A critical review of physics-informed machine learning applications in subsurface energy systems

A Latrach, ML Malki, M Morales, M Mehana… - Geoenergy Science and …, 2024 - Elsevier
Abstract Machine learning has emerged as a powerful tool in various fields, including
computer vision, natural language processing, and speech recognition. It can unravel …

Incorporation of physics into machine learning for production prediction from unconventional reservoirs: A brief review of the gray-box approach

HH Liu, J Zhang, F Liang, C Temizel… - … Reservoir Evaluation & …, 2021 - onepetro.org
Prediction of well production from unconventional reservoirs is often a complex problem with
an incomplete understanding of physics and a considerable amount of data. The most …

Prediction of solubility of N-alkanes in supercritical CO2 using RBF-ANN and MLP-ANN

M Abdi-Khanghah, A Bemani, Z Naserzadeh… - Journal of CO2 …, 2018 - Elsevier
Recently, due to declination of oil production the importance of enhancement of oil recovery
becomes highlighted. CO 2 injection as one of popular approaches because of …

A videographic assessment of ferrofluid during magnetic drug targeting: an application of artificial intelligence in nanomedicine

A Sohail, M Fatima, R Ellahi, KB Akram - Journal of Molecular Liquids, 2019 - Elsevier
Forecasting the thresholds via the computational analysis of magnetic drug targeting, is a
useful approach since it can help to design the nanoscale experiments to get the best results …

A generalized machine learning-assisted phase-equilibrium calculation model for shale reservoirs

F Chen, S Luo, S Wang, H Nasrabadi - Fluid Phase Equilibria, 2022 - Elsevier
In compositional reservoir simulation, a significant portion of the CPU time is consumed in
phase equilibrium calculations. Previous studies have incorporated the machine learning …

A thorough review of machine learning applications in oil and gas industry

C Temizel, CH Canbaz, Y Palabiyik, H Aydin… - SPE Asia Pacific Oil …, 2021 - onepetro.org
Reservoir engineering constitutes a major part of the studies regarding oil and gas
exploration and production. Reservoir engineering has various duties, including conducting …

[Књига][B] Data analytics in reservoir engineering

S Sankaran, S Matringe, M Sidahmed, L Saputelli… - 2020 - researchgate.net
Data Analytics in Reservoir Engineering Page 1 PetroBrief Data Analytics in Reservoir
Engineering Sathish Sankaran Sebastien Matringe Mohamed Sidahmed Luigi Saputelli …

Combining Machine Learning with Traditional Reservoir Physics for Predictive Modeling and Optimization of a Large Mature Waterflood Project in the Gulf of San …

C Calad, F Gutierrez, P Pastor, P Sarma - SPE Latin America and …, 2020 - onepetro.org
A novel technology that combines the benefits of speed of data sciences with the predictivity
capabilities of traditional simulation is being applied to model two blocks of a large …

Enhancing the Accuracy and Predictability of the Oxy Field Optimizer for Dynamic Steam Allocation in the Mukhaizna Steamflood Field

C Gao, D Le, N Al Qasabi, MM Al Mujaini, DM Dornier… - SPE Journal, 2024 - onepetro.org
The main challenge for the Mukhaizna steamflood field is to allocate steam dynamically
throughout the entire field, which consists of more than 3,200 wells, to obtain the most …

Predictive Model for Relative Permeability Using Physically-Constrained Artificial Neural Networks

HF Yoga, RT Johns, P Purswani - SPE Journal, 2024 - onepetro.org
Hysteresis of transport properties like relative permeability (kr) can lead to computational
problems and inaccuracies for various applications including CO 2 sequestration and …