Modeling viscosity of moderate and light dead oils in the presence of complex aromatic structure

Aily, M. El; Mansour, E.M.; Desouky, S.M.; Helmi, Mohamad

doi:10.1016/j.petrol.2018.10.024

Cited by 12 publications

(16 citation statements)

References 11 publications

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“…Nanocomposite polymer solution samples were prepared using different concentrations of the nanocomposite polymer (1, 1.5, 2, 2.5, 3, 3.5 and 4 g/L) at salinity of 30,000 ppm. These samples were subjected to viscosity analysis test (Mansour et al 2018;El Aily et al 2019). The viscosity measurements were taken at the reservoir temperature, atmospheric pressure and different shear rates.…”

Section: Solution Properties Measurementsmentioning

confidence: 99%

Effects of nanocomposite polymer flooding on Egyptian crude oil recovery

Helmi

Ela

Desouky

et al. 2020

J Petrol Explor Prod Technol

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In this work, a laboratory study of enhanced oil recovery (EOR) was carried out using Egyptian crude oil of 37°API extracted from a reservoir in the Western Desert to identify the optimum conditions for the application of locally prepared nanocomposite polymer flooding under harsh reservoir condition. In contrary to the other studies, we tested the ability of nanocomposite polymer where nanoparticles are involved in the polymer matrix during polymerization process. Measurements of viscosity and shear rate of several solutions were taken. Displacement runs were conducted at different conditions of nanocomposite polymer salinities (10,000, 20,000, 30,000, 40,000, 50,000, 60,000 and 65,000 ppm), concentrations (1, 1.5, 2, 2.5, 3, 3.5 and 4 g/L) and slug sizes (0.2, 0.4, 0.6 and 0.8 PV). A linear sandpack (length of 62.5 cm and diameter of 2″) was prepared and wrapped with thermal jacket to simulate several reservoir temperatures. It was filled by selected sand size to produce linear sandpack model with reasonable porosity (22%) and permeability (129–157 mD) values. The model was used to perform several displacements runs for waterflooding and nanocomposite polymer flooding. The results of the flood runs are analyzed using the water–oil relative permeability curves. The measurements of the solutions properties showed that the critical concentration of the used nanocomposite polymer in the solution is 2 g/L. Also, it was observed that the used nanocomposite polymer solution could withstand a salinity of 60,000 ppm. As a result of the flooding, it was found that the optimum economical slug size of the used nanocomposite polymer is 0.4 PV at reservoir temperature of 40 °C. The results indicated also that the used nanocomposite polymer could withstand a reservoir temperature of 90 °C. The water–oil relative permeability curves showed an enhancement of oil relative permeability and a decrease in the water relative permeability using nanocomposite polymer over waterflooding. The cost of the used nanocomposite polymer with a concentration of 2 g/L and slug size of 0.4 PV is 0.626 $ for each barrel of the incremental oil recovery. Based on the results of this work, it is clear that involving nanoparticles such as silica in the polymer matrix composition improves its properties, thermal and salinity resistivity. Such study is an original contribution to carry out successful nanocomposite polymer EOR projects.

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Section: Solution Properties Measurementsmentioning

confidence: 99%

Effects of nanocomposite polymer flooding on Egyptian crude oil recovery

Helmi

Ela

Desouky

et al. 2020

J Petrol Explor Prod Technol

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“…The development of the correlation occurred by using a regression analysis that depends on the variable's nature of them. The regression analysis concept depends on fitting the independent variables to predict one dependent variable [16]. Non-linear multiple regression analysis explains the relations between the dependent variables and independent variables [37].…”

Section: Modification Of Wilson Equation (K-values)mentioning

confidence: 99%

“…In the absence of such experimental analysis, the bubble point pressure may be estimated by the equation of state, empirical correlations or K-values [14]. PVT computation methods are used for the following reasons: (i) insufficient sample volume (ii) cost reduction (iii), not representative samples (iv) unavailable lab measurements [15][16][17]. Most popular bubble point pressure correlations informed in the literature are involved in this paper as shown in Appendix A.…”

Section: Introductionmentioning

confidence: 99%

Improvement Wilson equation (K-Values) of gas-liquid equilibrium for Advancing Estimating Bubble Point Pressure

Mansour

Desouky

2019

Egypt. J. Chem.

