A Novel Multi-Input AlexNet Prediction Model for Oil and Gas Production
Joint Authors
Wang, Yang
Lv, Yin
Guo, Dali
Zhang, Shu
Jiao, Shixiang
Source
Mathematical Problems in Engineering
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-12-04
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
In the process of oilfield development, it is important to predict the oil and gas production.
The predicted value of oil production is the amount of oil that may be obtained within a certain area over a certain period.
Because of the current demand for oil and gas production prediction, a prediction model using a multi-input convolutional neural network based on AlexNet is proposed in this paper.
The model predicts real oilfield data and achieves good results: increasing prediction accuracy by 17.5%, 20.8%, 11.6%, 8.9%, 6.9%, and 14.9% with respect to the backpropagation neural network, support vector machine, artificial neural network, radial basis function neural network, K-nearest neighbor, and decision tree methods, respectively.
It addresses the uncertainty of oil and gas production caused by the change in parameter values during the process of petroleum exploitation and has far-reaching application significance.
American Psychological Association (APA)
Wang, Yang& Lv, Yin& Guo, Dali& Zhang, Shu& Jiao, Shixiang. 2018. A Novel Multi-Input AlexNet Prediction Model for Oil and Gas Production. Mathematical Problems in Engineering،Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1207807
Modern Language Association (MLA)
Wang, Yang…[et al.]. A Novel Multi-Input AlexNet Prediction Model for Oil and Gas Production. Mathematical Problems in Engineering No. 2018 (2018), pp.1-9.
https://search.emarefa.net/detail/BIM-1207807
American Medical Association (AMA)
Wang, Yang& Lv, Yin& Guo, Dali& Zhang, Shu& Jiao, Shixiang. A Novel Multi-Input AlexNet Prediction Model for Oil and Gas Production. Mathematical Problems in Engineering. 2018. Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1207807
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1207807