Evaluates A PVT Correlation to Estimate Dead Oil Viscosity for Libyan Crudes Using 104 Samples from Different Reservoirs

Viscosity is defined as the resistance of the fluid to flow. It plays a very significant role in most oil and gas engineering applications. The measured viscosity for any crude oil at surface condition is called by the dead oil viscosity, where the dead oil viscosity is a function in any correlation to calculate the viscosity of the crude oil. Thus, the dead oil viscosity is important in most applications related to the petroleum engineering. Accordingly, a new mathematical and artificial neural network (ANN) dead oil viscosity correlations were developed for Libyan crudes and compared with renowned dead oil viscosity correlations using 104 samples from different reservoirs. The evaluation in this study has been done by statistical and graphical error analysis. The results shown that the ANN model has proven to be a useful tool for predicting where the ANN model has given the best result with low error AAD was 14.40509 % and R^2 was 95.91%. The ANN model and mathematical model gave the lowest error when they compared with different empirical correlations.

Methodology for Static and Dynamic Modeling of Hydrocarbon Systems Having Sharp Viscosity Gradient

This paper describes a methodology for delineating tar surface, incorporating it into a geological model, and the process for numerical modeling of oil viscosity variation with depth above the tar surface. The methodology integrates well log data and compositional fluid analysis to develop a mathematical model that mimics the oil's property variation with depth. While there are a good number of reservoirs that fit this description globally, there is a knowledge gap in literature regarding best practices for dealing with the peculiar challenges of such reservoirs. These challenges include; (i) how to delineate the top-of-tar across the field, (ii) modeling of Saturation Height Function (SHF) in a system where density and wettability is changing with depth, and (iii) the methodology for representing the depth-dependent oil properties (especially viscosity) in reservoir simulation. Nuclear magnetic resonance (NMR) logs were used to predict fluid viscosity using a technique discussed by Hursan et al. (2016). Viscosity regions are identified at every well that has an NMR log, and these regions are mapped from well to well across the reservoir. Within each viscosity region, the analysis results of fluid samples collected from wells are used to develop mathematical models of fluid composition variation with depth. A reliable SHF model was achieved by incorporating depth-varying oil density and depth varying wettability into the calculation of J-Function. A compositional reservoir simulation was set-up, using the viscosity regions and the mathematical models describing composition variation with depth, for the respective regions. Using information obtained from literature as a starting point, residual oil saturation was modeled as a function of oil viscosity. Original reservoir understanding places the top of non-movable oil (tar) at a constant fieldwide subsurface depth, which corresponds to the shallowest historical no-flow drillstem test (DST) depth. Mapping of the NMR viscosity regions across the field resulted in a sloping tar-oil contact (TOC), which resulted in an increase of movable hydrocarbon pore volume. The viscosity versus depth profile from the simulation model matched the observed data, and allow the simulation model better predict well performance. In addition, the simulation model results also matched the depth-variation of observed formation volume factor (FVF) and reservoir fluid density. Some wells that have measured viscosity data but no NMR logs were used as blind-test wells. The simulation model results also matched the measured viscosity at those blind-test wells. These good matches of the oil property variation with depth gave confidence, that the simulation model could be used as an efficient planning tool for ensuring that injectors are placed just-above the tar mat. The use of the simulation model for well planning could reduce the need for geosteering while drilling flank wells, leading to savings in financial costs. This paper contains a generalized approach that can be used in static and dynamic modeling of reservoirs, where oil changes from light to medium to heavy oil, underlain by tar. It contains recommendations and guidelines to construct a reliable simulation model of such systems.

Experimental measurements and modelling of viscosity and density of calcium and potassium chlorides ternary solutions

Measured viscosity and density data for ternary aqueous solutions of CaCl2 and KCl are presented at temperatures between 293 and 323 K with 5 K increment. A modified Jones–Dole was introduced by adding extra terms and proved to be suitable for modelling of the viscosity data. Goldsack and Franchetto, Hu and Exponential models are used to correlate the viscosity data, too. Al models are correlated as a function of temperature and concentration. All models had successfully predicted the viscosity with high precision reaching a maximum average absolute deviation (AAD) of less than 2.3%. The modified Jones–Dole showed the best results among other models. Viscosity of the ternary solution is higher than the viscosity of water by about 15% at low concentrations and reaches about 270% at the highest concentrations. The amount of CaCl2 has more significant effect on the ternary mixture viscosity compared to KCl. This has created difficulty in measuring the viscosity and consequently the challenge in finding the different models parameters. Ternary solution densities were successfully correlate with Kumar’s model with AAD of less than 0.4%. Comparison of the ternary solution density and viscosity with the few available data literature showed a good agreement.

