LAPSE:2024.0633
Published Article

LAPSE:2024.0633
Application of Intercriteria and Regression Analyses and Artificial Neural Network to Investigate the Relation of Crude Oil Assay Data to Oil Compatibility
June 5, 2024
Abstract
The compatibility of constituents making up a petroleum fluid has been recognized as an important factor for trouble-free operations in the petroleum industry. The fouling of equipment and desalting efficiency deteriorations are the results of dealing with incompatible oils. A great number of studies dedicated to oil compatibility have appeared over the years to address this important issue. The full analysis of examined petroleum fluids has not been juxtaposed yet with the compatibility characteristics in published research that could provide an insight into the reasons for the different values of colloidal stability indices. That was the reason for us investigating 48 crude oil samples pertaining to extra light, light, medium, heavy, and extra heavy petroleum crudes, which were examined for their colloidal stability by measuring solvent power and critical solvent power utilizing the n-heptane dilution test performed by using centrifuge. The solubility power of the investigated crude oils varied between 12.5 and 74.7, while the critical solubility power fluctuated between 3.3 and 37.3. True boiling point (TBP) analysis, high-temperature simulation distillation, SARA analysis, viscosity, density and sulfur distribution of narrow petroleum fractions, and vacuum residue characterization (SARA, density, Conradson carbon, asphaltene density) of the investigated oils were performed. All the experimentally determined data in this research were evaluated by intercriteria and regression analyses. Regression and artificial neural network models were developed predicting the critical solubility power with correlation coefficients R of 0.80 and 0.799, respectively.
The compatibility of constituents making up a petroleum fluid has been recognized as an important factor for trouble-free operations in the petroleum industry. The fouling of equipment and desalting efficiency deteriorations are the results of dealing with incompatible oils. A great number of studies dedicated to oil compatibility have appeared over the years to address this important issue. The full analysis of examined petroleum fluids has not been juxtaposed yet with the compatibility characteristics in published research that could provide an insight into the reasons for the different values of colloidal stability indices. That was the reason for us investigating 48 crude oil samples pertaining to extra light, light, medium, heavy, and extra heavy petroleum crudes, which were examined for their colloidal stability by measuring solvent power and critical solvent power utilizing the n-heptane dilution test performed by using centrifuge. The solubility power of the investigated crude oils varied between 12.5 and 74.7, while the critical solubility power fluctuated between 3.3 and 37.3. True boiling point (TBP) analysis, high-temperature simulation distillation, SARA analysis, viscosity, density and sulfur distribution of narrow petroleum fractions, and vacuum residue characterization (SARA, density, Conradson carbon, asphaltene density) of the investigated oils were performed. All the experimentally determined data in this research were evaluated by intercriteria and regression analyses. Regression and artificial neural network models were developed predicting the critical solubility power with correlation coefficients R of 0.80 and 0.799, respectively.
Record ID
Keywords
ANN, asphaltenes, intercriteria analysis, oil colloidal stability, Petroleum, regression, SARA
Suggested Citation
Shiskova I, Stratiev D, Tavlieva M, Nedelchev A, Dinkov R, Kolev I, van den Berg F, Ribagin S, Sotirov S, Nikolova R, Veli A, Georgiev G, Atanassov K. Application of Intercriteria and Regression Analyses and Artificial Neural Network to Investigate the Relation of Crude Oil Assay Data to Oil Compatibility. (2024). LAPSE:2024.0633
Author Affiliations
Shiskova I: LUKOIL Neftohim Burgas, 8104 Burgas, Bulgaria [ORCID]
Stratiev D: LUKOIL Neftohim Burgas, 8104 Burgas, Bulgaria; Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Georgi Bonchev 105, 1113 Sofia, Bulgaria [ORCID]
Tavlieva M: LUKOIL Neftohim Burgas, 8104 Burgas, Bulgaria
Nedelchev A: LUKOIL Neftohim Burgas, 8104 Burgas, Bulgaria
Dinkov R: LUKOIL Neftohim Burgas, 8104 Burgas, Bulgaria [ORCID]
Kolev I: LUKOIL Neftohim Burgas, 8104 Burgas, Bulgaria
van den Berg F: Black Oil Solutions, 2401 Alphen aan den Rijn, The Netherlands [ORCID]
Ribagin S: Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Georgi Bonchev 105, 1113 Sofia, Bulgaria; Department of Health and Pharmaceutical Care, University Prof. Dr. Assen Zlatarov, Professor Yakimov 1, 8010 Burgas, Bulgaria
Sotirov S: Laboratory of Intelligent Systems, University Prof. Dr. Assen Zlatarov, Professor Yakimov 1, 8010 Burgas, Bulgaria
Nikolova R: Central Research Laboratory, University Prof. Dr. Assen Zlatarov, Professor Yakimov 1, 8010 Burgas, Bulgaria [ORCID]
Veli A: Central Research Laboratory, University Prof. Dr. Assen Zlatarov, Professor Yakimov 1, 8010 Burgas, Bulgaria
Georgiev G: LUKOIL Neftohim Burgas, 8104 Burgas, Bulgaria
Atanassov K: Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Georgi Bonchev 105, 1113 Sofia, Bulgaria [ORCID]
Stratiev D: LUKOIL Neftohim Burgas, 8104 Burgas, Bulgaria; Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Georgi Bonchev 105, 1113 Sofia, Bulgaria [ORCID]
Tavlieva M: LUKOIL Neftohim Burgas, 8104 Burgas, Bulgaria
Nedelchev A: LUKOIL Neftohim Burgas, 8104 Burgas, Bulgaria
Dinkov R: LUKOIL Neftohim Burgas, 8104 Burgas, Bulgaria [ORCID]
Kolev I: LUKOIL Neftohim Burgas, 8104 Burgas, Bulgaria
van den Berg F: Black Oil Solutions, 2401 Alphen aan den Rijn, The Netherlands [ORCID]
Ribagin S: Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Georgi Bonchev 105, 1113 Sofia, Bulgaria; Department of Health and Pharmaceutical Care, University Prof. Dr. Assen Zlatarov, Professor Yakimov 1, 8010 Burgas, Bulgaria
Sotirov S: Laboratory of Intelligent Systems, University Prof. Dr. Assen Zlatarov, Professor Yakimov 1, 8010 Burgas, Bulgaria
Nikolova R: Central Research Laboratory, University Prof. Dr. Assen Zlatarov, Professor Yakimov 1, 8010 Burgas, Bulgaria [ORCID]
Veli A: Central Research Laboratory, University Prof. Dr. Assen Zlatarov, Professor Yakimov 1, 8010 Burgas, Bulgaria
Georgiev G: LUKOIL Neftohim Burgas, 8104 Burgas, Bulgaria
Atanassov K: Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Georgi Bonchev 105, 1113 Sofia, Bulgaria [ORCID]
Journal Name
Processes
Volume
12
Issue
4
First Page
780
Year
2024
Publication Date
2024-04-12
ISSN
2227-9717
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PII: pr12040780, Publication Type: Journal Article
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LAPSE:2024.0633
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https://doi.org/10.3390/pr12040780
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Jun 5, 2024
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