NDLI: Prediction of oil well production using multi-neural networks

Content Provider	IEEE Xplore Digital Library
Author	Nguyen, H.H. Chan, C.W. Wilson, M.
Copyright Year	2002
Description	Author affiliation: Dept. of Comput. Sci., Regina Univ., Sask., Canada (Nguyen, H.H.)
Abstract	This study presents an application of a multiple artificial neural network (MNN) model to estimate the future production performance of oil wells based on monthly-production time series data. Several engineering techniques that require expertise and data that are difficult to obtain are used currently to predict well performance. By comparison, a MNN model relies only on a large amount of available historical data on well production. Single artificial neural network (ANN) models have been used to predict short-term production performance. However, the accuracy of such a model may be seriously compromised when it is used for making long-term multistep predictions. Hence, instead of using single-ANN models, we propose using multiple artificial neural networks to improve accuracy of the model while also Minimizing complexity. A MNN is a group of ANNs that work together to solve a problem. Each ANN makes a Prediction for a different time period The MNN model for prediction of future well performance is applied to the time series data obtained from four pools of wells in the southwestern region of Saskatchewan, Canada The results showed that a MNN model performed well and slightly better than a single-ANN model for long term prediction.
Sponsorship	Dept. Electr. & Comput. Eng. Faculty of Graduate Studies Faculty of Eng. TRLabs Internet Innovation Centre NRC Inst. Biodiagnostics APEGM IEEE Winnipeg Sec. IEEE Canada & IEEE Manitoba Hydro Vansco Electron. Bristol Aerosp. Nygard Int. Norsat
Starting Page	798
Ending Page	802
File Size	456858
Page Count	5
File Format	PDF
ISBN	0780375149
ISSN	08407789
DOI	10.1109/CCECE.2002.1013044
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-05-12
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Petroleum Production Predictive models Artificial neural networks Economic forecasting Computer networks Neural networks Data engineering Power engineering and energy Informatics
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Hardware and Architecture

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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