Unconventional hydrocarbon resources: geological statistics, petrophysical characterization, and field development strategies
Hydrocarbons exist in abundant quantity beneath the earth's surface. These hydrocarbons
are generally classified as conventional and unconventional hydrocarbons depending upon …
are generally classified as conventional and unconventional hydrocarbons depending upon …
Machine learning-based shale wettability prediction: Implications for H2, CH4 and CO2 geo-storage
Shale wettability determines shale gas productivities and gas (H 2, CH 4 and CO 2) geo-
storage efficiencies. However, shale wettability is a complex parameter which depends on …
storage efficiencies. However, shale wettability is a complex parameter which depends on …
[PDF][PDF] Pore-GNN: A graph neural network-based framework for predicting flow properties of porous media from micro-CT images.
This paper presents a hybrid deep learning framework that combines graph neural networks
with convolutional neural networks to predict porous media properties. This approach …
with convolutional neural networks to predict porous media properties. This approach …
A general-purpose tool for modeling multifunctional thin porous media (POREnet): From pore network to effective property tensors
POREnet, a novel approach to model effective properties of thin porous media, TPM, is
presented. The methodology allows the extraction of local effective property tensors by …
presented. The methodology allows the extraction of local effective property tensors by …
[HTML][HTML] Microfluidic droplet detection via region-based and single-pass convolutional neural networks with comparison to conventional image analysis methodologies
As the complexity of microfluidic experiments and the associated image data volumes scale,
traditional feature extraction approaches begin to struggle at both detection and analysis …
traditional feature extraction approaches begin to struggle at both detection and analysis …
Geological reservoir characterization tasks based on computer vision techniques
Reservoir characterization is of great importance in oil and gas exploration and production.
To automate and improve the procedures involved in this task, several approaches in the …
To automate and improve the procedures involved in this task, several approaches in the …
MicroGraphNets: Automated characterization of the micro-scale wettability of porous media using graph neural networks
This study introduces MicroGraphNets, a deep learning framework for automating the
microscopic characterization of wettability in porous media using graph neural networks …
microscopic characterization of wettability in porous media using graph neural networks …
2D-to-3D image translation of complex nanoporous volumes using generative networks
Image-based characterization offers a powerful approach to studying geological porous
media at the nanoscale and images are critical to understanding reactive transport …
media at the nanoscale and images are critical to understanding reactive transport …
Multivariate Geostatistical Simulation and Deep Q-Learning to optimize mining decisions
In open pit mines, the long-term scheduling defines how the mine should be developed.
Uncertainties in geological attributes makes the search for an optimal scheduling a …
Uncertainties in geological attributes makes the search for an optimal scheduling a …
Automatic well test interpretation method for circular reservoirs with changing wellbore storage using one-dimensional convolutional neural network
X Liu, W Zha, D Li, X Li, L Shen - Journal of Energy …, 2023 - asmedigitalcollection.asme.org
In order to develop reservoirs rationally, accurate reservoir parameters are usually obtained
through well test analysis. However, a good deal of well test data with changing wellbore …
through well test analysis. However, a good deal of well test data with changing wellbore …