A Compositional Reservoir Simulation and Experimental Investigation of Asphaltene Onset Pressure

Asphaltene precipitation is one of the most common problems in many reservoirs and may lead to many safeties operational issues which affects on oil recovery; therefore, identifying start of asphaltene activation is known as a key parameter to control production efficiency. This study includes predicting onset pressure with Multiphase Flash test and compare its result with experimental data generated by Asphaltene Static Apparatus. Safety pressure was obtained by performing Multiphase Flash test for each component. In order to prevent adsorption, mechanical entrapment, and blockage, reservoir pressure must be higher than this safety pressure. SARA test is widely used to identify the fraction of crude oil that affect the asphaltene stability. IP143 standard test was used to measure precipitated asphaltene. Natural depletion test was designed at four steps, including 4400, 3000, 1550, and 1020 Psia and reservoir temperature is 205°F. It was seen that with decreasing pressure from reservoir pressure to saturation pressure asphaltene precipitation from PVT cell was increased and at pressures below saturation pressure with pressure reduction, asphaltene precipitations was decreased. Also it was concluded that above saturation pressure solubility model is dominant and below saturation pressure colloidal model is dominant. The results of IP143 show that initial content of asphaltene are 12.8%. SARA test result shows this kind of fluid located at unstable asphaltene precipitation region. Comparison of safety pressure between Multiphase Flash test and experimental data are investigated and discussed. Onset pressure of 18000 Pisa was obtained from Multiphase Flash test, which is in good agreement with experimental result.     

Comparison of Simulation Methods in Gas Condensate Reservoirs

Formation of condensate bank around a gas condensate well due to reduction in bottom-hole pressure below its dew point will impose a significant reduction on the well productivity owing to a sharp fall in the gas effective permeability. Hence a better understanding and modeling of this behavior will lead to a more precise prediction of reservoir performance. Therefore the objective of this work was to make a comparison between different simulation methods of gas condensate reservoirs in order to check the validity of them respect to the most accurate one (i.e., fine grid) fully compositional model. This study shows that the modified black oil model would be adequate to simulate depletion cases above and below gas condensate dew point. However, the two phase pseudo-pressure method is not always applicable in simulating gas condensate reservoirs especially in the presence of high velocity flow effects.     

Stability Analysis of Stakeholders’ Cooperation in Inter-Basin Water Transfer Projects: a Case Study

Water Resources Management - This study investigates the conflict resolution among different stakeholders in a water transfer project. The portion of the Beheshtabad Water Transfer Project in Iran...     

Experimental and modeling asphaltene precipitation in presence of DBSA using PC-SAFT EOS

Asphaltene precipitation and subsequent deposition in production tubing and topside facilities present significant cost penalties to crude oil production. Therefore, it is highly desirable to predict their phase behavior and the efficiency of dispersants in preventing or delaying deposition. Very few studies have been carried out on the molecular interactions between asphaltenes and different dispersants. As a result, the mechanisms by which dispersants stabilize asphaltenes are still open to discussion. The authors introduced a new method to characterize asphaltenes in perturbed chain statistical association fluid theory equation of state (EOS; perturbed-chain statistical association fluid theory EOS [PC-SAFT-EOS]) and correctly model the effect of dodecyl benzene sulfonic acid (DBSA) dispersant on the thermodynamic behavior of asphaltenes. Using the filtration method the effect of the ionic dispersant (DBSA) on asphaltene precipitation for different concentrations of n-heptane was measured experimentally, then modeled through PC-SAFT EOS. In the approach only the hard-chain and the dispersion terms are taken into consideration, and PC-SAFT parameters were calculated based on Gonzales et al. (2007 Gonzalez, D. L., Hirasaki, G. J., Creek, J., and Chapman, W. G. (2007). Modeling of asphaltene precipitation due to changes in composition using the perturbed chain statistical associating fluid theory equation of state. Energy Fuels 21:1231–1242.[Crossref], [Web of Science ®] , [Google Scholar]) based on molecular weight (Mw) and aromaticity factor (γ). Additionally, the model could correctly predict the amount of asphaltene precipitation upon addition of DBSA dispersant.     

Exploration of Electrochemical Reactions at Organic–Inorganic Halide Perovskite Interfaces via Machine Learning in In Situ Time‐of‐Flight Secondary Ion Mass Spectrometry

The instability of hybrid organic–inorganic perovskite (HOIP) devices is one of the significant challenges preventing commercialization. Exploring these phenomena is severely limited by the complexity of the intrinsic electrochemistry of HOIPs, the presence of multiple volatile and mobile ionic species, and the possible role of environmentally induced reactions at surfaces and triple‐phase junctions. Here, in situ studies of the electrochemistry of methylammonium lead bromide perovskite with the Au electrode interface are reported via light‐ and voltage‐dependent time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) imaging of lateral perovskite heterostructures. While ToF‐SIMS allows for the visualization of the chemical composition along the surface and its evolution with light and electrical bias, the interpretation of the multidimensional data obtained is often limited due to strong correlations between chemical signatures and the need to track multiple peaks at once. Here, a machine learning workflow combining the Hough transform and non‐negative matrix factorization and non‐negative tensor decomposition is developed to avoid this limitation and extract salient features of associated chemical changes and to separate the light‐ and voltage‐dependent dynamics. Combining these in situ characterizations and the machine learning workflow provides comprehensive information on the chemical nature of moving species, ion accumulation, and interfacial electrochemical reactions in HOIP devices.     