Searching accurate predicted viscosities near the melting point in a non-strong glass-forming liquid

The accurate predicted viscosities near the melting point [Formula: see text] have been searched. In order to find the temperature ranges, where the measured viscosity data applied to obtain the accurate fitting viscosity data and the accurate fitting expressions near [Formula: see text] lie, the measured data in 15 different temperature ranges (a)–(o) are applied to obtain the fitting viscosity data and the fitting expressions by the Vogel–Fulcher–Tammann (VFT) relation. The accuracy of the fitting viscosity near [Formula: see text] will varies when the measured data in different temperature ranges are applied to obtain the fitting viscosity data by VFT relation. It is found that the accurate fitting viscosity data with the coefficients of determination [Formula: see text] in temperature range 397.3–583.6 K (0.84–1.24[Formula: see text]) near [Formula: see text] can be acquired using the measured data in temperature ranges (g)–(h) and (k) by the VFT relation. In other words, we found the temperature ranges (namely, temperature ranges (g)–(h) and (k)), in which the measured viscosity data applied to obtain the accurate fitting viscosity data and the accurate fitting expressions near [Formula: see text] exist.

Effects of Sonication Time on the Stability and Viscosity of Functionalized MWCNT-Based Nanolubricants

Background: Active scholars in the nanofluid field have continuously attempted to remove the associated challenge of the stability of nanofluids via various approaches, such as functionalization and adding a surfactant. After preparing a stable nanofluid, one must measure the properties, as this is vital in the design of thermal systems. Objective: Authors aimed to investigate the stability and viscosity of refrigeration lubrication oilbased nanofluids containing functionalized MWCNTs. The effects of concentration and temperature on viscosity were studied. Furthermore, the present study focused on the effect of sonication time on the stability and viscosity of the prepared samples. Methods: After the preparation of chemically functionalized MWCNTs, solutions were dispersed with an ultrasonic homogenizer for 2, 4 and 8 hours sonication at maximum power. Viscosity measurements for all samples were made 10 minutes after sonication by adjusting the proper spinning velocity using a digital rotary viscometer. Results: The first part deals with the stability of the nanofluid as a nanolubricant, and the second one investigates the viscosity of the nanofluid and the effects of various parameters on it. The last one is related to the validation of the measured viscosity values by means of well-known empirical correlations. The measured data are given for validation issues. Conclusion: The samples will have higher stability by increasing the time of sonication. The viscosity of a nanofluid does not change with the increase of sonication time to two hours and higher. Up to mass concentration of 0.1%, the effective viscosity increases with adding nanotubes linearly.

Non-Newtonian numerical modelling of solder paste viscosity measurement

PurposeThe purpose of this paper is to present the establishment of a computational fluid dynamics model for investigating different non-Newtonian rheological models of solder pastes by simulating solder paste viscosity measurement. A combined material model was established which can follow the measured, apparent viscosity values with lower error.Design/methodology/approachThe model included a parallel plate arrangement of rheometers. The diameter of the plate was 50 mm, whereas the gap between the plates was 0.5 mm. Only one quarter of the plate was modelled to enable using fine enough mesh, while keeping the calculation time low. Non-Newtonian properties were set using user defined function in Ansys, based on the Cross and Carreau–Yasuda material models. The viscosity values predicted by the mathematical models were compared to measured viscosity values of different types of solder pastes.FindingsIt was found that the Cross model predicts the apparent viscosity with a relatively high error (even approximately 50 per cent) at lower shear rates, whereas the Carerau–Yasuda model has higher errors at higher shear rates. The application of the proposed, combined model can result in a much lower error in the apparent viscosity between the calculated and measured viscosity values.Originality/valueThe error of Cross and Carreau–Yasuda material models has not been investigated yet in details. The proposed, combined material model can be applied for subsequent simulations via the described UDF, e.g. in the numerical modelling of the stencil printing. This can result in a more accurate modelling of the stencil printing process, which is inevitable considering the printing of solder paste for today fine-pitch, small size components.

Effect of Substituting CaO with BaO and CaO/Al2O3 Ratio on the Viscosity of CaO–BaO–Al2O3–CaF2–Li2O Mold Flux System

The effect of substituting CaO with BaO and CaO/Al2O3 ratio on the viscosity of CaO–BaO–Al2O3–CaF2–Li2O mold flux system was studied by rotational viscosity method. The results showed that the viscosity increased with increasing BaO as a substitute for CaO, while the viscosity decreased with the increase in CaO/Al2O3 ratio. The viscous activation energy of the slags is from 92.1 kJ·mol−1 to 133.4 kJ·mol−1. Either the Arhenius or the Weymann–Frenkel equation can be applied to establish the viscosity prediction model. In this paper, the Weymann–Frenkel equation and a new optical basicity with regard to Al2O3 as an acidic oxide were applied to the modified NPL model for predicting the viscosity of CaO–BaO–Al2O3–CaF2–Li2O mold flux system. The estimated viscosity is in good agreement with the measured viscosity.