Efficient surrogate modeling based on improved vision transformer neural network for history matching
D Zhang, H Li - SPE Journal, 2023 - onepetro.org
For history-matching problems, simulations of reservoir models usually involve high
computational costs. Surrogate modeling based on deep learning has proved to be an …
computational costs. Surrogate modeling based on deep learning has proved to be an …
Recent developments in denoising medical images using deep learning: an overview of models, techniques, and challenges.
Medical imaging plays a critical role in diagnosing and treating various medical conditions.
However, interpreting medical images can be challenging even for expert clinicians, as they …
However, interpreting medical images can be challenging even for expert clinicians, as they …
An oil production prediction approach based on variational mode decomposition and ensemble learning model
J Fang, Z Yan, X Lu, Y Xiao, Z Zhao - Computers & Geosciences, 2024 - Elsevier
Well production forecasting can provide scientific guidance for oilfield production and
management, which is an indispensable part of the oilfield development process. In this …
management, which is an indispensable part of the oilfield development process. In this …
A systematic evaluation of machine learning approaches for petroleum production forecasting
Accurate production forecasting from oil well is an important decision-making variable for the
Oil & Gas industry. Conventionally, future production is estimated using mechanistic …
Oil & Gas industry. Conventionally, future production is estimated using mechanistic …
Dynamic physics-guided deep learning for long-term production forecasting in unconventional reservoirs
Neural network predictive models are popular for production forecasting in unconventional
reservoirs due to their ability to learn complex relationships between well properties and …
reservoirs due to their ability to learn complex relationships between well properties and …
Deep learning-based prediction of subsurface oil reservoir pressure using spatio-temporal data
Prediction of subsurface oil reservoir pressure are critical to hydrocarbon production.
However, the accurate pressure estimation faces great challenges due to the complexity and …
However, the accurate pressure estimation faces great challenges due to the complexity and …
[HTML][HTML] Integrating Machine Learning with Intelligent Control Systems for Flow Rate Forecasting in Oil Well Operations
B Amangeldy, N Tasmurzayev, S Shinassylov… - Automation, 2024 - mdpi.com
This study addresses the integration of machine learning (ML) with supervisory control and
data acquisition (SCADA) systems to enhance predictive maintenance and operational …
data acquisition (SCADA) systems to enhance predictive maintenance and operational …
Long-term, multi-variate production forecasting using non-stationary transformer
A Kumar - International Petroleum Technology Conference, 2024 - onepetro.org
Petroleum production forecasting plays an important role in business decisions related to
field development planning. Machine learning and artificial intelligence have been used …
field development planning. Machine learning and artificial intelligence have been used …
A Data-Driven Approach for Stylolite Detection
Stylolite is a specific geopattern that can occur in both sedimentary rocks and deformed
zones, which could change porosity of the reservoir, modify the permeability, and even result …
zones, which could change porosity of the reservoir, modify the permeability, and even result …
NTformer: Near Time Transformer for multi-step prediction of wellhead pressure in fracturing operations
Y Zhong, J Zhou, T Zhang, J Yang, P Li… - Geoenergy Science and …, 2023 - Elsevier
Fracturing process technology is a major factor in the effectiveness of fracturing for
increasing production. One of the most critical issues during the construction of fracturing is …
increasing production. One of the most critical issues during the construction of fracturing is …