Performance of asphaltene stability predicting models in field environment and development of new stability predicting model (ANJIS)

Abstract Asphaltene Precipitation is a major issue in both upstream and downstream sectors of the Petroleum Industry. This problem could occur at different locations of the hydrocarbon production system i.e., in the reservoir, wellbore, flowlines network, separation and refining facilities, and during transportation process. Asphaltene precipitation begins due to certain factors which include variation in crude oil composition, changes in pressure and temperature, and electrokinetic effects. Asphaltene deposition may offer severe technical and economic challenges to operating Exploration and Production companies with respect to losses in hydrocarbon production, facilities damages, and costly preventive and treatment solutions. Therefore, asphaltene stability monitoring in crude oils is necessary for the prevention of aggravation of problem related to the asphaltene deposition. This study will discuss the performance of eleven different stability parameters or models already developed by researchers for the monitoring of asphaltene stability in crude oils. These stability parameters include Colloidal Instability Index, Stability Index, Colloidal Stability Index, Chamkalani’s stability classifier, Jamaluddin’s method, Modified Jamaluddin’s method, Stankiewicz plot, QQA plots and SCP plots. The advantage of implementing these stability models is that they utilize less input data as compared to other conventional modeling techniques. Moreover, these stability parameters also provide quick crude oils stability outcomes than expensive experimental methods like Heithaus parameter, Toluene equivalence, spot test, and oil compatibility model. This research study will also evaluate the accuracies of stability parameters by their implementation on different stability known crude oil samples present in the published literature. The drawbacks and limitations associated with these applied stability parameters will also be presented and discussed in detail. This research found that CSI performed best as compared to other SARA based stability predicting models. However, considering the limitation of CSI and other predictors, a new predictor, namely ANJIS (Abdus, Nimra, Javed, Imran & Shaine) Asphaltene stability predicting model is proposed. ANJIS when used on oil sample of different conditions show reasonable accuracy. The study helps Petroleum companies, both upstream and downstream sector, to determine the best possible SARA based parameter and its associated risk used for the screening of asphaltene stability in crude oils. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Saboor, Abdus [verfasserIn] Yousaf, Nimra Haneef, Javed Ali, Syed Imran Lalji, Shaine Mohammadali

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Asphaltene SARA Stability Precipitation ANJIS Prediction

Anmerkung:	© The Author(s) 2021

Übergeordnetes Werk:	Enthalten in: Journal of petroleum exploration and production technology - Berlin : Springer, 2011, 12(2021), 5 vom: 07. Dez., Seite 1423-1436
Übergeordnetes Werk:	volume:12 ; year:2021 ; number:5 ; day:07 ; month:12 ; pages:1423-1436

Links:	Volltext

DOI / URN:	10.1007/s13202-021-01407-8

Katalog-ID:	SPR046931481

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allfields	10.1007/s13202-021-01407-8 doi (DE-627)SPR046931481 (SPR)s13202-021-01407-8-e DE-627 ger DE-627 rakwb eng Saboor, Abdus verfasserin aut Performance of asphaltene stability predicting models in field environment and development of new stability predicting model (ANJIS) 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract Asphaltene Precipitation is a major issue in both upstream and downstream sectors of the Petroleum Industry. This problem could occur at different locations of the hydrocarbon production system i.e., in the reservoir, wellbore, flowlines network, separation and refining facilities, and during transportation process. Asphaltene precipitation begins due to certain factors which include variation in crude oil composition, changes in pressure and temperature, and electrokinetic effects. Asphaltene deposition may offer severe technical and economic challenges to operating Exploration and Production companies with respect to losses in hydrocarbon production, facilities damages, and costly preventive and treatment solutions. Therefore, asphaltene stability monitoring in crude oils is necessary for the prevention of aggravation of problem related to the asphaltene deposition. This study will discuss the performance of eleven different stability