Prediction of air quality index using machine learning techniques: a comparative analysis

NS Gupta, Y Mohta, K Heda, R Armaan… - … and Public Health, 2023 - Wiley Online Library
An index for reporting air quality is called the air quality index (AQI). It measures the impact
of air pollution on a person's health over a short period of time. The purpose of the AQI is to …

Permeability prediction of heterogeneous carbonate gas condensate reservoirs applying group method of data handling

MZ Kamali, S Davoodi, H Ghorbani, DA Wood… - Marine and Petroleum …, 2022 - Elsevier
Carbonate petroleum reservoirs typically have lower permeabilities and recovery factors
than sandstone reservoirs, so the natural fractures they often incorporate have positive …

Predicting shear wave velocity from conventional well logs with deep and hybrid machine learning algorithms

M Rajabi, O Hazbeh, S Davoodi, DA Wood… - Journal of Petroleum …, 2023 - Springer
Shear wave velocity (VS) data from sedimentary rock sequences is a prerequisite for
implementing most mathematical models of petroleum engineering geomechanics …

Optimized machine learning models for natural fractures prediction using conventional well logs

S Tabasi, PS Tehrani, M Rajabi, DA Wood, S Davoodi… - Fuel, 2022 - Elsevier
Identifying and characterizing natural fractures is essential for understanding fluid flow and
drainage in many oil and gas reservoirs, particularly carbonate. The presence of fractures …

Machine learning-a novel approach to predict the porosity curve using geophysical logs data: an example from the Lower Goru sand reservoir in the Southern Indus …

W Hussain, M Luo, M Ali, SM Hussain, S Ali… - Journal of Applied …, 2023 - Elsevier
Porosity estimation is one of the essential issues in oil and natural gas industries to evaluate
the reservoir characteristics properly. Therefore, it is imperative to predict porosity with the …

[HTML][HTML] A robust approach to pore pressure prediction applying petrophysical log data aided by machine learning techniques

G Zhang, S Davoodi, SS Band, H Ghorbani, A Mosavi… - Energy Reports, 2022 - Elsevier
Determination of pore pressure (PP), a key reservoir parameter that is beneficial for
evaluating geomechanical parameters of the reservoir, is so important in oil and gas fields …

Novel hybrid machine learning optimizer algorithms to prediction of fracture density by petrophysical data

M Rajabi, S Beheshtian, S Davoodi, H Ghorbani… - Journal of Petroleum …, 2021 - Springer
One of the challenges in reservoir management is determining the fracture density (FVDC)
in reservoir rock. Given the high cost of coring operations and image logs, the ability to …

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 …
M Ali, M Ehsan… - Geoenergy Science and …, 2023 - Elsevier
The present study aims to better understand the mineralogy and thermal structure of the
Yingxiu-Beichuan fault zone (YBFZ), Sichuan basin, China, which was lacking previously …

[HTML][HTML] Data driven models to predict pore pressure using drilling and petrophysical data

F Jafarizadeh, M Rajabi, S Tabasi, R Seyedkamali… - Energy Reports, 2022 - Elsevier
The mud weight window (MW) determination is one of the most important parameters in
drilling oil and gas wells, where accurate design can secure the drilled well and deliver a …