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.

Synergistic Effect of Low Salinity Surfactant Nanofluid on the Interfacial Tension of Oil–Water Systems, Wettability Alteration, and Surfactant Adsorption on the Quartz Surface

“…The colloidal instability index formula CII is used to calculate the stability of asphaltene in crude oil in the bulk phase. If CII > 0.9 asphaltene is unstable in crude oil, CII < 0.7 asphaltene is stable, and 0.9 < CII < 0.7 asphaltene stability is uncertain . The CII formula is given below. normalC normalI normalI = normalS + normalA normals normalR + normalA normalr where S, As, R, and Ar are saturates, asphaltenes, resin, and aromatics in weight percentage (wt %) .…”

“…If CII > 0.9 asphaltene is unstable in crude oil, CII < 0.7 asphaltene is stable, and 0.9 < CII < 0.7 asphaltene stability is uncertain. 86 The CII formula is given below.…”

Application of Intercriteria and Regression Analyses and Artificial Neural Network to Investigate the Relation of Crude Oil Assay Data to Oil Compatibility

“…This can explain why light crude oils are reported by some authors to be more prone to cause asphaltene precipitations than heavy oils [14][15][16][17]. Apart from the abovementioned methods, various predictive models and experimental techniques have been implemented to estimate oil stability throughout the years, such as the colloidal instability index (CII) [18][19][20][21], colloidal stability index (CSI) [19,21,22], stability index (SI) [19,22,23], Stankiewicz plot (SP) [19,21,24], qualitativequantitative analysis (QQA) [21], stability cross plot (SCP) [21,25], Heithaus parameter (or parameter P) [26], heptane dilution (HD) [27,28], toluene equivalence (TE) [3,29,30], spot test [26,[31][32][33][34][35] and separability number (SN) [26,36,37], Jamaluddin method (JM) [38][39][40], modified Jamaluddin method (MJM) [41], density-based asphaltene stability envelope (DBASE) [19], in-line filtration method [42], 1D low-field nuclear magnetic resonance relaxometry and chemometric methods [43,…”

Determination of Asphaltene Stability in Crude Oils Using a Deposit Level Test Coupled with a Spot Test: A Simple and Qualitative Approach

“… 1 , 22 Different models like the colloidal instability index (CII), stability index (SI), colloidal stability index (CSI), Stankiewicz plot (SP), ANJIS Model, qualitative–quantitative analysis (QQA), and stability cross plot (SCP) were developed and applied in the past, which works well on this concept. 41 − 49 Although these models are very easy and simple to apply as it involves less computational work, their reliability and accuracy are debatable. 1 , 40 These indices suffer inaccuracies due to the absence of the incorporation of other factors like asphaltene and resins compositional and structural properties and operational conditions such as temperature and pressure conditions which are equally important for judging asphaltene stability in crude oils.…”

“…In the literature, there are various stability assessment methods proposed to determine the asphaltene deposition risk potential in crude oils. Among them, one of the methods is based on determining asphaltene stability in crude oil on the basis of crude oil SARA values. , The basic concept behind this method is that crude oil is found to be more risky in terms of asphaltene deposition if it contains higher contents of saturates and asphaltene and lower contents of resins and aromatic contents. , Different models like the colloidal instability index (CII), stability index (SI), colloidal stability index (CSI), Stankiewicz plot (SP), ANJIS Model, qualitative–quantitative analysis (QQA), and stability cross plot (SCP) were developed and applied in the past, which works well on this concept. − Although these models are very easy and simple to apply as it involves less computational work, their reliability and accuracy are debatable. , These indices suffer inaccuracies due to the absence of the incorporation of other factors like asphaltene and resins compositional and structural properties and operational conditions such as temperature and pressure conditions which are equally important for judging asphaltene stability in crude oils . In addition, these parameters take SARA values as input which are determined through various techniques and found to have significant differences in values from each other; therefore, in this context it may produce different stability outcomes for same crude oil. ,,, Moreover, recent studies reported that these SARA-based parameters produce biased predictions, i.e., predict certain classes better, and because of this a significant difference is usually found in the prediction outcomes of these models. , The second way of determining asphaltene stability in crude oils is to use thermodynamic models .…”