parameters or models already developed by researchers for the monitoring of asphaltene stability in crude oils. These stability parameters include Colloidal Instability Index, Stability Index, Colloidal Stability Index, Chamkalani’s stability classifier, Jamaluddin’s method, Modified Jamaluddin’s method, Stankiewicz plot, QQA plots and SCP plots. The advantage of implementing these stability models is that they utilize less input data as compared to other conventional modeling techniques. Moreover, these stability parameters also provide quick crude oils stability outcomes than expensive experimental methods like Heithaus parameter, Toluene equivalence, spot test, and oil compatibility model. This research study will also evaluate the accuracies of stability parameters by their implementation on different stability known crude oil samples present in the published literature. The drawbacks and limitations associated with these applied stability parameters will also be presented and discussed in detail. This research found that CSI performed best as compared to other SARA based stability predicting models. However, considering the limitation of CSI and other predictors, a new predictor, namely ANJIS (Abdus, Nimra, Javed, Imran & Shaine) Asphaltene stability predicting model is proposed. ANJIS when used on oil sample of different conditions show reasonable accuracy. The study helps Petroleum companies, both upstream and downstream sector, to determine the best possible SARA based parameter and its associated risk used for the screening of asphaltene stability in crude oils. Asphaltene (dpeaa)DE-He213 SARA (dpeaa)DE-He213 Stability (dpeaa)DE-He213 Precipitation (dpeaa)DE-He213 ANJIS (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 Yousaf, Nimra (orcid)0000-0003-0424-5518 aut Haneef, Javed aut Ali, Syed Imran aut Lalji, Shaine Mohammadali aut Enthalten in Journal of petroleum exploration and production technology Berlin : Springer, 2011 12(2021), 5 vom: 07. Dez., Seite 1423-1436 (DE-627)647654148 (DE-600)2595714-4 2190-0566 nnns volume:12 year:2021 number:5 day:07 month:12 pages:1423-1436 https://dx.doi.org/10.1007/s13202-021-01407-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2021 5 07 12 1423-1436
spelling	10.1007/s13202-021-01407-8 doi (DE-627)SPR046931481 (SPR)s13202-021-01407-8-e DE-627 ger DE-627 rakwb eng Saboor, Abdus verfasserin aut Performance of asphaltene stability predicting models in field environment and development of new stability predicting model (ANJIS) 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract Asphaltene Precipitation is a major issue in both upstream and downstream sectors of the Petroleum Industry. This problem could occur at different locations of the hydrocarbon production system i.e., in the reservoir, wellbore, flowlines network, separation and refining facilities, and during transportation process. Asphaltene precipitation begins due to certain factors which include variation in crude oil composition, changes in pressure and temperature, and electrokinetic effects. Asphaltene deposition may offer severe technical and economic challenges to operating Exploration and Production companies with respect to losses in hydrocarbon production, facilities damages, and costly preventive and treatment solutions. Therefore, asphaltene stability monitoring in crude oils is necessary for the prevention of aggravation of problem related to the asphaltene deposition. This study will discuss the performance of eleven different stability parameters or models already developed by researchers for the monitoring of asphaltene stability in crude oils. These stability parameters include Colloidal Instability Index, Stability Index, Colloidal Stability Index, Chamkalani’s stability classifier, Jamaluddin’s method, Modified Jamaluddin’s method, Stankiewicz plot, QQA plots and SCP plots. The advantage of implementing these stability models is that they utilize less input data as compared to other conventional modeling techniques. Moreover, these stability parameters also provide quick crude oils stability outcomes than expensive experimental methods like Heithaus parameter, Toluene equivalence, spot test, and oil compatibility model. This research study will also evaluate the accuracies of stability parameters by their implementation on different stability known crude oil samples present in the published literature. The drawbacks and limitations associated with these applied stability parameters will also be presented and discussed in detail. This research found that CSI performed best as compared to other SARA based stability predicting models. However, considering