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Production Forecasting of Petroleum Reservoir applying Higher-Order Neural Networks (HONN) with Limited Reservoir Data

by Chithra Chakra N C, Ki-young Song, Deoki N Saraf, Madan M Gupta
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 72 - Number 2
Year of Publication: 2013
Authors: Chithra Chakra N C, Ki-young Song, Deoki N Saraf, Madan M Gupta
10.5120/12466-8834

Chithra Chakra N C, Ki-young Song, Deoki N Saraf, Madan M Gupta . Production Forecasting of Petroleum Reservoir applying Higher-Order Neural Networks (HONN) with Limited Reservoir Data. International Journal of Computer Applications. 72, 2 ( June 2013), 23-35. DOI=10.5120/12466-8834

@article{ 10.5120/12466-8834,
author = { Chithra Chakra N C, Ki-young Song, Deoki N Saraf, Madan M Gupta },
title = { Production Forecasting of Petroleum Reservoir applying Higher-Order Neural Networks (HONN) with Limited Reservoir Data },
journal = { International Journal of Computer Applications },
issue_date = { June 2013 },
volume = { 72 },
number = { 2 },
month = { June },
year = { 2013 },
issn = { 0975-8887 },
pages = { 23-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume72/number2/12466-8834/ },
doi = { 10.5120/12466-8834 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:36:51.016674+05:30
%A Chithra Chakra N C
%A Ki-young Song
%A Deoki N Saraf
%A Madan M Gupta
%T Production Forecasting of Petroleum Reservoir applying Higher-Order Neural Networks (HONN) with Limited Reservoir Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 72
%N 2
%P 23-35
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Accurate and reliable production forecasting is certainly a significant step for the management and planning of the petroleum reservoirs. This paper presents a new neural approach called higher-order neural network (HONN) to forecast the oil production of a petroleum reservoir. In HONN, the neural input variables are correlated linearly as well as nonlinearly, which overcomes the limitation of the conventional neural network. Hence, HONN is a promising technique for petroleum reservoir production forecasting without sufficient network training data. A sandstone reservoir located in Gujarat, India was chosen for simulation studies, to prove the efficiency of HONNs in oil production forecasting with insufficient data available. In order to reduce noise in the measured data from the oil field a pre-processing procedure that consists of a low pass filter was used. Also an autocorrelation function (ACF) and cross-correlation function (CCF) was employed for selecting the optimal input variables. The results from these simulation studies show that the HONN models have enhanced forecasting capability with higher accuracy in the prediction of oil production.

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Index Terms

Computer Science
Information Sciences

Keywords

Production forecasting reservoir performance higher-order neural network higher-order synaptic operation

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