Improving normalization method of higher-order neural network in the forecasting of oil production

被引：4

作者：

Prasetyo, Joko ^{[1
]}

Setiawan, Noor Akhmad ^{[1
]}

Adji, Teguh Bharata ^{[1
]}

机构：

[1] Univ Gadjah Mada, Dept Elect Engn, Yogyakarta, Indonesia

来源：

1ST GEOSCIENCES AND ENVIRONMENTAL SCIENCES SYMPOSIUM (ICST 2020) | 2020年 / 200卷

关键词：

oil production forecast; time-series; higher-order neural network; normalization; PREDICTION;

D O I：

10.1051/e3sconf/202020002016

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

One of the challenges in the oil industry is to predict well production in the absence of frequent flow measurement. Many researches have been done to develop production forecasting in the petroleum area. One of the machine learning approach utilizing higher-order neural network (HONN) have been introduced in the previous study. In this study, research focus on normalization impact to the HONN model, specifically for univariate time-series dataset. Normalization is key aspect in the pre-processing stage, moreover in neural network model.

引用

页数：5

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