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A CNN-BiGRU-AM neural network for AI applications in shale oil production prediction
G Zhou, Z Guo, S Sun, Q ** - Applied Energy, 2023 - Elsevier
In the coming decades, the demand for shale oil will likely surge because of predicted
increases in the global population and productivity. Efficiently predicting shale oil production …
increases in the global population and productivity. Efficiently predicting shale oil production …
[HTML][HTML] Total organic carbon content logging prediction based on machine learning: A brief review
The total organic carbon content usually determines the hydrocarbon generation potential of
a formation. A higher total organic carbon content often corresponds to a greater possibility …
a formation. A higher total organic carbon content often corresponds to a greater possibility …
A review on intelligent recognition with logging data: tasks, current status and challenges
X Zhu, H Zhang, Q Ren, L Zhang, G Huang… - Surveys in …, 2024 - Springer
Geophysical logging series are valuable geological data that record the physical and
chemical information of borehole walls and in-situ formations, and are widely used by …
chemical information of borehole walls and in-situ formations, and are widely used by …
[HTML][HTML] Surface roughness prediction of machined aluminum alloy with wire electrical discharge machining by different machine learning algorithms
Aluminum alloys are preferred in aviation, aerospace and automotive industries because of
their high strength and durability compared to their lightness. Precision production of parts is …
their high strength and durability compared to their lightness. Precision production of parts is …
[HTML][HTML] Machine learning and data-driven prediction of pore pressure from geophysical logs: A case study for the Mangahewa gas field, New Zealand
Pore pressure is an essential parameter for establishing reservoir conditions, geological
interpretation and drilling programs. Pore pressure prediction depends on information from …
interpretation and drilling programs. Pore pressure prediction depends on information from …
An improved neural network for TOC, S1 and S2 estimation based on conventional well logs
H Wang, W Wu, T Chen, X Dong, G Wang - Journal of Petroleum Science …, 2019 - Elsevier
Total organic carbon (TOC), volatile hydrocarbon (S 1) and remaining hydrocarbon (S 2) are
significant factors for shale oil and gas exploration and development. However, the TOC, S 1 …
significant factors for shale oil and gas exploration and development. However, the TOC, S 1 …
Application of machine learning models for real-time prediction of the formation lithology and tops from the drilling parameters
Lithology changes significantly affect the drilling program and the total cost of drilling an oil
well, therefore, it is very important to detect the lithology variation and formation tops while …
well, therefore, it is very important to detect the lithology variation and formation tops while …
A new and reliable dual model-and data-driven TOC prediction concept: A TOC logging evaluation method using multiple overlap** methods integrated with semi …
The total organic carbon content (TOC) is the most important parameter when determining
the source rock quality. At present, there are two main types of TOC well logging calculation …
the source rock quality. At present, there are two main types of TOC well logging calculation …
Forming a new small sample deep learning model to predict total organic carbon content by combining unsupervised learning with semisupervised learning
The total organic carbon (TOC) content is a parameter that is directly used to evaluate the
hydrocarbon generation capacity of a reservoir. For a reservoir, accurately calculating TOC …
hydrocarbon generation capacity of a reservoir. For a reservoir, accurately calculating TOC …
A machine learning methodology for multivariate pore-pressure prediction
Accurate pore-pressure prediction is of essential importance to hydrocarbon exploration and
development. A multivariate prediction model of multiple petrophysical data is required to …
development. A multivariate prediction model of multiple petrophysical data is required to …