Two-phase flow pressure drop modelling in horizontal pipes with different diameters

Faraji, Foad; Santim, C.G.S.; Chong, Perk Lin; Hamad, Faik

doi:10.1016/j.nucengdes.2022.111863

Cited by 6 publications

(5 citation statements)

References 75 publications

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“…Hybrid forms of MLP and LSSVM algorithms with PSO and GA optimizers have presented themselves as promising hybrid methods for diverse prediction purposes in different engineering sections, particularly in the oil and gas industry. For instance, these hybrid methods have shown significant accuracy in the prediction of different parameters, such as shear wave velocity (Ghorbani et al, 2021;Miah, 2021;Rajabi et al, 2022a), viscosity of waxy crude oils (Madani et al, 2021), estimating formation pore pressure (Rajabi et al, 2022b), safe mud window (Beheshtian et al, 2022), gas flow rate (Abad et al, 2022), casing collapse (Mohamadian et al, 2021), rock porosity and permeability (Nourani et al, 2022), two-phase flow pressure drop modelling (Faraji et al, 2022), rate of penetration in drilling (Hashemizadeh et al, 2022), gas condensate viscosity (Abad et al, 2021a), prediction based on biodiesel distillation (Vera-Rozo et al, 2022), oil holdup (Zhang et al, 2011). The promising accuracy achieved by the optimized LSSVM and MLP models for different prediction purposes trigged the idea to establish solid hybrid methods based on these algorithms and evaluate their performance in the FVDC prediction using conventional well logs.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Prediction of fracture density in a gas reservoir using robust computational approaches

Gao

Hazbeh²,

Davoodi³

et al. 2023

Front. Earth Sci.

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One of the challenges that reservoir engineers, drilling engineers, and geoscientists face in the oil and gas industry is determining the fracture density (FVDC) of reservoir rock. This critical parameter is valuable because its presence in oil and gas reservoirs boosts productivity and is pivotal for reservoir management, operation, and ultimately energy management. This valuable parameter is determined by some expensive operations such as FMI logs and core analysis techniques. As a result, this paper attempts to predict this important parameter using petrophysics logs routinely collected at oil and gas wells and by applying four robust computational algorithms and artificial intelligence hybrids. A total of 6067 data points were collected from three gas wells (#W1, #W2, and #W3) in one gas reservoir in Southwest Asia. Following feature selection, the input variables include spectral gamma ray (SGR); sonic porosity (PHIS); potassium (POTA); photoelectric absorption factor (PEF); neutron porosity (NPHI); sonic transition time (DT); bulk density (RHOB); and corrected gamma ray (CGR). In this study, four hybrids of two networks were used, including least squares support vector machine (LSSVM) and multi-layer perceptron (MLP) with two optimizers particle swarm optimizer (PSO) and genetic algorithm (GA). Four robust hybrid machine learning models were applied, and these are LSSVM-PSO/GA and MLP-PSO/GA, which had not previously used for prediction of FVDC. In addition, the k-fold cross validation method with k equal to 8 was used in this article. When the performance accuracy of the hybrid algorithms for the FVDC prediction is compared, the revealed result is LSSVM-PSO > LSSVM-GA > MLP-PSO > MLP-GA. The study revealed that the best algorithm for predicting FVDC among the four algorithms is LSSVM-PSO (for total dataset RMSE = 0.0463 1/m; R2 = 0.9995). This algorithm has several advantages, including: 1) lower adjustment parameters, 2) high search efficiency, 3) fast convergence speed, 4) increased global search capability, and 5) preventing the local optimum from falling. When compared to other models, this model has the lowest error.

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

Prediction of fracture density in a gas reservoir using robust computational approaches

Gao

Hazbeh²,

Davoodi³

et al. 2023

Front. Earth Sci.

