Application of supervised machine learning paradigms in the prediction of petroleum reservoir properties: Comparative analysis of ANN and SVM models
Abstract The advent of Artificial Intelligence (AI) in the petroleum industry has seen an
increase in its use in exploration, development, production, reservoir engineering and …
increase in its use in exploration, development, production, reservoir engineering and …
Classification of earthquakes, explosions and mining-induced earthquakes based on XGBoost algorithm
T Wang, Y Bian, Y Zhang, X Hou - Computers & Geosciences, 2023 - Elsevier
The classification of low-magnitude tectonic earthquakes, explosions and mining-induced
earthquakes is an important task in regional earthquake monitoring. Seismic events …
earthquakes is an important task in regional earthquake monitoring. Seismic events …
Porosity and permeability prediction using a transformer and periodic long short-term network
Effective reservoir parameter prediction is important for subsurface characterization and
understanding fluid migration. However, conventional methods for obtaining porosity and …
understanding fluid migration. However, conventional methods for obtaining porosity and …
High-fidelity permeability and porosity prediction using deep learning with the self-attention mechanism
Accurate estimation of reservoir parameters (eg, permeability and porosity) helps to
understand the movement of underground fluids. However, reservoir parameters are usually …
understand the movement of underground fluids. However, reservoir parameters are usually …
Improved bias value and new membership function to enhance the performance of fuzzy support vector Machine
Y Dhanasekaran, P Murugesan - Expert Systems with Applications, 2022 - Elsevier
The effect of an outlier in classification is a problem in the Fuzzy Support Vector Machine
(FSVM). In this paper, two new methodologies have been implemented to compute fuzzy …
(FSVM). In this paper, two new methodologies have been implemented to compute fuzzy …
Pre-earthquake anomaly extraction from borehole strain data based on machine learning
C Chi, C Li, Y Han, Z Yu, X Li, D Zhang - Scientific Reports, 2023 - nature.com
Borehole strain monitoring plays a critical role in earthquake precursor research. With the
accumulation of observation data, traditional data processing methods struggle to handle …
accumulation of observation data, traditional data processing methods struggle to handle …
Machine-learning-based earthquake locations reveal the seismogenesis of the 2020 Mw 5.0 Qiaojia, Yunnan earthquake
SUMMARY A moment magnitude (M w) 5.0 earthquake hit Qiaojia, Yunnan, China on 18
May 2020. Its hypocentre is only approximately 20 km away from the Baihetan reservoir, the …
May 2020. Its hypocentre is only approximately 20 km away from the Baihetan reservoir, the …
Recent advances in earthquake seismology using machine learning
Given the recent developments in machine-learning technology, its application has rapidly
progressed in various fields of earthquake seismology, achieving great success. Here, we …
progressed in various fields of earthquake seismology, achieving great success. Here, we …
On the Use of Accelerometric Data to Monitor the Seismic Performance of Non-Structural Elements in Existing Buildings: A Case Study
Monitoring of non-structural elements is not usually implemented, despite the seismic
vulnerability of these components and the significant cost associated with their replacement …
vulnerability of these components and the significant cost associated with their replacement …
Advancing Local Distance Discrimination of Explosions and Earthquakes With Joint P/S and ML‐MC Classification
Classification of local‐distance, low‐magnitude seismic events is challenging because
signals can be numerous and difficult to characterize with approaches developed for larger …
signals can be numerous and difficult to characterize with approaches developed for larger …