Exploring the power of machine learning to predict carbon dioxide trapping efficiency in saline aquifers for carbon geological storage project
Carbon geological sequestration (CGS) in saline aquifers is an effective carbon utilization
approach to decrease the effect of greenhouse gases on the atmosphere. However, the …
approach to decrease the effect of greenhouse gases on the atmosphere. However, the …
Application of robust intelligent schemes for accurate modelling interfacial tension of CO2 brine systems: Implications for structural CO2 trapping
Given the current global climate change, renewable energy sources, carbon capture,
utilization, and storage (CCUS) are being considered as a potential solutions to this critical …
utilization, and storage (CCUS) are being considered as a potential solutions to this critical …
Seismic random noise suppression by using deep residual U-Net
T Zhong, M Cheng, X Dong, Y Li, N Wu - Journal of Petroleum Science and …, 2022 - Elsevier
Pre-stack seismic denoising is one of most important processing steps in seismic
exploration, which can significantly enhance the signal-to-noise ratio (SNR) and resolution …
exploration, which can significantly enhance the signal-to-noise ratio (SNR) and resolution …
Modeling interfacial tension of surfactant–hydrocarbon systems using robust tree-based machine learning algorithms
A Rashidi-Khaniabadi, E Rashidi-Khaniabadi… - Scientific Reports, 2023 - nature.com
Interfacial tension (IFT) between surfactants and hydrocarbon is one of the important
parameters in petroleum engineering to have a successful enhanced oil recovery (EOR) …
parameters in petroleum engineering to have a successful enhanced oil recovery (EOR) …
Prediction of hydrogen solubility in aqueous solutions: Comparison of equations of state and advanced machine learning-metaheuristic approaches
Hydrogen is the primary carrier of renewable energy stored underground. Understanding
the solubility of hydrogen in water is critical for subsurface storage. Accurately measuring the …
the solubility of hydrogen in water is critical for subsurface storage. Accurately measuring the …
Pore structure characterization of solvent extracted shale containing kerogen type III during artificial maturation: Experiments and tree-based machine learning …
B Liu, MR Mohammadi, Z Ma, L Bai, L Wang, Y Xu… - Energy, 2023 - Elsevier
Shale samples with type III kerogen from the Damoguaihe formation were exposed to
hydrous and anhydrous pyrolysis (HP and AHP) in the temperature range of 300–450° C …
hydrous and anhydrous pyrolysis (HP and AHP) in the temperature range of 300–450° C …
Modeling Viscosity of CO2–N2 Gaseous Mixtures Using Robust Tree-Based Techniques: Extra Tree, Random Forest, GBoost, and LightGBM
H Zheng, A Mahmoudzadeh, B Amiri-Ramsheh… - ACS …, 2023 - ACS Publications
Carbon dioxide (CO2) has an essential role in most enhanced oil recovery (EOR) methods
in the oil industry. Oil swelling and viscosity reduction are the dominant mechanisms in an …
in the oil industry. Oil swelling and viscosity reduction are the dominant mechanisms in an …
[HTML][HTML] Exploring the power of machine learning in analyzing the gas minimum miscibility pressure in hydrocarbons
Abstract Minimum Miscibility Pressure (MMP) plays a crucial role in subsurface gas injection
processes. Hence, the accurate determination and analysis of the effective parameters on …
processes. Hence, the accurate determination and analysis of the effective parameters on …
Modeling wax deposition of crude oils using cascade forward and generalized regression neural networks: Application to crude oil production
B Amiri-Ramsheh, R Zabihi… - Geoenergy Science and …, 2023 - Elsevier
Crude oil is made of different compositions and materials, namely hydrocarbons, oxygen,
nitrogen, sulfur, and various metals. Deposition of heavy components can lead to a …
nitrogen, sulfur, and various metals. Deposition of heavy components can lead to a …
[HTML][HTML] Knowledge-based rigorous machine learning techniques to predict the deliverability of underground natural gas storage sites for contributing to sustainable …
This study presents a method to develop a series of unique deliverability smart models for
underground natural gas storage (UNGS) in different types of target formations. The natural …
underground natural gas storage (UNGS) in different types of target formations. The natural …