Connectionist Model to Estimate Performance of Steam-Assisted Gravity Drainage in Fractured and Unfractured Petroleum Reservoirs: Enhanced Oil Recovery Implications

Zendehboudi, Sohrab; Rajabzadeh, Amin Reza; Bahadori, Alireza; Chatzis, Ioannis; Dusseault, Maurice B.; Elkamel, Ali; Lohi, Ali; Fowler, Michael

doi:10.1021/ie303106z

Cited by 28 publications

(15 citation statements)

References 69 publications

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“…Further, since the formation of emulsions result from steam condensate contacting oil near the boundary of the steam chamber (Mohammadzadeh et al, 2012;Zendehboudi et al, 2014;Kar et al, 2014), the role of emulsions on oil flow and solvent mass transfer also needs more study.…”

Section: Proposed Steam-solvent Injection Strategy To Improve Es-sagdmentioning

confidence: 99%

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Analysis of steam–solvent–bitumen phase behavior and solvent mass transfer for improving the performance of the ES-SAGD process

Dong

Chen

2015

Journal of Petroleum Science and Engineering

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Section: Proposed Steam-solvent Injection Strategy To Improve Es-sagdmentioning

confidence: 99%

“…It has been demonstrated that heavy oil or bitumen can be produced at rates from 100 to 400 m 3 /day, with the cSOR varying from 2 to 10 m 3 /m 3 (Butler, 1998). Costs are more adverse for a highly fractured reservoir with a typically high cSOR and low oil recovery factor (Zendehboudi et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Analysis of steam–solvent–bitumen phase behavior and solvent mass transfer for improving the performance of the ES-SAGD process

Dong

Chen

2015

Journal of Petroleum Science and Engineering

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“…The number of neurons in the input and output layers is decided by the number of input and output variables that are planned for the predictive tool. However, the optimal number of neurons in hidden layer(s) is a strong function of nonlinearity and dimensionality of the problem under study [13][14][15][16][17][18][19][20][21][22][23][24].…”

Section: Artificial Neural Network and Particle Swarm Optimizationmentioning

confidence: 99%

“…An activation function processes this sum and gives out the output, . Indeed, the resulting sum is processed by a neuron activation function to obtain the ultimate output of the neuron as follows [13][14][15][16][17][18][19][20][21][22][23][24][25][26]:…”

Section: Artificial Neural Network and Particle Swarm Optimizationmentioning

confidence: 99%

“…The superscript stands for output layer. relationships for these functions are defined, respectively, by (2)-(4), as given below [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]: [13,16], unified particle swarm optimization (UPSO) [14], and imperialist competitive algorithm (ICA) [17,20,23] for weight training of neural networks have been used. Kennedy [27] introduced the PSO as a strong stochastic optimization technique which simulates the social manners of birds within a group, based on population concept.…”

Section: Input Layermentioning

confidence: 99%

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Developing a Robust Surrogate Model of Chemical Flooding Based on the Artificial Neural Network for Enhanced Oil Recovery Implications

Ahmadi

2015

Mathematical Problems in Engineering

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Application of chemical flooding in petroleum reservoirs turns into hot topic of the recent researches. Development strategies of the aforementioned technique are more robust and precise when we consider both economical points of view (net present value, NPV) and technical points of view (recovery factor, RF). In current study many attempts have been made to propose predictive model for estimation of efficiency of chemical flooding in oil reservoirs. To gain this end, a couple of swarm intelligence and artificial neural network (ANN) is employed. Also, lucrative and high precise chemical flooding data banks reported in previous attentions are utilized to test and validate proposed intelligent model. According to the mean square error (MSE), correlation coefficient, and average absolute relative deviation, the suggested swarm approach has acceptable reliability, integrity and robustness. Thus, the proposed intelligent model can be considered as an alternative model to predict the efficiency of chemical flooding in oil reservoir when the required experimental data are not available or accessible.

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Hybrid smart model to determine concentration of acidic gases in absorption tower of sweetening process

Nejad

Amin

Mohsenipour³

et al. 2022

Can J Chem Eng

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The natural gases produced from underground reserves may contain sour gases such as H 2 S and CO 2 . If the amount of these gases is greater than the standard level, it will be harmful to humans and the environment. Hence, it appears important to accurately determine the concentration of H 2 S and CO 2 in various equipment/units of a natural gas sweetening plant. In this study, a new connectionist approach is introduced to obtain the concentration of outlet acid gases of the sweetening tower of the South Pars gas refinery (in Iran), by utilizing a conventional connectionist tool optimized by an imperialist competitive algorithm (ICA). This ICA strategy is applied to optimize the weights, biases, and number of neurons of the artificial neural network (ANN) model.The input parameters in this modelling strategy include time, the amount of H 2 S in the output amine, the flow rate of input sour gas, the temperature of sea water for cooling, the mass flow rate of low-pressure steam to the amineamine heat exchanger, and the flow rate of input amine. The performance of the hybrid deterministic tool, ANN-ICA, is compared with the ANN-back propagation (BP) method. The coefficient of determination and mean squared error to forecast the concentration of output H 2 S are 0.8931 and 0.0125 for the ANN-BP algorithm and 0.9057 and 0.0104 for the ANN-ICA algorithm, respectively. These statistical values are 0.8428 and 0.0001 for the ANN-BP model and 0.9307 and 0.000 05 for the ANN-ICA model, respectively, while predicting output CO 2 concentration. The results confirm that the ANN-ICA is a more reliable approach, compared to ANN-BP, to forecast the concentration of acidic gases leaving the absorption tower.

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Connectionist Model to Estimate Performance of Steam-Assisted Gravity Drainage in Fractured and Unfractured Petroleum Reservoirs: Enhanced Oil Recovery Implications

Abstract: Supporting InformationA portion of the actual data employed in the current study is given in Table S1.

Cited by 28 publications

References 69 publications

Analysis of steam–solvent–bitumen phase behavior and solvent mass transfer for improving the performance of the ES-SAGD process

Analysis of steam–solvent–bitumen phase behavior and solvent mass transfer for improving the performance of the ES-SAGD process

Developing a Robust Surrogate Model of Chemical Flooding Based on the Artificial Neural Network for Enhanced Oil Recovery Implications

Hybrid smart model to determine concentration of acidic gases in absorption tower of sweetening process

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