A novel method to identify the flow pattern of oil–water two-phase flow

Li, Zhongcheng; Fan, Chunling

doi:10.1007/s13202-020-00987-1

Cited by 15 publications

(6 citation statements)

References 25 publications

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“…This means that it is not possible to establish a universal formulation to determine the expected flow pattern for a specific system. To overcome this problem, Li and Fan proposed a new method that combines an extreme learning machine and multiple empirical mode decompositions to identify biphasic flow patterns for horizontal oil–water flows. The method of Li and Fan enabled the identification of six typical horizontal flow patterns: “stratified flow (ST), stratified flow with mixing at an interface (ST&MI), dispersion of oil in water and water flow (DO/W&W), dispersion of water in oil and oil in water flow pattern (DW/O&D O/W), dispersion of oil in water flow pattern (DO/W), dispersion of water in oil flow pattern (DW/O)”.…”

Section: Influence Of Water On the Emulsionmentioning

confidence: 99%

“…This means that it is not possible to establish a universal formulation to determine the expected flow pattern for a specific system. To overcome this problem, Li and Fan 113 proposed a new method that combines an extreme learning machine and multiple empirical mode decompositions to identify biphasic flow patterns for horizontal oil−water flows. The method of Li and Fan enabled the identification of six typical horizontal flow patterns: 113 The comparison between results provided in the literature is not straightforward since there is still a tremendous diversity of denominations for horizontal flow patterns.…”

Section: Influence Of Water On the Emulsionmentioning

confidence: 99%

“…To overcome this problem, Li and Fan 113 proposed a new method that combines an extreme learning machine and multiple empirical mode decompositions to identify biphasic flow patterns for horizontal oil−water flows. The method of Li and Fan enabled the identification of six typical horizontal flow patterns: 113 The comparison between results provided in the literature is not straightforward since there is still a tremendous diversity of denominations for horizontal flow patterns. For instance, Arirachakaran et al 89 However, it should be noted that experimental conditions may have had an influence on the reported patterns.…”

Section: Influence Of Water On the Emulsionmentioning

confidence: 99%

See 2 more Smart Citations

Properties of Crude Oil-in-Water and Water-in-Crude Oil Emulsions: A Critical Review

Sousa

Matos

Pereira

2021

Ind. Eng. Chem. Res.

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Water and crude oil emulsions can be produced during oilfield operations. Since most oilfields are in the secondary or tertiary recovery stages, the amount of produced water is not negligible. Furthermore, enhanced oil recovery makes use of surfactants injection (chemical substances that lower the surface tension of the liquid) and polymer flooding (polymers are large molecules, composed of repeating blocks bounded together, which are injected to decrease the water–oil mobility ratio), leading to increased emulsion problems. Knowledge of fluid petrochemical properties and behavior is fundamental for designing the production system, transport infrastructures, and refinery layout. Depending on the fluid composition, the flow rate, and the PVT conditions, different physical behaviors and flow patterns will occur. Even though significant effort has been carried out to take a broader view of liquid–liquid models, there is a wide range of correlations to consider with different crude oil compositions and diverse field conditions. In this work, a critical review was carried out to assess the main principles that affect an emulsion, how the flow patterns are influenced by the water fraction, which equations can be used to estimate the main physical properties, and last what are the methods or techniques for identifying or characterizing an emulsion. As a result, a strong background in this matter is offered, and future research directions are synthesized.

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Section: Influence Of Water On the Emulsionmentioning

confidence: 99%

Section: Influence Of Water On the Emulsionmentioning

confidence: 99%

Section: Influence Of Water On the Emulsionmentioning

confidence: 99%

See 1 more Smart Citation

Properties of Crude Oil-in-Water and Water-in-Crude Oil Emulsions: A Critical Review

Sousa

Matos

Pereira

2021

Ind. Eng. Chem. Res.

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“…In which , , , and are output weights, activation function, biases, and input weights for i-th neuron, respectively. The formulation, as mentioned earlier, could be written in another way as follows: where is the hidden layer output matrix, which could be defined as follows: where, and 50 . The main regularization parameter in this approach is the number of neurons in the hidden layer, which is obtained empirically.…”

Section: Model Developmentmentioning

confidence: 99%

Artificial intelligence-based framework for precise prediction of asphaltene particle aggregation kinetics in petroleum recovery

Sharifzadegan,

Behnamnia,

Dehghan Monfared

2023

Sci Rep

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The precipitation and deposition of asphaltene on solid surfaces present a significant challenge throughout all stages of petroleum recovery, from hydrocarbon reservoirs in porous media to wellbore and transfer pipelines. A comprehensive understanding of asphaltene aggregation phenomena is crucial for controlling deposition issues. In addition to experimental studies, accurate prediction of asphaltene aggregation kinetics, which has received less attention in previous research, is essential. This study proposes an artificial intelligence-based framework for precisely predicting asphaltene particle aggregation kinetics. Different techniques were utilized to predict the asphaltene aggregate diameter as a function of pressure, temperature, oil specific gravity, and oil asphaltene content. These methods included the adaptive neuro-fuzzy interference system (ANFIS), radial basis function (RBF) neural network optimized with the Grey Wolf Optimizer (GWO) algorithm, extreme learning machine (ELM), and multi-layer perceptron (MLP) coupled with Bayesian Regularization (BR), Levenberg–Marquardt (LM), and Scaled Conjugate Gradient (SCG) algorithms. The models were constructed using a series of published data. The results indicate the excellent correlation between predicted and experimental values using various models. However, the GWO-RBF modeling strategy demonstrated the highest accuracy among the developed models, with a determination coefficient, average absolute relative deviation percent, and root mean square error (RMSE) of 0.9993, 1.1326%, and 0.0537, respectively, for the total data.

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“…Oil-water two-phase flow is commonly seen throughout the entire process of crude oil production and transportation [1]. The long history of oil recovery and the application of various secondary and tertiary recovery techniques have brought a higher water cut to the production in many oil fields [2], and such a problem is a general challenge faced by certain middle and late stages of reservoir development [3].…”

Section: Introductionmentioning

confidence: 99%

Effect of Temperature on Oil–Water Separations Using Membranes in Horizontal Separators

Tao

Sun

2022

Membranes

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The effect of temperature on oil–water separations is studied in this paper, focusing on the changed penetration velocities of water droplets on the separation membrane in a horizontal separator. A compact numerical scheme is developed based on the phase-field model, and the temperature effect is first theoretically analyzed regarding the key thermodynamic properties that may affect the separation performance. The computational scenario is designed based on practical horizontal separators in the oil field, and the droplet motions in the oil–water two-phase flow are simulated using our scheme under various operation conditions. It was found that a higher temperature may result in a faster penetration of the water droplets, and a larger density difference in the oil–water system is also preferred to accelerate the separation using membranes. Furthermore, increasing the operation temperature is proved to benefit the separation of water and heavy oil.

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A novel method to identify the flow pattern of oil–water two-phase flow

Cited by 15 publications

References 25 publications

Properties of Crude Oil-in-Water and Water-in-Crude Oil Emulsions: A Critical Review

Properties of Crude Oil-in-Water and Water-in-Crude Oil Emulsions: A Critical Review

Artificial intelligence-based framework for precise prediction of asphaltene particle aggregation kinetics in petroleum recovery

Effect of Temperature on Oil–Water Separations Using Membranes in Horizontal Separators

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