the limitation of CSI and other predictors, a new predictor, namely ANJIS (Abdus, Nimra, Javed, Imran & Shaine) Asphaltene stability predicting model is proposed. ANJIS when used on oil sample of different conditions show reasonable accuracy. The study helps Petroleum companies, both upstream and downstream sector, to determine the best possible SARA based parameter and its associated risk used for the screening of asphaltene stability in crude oils. Asphaltene (dpeaa)DE-He213 SARA (dpeaa)DE-He213 Stability (dpeaa)DE-He213 Precipitation (dpeaa)DE-He213 ANJIS (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 Yousaf, Nimra (orcid)0000-0003-0424-5518 aut Haneef, Javed aut Ali, Syed Imran aut Lalji, Shaine Mohammadali aut Enthalten in Journal of petroleum exploration and production technology Berlin : Springer, 2011 12(2021), 5 vom: 07. Dez., Seite 1423-1436 (DE-627)647654148 (DE-600)2595714-4 2190-0566 nnns volume:12 year:2021 number:5 day:07 month:12 pages:1423-1436 https://dx.doi.org/10.1007/s13202-021-01407-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2021 5 07 12 1423-1436
allfields_unstemmed	10.1007/s13202-021-01407-8 doi (DE-627)SPR046931481 (SPR)s13202-021-01407-8-e DE-627 ger DE-627 rakwb eng Saboor, Abdus verfasserin aut Performance of asphaltene stability predicting models in field environment and development of new stability predicting model (ANJIS) 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract Asphaltene Precipitation is a major issue in both upstream and downstream sectors of the Petroleum Industry. This problem could occur at different locations of the hydrocarbon production system i.e., in the reservoir, wellbore, flowlines network, separation and refining facilities, and during transportation process. Asphaltene precipitation begins due to certain factors which include variation in crude oil composition, changes in pressure and temperature, and electrokinetic effects. Asphaltene deposition may offer severe technical and economic challenges to operating Exploration and Production companies with respect to losses in hydrocarbon production, facilities damages, and costly preventive and treatment solutions. Therefore, asphaltene stability monitoring in crude oils is necessary for the prevention of aggravation of problem related to the asphaltene deposition. This study will discuss the performance of eleven different stability parameters or models already developed by researchers for the monitoring of asphaltene stability in crude oils. These stability parameters include Colloidal Instability Index, Stability Index, Colloidal Stability Index, Chamkalani’s stability classifier, Jamaluddin’s method, Modified Jamaluddin’s method, Stankiewicz plot, QQA plots and SCP plots. The advantage of implementing these stability models is that they utilize less input data as compared to other conventional modeling techniques. Moreover, these stability parameters also provide quick crude oils stability outcomes than expensive experimental methods like Heithaus parameter, Toluene equivalence, spot test, and oil compatibility model. This research study will also evaluate the accuracies of stability parameters by their implementation on different stability known crude oil samples present in the published literature. The drawbacks and limitations associated with these applied stability parameters will also be presented and discussed in detail. This research found that CSI performed best as compared to other SARA based stability predicting models. However, considering the limitation of CSI and other predictors, a new predictor, namely ANJIS (Abdus, Nimra, Javed, Imran & Shaine) Asphaltene stability predicting model is proposed. ANJIS when used on oil sample of different conditions show reasonable accuracy. The study helps Petroleum companies, both upstream and downstream sector, to determine the best possible SARA based parameter and its associated risk used for the screening of asphaltene stability in crude oils. Asphaltene (dpeaa)DE-He213 SARA (dpeaa)DE-He213 Stability (dpeaa)DE-He213 Precipitation (dpeaa)DE-He213 ANJIS (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 Yousaf, Nimra (orcid)0000-0003-0424-5518 aut Haneef, Javed aut Ali, Syed Imran aut Lalji, Shaine Mohammadali aut Enthalten in Journal of petroleum exploration and production technology Berlin : Springer, 2011 12(2021), 5 vom: 07. Dez., Seite 1423-1436 (DE-627)647654148 (DE-600)2595714-4 2190-0566 nnns volume:12 year:2021 number:5 day:07 month:12 pages:1423-1436 https://dx.doi.org/10.1007/s13202-021-01407-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2021 5 07 12 1423-1436
