Predicting formation damage of oil fields due to mineral scaling during water-flooding operations: Gradient boosting decision tree and cascade-forward back …

A Larestani, SP Mousavi, F Hadavimoghaddam… - Journal of Petroleum …, 2022 - Elsevier
Water-flooding is one of the main options employed by the oil industry to meet the world's
ever-increasing demand for oil, as the primary source of energy. This approach is highly …

Real-time prognosis of flowing bottom-hole pressure in a vertical well for a multiphase flow using computational intelligence techniques

Z Tariq, M Mahmoud, A Abdulraheem - Journal of Petroleum Exploration …, 2020 - Springer
An accurate prediction of well flowing bottom-hole pressure (FBHP) is highly needed in
petroleum engineering applications such as for the field production optimization, cost per …

Modeling of asphaltic sludge formation during acidizing process of oil well reservoir using machine learning methods

S Shakouri, M Mohammadzadeh-Shirazi - Energy, 2023 - Elsevier
Considering the global need for fossil fuels and its limited resources, maximum production
from oil reservoirs is important. Acid treatment is a common method to stimulate oil …

Interpretable knowledge-guided framework for modeling reservoir water-sensitivity damage based on Light Gradient Boosting Machine using Bayesian optimization …

K Sheng, G Jiang, M Du, Y He, T Dong… - Engineering Applications of …, 2024 - Elsevier
Reservoir water sensitivity damage significantly contributes to production declines in low-
permeability oil and gas fields. An accurate and rapid assessment of water sensitivity is …

[HTML][HTML] Machine learning approaches for assessing stability in acid-crude oil emulsions: application to mitigate formation damage

S Shakouri, M Mohammadzadeh-Shirazi - Petroleum Science, 2024 - Elsevier
The stability of acid-crude oil emulsion poses manifold issues in the oil industry.
Experimentally evaluating this phenomenon may be costly and time-consuming. In contrast …

A new method for building porosity and permeability models of a fractured granite basement reservoir

TBN Nguyen, W Bae, LA Nguyen… - Petroleum science and …, 2014 - Taylor & Francis
This paper presents the procedure and results of building porosity and permeability models
for a fractured basement reservoir in offshore Vietnam by using Artificial Neural Network …

A Data-Driven Machine Learning Approach to Predict the Natural Gas Density of Pure and Mixed Hydrocarbons

Z Tariq, A Hassan, UB Waheed… - Journal of …, 2021 - asmedigitalcollection.asme.org
Natural gas is one of the main fossil energy resources, and its density is an effective
thermodynamic property, which is required in almost every pressure–volume–temperature …

Predicting aqueous phase trapping damage in tight reservoirs using quantum neural networks

Y Sun, J Zhao, M Bai - Environmental Earth Sciences, 2015 - Springer
Formation damage associated with aqueous phase trapping (APT) often occurs during
drilling wells using water-based fluids in tight reservoirs. Prediction of a reservoir's APT …

Improving history match using artificial neural networks

MA Habib, AM Abdulaziz… - International Journal of …, 2016 - inderscienceonline.com
Of late, the application of the artificial intelligence in oil industry has been increasing and
seems promising in reservoir simulation studies. In this research, an artificial neural network …

Modeling formation damage due to flocculated asphaltene deposition

A Rezaian, MH Sefat, M Alipanah… - Petroleum science …, 2012 - Taylor & Francis
In this article, a hybrid model of an analytical and artificial neural network simulation and
corresponding analytical method are applied using laboratory data obtained by performing …