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Prediction of non-hydrocarbon gas components in separator by using Hybrid Computational Intelligence models

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Abstract

Accurate prediction of non-hydrocarbon (Non-HC) gas components in the gas-oil separators reduces the cost of gas and oil production in petroleum engineering. However, this task is difficult because there is no known relation among the properties of crude oil and the separators. There are studies that attempt to predict hydrocarbons (HCs) components using either Computational Intelligence (CI) techniques or conventional techniques like Equitation-of-State (EOS) and Empirical Correlation (EC). In this paper, we explore the applicability of CI techniques such as Artificial Neural Network, Support Vector Regressions, and Adaptive Neuro-Fuzzy Inference System to predict the Non-HC gas components in gas-oil separator tank. Further, we incorporate Genetic Algorithms (GA) into the Hybrid Computational Intelligence (HCI) models to enhance the accuracy of prediction. GA is used to determine the most favorable values of the tuning parameters in the CI models. The performances of the CI and HCI models are compared with the performance of the conventional techniques like EOS and EC. The experimental results show that accuracy of prediction by CI and HCI models outperform the conventional methods for N2 and H2S gas components. Furthermore, the HCI models perform better than the non-optimized CI models while predicting the Non-HC gas components.

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Abbreviations

ANFIS:

Adaptive neuro-fuzzy inference system

ANN:

Artificial neural network

CC:

Correlation coefficient

CI:

Computational intelligence

CO2 :

Carbon di oxide

EC:

Empirical correlations

EOS:

Equation-of-states

FIS:

Fuzzy inference system

FL:

Fuzzy logic

GA:

Genetic algorithm

GOSP:

Gas oil separation plant

H2S:

Hydrogen sulfide

HC:

Hydrocarbon

HCI:

Hybrid computational intelligence

LM:

Levenberg–Marquardt

MLP:

Multi-layer perceptron

MW:

Molecular weight

N2 :

Nitrogen

Non-HC:

Non-hydrocarbon

P:

Pressure (psi)

Pb :

Bubble point pressure (psi)

PR-EOS:

Peng–Robinson EOS

PEPs:

Petroleum engineering problems

RMSE:

Root-mean-square error

Rprop:

Resilient back-propagation

RT:

Reservoir temperature (°F)

SP:

Separator pressure (psi)

ST API:

Stock Tank American Petroleum Institute

ST:

Separator temperature (°F)

Subclust:

Subtractive clustering

SVM:

Support vector machine

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Acknowledgments

This research is funded by King Abdulaziz City for Science and Technology (KACST) through the Science and Technology Unit at KFUPM under the Project No GSP-18-101. The authors would like to thank Dr. Saifur Rahman, Mr. Nofal, Mr. Fatai, Mr. Shujath and Mr. Nizamuddin of the Research Institute and Mr. Mohammadain of Petroleum Engineering department at King Fahd University of Petroleum and Minerals (KFUPM) for suggestion and valuable comments. Warm regards to Dr. Jaubert [29] for providing a part of the data. Thanks are extended to KFUPM for providing the supporting research facilities.

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Authors and Affiliations

  1. Department of Information and Computer Science, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia

    Tarek Helmy, Muhammad Imtiaz Hossain & Md. Rafiul Hassan

  2. Department of Petroleum Engineering, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia

    Abdulazeez Adbulraheem

  3. Center for Environment and Water, Research Institute, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia

    S. M. Rahman

  4. Department of Systems Engineering, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia

    Amar Khoukhi & M. Elshafei

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  1. Tarek Helmy
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Correspondence to Tarek Helmy.

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Helmy, T., Hossain, M.I., Adbulraheem, A. et al. Prediction of non-hydrocarbon gas components in separator by using Hybrid Computational Intelligence models. Neural Comput & Applic 28, 635–649 (2017). https://doi.org/10.1007/s00521-015-2088-4

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