allfieldsGer	10.1007/s13202-021-01407-8 doi (DE-627)SPR046931481 (SPR)s13202-021-01407-8-e DE-627 ger DE-627 rakwb eng Saboor, Abdus verfasserin aut Performance of asphaltene stability predicting models in field environment and development of new stability predicting model (ANJIS) 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract Asphaltene Precipitation is a major issue in both upstream and downstream sectors of the Petroleum Industry. This problem could occur at different locations of the hydrocarbon production system i.e., in the reservoir, wellbore, flowlines network, separation and refining facilities, and during transportation process. Asphaltene precipitation begins due to certain factors which include variation in crude oil composition, changes in pressure and temperature, and electrokinetic effects. Asphaltene deposition may offer severe technical and economic challenges to operating Exploration and Production companies with respect to losses in hydrocarbon production, facilities damages, and costly preventive and treatment solutions. Therefore, asphaltene stability monitoring in crude oils is necessary for the prevention of aggravation of problem related to the asphaltene deposition. This study will discuss the performance of eleven different stability parameters or models already developed by researchers for the monitoring of asphaltene stability in crude oils. These stability parameters include Colloidal Instability Index, Stability Index, Colloidal Stability Index, Chamkalani’s stability classifier, Jamaluddin’s method, Modified Jamaluddin’s method, Stankiewicz plot, QQA plots and SCP plots. The advantage of implementing these stability models is that they utilize less input data as compared to other conventional modeling techniques. Moreover, these stability parameters also provide quick crude oils stability outcomes than expensive experimental methods like Heithaus parameter, Toluene equivalence, spot test, and oil compatibility model. This research study will also evaluate the accuracies of stability parameters by their implementation on different stability known crude oil samples present in the published literature. The drawbacks and limitations associated with these applied stability parameters will also be presented and discussed in detail. This research found that CSI performed best as compared to other SARA based stability predicting models. However, considering the limitation of CSI and other predictors, a new predictor, namely ANJIS (Abdus, Nimra, Javed, Imran & Shaine) Asphaltene stability predicting model is proposed. ANJIS when used on oil sample of different conditions show reasonable accuracy. The study helps Petroleum companies, both upstream and downstream sector, to determine the best possible SARA based parameter and its associated risk used for the screening of asphaltene stability in crude oils. Asphaltene (dpeaa)DE-He213 SARA (dpeaa)DE-He213 Stability (dpeaa)DE-He213 Precipitation (dpeaa)DE-He213 ANJIS (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 Yousaf, Nimra (orcid)0000-0003-0424-5518 aut Haneef, Javed aut Ali, Syed Imran aut Lalji, Shaine Mohammadali aut Enthalten in Journal of petroleum exploration and production technology Berlin : Springer, 2011 12(2021), 5 vom: 07. Dez., Seite 1423-1436 (DE-627)647654148 (DE-600)2595714-4 2190-0566 nnns volume:12 year:2021 number:5 day:07 month:12 pages:1423-1436 https://dx.doi.org/10.1007/s13202-021-01407-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2021 5 07 12 1423-1436
allfieldsSound	10.1007/s13202-021-01407-8 doi (DE-627)SPR046931481 (SPR)s13202-021-01407-8-e DE-627 ger DE-627 rakwb eng Saboor, Abdus verfasserin aut Performance of asphaltene stability predicting models in field environment and development of new stability predicting model (ANJIS) 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract Asphaltene Precipitation is a major issue in both upstream and downstream sectors of the Petroleum Industry. This problem could occur at different locations of the hydrocarbon production system i.e., in the reservoir, wellbore, flowlines network, separation and refining facilities, and during transportation process. Asphaltene precipitation begins due to certain factors which include variation in crude oil composition, changes in pressure and temperature, and electrokinetic effects. Asphaltene deposition may offer severe technical and economic challenges to operating Exploration and Production companies with respect to losses in hydrocarbon production, facilities damages, and costly preventive and treatment solutions. Therefore, asphaltene stability monitoring in crude oils is necessary for