Correlations between SARA Fractions, Density, and RI to Investigate the Stability of Asphaltene

Chamkalani, Ali

doi:10.5402/2012/219276

Cited by 33 publications

(47 citation statements)

References 41 publications

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“…Due to drawbacks encountered in various methods of experimental measurement, development of a crude oil RI model could facilitate exact calculation of crude oil RI from readily available experimental data. Several researchers have tried to develop a predictive model for determining the RI (Chamkalani et al 2014;Fan et al 2002;Buckley and Wang 2002;Chamkalani et al 2012;Chamkalani 2012;Gholami et al 2014a, b;Vargas and Chapman 2010;Zargar et al 2015;Tatar et al 2015). The published models available in the literature for estimating the value of crude oil RI can be divided into two main categories.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of crude oil refractive index through optimized support vector regression: a competition between optimization techniques

Gholami

Ansari

Hosseini

2016

J Petrol Explor Prod Technol

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Refractive index (RI) provides valuable information about various reservoir engineering calculations, making it a key parameter for characterizing crude oils. Determination of this index through experiment is capitalintensive, time consuming, and also toil. Hence, it is essential to search for an efficient and accurate estimation of crude oil RI. In this study, an intelligent approach, based on optimized support vector regression (SVR), is introduced to find a quantitative correlation between crude oil RI and SARA (saturate, aromatic, resin, and asphaltene) fraction data. Optimization of SVR is implemented through three searching approaches, viz. hybrid of grid and pattern search (HGP), genetic algorithm (GA), and imperialist competitive algorithm (ICA). Using these approaches, three models are constructed and tested on experimental data gathered from open source literature. To evaluate the performance of these models, their outputs are compared with corresponding experimental data in terms of statistical criteria. The comparative study clearly shows the advantage of ICA over its rivals (GA and HGP) in optimizing the SVR parameters. ICA optimized support vector regression results in an R 2 of 0.9971 and MSE of 1.48548e-05 demonstrating its efficacy in obtaining crude oil refractive index form SARA data.

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Section: Introductionmentioning

confidence: 99%

“…They developed an empirical correlation between the SARA fraction data and RI of crude oil. Chamkalani (2012) established another empirical correlation to estimate the value of crude oils RI. Although aforementioned empirical correlations are valuable, they are faced with some shortcomings, the main one being unsatisfactory accuracy.…”

Section: Introductionmentioning

confidence: 99%

Prediction of crude oil refractive index through optimized support vector regression: a competition between optimization techniques

Gholami

Ansari

Hosseini

2016

J Petrol Explor Prod Technol

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“…It is rational to state that the stability of the asphaltene fraction in the bulk of crude oil is reduced if the RI of maltene fraction lowers (note: The crude oil is named maltene if its asphaltene is removed). It is important to note that an elevated temperature may lead to thermal disaggregation, even if the RI is reduced …”

Section: Theorymentioning

confidence: 99%

“…Recently, Chamkalani developed a linear relationship between SARA fractions, density, and RI to examine asphaltene stability. According to the statistical analysis presented for the correlations obtained in his study, R 2 was determined to be ∼0.73, which is fairly low for the linear regressive equation . Thus, a moderately high value for MSE was attained based on the measured data and predicted outputs .…”

Section: Introductionmentioning

confidence: 99%

New efficient tool diagnoses asphaltene stability: Utilization of refractive index

Ahmadi

Zendehboudi

2019

Can J Chem Eng

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Asphaltene precipitation/deposition is considered a critical issue in the petroleum industry as it causes damages and blockage in various process equipment (e.g., pipe, pump, and vessel) and porous formation. To prevent this obstruction, it seems necessary to attain a reasonable estimate of the precipitation time, in other words, the time of asphaltene stability in crude oils. The gene expression programming (GEP) technique is utilized in this study to develop a correlation for the prediction of this important parameter using real refractive index (RI) data. The independent variables considered for the development of the correlation are the composition of aromatics, saturates, and resins in weight percentage. The predicted outputs show a better match with the real data, compared to the results obtained from other available predictive tools and/or correlations. Besides its simplicity, the developed correlation can have a broad range of applications in oil production plants in order to ensure that proper strategies are determined to avoid asphaltene precipitation/deposition throughout various stages of oil production and processing.

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“…Several researchers have attempted to develop valuable empirical correlations using regression technique. [14,17] Although these correlations are useful, they possess major flaws. These correlations usually do not achieve accurate estimations of the refractive index.…”

Section: Introductionmentioning

confidence: 99%

Smart Determination of Difference Index for Asphaltene Stability Evaluation

Gholami

Asoodeh

Bagheripour

2014

Journal of Dispersion Science and Technology

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Precipitation and deposition of asphaltene during different stages of petroleum production is recognized as problematic in oil industry because of the increase in production cost and the inhibition of a consistent flow of crude oil in different medium. Numerous correlations have been developed to determine asphaltene stability in crude oil. In this study, a novel ONN method was used to estimate difference index from SARA fraction data for rapid, accurate, and cost-effective determination of asphaltene stability. Neural networks are highly in danger of trapping in local minima. To eliminate this flaw, a hybrid genetic algorithm-pattern search technique was used instead of common back-propagation algorithm for training the employed neural network. A comparison between neural network and optimized neural network indicated superiority of optimized neural network.

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Correlations between SARA Fractions, Density, and RI to Investigate the Stability of Asphaltene

Cited by 33 publications

References 41 publications

Prediction of crude oil refractive index through optimized support vector regression: a competition between optimization techniques

Prediction of crude oil refractive index through optimized support vector regression: a competition between optimization techniques

New efficient tool diagnoses asphaltene stability: Utilization of refractive index

Smart Determination of Difference Index for Asphaltene Stability Evaluation

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