix and Apparent Permeability Modelling in DFNWORKS for Shale Reservoir Simulation
Modelling non-Darcy flow behaviour in shale rocks, composed of nanometer-sized pores
and multi-scale fracture networks, is crucial for various subsurface energy applications …
and multi-scale fracture networks, is crucial for various subsurface energy applications …