An intelligent modeling approach for prediction of thermal conductivity of CO 2

Shams, Reza; Sajjad, Esmaili; Rashid, Saeed; Suleymani, Muhammad

doi:10.1016/j.jngse.2015.08.050

Cited by 17 publications

(5 citation statements)

References 56 publications

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“…Rashid et al used the collected 276 data and radial basis function-genetic algorithm (RBF-GA) neural network to estimate the flow rate via the wellhead chokes. In this study, the R 2 values for training and test data were obtained 0.9885 and 0.9795, respectively 21 , 22 . Mirzaei-paiaman & Salavati using 102 production test data and adding the specific gravity of oil and gas to the general equation of Gilbert reached the following Eq.…”

Section: Introductionmentioning

confidence: 78%

Modeling liquid rate through wellhead chokes using machine learning techniques

Dabiri,

Hadavimoghaddam,

Ashoorian

et al. 2024

Sci Rep

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Precise measurement and prediction of the fluid flow rates in production wells are crucial for anticipating the production volume and hydrocarbon recovery and creating a steady and controllable flow regime in such wells. This study suggests two approaches to predict the flow rate through wellhead chokes. The first is a data-driven approach using different methods, namely: Adaptive boosting support vector regression (Adaboost-SVR), multivariate adaptive regression spline (MARS), radial basis function (RBF), and multilayer perceptron (MLP) with three algorithms: Levenberg–Marquardt (LM), bayesian-regularization (BR), and scaled conjugate gradient (SCG). The second is a developed correlation that depends on wellhead pressure (Pwh), gas-to-liquid ratio (GLR), and choke size (Dc). A dataset of 565 data points is available for model development. The performance of the two suggested approaches is compared with earlier correlations. Results revealed that the proposed models outperform the existing ones, with the Adaboost-SVR model showing the best performance with an average absolute percent relative error (AAPRE) of 5.15% and a correlation coefficient of 0.9784. Additionally, the results indicated that the developed correlation resulted in better predictions compared to the earlier ones. Furthermore, a sensitivity analysis of the input variable was also investigated in this study and revealed that the choke size variable had the most significant effect, while the Pwh and GLR showed a slight effect on the liquid rate. Eventually, the leverage approach showed that only 2.1% of the data points were in the suspicious range.

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Section: Introductionmentioning

confidence: 78%

Modeling liquid rate through wellhead chokes using machine learning techniques

Dabiri,

Hadavimoghaddam,

Ashoorian

et al. 2024

Sci Rep

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show abstract

“…It cannot be compared with semi‐empirical regression models, e.g., the equation of Jarrahian and Heidaryan 52. In comparison, the NN and machine learning models 10, 58–61 show high accuracy in all phase regions including the RACP. However, they are highly dependent on the characteristics of both the training dataset and the optimization algorithm itself.…”

Section: Resultsmentioning

confidence: 99%

“…An improved radial basis function (RBF) network based on the particle swarm optimization (PSO) algorithm [58] shows higher training accuracy in almost all networks, but its generalization capability is usually very limited. Another new algorithm, the leastsquares support vector regression (LSSVR) [59], models the thermal conductivity using a hyperplane to fit the data and using least squares to simultaneously reduce the solution difficulty and speed up the convergence. Also, the thermal conductivity prediction model based on the adaptive neuro-fuzzy inference system (ANFIS) [60] takes into account the interpretability and adaptive learning capability of the fuzzy inference system.…”

Section: Comparison Of Ai-based Modeling Approachesmentioning

confidence: 99%

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Modeling Thermophysical Properties of Carbon Dioxide: Performance Comparison and Assessment

2022

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Although many mathematical models have been developed for predicting the density, viscosity, and thermal conductivity of CO2, their accuracy and applicability have not been sufficiently established in the gas/liquid and supercritical regions and, in particular, in the region around the critical point. To understand their performances, the models are compared and analyzed, including (i) the cubic equations of state, multi‐parameter equations, and the Helmholtz free energy‐based model for density, (ii) residual theory‐ and regression‐based models for viscosity and thermal conductivity, and (iii) artificial intelligence‐based models (neural networks/machine learning) for these properties. A technical assessment is then performed. The results may provide a positive reference for determining the thermophysical properties of CO2.

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“…Eslamloueyan and Khademi [20] used an ANN model based on feed forward three-layer to model the TC of pure gases versus molecular weight, critical temperature and critical pressure at atmospheric pressure. Shams et al [21] applied 550 data points and presented a calculation approach of least square support vector machine (LSSVM) for describing TC of CO 2 . Di Nicola et al [13] improved their previous correlation for calculating TC [22] versus reduced temperature for the refrigerant family.…”

Section: Introductionmentioning

confidence: 99%

A New Correlation Method for Estimating Thermal Conductivity of Carbon Dioxide in Liquid, Vapor and Supercritical Phases

Rostamian

Lotfollahi

2019

Period. Polytech. Chem. Eng.

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In this study, a new correlation for estimating thermal conductivity (TC) of carbon dioxide was developed based on 2319 data points. The data points were at the temperature ranging from 250 to 1100 K, pressure ranging from 1 to 3000 bar and density ranging from 0.3 to 1400 Kg.m-3 in different phases of liquid, vapor and supercritical. The statistical parameters including average absolute deviation (AAD%), average percent relative error (ARE%), sum of absolute residual (SAR) and the coefficient of determination (R2) have been calculated to evaluate the accuracy of present correlation. The obtained values of AAD%, ARE%, SAR and R2 were 1.98, −0.64, 3510.1 and 0.995, respectively. The predictions of proposed correlation were also compared with three widely used correlations. The results showed that the proposed correlation is able to accurately calculate thermal conductivity of carbon dioxide. In addition, the proposed model is superior to all the existing empirical models considered.

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An intelligent modeling approach for prediction of thermal conductivity of CO 2

Cited by 17 publications

References 56 publications

Modeling liquid rate through wellhead chokes using machine learning techniques

Modeling liquid rate through wellhead chokes using machine learning techniques

Modeling Thermophysical Properties of Carbon Dioxide: Performance Comparison and Assessment

A New Correlation Method for Estimating Thermal Conductivity of Carbon Dioxide in Liquid, Vapor and Supercritical Phases

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