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Research Papers: Petroleum Engineering

Application of Artificial Neural Network-Particle Swarm Optimization Algorithm for Prediction of Asphaltene Precipitation During Gas Injection Process and Comparison With Gaussian Process Algorithm

[+] Author and Article Information
Abbas Khaksar Manshad

Department of Petroleum Engineering,
Abadan Faculty of Petroleum Engineering,
Petroleum University of Technology,
Abadan, Iran

Habib Rostami

Department of Computer Engineering,
School of Engineering,
Persian Gulf University,
Bushehr 7516913817, Iran
e-mail: habib@pgu.ac.ir

Hojjat Rezaei, Seyed Moein Hosseini

Department of Petroleum Engineering,
Ahwaz Faculty of Petroleum Engineering,
Petroleum University of Technology (PUT),
Ahwaz, Iran

1Corresponding author.

Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received September 9, 2014; final manuscript received July 9, 2015; published online July 27, 2015. Assoc. Editor: Egidio Marotta.

J. Energy Resour. Technol 137(6), 062904 (Jul 27, 2015) (5 pages) Paper No: JERT-14-1291; doi: 10.1115/1.4031042 History: Received September 09, 2014

Asphaltene precipitation is a major problem in the oil production and transportation of oil. Changes in pressure, temperature, and composition of oil can lead to asphaltene precipitation. In the case of gas injection into oil reservoirs, the injected gas causes a change in oil composition and may lead to asphaltene precipitation. Accurate determination and prediction of the precipitated amount are vital, for this purpose there are several approaches such as experimental method, scaling equation, thermodynamics models, and neural network as the most recent ones. In this paper, we propose a new artificial neural network (ANN) optimized by particle swarm optimization (PSO) to predict the amount of asphaltene precipitation. This is conducted during the process of gas injection into oil reservoirs for enhanced oil recovery purposes. In the developed models, (1) oil composition, (2) temperature, (3) pressure, (4) oil specific gravity, (5) solvent mole percent, (6) solvent molecular weight, and (7) asphaltene content are considered as input parameters to the neural network. The weight of asphaltene and asphaltene content are considered as input parameters to the neural network and the weight of asphaltene precipitation as an output parameter. A comparison between the results of the proposed new model with Gaussian Process algorithm and previous research shows that the predictive model is more accurate.

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Figures

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Fig. 1

Structural design of three layer feed-forward ANNs

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Fig. 2

ANN inputs for prediction of asphaltene precipitation

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Fig. 3

Flow chart for ANN-PSO

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Fig. 4

Network performance in data training

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Fig. 5

Network performance in data validating

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Fig. 6

Network performance in data testing

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Fig. 7

Scatter plot of model prediction values versus measured value

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Fig. 8

Comparison of model prediction values with measured value

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Fig. 9

Impact of input variables on asphaltene precipitation amount

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