Artificial intelligence techniques and their application in oil and gas industry
S Choubey, GP Karmakar - Artificial Intelligence Review, 2021 - Springer
Data are being continuously generated from various operational steps in the oil and gas
industry. The recordings of these data and their proper utilization have become a major …
industry. The recordings of these data and their proper utilization have become a major …
Application of cascade forward neural network and group method of data handling to modeling crude oil pyrolysis during thermal enhanced oil recovery
MR Mohammadi, A Hemmati-Sarapardeh… - Journal of Petroleum …, 2021 - Elsevier
Oil recovery during in situ combustion is majorly controlled by hydrocarbon oxidation and
pyrolysis reactions, which govern fuel formation and heat evolution. Fuel deposition, in turn …
pyrolysis reactions, which govern fuel formation and heat evolution. Fuel deposition, in turn …
[HTML][HTML] Probing Solubility and pH of CO2 in aqueous solutions: Implications for CO2 injection into oceans
E Mohammadian, F Hadavimoghaddam… - Journal of CO2 …, 2023 - Elsevier
CO 2 sequestration is among the most anticipated methods to mitigate the already
detrimental concentrations of CO 2 in the atmosphere. Among sequestration methods, CO 2 …
detrimental concentrations of CO 2 in the atmosphere. Among sequestration methods, CO 2 …
Hybrid machine learning algorithms to predict condensate viscosity in the near wellbore regions of gas condensate reservoirs
ARB Abad, S Mousavi, N Mohamadian… - Journal of Natural Gas …, 2021 - Elsevier
Gas condensate reservoirs display unique phase behavior and are highly sensitive to
reservoir pressure changes. This makes it difficult to determine their PVT characteristics …
reservoir pressure changes. This makes it difficult to determine their PVT characteristics …
Modeling of methane adsorption capacity in shale gas formations using white-box supervised machine learning techniques
MN Amar, A Larestani, Q Lv, T Zhou… - Journal of Petroleum …, 2022 - Elsevier
Energy demand is increasing worldwide and shale gas formations have gained increasing
attention and have become crucial energy sources. Therefore, accurate determination of …
attention and have become crucial energy sources. Therefore, accurate determination of …
Prediction of CO2 diffusivity in brine using white-box machine learning
MN Amar, AJ Ghahfarokhi - Journal of Petroleum Science and Engineering, 2020 - Elsevier
Accurate knowledge of the diffusivity coefficient of CO 2 in brine has a significant effect on
the design success and monitoring of CO 2 storage in saline aquifers, which is a part of …
the design success and monitoring of CO 2 storage in saline aquifers, which is a part of …
[HTML][HTML] Predicting thermal conductivity of carbon dioxide using group of data-driven models
MN Amar, AJ Ghahfarokhi, N Zeraibi - Journal of the Taiwan Institute of …, 2020 - Elsevier
Thermal conductivity of carbon dioxide (CO 2) is a vital thermophysical parameter that
significantly affects the heat transfer modeling related to CO 2 transportation, pipelines …
significantly affects the heat transfer modeling related to CO 2 transportation, pipelines …
Review of modeling schemes and machine learning algorithms for fluid rheological behavior analysis
Abstract Machine learning's prowess in extracting insights from data has significantly
advanced fluid rheological behavior prediction. This machine-learning-based approach …
advanced fluid rheological behavior prediction. This machine-learning-based approach …
Modeling minimum miscibility pressure of pure/impure CO2-crude oil systems using adaptive boosting support vector regression: Application to gas injection …
A Dargahi-Zarandi, A Hemmati-Sarapardeh… - Journal of Petroleum …, 2020 - Elsevier
Carbon dioxide based enhanced oil recovery (CO2-EOR) techniques have been of great
interest due to CO2 effectiveness as an oil solvent under supercritical condition and …
interest due to CO2 effectiveness as an oil solvent under supercritical condition and …
A hybrid wavelet decomposer and GMDH-ELM ensemble model for Network function virtualization workload forecasting in cloud computing
S Jeddi, S Sharifian - Applied Soft Computing, 2020 - Elsevier
Nowadays Network function virtualization (NFV) has drawn immense attention from many
cloud providers because of its benefits. NFV enables networks to virtualize node functions …
cloud providers because of its benefits. NFV enables networks to virtualize node functions …