Prediction of Crude Oil Viscosity and Gas/Oil Ratio Curves Using Recent Advances to Neural Networks

The following article has been withdrawn on the request of the author of the journal Recent Advances in Computer Science and Communications (Recent Patents on Computer Science): Title: Diabetes Mellitus Prognosis Using Fuzzy Logic and Neural Networks Case Study: Alexandria Vascular Center (AVC) Authors: Abeer A. Amer and Soha M. Ismail* Bentham Science apologizes to the readers of the journal for any inconvenience this may cause BENTHAM SCIENCE DISCLAIMER: It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously submitted or published elsewhere. Furthermore, any data, illustration, structure or table that has been published elsewhere must be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submitting the article for publication the authors agree that the publishers have the legal right to take appropriate action against the authors, if plagiarism or fabricated information is discovered. By submitting a manuscript, the authors agree that the copyright of their article is transferred to the publishers if and when the article is accepted for publication.

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Heavy crude oil viscosity reduction and rheology for pipeline transportation

Fuel ◽

10.1016/j.fuel.2009.12.021 ◽

2010 ◽

Vol 89 (5) ◽

pp. 1095-1100 ◽

Cited By ~ 225

Author(s):

Shadi W. Hasan ◽

Mamdouh T. Ghannam ◽

Nabil Esmail

Keyword(s):

Crude Oil ◽

Viscosity Reduction ◽

Heavy Crude Oil ◽

Pipeline Transportation ◽

Oil Viscosity ◽

Heavy Crude

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Prediction of crude oil viscosity curve using artificial intelligence techniques

Journal of Petroleum Science and Engineering ◽

10.1016/j.petrol.2012.03.029 ◽

2012 ◽

Vol 86-87 ◽

pp. 111-117 ◽

Cited By ~ 20

Author(s):

M.A. Al-Marhoun ◽

S. Nizamuddin ◽

A.A. Abdul Raheem ◽

S. Shujath Ali ◽

A.A. Muhammadain

Keyword(s):

Artificial Intelligence ◽

Crude Oil ◽

Oil Viscosity ◽

Artificial Intelligence Techniques ◽

Viscosity Curve

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Use CO2 Soluble Surfactant to Decrease the Minimum Miscibility Pressure of CO2 Flooding in Oil Reservoir

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.239-242.2650 ◽

2011 ◽

Vol 239-242 ◽

pp. 2650-2654

Author(s):

Fu Chen ◽

Jie He ◽

Ping Guo ◽

Yuan Xu ◽

Cheng Zhong

Keyword(s):

Citric Acid ◽

Crude Oil ◽

Water System ◽

Viscosity Reduction ◽

Mass Ratio ◽

Oil Viscosity ◽

Minimum Miscibility Pressure ◽

Oil Water ◽

Carbon Dioxide Miscible Flooding ◽

Soluble Surfactant

According to the mechanisms of carbon dioxide miscible flooding and previous researchers’ work on synthesis of CO2-soluble surfactant, Citric acid isoamyl ester was synthesized, and it’s oil solubility and the rate of viscosity reduction both in oil-water system and oil were evaluated. And then we found that this compound can solve in oil effectively; the optimum mass of Citric acid isoamyl ester introduced in oil-water system is 0.12g when the mass ratio of oil and water is 7:3 (crude oil 23.4g, formation water 10g) and the experimental temperature is 50°C , the rate of viscosity reduction is 47.2%; during the evaluation of the ability of Citric acid isoamyl ester to decrease oil viscosity, we found that the optimum dosage of this compound in 20g crude oil is 0.2g when the temperature is 40°C, and the rate of viscosity reduction is 7.37% at this point.

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Recent Advances for Neural Networks Accelerators and Optimizations

High Performance Computing for Big Data ◽

10.1201/9781315155524-11 ◽

2017 ◽

pp. 171-190

Author(s):

Fan Sun ◽

Chao Wang ◽

Lei Gong ◽

Xi Li ◽

Aili Wang ◽

...

Keyword(s):

Neural Networks ◽

Recent Advances

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Data Size Requirement for Forecasting Daily Crude Oil Price with Neural Networks

Scientific Annals of Economics and Business ◽

10.47743/saeb-2019-0027 ◽

2019 ◽

Vol 66 (3) ◽

pp. 363-388

Author(s):

Serkan Aras ◽

Manel Hamdi

Keyword(s):

Neural Networks ◽

Crude Oil ◽

Significant Interaction ◽

Statistical Tests ◽

Oil Price ◽

Training Data ◽

Forecasting Model ◽

Crude Oil Price ◽

Crude Oil Prices ◽

Selection Of

When the literature regarding applications of neural networks is investigated, it appears that a substantial issue is what size the training data should be when modelling a time series through neural networks. The aim of this paper is to determine the size of training data to be used to construct a forecasting model via a multiple-breakpoint test and compare its performance with two general methods, namely, using all available data and using just two years of data. Furthermore, the importance of the selection of the final neural network model is investigated in detail. The results obtained from daily crude oil prices indicate that the data from the last structural change lead to simpler architectures of neural networks and have an advantage in reaching more accurate forecasts in terms of MAE value. In addition, the statistical tests show that there is a statistically significant interaction between data size and stopping rule.

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Different Nonlinear Regression Techniques and Sensitivity Analysis as Tools to Optimize Oil Viscosity Modeling

Resources ◽

10.3390/resources10100099 ◽

2021 ◽

Vol 10 (10) ◽

pp. 99

Author(s):

Dicho Stratiev ◽

Svetoslav Nenov ◽

Dimitar Nedanovski ◽

Ivelina Shishkova ◽

Rosen Dinkov ◽

...

Keyword(s):

Sensitivity Analysis ◽

Nonlinear Regression ◽

Information Criterion ◽

Least Square ◽

Gas Oil ◽

Oil Viscosity ◽

Prediction Ability ◽

Regression Techniques ◽

Comparable Accuracy ◽

Relative Errors

Four nonlinear regression techniques were explored to model gas oil viscosity on the base of Walther’s empirical equation. With the initial database of 41 primary and secondary vacuum gas oils, four models were developed with a comparable accuracy of viscosity calculation. The Akaike information criterion and Bayesian information criterion selected the least square relative errors (LSRE) model as the best one. The sensitivity analysis with respect to the given data also revealed that the LSRE model is the most stable one with the lowest values of standard deviations of derivatives. Verification of the gas oil viscosity prediction ability was carried out with another set of 43 gas oils showing remarkably better accuracy with the LSRE model. The LSRE was also found to predict better viscosity for the 43 test gas oils relative to the Aboul Seoud and Moharam model and the Kotzakoulakis and George.

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