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An Artificial Neural Network Model for Predicting the Hydrate Formation Temperature

  • Research Article-Petroleum Engineering
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Abstract

Gas hydrate is one of the crucial flow assurance problems in the petroleum industry. Inaccurate predictions of gas-hydrate conditions could lead to severe technical and economic defects especially when results in plugging the gas streamline. The prediction techniques could be classified into six approaches relevant to pure and complex hydrocarbon mixtures. Up till now, many correlations have been developed to cover the hydrate formation conditions point; however, most of these correlations are presented graphically that which makes them incompatible with computer simulation packages. Furthermore, by reviewing the literature, the previous artificial neural networks have a deficiency in providing the corresponding mathematical model to be employed to reproduce results using other data points or to be compared to other developed artificial neural network (ANN) models. The main purpose of this paper is to develop a comprehensive ANN model with its corresponding mathematical model to facilitate the prediction of gas-hydrate formation temperature in the case of natural gas-pure water systems. In addition to the mathematical model, a MATLAB code and a standalone computer program are also established to provide a high degree of compatibility with simulation and design software packages. The ANN model was built based on (1469) datasets of gas-hydrate formation pressure, temperature, and specific gravity, and the data points are captured from Katz's (Transactions of the AIME 160:140–9, 1945) gravity chart. The proposed model achieves a coefficient of determination equal to (0.999), with an average relative error of (− 0.0005%), the average absolute relative error of (0.1145%), root mean square error (0.073), and standard deviation of (0.00398).

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Abbreviations

ANN:

Artificial neural network

N :

Number of neurons

HFT:

Hydrate formation temperature

HFP:

Hydrate formation pressure

\(y_{i}\) :

Mole fraction of ith hydrocarbon component exists in the gas phase on a water-free basis

\(x_{i}\) :

Mole fraction of ith hydrocarbon component exists in the solid phase on a water-free basis

R 2 :

Coefficient of determination

R :

Correlation coefficient

ARE:

Average relative error

AARE:

Average absolute relative error

\(\gamma_{g}\) :

Gas specific gravity

MSE:

Mean square error

RMSE:

Root mean square error

LM:

Levenberg–Marquardt (Training algorithm)

°C:

Celsius

°F:

Fahrenheit

R:

Rankin

KPa:

Kilopascal

MPa:

Megapascal

Atm:

Atmospheric pressure

Psi:

Pound per square inch

T n :

Normalized

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Author information

Authors and Affiliations

  1. PVT-Lab, Production Department, Egyptian Petroleum Research Institute, Nasr City, Cairo, Egypt

    A. N. El-hoshoudy

  2. Petroleum Engineering and Gas Technology Department, Faculty of Engineering, The British University in Egypt, El Sherouk City, Cairo, Egypt

    Abdelrahman Ahmed, Sayed Gomaa & Atef Abdelhady

  3. Mining and Petroleum Engineering Department, Faculty of Engineering, Al-Azhar University, Cairo, Egypt

    Sayed Gomaa

Authors
  1. A. N. El-hoshoudy
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  2. Abdelrahman Ahmed
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  4. Atef Abdelhady
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Correspondence to A. N. El-hoshoudy.

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El-hoshoudy, A.N., Ahmed, A., Gomaa, S. et al. An Artificial Neural Network Model for Predicting the Hydrate Formation Temperature. Arab J Sci Eng 47, 11599–11608 (2022). https://doi.org/10.1007/s13369-021-06340-w

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