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A critical review of physics-informed machine learning applications in subsurface energy systems
Abstract Machine learning has emerged as a powerful tool in various fields, including
computer vision, natural language processing, and speech recognition. It can unravel …
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
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 …
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
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 …
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
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 …
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
In compositional reservoir simulation, a significant portion of the CPU time is consumed in
phase equilibrium calculations. Previous studies have incorporated the machine learning …
phase equilibrium calculations. Previous studies have incorporated the machine learning …
A thorough review of machine learning applications in oil and gas industry
Reservoir engineering constitutes a major part of the studies regarding oil and gas
exploration and production. Reservoir engineering has various duties, including conducting …
exploration and production. Reservoir engineering has various duties, including conducting …
[Књига][B] Data analytics in reservoir engineering
Data Analytics in Reservoir Engineering Page 1 PetroBrief Data Analytics in Reservoir
Engineering Sathish Sankaran Sebastien Matringe Mohamed Sidahmed Luigi Saputelli …
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 …
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 …
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
Hysteresis of transport properties like relative permeability (kr) can lead to computational
problems and inaccuracies for various applications including CO 2 sequestration and …
problems and inaccuracies for various applications including CO 2 sequestration and …