Prediction of pore-scale clogging using artificial intelligence algorithms
C Lei, M Samari-Kermani, H Aslannejad… - … Research and Risk …, 2023 - Springer
We use five established, but conceptually different artificial intelligence algorithms for
analysing clogging and quantifying colloid transport at pore scale: artificial neural networks …
analysing clogging and quantifying colloid transport at pore scale: artificial neural networks …
Structural reconstruction and thermophysical properties of alumina agglomerate based on QSGS calculation
M Li, J Wang, B Cheng, H Li, W Hou - Computational Particle Mechanics, 2024 - Springer
The presence of alumina agglomerates seriously affects the current efficiency of the
aluminum electrolysis process. The microstructure of agglomerate is difficult to obtain while it …
aluminum electrolysis process. The microstructure of agglomerate is difficult to obtain while it …
A Critical Literature Review on Rock Petrophysical Properties Estimation from Images Based on Direct Simulation and Machine Learning Techniques
Estimation of petrophysical properties is essential for accurate reservoir predictions. In
recent years, extensive work has been dedicated into training different machine-learning …
recent years, extensive work has been dedicated into training different machine-learning …
Effects of stress and temperature on the permeability of gas-saturated wet coal
J Wang, Y Wang, Z Wan, H Zhang, J Cheng… - Energy & …, 2020 - ACS Publications
Fracturing and heat injection are promising methods for in situ modification of low-
permeability reservoirs to enhance coalbed methane (CBM) recovery. The technical …
permeability reservoirs to enhance coalbed methane (CBM) recovery. The technical …
The use of machine learning in oil well petrophysics and original oil in place estimation: a systematic literature review approach
Machine learning is a form of artificial intelligence that is applicable in all fields of study. It
incorporates many algorithms used in carrying out various tasks such as classification …
incorporates many algorithms used in carrying out various tasks such as classification …
Fluid classification through well logging is conducted using the extreme gradient boosting model based on the adaptive piecewise flatness-based fast transform …
Y Sun, J Zhang, Y Zhang - Physics of Fluids, 2024 - pubs.aip.org
In recent years, fluid prediction through well logging has assumed a pivotal role in the realm
of oil and gas exploration. Seeking to enhance prediction accuracy, this paper introduces an …
of oil and gas exploration. Seeking to enhance prediction accuracy, this paper introduces an …
[PDF][PDF] 基于深度学习的多孔介质渗透率预测
刘浩， 须颖， 罗杨泉， 肖海善 - 机械工程学报, 2022 - qikan.cmes.org
渗透率是多孔介质的重要属性, 衡量多孔介质对流体的阻碍能力. 现有渗透率计算方法如有限
体积法(Finite volume method, FVM), 格子玻尔兹曼(Lattice Boltzmann method, LBM) …
体积法(Finite volume method, FVM), 格子玻尔兹曼(Lattice Boltzmann method, LBM) …
Dual neural network architecture for determining permeability and associated uncertainty
The computation of permeability is vital for reservoir characterization because it is a key
parameter in the reservoir models used for estimating and optimizing hydrocarbon …
parameter in the reservoir models used for estimating and optimizing hydrocarbon …
[HTML][HTML] Hydrocarbon detections using multi-attributes based quantum neural networks in a tight sandstone gas reservoir in the Sichuan Basin, China
A direct hydrocarbon detection is performed by using multi-attributes based quantum neural
networks with gas fields. The proposed multi-attributes based quantum neural networks for …
networks with gas fields. The proposed multi-attributes based quantum neural networks for …
A circle/sphere populating method to generate 2D/3D stochastic microstructures
Y Li, D Liu, W Yan - Heliyon, 2023 - cell.com
A circle/sphere populating method is proposed to generate 2D/3D stochastic
microstructures. The proposed method uses circles/spheres as the basic elements and …
microstructures. The proposed method uses circles/spheres as the basic elements and …