A new model for estimating the gas compressibility factor using Group Method of Data Handling algorithm (case study)

2019 ◽  
Vol 14 (3) ◽  
pp. e2307 ◽  
Author(s):  
Soroush Shariaty ◽  
Mohammad Reza Khorsand Movaghar ◽  
Ashkan Vatandoost
Keyword(s):  
Compressibility Factor ◽  
Group Method ◽  
Data Handling ◽  
New Model ◽  
Gas Compressibility

scholarly journals Modeling Natural Gas Compressibility Factor Using a Hybrid Group Method of Data Handling

2019 ◽  
Author(s):  
Abdolhossein Hemmati-Sarapardeh ◽  
Sassan Hajirezaie ◽  
Mohamad Reza Soltanian ◽  
Amir Mosavi ◽  
Shahab Shamshirband
Keyword(s):  
Natural Gas ◽  
Equations Of State ◽  
Compressibility Factor ◽  
Absolute Error ◽  
Group Method ◽  
Data Handling ◽  
Hydrocarbon Reservoir ◽  
Compression And Expansion ◽  
Natural Gas Transport ◽  
Gas Compressibility

A Natural gas is increasingly being sought after as a vital source of energy, given that its production is very cheap and does not cause the same environmental harms that other resources, such as coal combustion, do. Understanding and characterizing the behavior of natural gas is essential in hydrocarbon reservoir engineering, natural gas transport, and process. Natural gas compressibility factor, as a critical parameter, defines the compression and expansion characteristics of natural gas under different conditions. In this study, a simple second-order polynomial model based on the group method of data handling (GMDH) is presented to determine the compressibility factor of different natural gases at different conditions, using corresponding state principles. The accuracy of the model evaluated through graphical and statistical analyses. The results show that the model is capable of predicting natural gas compressibility with an average absolute error of only 2.88%, a root means square of 0.03, and a regression coefficient of 0.92. The performance of the developed model compared to widely known, previously published equations of state (EOSs) and correlations, and the precision of the results demonstrates its superiority over all other correlations and EOSs.

scholarly journals Modeling natural gas compressibility factor using a hybrid group method of data handling

2019 ◽  
Vol 14 (1) ◽  
pp. 27-37 ◽  
Author(s):  
Abdolhossein Hemmati-Sarapardeh ◽  
Sassan Hajirezaie ◽  
Mohamad Reza Soltanian ◽  
Amir Mosavi ◽  
Narjes Nabipour ◽  
...  
Keyword(s):  
Natural Gas ◽  
Compressibility Factor ◽  
Group Method ◽  
Data Handling ◽  
Gas Compressibility ◽  
Hybrid Group

Predicting tunnel boring machine performance through a new model based on the group method of data handling

2018 ◽  
Vol 78 (5) ◽  
pp. 3799-3813 ◽  
Author(s):  
Mohammadreza Koopialipoor ◽  
Sayed Sepehr Nikouei ◽  
Aminaton Marto ◽  
Ahmad Fahimifar ◽  
Danial Jahed Armaghani ◽  
...  
Keyword(s):  
Tunnel Boring Machine ◽  
Group Method ◽  
Data Handling ◽  
New Model ◽  
Tunnel Boring ◽  
Model Based ◽  
Machine Performance

Engine Health Assessment and Prediction Using the Group Method of Data Handling and the Method of Match Matrix: Autoregressive Moving Average

2007 ◽  
Author(s):  
Haixia Wang ◽  
Jay Lee ◽  
Takahiro Ueda ◽  
Kondo H. Adjallah ◽  
Masoud Ghaffari
Keyword(s):  
Anomaly Detection ◽  
Moving Average ◽  
Inductive Learning ◽  
Health Assessment ◽  
Group Method ◽  
Autoregressive Moving Average ◽  
Data Handling ◽  
Learning Technique

This paper presents a methodology for engine health assessment and prediction, which includes the following two steps: (1) engine health assessment and anomaly detection is conducted based on an inductive learning technique called Group Method of Data Handling (GMDH), and (2) engine health prediction is conducted based on the method of Match Matrix - Autoregressive Moving Average. Results from an industry case study illustrate the effectiveness of the presented methodology in engine health assessment and prediction.

scholarly journals New model for standpipe pressure prediction while drilling using Group Method of Data Handling

Petroleum ◽  
2021 ◽  
Author(s):  
Mohamed Riad Youcefi ◽  
Ahmed Hadjadj ◽  
Farouk Said Boukredera
Keyword(s):  
Group Method ◽  
Data Handling ◽  
New Model

Application of Group Method of Data Handling with a Shift Operator to Forecasting Financial Indices of Local Budgets

2008 ◽  
Vol 40 (6) ◽  
pp. 17-26
Author(s):  
Valentin N. Tomashevskiy ◽  
Alexander N. Vinogradov ◽  
Yuriy A. Oleinik
Keyword(s):  
Shift Operator ◽  
Group Method ◽  
Data Handling ◽  
Financial Indices

APPLICATION OF ONE WEEK AHEAD WAVE PREDICTION FOR USING GROUP METHOD OF DATA HANDLING

2020 ◽  
Vol 76 (2) ◽  
pp. I_229-I_234
Author(s):  
Keishiro CHIYONOBU ◽  
Sooyoul KIM ◽  
Masahide TAKEDA ◽  
Chisato HARA ◽  
Hajime MASE ◽  
...  
Keyword(s):  
Group Method ◽  
Data Handling ◽  
Wave Prediction
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