Multi-well placement optimisation using sequential artificial neural networks and multi-level grid system

2020 ◽  
Vol 24 (4) ◽  
pp. 445 ◽  
Author(s):  
Ilsik Jang ◽  
Seeun Oh ◽  
Hyunjeong Kang ◽  
Juhwan Na ◽  
Baehyun Min
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Grid System ◽  
Well Placement ◽  
Multi Level ◽  
Artificial Neural

Automated multi-level pathology identification techniques for abnormal retinal images using artificial neural networks

2011 ◽  
Vol 96 (2) ◽  
pp. 220-223 ◽  
Author(s):  
J Anitha ◽  
C Kezi Selva Vijila ◽  
A Immanuel Selvakumar ◽  
A Indumathy ◽  
D Jude Hemanth
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Retinal Images ◽  
Multi Level ◽  
Artificial Neural ◽  
Identification Techniques

scholarly journals Well-placement optimisation using sequential artificial neural networks

2017 ◽  
Vol 36 (3) ◽  
pp. 433-449 ◽  
Author(s):  
Ilsik Jang ◽  
Seeun Oh ◽  
Yumi Kim ◽  
Changhyup Park ◽  
Hyunjeong Kang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Global Solution ◽  
Search Space ◽  
Superior Performance ◽  
Training Dataset ◽  
Well Placement ◽  
Artificial Neural

In this study, a new algorithm is proposed by employing artificial neural networks in a sequential manner, termed the sequential artificial neural network, to obtain a global solution for optimizing the drilling location of oil or gas reservoirs. The developed sequential artificial neural network is used to successively narrow the search space to efficiently obtain the global solution. When training each artificial neural network, pre-defined amount of data within the new search space are added to the training dataset to improve the estimation performance. When the size of the search space meets a stopping criterion, reservoir simulations are performed for data in the search space, and a global solution is determined among the simulation results. The proposed method was applied to optimise a horizontal well placement in a coalbed methane reservoir. The results show a superior performance in optimisation while significantly reducing the number of simulations compared to the particle-swarm optimisation algorithm.

Multi-Level Interval Estimation for Locating damage in Structures by Using Artificial Neural Networks

2010 ◽  
Author(s):  
Pan Dan-guang ◽  
Gao Yan-hua ◽  
Song Jun-lei ◽  
Jane W. Z. Lu ◽  
Andrew Y. T. Leung ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Interval Estimation ◽  
Multi Level ◽  
Artificial Neural
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