the prevention of aggravation of problem related to the asphaltene deposition. This study will discuss the performance of eleven different stability parameters or models already developed by researchers for the monitoring of asphaltene stability in crude oils. These stability parameters include Colloidal Instability Index, Stability Index, Colloidal Stability Index, Chamkalani’s stability classifier, Jamaluddin’s method, Modified Jamaluddin’s method, Stankiewicz plot, QQA plots and SCP plots. The advantage of implementing these stability models is that they utilize less input data as compared to other conventional modeling techniques. Moreover, these stability parameters also provide quick crude oils stability outcomes than expensive experimental methods like Heithaus parameter, Toluene equivalence, spot test, and oil compatibility model. This research study will also evaluate the accuracies of stability parameters by their implementation on different stability known crude oil samples present in the published literature. The drawbacks and limitations associated with these applied stability parameters will also be presented and discussed in detail. This research found that CSI performed best as compared to other SARA based stability predicting models. However, considering the limitation of CSI and other predictors, a new predictor, namely ANJIS (Abdus, Nimra, Javed, Imran & Shaine) Asphaltene stability predicting model is proposed. ANJIS when used on oil sample of different conditions show reasonable accuracy. The study helps Petroleum companies, both upstream and downstream sector, to determine the best possible SARA based parameter and its associated risk used for the screening of asphaltene stability in crude oils. Asphaltene (dpeaa)DE-He213 SARA (dpeaa)DE-He213 Stability (dpeaa)DE-He213 Precipitation (dpeaa)DE-He213 ANJIS (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 Yousaf, Nimra (orcid)0000-0003-0424-5518 aut Haneef, Javed aut Ali, Syed Imran aut Lalji, Shaine Mohammadali aut Enthalten in Journal of petroleum exploration and production technology Berlin : Springer, 2011 12(2021), 5 vom: 07. Dez., Seite 1423-1436 (DE-627)647654148 (DE-600)2595714-4 2190-0566 nnns volume:12 year:2021 number:5 day:07 month:12 pages:1423-1436 https://dx.doi.org/10.1007/s13202-021-01407-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2021 5 07 12 1423-1436
language	English
source	Enthalten in Journal of petroleum exploration and production technology 12(2021), 5 vom: 07. Dez., Seite 1423-1436 volume:12 year:2021 number:5 day:07 month:12 pages:1423-1436
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topic_facet	Asphaltene SARA Stability Precipitation ANJIS Prediction
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authorswithroles_txt_mv	Saboor, Abdus @@aut@@ Yousaf, Nimra @@aut@@ Haneef, Javed @@aut@@ Ali, Syed Imran @@aut@@ Lalji, Shaine Mohammadali @@aut@@
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These stability parameters include Colloidal Instability Index, Stability Index, Colloidal Stability Index, Chamkalani’s stability classifier, Jamaluddin’s method, Modified Jamaluddin’s method, Stankiewicz plot, QQA plots and SCP plots. The advantage of implementing these stability models is that they utilize less input data as compared to other conventional modeling techniques. Moreover, these stability parameters also provide quick crude oils stability outcomes than expensive experimental methods like Heithaus parameter, Toluene equivalence, spot test, and oil compatibility model. This research study will also evaluate the accuracies of stability parameters by their implementation on different stability known crude oil samples present in the published literature. The drawbacks and limitations associated with these applied stability parameters will also be presented and discussed in detail. 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author	Saboor, Abdus
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topic_title	Performance of asphaltene stability predicting models in field environment and development of new stability predicting model (ANJIS) Asphaltene (dpeaa)DE-He213 SARA (dpeaa)DE-He213 Stability (dpeaa)DE-He213 Precipitation (dpeaa)DE-He213 ANJIS (dpeaa)DE-He213 Prediction (dpeaa)DE-He213
topic	misc Asphaltene misc SARA misc Stability misc Precipitation misc ANJIS misc Prediction
topic_unstemmed	misc Asphaltene misc SARA misc Stability misc Precipitation misc ANJIS misc Prediction
topic_browse	misc Asphaltene misc SARA misc Stability misc Precipitation misc ANJIS misc Prediction