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“…They are visible in Figs. (2)(3)(4)(5) and Fig. (A1-A6) (in Appendix) for the maximum-minimum and clip normalization datasets.…”

Section: Performance Of the Developed Neural Networkmentioning

confidence: 99%

“…On the other hand, the performance of the networks trained with the clip-normalized datasets is in Figs. (4)(5). In these figures, the general performance of the various topologies (i.e., 2-3-1, 2-4-1, and 2-5-1) is in sync with the 0.00E+00 Comparing the performance of the different network topologies, the overall R and MSE values showed that the 2-5-1 network for both maximum-minimum and clip-normalized datasets performed slightly better than the 2-3-1 and 2-4-1 networks.…”

Section: Performance Of the Developed Neural Networkmentioning

confidence: 99%

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Interpretation of Neural Network Models – New Insights of Estimating Static Bottom-Hole Pressures of Gas Wells in Nigerian Petroleum Provinces

Udoma,

Okon,

Udoeyop

et al. 2023

Int. J. Petrol. Technol.

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Methods to determine static bottom-hole pressure (BHP) from surface measurements include the average temperature and z-factor method, the Sukkar-Cornell method, the Cullender-Smith method, and the Poettmann method. Among these methods, the Poettmann method is preferable in the petroleum industry but with a concern for software developers, as the integral values to determine the static BHP are tabular. In this study, neural network-based models to predict the integral values using pseudo-reduced pressures and temperatures were developed. The 2-3-1, 2-4-1, and 2-5-1 neural-based models had overall correlation coefficients (R) of 0.9974, 0.99835, and 0.99745, respectively, for the maximum-minimum normalization method and R of 0.99745, 0.99805, and 0.9992 for the clip-scaling method. Comparing the models' predictions with the Lagrangian interpolated values resulted in R of 0.99895 and 0.9995 for the maximum-minimum and clip-scaling-based models. Thus, the developed models can predict Poettmann's integral values without table look-up to estimate static BHP in gas wells.

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“…Two-phase flow on mini pipes differs from pipes of larger diameter [7]. In mini tubes, the surface tension is found a lot, while in heat transfer, it is not found, and it can be said that no phase change occurs in mini pipes [8][9][10].…”

Section: Introductionmentioning

confidence: 96%

Void Fraction of Two-Phase Flow of Air – Emulsion of Water and Oil in Horizontal Mini Pipe

Sukamta Sukamta,

Dwi Ari Saputra,

Sudarja Sudarja

et al. 2023

ARFMTS

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Two-phase flow is the most straightforward flow of multi-phase flow. The void fraction is one of the significant parameters affecting the two-phase flow pattern on both small and mini channels. The flow pattern will be significantly sure, and fluctuations in pressure will change. If the pressure fluctuations are very high, it will affect the piping network system or channel and cause damage. Somebody can find the application of this case of damage to mini heat exchangers, mini boilers, and other small and mini equipment. This paper presents a significant correlation between void fractions and flow patterns. This study used a glass pipe with a length of 160 mm and a diameter of 1.6 mm with a horizontal position. The working fluids used are an emulsion of water and coconut oil with a concentration of 350 mg/dl and 500 mg/dl. The superficial velocity is gas (JG) = 0.08 m/s to 74.6 m/s, and the superficial velocity of the Liquid (JL)= 0.04 m/s to 4.15 m/s. Data measurements use a high-speed camera and are processed using MATLAB R2014a software. The research results showed that the difference in the concentration of the emulsion of water and coconut oil sufficiently affected the value of the void fraction significantly. The void fraction is because the increased concentration of such emulsions affects the increase in the viscosity value of the Liquid. The increase in the viscosity of the Liquid affects the speed of the Liquid tends to decrease. Under these conditions, the increasing superficial velocity of the gas affects the value of the growing void fraction.

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Two-phase flow pressure drop modelling in horizontal pipes with different diameters

Cited by 6 publications

References 75 publications

Prediction of fracture density in a gas reservoir using robust computational approaches

Prediction of fracture density in a gas reservoir using robust computational approaches

Interpretation of Neural Network Models – New Insights of Estimating Static Bottom-Hole Pressures of Gas Wells in Nigerian Petroleum Provinces

Void Fraction of Two-Phase Flow of Air – Emulsion of Water and Oil in Horizontal Mini Pipe

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