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K NOWING the bubble point pressure of the oil reservoirs is a key factor for the petroleum engineers in order to optimize the development of the oil field starting from the discovery phase until the abandonment. Thus any considerable error in estimating the bubble point pressure will lead to errors in the production and reservoirs engineering calculations. More than six hundred Egyptian oil samples of PVT analysis were used to improve the Wilson equation (K-Values) for estimating bubble point pressure. None of the published bubble point pressure correlations is particularly accurate when applied to Egyptian crude oils. Experimental work was conducted in the PVT-Services Center of the Egyptian Petroleum Research Institute (EPRI). In this work, the K-values correlation proposed by Wilson equation was modified by adding new terms such as API, GOR, and heptanes plus-fraction (C 7 +) of compositional analysis of the reservoir fluid. Multiple non-linear regression analysis is used to calculate the new equation constant after adding the new terms. The statistical error analysis shows that the Correlation coefficient () 2 ,% r , the average percent relative error () ARE , the standard deviation () ,% S and the average absolute percent relative error () AARE of the modifying are 99.75 %, 1.77%, 8.08%, and 6.84 %, respectively. This correlation of K-factor can be applied not only to low and moderate pressure but also to higher pressures up to (4500 psi) by this modification. Evaluation among the bubble point pressure calculated from the modified Wilson equation and the literature published ones were done. Results confirm that the modified Wilson equation is more accurate one and it can be used for estimating the reservoir fluids characteristics in the absence of data measurement.

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“…The average error of -1.83% have been recorded during testing for proposed correlation. Their correlation is covering solution gas to oil ratio (Rs) within the range of 20 to 2070 scf/STB, oil gravity of 16 to 58°API, pressure range of 0 to 5250 and temperature of 70 to 295°F (Beggs and Robinson, 1975;El Aily et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…All aforementioned correlations developed from crude oil, which has compositional differences with gas condensate fluid composition. Moreover, they are direct function of dead oil viscosity, which is one of the most unreliable properties to be predicted by correlations due to the large effect that oil type (paraffinicity, aromaticity and asphaltene content) has on viscosity (Aily et al, 2019;Whitson et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

Modelling viscosity of liquid dropout near wellbore region in gas condensate reservoirs using modern numerical approaches

Faraji

Ugwu

Chong

et al. 2020

Journal of Petroleum Science and Engineering

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Liquid dropout occurs in gas condensate reservoirs below the dew point pressure around near wellbore region as a result of depletion from production of such reservoirs. Forecasting production as well as optimizing future recoveries of gas condensate reservoirs are highly desirable. This is not possible to achieve without accurate determination of liquid dropout viscosity () below the dew point. The focus of research in past decades has been on the development of accurate viscosity prediction models below the dew point pressure to ensure accurate condensate production forecast. Gas condensate production forecast and optimisation around this region and condition are complicated due to unique gas condensate behaviour that violates thermodynamic laws. Current methods are based on correlation estimation, however the accuracy of these correlations are less than satisfactory, and root cause is due to the miscapturing of complex behaviour of gas condensate reservoir near the wellbore region. These motivated the consideration of modern numerical approaches such as the Least Square Support Vector Machine (LSSVM) and Artificial Neural Network (ANN) used in this paper. These methods are considered as more data behaviour oriented, with the capability of capturing the fluid complexity of gas condensate in such conditions. In this study viscosity of condensate phase near the wellbore region was modelled using machine learning techniques including ANN and LSSVM. For this purpose, over 300 viscosity data sets were collected from published literature and experimental studies worldwide. This databank includes API gravity, reservoir temperature, solution gas to oil ratio (Rs), specific gas gravity, fluid compositions and reservoir pressure. Six well known previously published viscosity correlations refined using least-square approach to match the experimental data. Qualitative and quantitative error analysis of developed LSSVM and ANN showed their performance superiority over refined literature correlations. The new proposed models can be embedded as an extra feature of commercial reservoir simulation packages for optimization and future recoveries of gas condensate reservoirs.

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Modeling viscosity of moderate and light dead oils in the presence of complex aromatic structure

Cited by 12 publications

References 11 publications

Effects of nanocomposite polymer flooding on Egyptian crude oil recovery

Effects of nanocomposite polymer flooding on Egyptian crude oil recovery

Improvement Wilson equation (K-Values) of gas-liquid equilibrium for Advancing Estimating Bubble Point Pressure

Modelling viscosity of liquid dropout near wellbore region in gas condensate reservoirs using modern numerical approaches

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