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title	Performance of asphaltene stability predicting models in field environment and development of new stability predicting model (ANJIS)
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title_full	Performance of asphaltene stability predicting models in field environment and development of new stability predicting model (ANJIS)
author_sort	Saboor, Abdus
journal	Journal of petroleum exploration and production technology
journalStr	Journal of petroleum exploration and production technology
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author_browse	Saboor, Abdus Yousaf, Nimra Haneef, Javed Ali, Syed Imran Lalji, Shaine Mohammadali
container_volume	12
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author-letter	Saboor, Abdus
doi_str_mv	10.1007/s13202-021-01407-8
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title_sort	performance of asphaltene stability predicting models in field environment and development of new stability predicting model (anjis)
title_auth	Performance of asphaltene stability predicting models in field environment and development of new stability predicting model (ANJIS)
abstract	Abstract Asphaltene Precipitation is a major issue in both upstream and downstream sectors of the Petroleum Industry. This problem could occur at different locations of the hydrocarbon production system i.e., in the reservoir, wellbore, flowlines network, separation and refining facilities, and during transportation process. Asphaltene precipitation begins due to certain factors which include variation in crude oil composition, changes in pressure and temperature, and electrokinetic effects. Asphaltene deposition may offer severe technical and economic challenges to operating Exploration and Production companies with respect to losses in hydrocarbon production, facilities damages, and costly preventive and treatment solutions. Therefore, asphaltene stability monitoring in crude oils is necessary for the prevention of aggravation of problem related to the asphaltene deposition. This study will discuss the performance of eleven different stability parameters or models already developed by researchers for the monitoring of asphaltene stability in crude oils. These stability parameters include Colloidal Instability Index, Stability Index, Colloidal Stability Index, Chamkalani’s stability classifier, Jamaluddin’s method, Modified Jamaluddin’s method, Stankiewicz plot, QQA plots and SCP plots. The advantage of implementing these stability models is that they utilize less input data as compared to other conventional modeling techniques. Moreover, these stability parameters also provide quick crude oils stability outcomes than expensive experimental methods like Heithaus parameter, Toluene equivalence, spot test, and oil compatibility model. This research study will also evaluate the accuracies of stability parameters by their implementation on different stability known crude oil samples present in the published literature. The drawbacks and limitations associated with these applied stability parameters will also be presented and discussed in detail. This research found that CSI performed best as compared to other SARA based stability predicting models. However, considering the limitation of CSI and other predictors, a new predictor, namely ANJIS (Abdus, Nimra, Javed, Imran & Shaine) Asphaltene stability predicting model is proposed. ANJIS when used on oil sample of different conditions show reasonable accuracy. The study helps Petroleum companies, both upstream and downstream sector, to determine the best possible SARA based parameter and its associated risk used for the screening of asphaltene stability in crude oils. © The Author(s) 2021
abstractGer	Abstract Asphaltene Precipitation is a major issue in both upstream and downstream sectors of the Petroleum Industry. This problem could occur at different locations of the hydrocarbon production system i.e., in the reservoir, wellbore, flowlines network, separation and refining facilities, and during transportation process. Asphaltene precipitation begins due to certain factors which include variation in crude oil composition, changes in pressure and temperature, and electrokinetic effects. Asphaltene deposition may offer severe technical and economic challenges to operating Exploration and Production companies with respect to losses in hydrocarbon production, facilities damages, and costly preventive and treatment solutions. Therefore, asphaltene stability monitoring in crude oils is necessary for the prevention of aggravation of problem related to the asphaltene deposition. This study will discuss the performance of eleven different stability