Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part II

A Samnioti, V Gaganis - Energies, 2023 - mdpi.com
In recent years, Machine Learning (ML) has become a buzzword in the petroleum industry,
with numerous applications which guide engineers in better decision making. The most …

Performance forecasting for polymer flooding in heavy oil reservoirs

E Amirian, M Dejam, Z Chen - Fuel, 2018 - Elsevier
As a supply for future fuel and energy demand, 95% of the bitumen deposits in North
America are expected to become a major source. The Steam Assisted Gravity Drainage …

Modeling of wax disappearance temperature (WDT) using soft computing approaches: Tree-based models and hybrid models

B Amiri-Ramsheh, M Safaei-Farouji, A Larestani… - Journal of Petroleum …, 2022 - Elsevier
Solid scales can cause significant problems in oil production and transmission systems such
as oil flow rate reduction. Wax is one of the most critical substances that are highly prone to …

[HTML][HTML] Predicting the surfactant-polymer flooding performance in chemical enhanced oil recovery: Cascade neural network and gradient boosting decision tree

A Larestani, SP Mousavi, F Hadavimoghaddam… - Alexandria Engineering …, 2022 - Elsevier
Surfactant-polymer flooding is one of the most important enhanced oil recovery (EOR)
techniques, which refers to the injection of surfactant slugs and polymer drives. Two crucial …

Integrated cluster analysis and artificial neural network modeling for steam-assisted gravity drainage performance prediction in heterogeneous reservoirs

E Amirian, JY Leung, S Zanon, P Dzurman - Expert Systems with …, 2015 - Elsevier
Abstract Evaluation of steam-assisted gravity drainage (SAGD) performance that involves
detailed compositional simulations is usually deterministic, cumbersome, expensive …

Developing a robust surrogate model of chemical flooding based on the artificial neural network for enhanced oil recovery implications

MA Ahmadi - Mathematical Problems in Engineering, 2015 - Wiley Online Library
Application of chemical flooding in petroleum reservoirs turns into hot topic of the recent
researches. Development strategies of the aforementioned technique are more robust and …

Potential for prediction of water saturation distribution in reservoirs utilizing machine learning methods

Q Zhang, C Wei, Y Wang, S Du, Y Zhou, H Song - Energies, 2019 - mdpi.com
Machine learning technology is becoming increasingly prevalent in the petroleum industry,
especially for reservoir characterization and drilling problems. The aim of this study is to …

Integrated dynamic evaluation of depletion-drive performance in naturally fractured-vuggy carbonate reservoirs using DPSO–FCM clustering

D Wang, Y Li, Y Hu, B Li, X Deng, Z Liu - Fuel, 2016 - Elsevier
Compared to the widely distributed porous and fractured-porous carbonate reservoirs, the
naturally fractured-vuggy carbonate reservoirs (NFVCRs) found in Tarim Basin, China, suffer …

Integrating a robust model for predicting surfactant–polymer flooding performance

A Kamari, F Gharagheizi, A Shokrollahi… - Journal of Petroleum …, 2016 - Elsevier
The combination of surfactant and polymer in injecting water will improve the oil recovery
during a water flood. The surfactant–polymer (SP) flooding would be more effective if …

Artificial intelligence approach to predict drag reduction in crude oil pipelines

R Zabihi, D Mowla, HR Karami - Journal of Petroleum Science and …, 2019 - Elsevier
The addition of a small amount of drag reducing agents (DRAs) to a flowing fluid in a
pipeline causes reduction in pressure drop through the pipeline. As a result, energy …