[HTML][HTML] Estimation of the non records logs from existing logs using artificial neural networks

MM Salehi, M Rahmati, M Karimnezhad… - Egyptian Journal of …, 2017 - Elsevier
Finding the information of the hydrocarbon reservoirs from well logs is one of the main
objectives of the engineers. But, missing the log records (due to many reasons such as …

Synthetic geochemical well logs generation using ensemble machine learning techniques for the Brazilian pre-salt reservoirs

LAB De Oliveira, C de Carvalho Carneiro - Journal of Petroleum Science …, 2021 - Elsevier
Geochemical logs are an essential tool for hydrocarbon reservoir characterization. The rock
composition given by these logs is useful for porosity calculation, stratigraphic modeling …

Integration of deep neural networks and ensemble learning machines for missing well logs estimation

H Jian, L Chenghui, C Zhimin, M Haiwei - Flow Measurement and …, 2020 - Elsevier
Geophysical logging is one of the most important measurement techniques for the oil/gas
development and exploration industry. In practice, missing well logs estimation/prediction or …

Logging curve prediction method based on CNN-LSTM-attention

M Shi, B Yang, R Chen, D Ye - Earth Science Informatics, 2022 - Springer
Logging curves are an important basis for geological development planning and
hydrocarbon reserve exploration. However, in the actual logging process, there are often …

IDS fitted Q improvement using fuzzy approach for resource provisioning in cloud

M Amiri, MR Feizi-Derakhshi… - Journal of Intelligent …, 2017 - content.iospress.com
Reinforcement Learning (RL) is used to find the best policy. A policy is a rule that maps a
given state to an appropriate action. The RL is used to learn utility functions for dynamic …

An adaptive RL based approach for dynamic resource provisioning in Cloud virtualized data centers

F Bahrpeyma, H Haghighi, A Zakerolhosseini - Computing, 2015 - Springer
Because of numerous parameters existing in the Cloud's environment, it is helpful to
introduce a general solution for dynamic resource provisioning in Cloud that is able to …

[PDF][PDF] Reliability-aware: task scheduling in cloud computing using multi-agent reinforcement learning algorithm and neural fitted Q.

HAM Balla, CG Sheng, W Jing - Int. Arab J. Inf. Technol., 2021 - iajit.org
Cloud computing becomes the basic alternative platform for the most users application in the
recent years. The complexity increasing in cloud environment due to the continuous …

Using IDS fitted Q to develop a real-time adaptive controller for dynamic resource provisioning in Cloud's virtualized environment

F Bahrpeyma, A Zakerolhoseini, H Haghighi - Applied Soft Computing, 2015 - Elsevier
Reinforcement learning (RL) is a powerful solution to adaptive control when no explicit
model exists for the system being controlled. To handle uncertainty along with the lack of …

Photoelectric factor prediction using automated learning and uncertainty quantification

K Alsamadony, AF Ibrahim, S Elkatatny… - Neural Computing and …, 2023 - Springer
The photoelectric factor (PEF) is an important well-logging tool to distinguish between
different types of reservoir rocks because PEF measurement is sensitive to elements with …

A new clustering-based approach for target tracking to optimize energy consumption in wireless sensor networks

R Hosseini, H Mirvaziri - Wireless Personal Communications, 2020 - Springer
The emergence of wireless sensor networks is counted among the most significant
achievements of the late 20's. Nowadays, by the ever increasing utilization of new …