parameters or models already developed by researchers for the monitoring of asphaltene stability in crude oils. These stability parameters include Colloidal Instability Index, Stability Index, Colloidal Stability Index, Chamkalani’s stability classifier, Jamaluddin’s method, Modified Jamaluddin’s method, Stankiewicz plot, QQA plots and SCP plots. The advantage of implementing these stability models is that they utilize less input data as compared to other conventional modeling techniques. Moreover, these stability parameters also provide quick crude oils stability outcomes than expensive experimental methods like Heithaus parameter, Toluene equivalence, spot test, and oil compatibility model. This research study will also evaluate the accuracies of stability parameters by their implementation on different stability known crude oil samples present in the published literature. The drawbacks and limitations associated with these applied stability parameters will also be presented and discussed in detail. This research found that CSI performed best as compared to other SARA based stability predicting models. However, considering the limitation of CSI and other predictors, a new predictor, namely ANJIS (Abdus, Nimra, Javed, Imran & Shaine) Asphaltene stability predicting model is proposed. ANJIS when used on oil sample of different conditions show reasonable accuracy. The study helps Petroleum companies, both upstream and downstream sector, to determine the best possible SARA based parameter and its associated risk used for the screening of asphaltene stability in crude oils. © The Author(s) 2021
abstract_unstemmed	Abstract Asphaltene Precipitation is a major issue in both upstream and downstream sectors of the Petroleum Industry. This problem could occur at different locations of the hydrocarbon production system i.e., in the reservoir, wellbore, flowlines network, separation and refining facilities, and during transportation process. Asphaltene precipitation begins due to certain factors which include variation in crude oil composition, changes in pressure and temperature, and electrokinetic effects. Asphaltene deposition may offer severe technical and economic challenges to operating Exploration and Production companies with respect to losses in hydrocarbon production, facilities damages, and costly preventive and treatment solutions. Therefore, asphaltene stability monitoring in crude oils is necessary for the prevention of aggravation of problem related to the asphaltene deposition. This study will discuss the performance of eleven different stability parameters or models already developed by researchers for the monitoring of asphaltene stability in crude oils. These stability parameters include Colloidal Instability Index, Stability Index, Colloidal Stability Index, Chamkalani’s stability classifier, Jamaluddin’s method, Modified Jamaluddin’s method, Stankiewicz plot, QQA plots and SCP plots. The advantage of implementing these stability models is that they utilize less input data as compared to other conventional modeling techniques. Moreover, these stability parameters also provide quick crude oils stability outcomes than expensive experimental methods like Heithaus parameter, Toluene equivalence, spot test, and oil compatibility model. This research study will also evaluate the accuracies of stability parameters by their implementation on different stability known crude oil samples present in the published literature. The drawbacks and limitations associated with these applied stability parameters will also be presented and discussed in detail. This research found that CSI performed best as compared to other SARA based stability predicting models. However, considering the limitation of CSI and other predictors, a new predictor, namely ANJIS (Abdus, Nimra, Javed, Imran & Shaine) Asphaltene stability predicting model is proposed. ANJIS when used on oil sample of different conditions show reasonable accuracy. The study helps Petroleum companies, both upstream and downstream sector, to determine the best possible SARA based parameter and its associated risk used for the screening of asphaltene stability in crude oils. © The Author(s) 2021
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container_issue	5
title_short	Performance of asphaltene stability predicting models in field environment and development of new stability predicting model (ANJIS)
url	https://dx.doi.org/10.1007/s13202-021-01407-8
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author2	Yousaf, Nimra Haneef, Javed Ali, Syed Imran Lalji, Shaine Mohammadali
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