Predicting the performance of steam assisted gravity drainage (SAGD) method utilizing artificial neural network (ANN)

Ansari, Areeba; Heras, Marco; Nones, Julianne; Mohammadpoor, Mehdi; Torabi, Farshid

doi:10.1016/j.petlm.2019.04.001

Cited by 26 publications

(14 citation statements)

References 5 publications

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“…Followed by selecting the neural network design or architecture. The last step includes separating the data into three subsets: training, validation, and testing, in addition to choosing the number of hidden layers as well as the number of neurons selected in them [59] .…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…The efficiency of the developed ANN model was compared to previous empirical correlations and they concluded that the IFT estimation accuracy can be enhanced essentially using the ANN model. The prediction of reservoir oil production performance [58] , the prediction of ultimate recovery factor by steam-assisted gravity drainage (SAGD) [59] , the forecast of horizontal wells productivity [60] , and the prediction of waterflooding performance in heavy oil reservoirs [61] are other examples of petroleum application studies conducted using this powerful tool. ANN has been also utilised in the chemical EOR studies such as; surfactant–polymer (SP) flooding performance [53] , [62] , curing of polymer flooding [63] , and the formation and stability of oil/water emulsion [64] .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Experimental investigation, binary modelling and artificial neural network prediction of surfactant adsorption for enhanced oil recovery application

Belhaj

Elraies

Alnarabiji

et al. 2021

Chemical Engineering Journal

116

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Experimental investigation, binary modelling and artificial neural network prediction of surfactant adsorption for enhanced oil recovery application

Belhaj

Elraies

Alnarabiji

et al. 2021

Chemical Engineering Journal

116

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show abstract

“…Azizi et al [ 17 ] used an artificial neural network to predict the compressibility factor (z-factor) of natural gases. Ansari et al [ 18 ] trained an artificial neural network to predict the ultimate recovery factor of oil reservoirs by steam-assisted gravity drainage (SAGD).…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing Optical Film Lamination to Enhance the Luminance of TFT-LCD Displays Using the Taguchi-NNGA Method

Leu

Lin

2021

Materials

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Luminance is an essential quality of a TFT-LCD display. Manufacturers have attempted to improve the soft-to-hard lamination stage to enhance the luminance of their TFT-LCD displays. In addition, many customers have complained about the insufficient luminance of the TFT-LCD displays of the case company. While product engineers have kept tuning the control factors in the soft-to-hard lamination stage through the trial and error method, the improvement of the luminance was not good enough. This study aims to assist the product engineers to fine-tune the settings of the control factors using a new method composed of the Taguchi method, a neural network, and a genetic algorithm. The confirmation experiments showed that the proposed method had increased the average luminance of the TFT-LCD displays from 17.03 to 25.15, which was higher than the required luminance value of 25. As a result, the number of complaints on the TFT-LCD displays had been significantly reduced.

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“…The meta-heuristic techniques have been getting attention to improve the parameters of ANN. Therefore, we apply PSO algorithm to optimize ANN'S weights [14,27].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Modified Particle Swarm Optimization-Artificial Neural Network and Gene Expression Programing for Predicting High Temperature Oxidation Behavior of Ni–Cr–W-Mo Alloys

Hasibi

Mahmoudian

Khayati

2020

IJE

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This paper is an attempt to model the oxidation behavior of Ni-base alloys by considering the alloying elements, i.e., Cr, W, Mo, as variables. Modified particle swarm optimization-artificial neural network (MPSO-ANN) and gene expression programming (GEP) techniques were employed for modeling. Data set for construction of (MPSO-ANN) and GEP models selected from 66 cyclic oxidation performed in the temperature range of 400-1150 ᵒC for 27 different Ni-based alloy samples at various amounts of Cr, W, and Mo. The weight percent of alloying elements selected as input variables and the changes of weight during the oxidation cycle considered as output. To analyze the performance of proposed models, various statistical indices, viz. root mean squared error (RMSE) and the correlation coefficient between two data sets (R 2) were utilized. The collected data of GEP randomly divided into 21 training sets and 6 testing sets. The results confirmed that the possibility of oxidation behavior modeling using GEP by R 2 = 0.981, RMSE =0.0822. By consideration of oxidation resistance as criteria, Cr, Mo, and W enhanced the oxidation resistance of Ni-based alloys. The results showed that in the presence of Cr as alloying element, especially at Cr contents higher than 22 wt.%, the effect of W and Mo were negligible. However, the same trend was reversed at the sample with Cr content lower than 20 wt.%. In these cases, the effect of W and Mo on oxidation resistance were significantly enhanced.

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Predicting the performance of steam assisted gravity drainage (SAGD) method utilizing artificial neural network (ANN)

Cited by 26 publications

References 5 publications

Experimental investigation, binary modelling and artificial neural network prediction of surfactant adsorption for enhanced oil recovery application

Experimental investigation, binary modelling and artificial neural network prediction of surfactant adsorption for enhanced oil recovery application

Optimizing Optical Film Lamination to Enhance the Luminance of TFT-LCD Displays Using the Taguchi-NNGA Method

Modified Particle Swarm Optimization-Artificial Neural Network and Gene Expression Programing for Predicting High Temperature Oxidation Behavior of Ni–Cr–W-Mo Alloys

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