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Machine learning in geo-and environmental sciences: From small to large scale
In recent years significant breakthroughs in exploring big data, recognition of complex
patterns, and predicting intricate variables have been made. One efficient way of analyzing …
patterns, and predicting intricate variables have been made. One efficient way of analyzing …
Deep learning in pore scale imaging and modeling
Pore-scale imaging and modeling has advanced greatly through the integration of Deep
Learning into the workflow, from image processing to simulating physical processes. In …
Learning into the workflow, from image processing to simulating physical processes. In …
Machine learning enabled orthogonal camera goniometry for accurate and robust contact angle measurements
Abstract Characterization of surface wettability plays an integral role in physical, chemical,
and biological processes. However, the conventional fitting algorithms are not suitable for …
and biological processes. However, the conventional fitting algorithms are not suitable for …
Review of machine learning for hydrodynamics, transport, and reactions in multiphase flows and reactors
Artificial intelligence (AI), machine learning (ML), and data science are leading to a
promising transformative paradigm. ML, especially deep learning and physics-informed ML …
promising transformative paradigm. ML, especially deep learning and physics-informed ML …
An optimized XGBoost method for predicting reservoir porosity using petrophysical logs
S Pan, Z Zheng, Z Guo, H Luo - Journal of Petroleum Science and …, 2022 - Elsevier
To overcome the deficiencies of current porosity prediction methods, the XGBoost algorithm
is introduced to construct a model for porosity prediction, and the obtained model is …
is introduced to construct a model for porosity prediction, and the obtained model is …
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 …
Automated lithology classification from drill core images using convolutional neural networks
In hydrocarbon reservoir evaluation, lithology is a key characteristic for determination of
storage capacity and rock properties. Lithology is usually predicted from well log data or …
storage capacity and rock properties. Lithology is usually predicted from well log data or …
Advances in the application of deep learning methods to digital rock technology
Digital rock technology is becoming essential in reservoir engineering and petrophysics.
Three-dimensional digital rock reconstruction, image resolution enhancement, image …
Three-dimensional digital rock reconstruction, image resolution enhancement, image …
Machine learning for the advancement of membrane science and technology: A critical review
Abstract Machine learning (ML) has been rapidly transforming the landscape of natural
sciences and has the potential to revolutionize the process of data analysis and hypothesis …
sciences and has the potential to revolutionize the process of data analysis and hypothesis …
Leveraging machine learning in porous media
The emergence of artificial intelligence (AI) and, more particularly, machine learning (ML),
has had a significant impact on engineering and the fundamental sciences, resulting in …
has had a significant impact on engineering and the fundamental sciences, resulting in …