POLAROGRAPHIC CHARACTERIZATION OF AROMATIC STRUCTURES IN COAL LIQUID PRODUCTS

Reducible aromatic species in coal liquid products from five major processes (Synthoil, FMC COED, HRI H-Coal, PAMCO SRC, and Catalytic Inc. SRC) together with solvent elution chromatography fractions of a selected asphaltene has been evaluated. The half-wave potentials measurments are found to be consistant with other methods. The total polarographic reduction of measurement per average molecule for Synthoil solvent fractions, as measured by diffusion constant (i_d/C), increases in the following manner: gas oil → resin → asphaltene → carbene → carboid. The same trend was observed when color indices (I) and refractive indices (n₂₅) were individually studied.     

An experimental and theoretical study on the prediction of forming limit diagrams using new BBC yield criteria and M–K analysis

In the present paper, a comprehensive study on the prediction of forming limit diagrams (FLDs) for an AA3003-O aluminium alloy is developed theoretically and experimentally. For obtaining the experimental FLDs, an out-of-plane formability test was performed based on the technique proposed by Ozturk and Lee [F. Ozturk, D. Lee, J. Mater. Process. Technol. 170 (2005) 247–253]. The classical Marciniak–Kuczynski (M–K) model and some new yield criteria are utilized to simulate the necking phenomenon and calculate the limit strains theoretically. The employed yield functions are: the BBC2000, BBC2002, and BBC2003 yield criteria proposed by Banabic et al. [D. Banabic, S.D. Comsa, T. Balan, in: Proceedings of the Cold Metal Forming 2000 Conference, Cluj-Napoca, 2000, p. 217; D. Banabic, T. Kuwabara, T. Balan, D.S. Comsa, D. Julean, Int. J. Mech. Sci. 45 (2003) 797–811; D. Banabic, H. Aretz, D.S. Comsa, L. Paraianu, Int. J. Plast. 21 (2005) 493–512]. To calibrate and determine each particular coefficients of performed yield functions an appropriate error-function is defined and minimized by a Newton algorithm. To compare the calculated yield stresses and r-values with experimental data a relative root mean square deviation method presented by Leacock [Alan G. Leacock, J. Mech. Phys. Solids 54 (2006) 425–444] is used. Work-hardening effects on the FLD are analyzed by using Swift and Voce hardening laws. The effect of yield surface on the prediction of numerical FLDs and the number of experimental anisotropy parameters on the accuracy of yield functions are also studied.     

A miniaturised monopole antenna for ultra-wide band applications with band-notch filter

This study presents a new miniaturised printed monopole antenna. The size of the antenna is 18 x 18 mm². First simple design rules are given to arrive at an initial design for the antenna, then the antenna parameters are optimised for utilisation in ultra wide band (UWB) applications. The performance parameters like voltage standing wave ratio (VSWR) of the single antenna as well as transmission function, group delay and the fidelity factor of a two-antenna system are calculated and measured. It is shown that the band-notched performance can be obtained from the designed antenna by introducing simple p-shape or V-shape slots in its radiating element. Good agreements between simulated and measured results are observed.     

Optimization of STM/FIM nanotip aspect ratio based on the Taguchi method

This paper presents optimization of electrochemical etching parameters to achieve the optimum aspect ratio of the scanning tunneling microscopy/field ion microscopy tungsten nanotip by using Taguchi method. The combination of optimum level of process parameters was obtained by using the analysis of signal-to-noise ratio. The level of importance of the process parameters on the nanotip aspect ratio was determined by using analysis of variance. It was found that the optimum level of process parameters are electrolyte concentration of 2 M/lit, wire immersion length of 2.5 mm, cathode tube inner diameter of 40 mm, and voltage of 3.5 V. Within the range of experiments and the process parameters in terms of impact significance were found to be electrolyte concentration, process voltage, wire immersion length, and inner diameter of cathode tube, respectively. By using the optimum level of the process parameters, the nanotip aspect ratio was enhanced by 263% in comparison to the mean value of the experimental results. The nanotip aspect ratio of up to 163:1 was obtained in the present research.     

A least-squares support vector machine approach to predict temperature drop accompanying a given pressure drop for the natural gas production and processing systems

Precise estimation of temperature variations throughout gas production systems can enhance designing the production amenities. Routine methods for determining the temperature profiles in gas production systems are based on the gas composition and flash calculations. However, if the gas compositions are not available, the gas production system can be modelled by employing a black-oil approach, which is also a method for calculating the oil/gas resources and for modelling the gas reservoir operation. Accordingly, for black-oil models and when the natural gas compositions are not accessible, applying robust predictive tools in this research is of high interest in natural production systems. The current study places emphasis on applying the predictive model based on the least- squares support vector machine (LSSVM) to estimate precisely the proper temperature drop associated with a given pressure drop throughout the natural gas production systems based on the black-oil approach to acquire an accurate result for the temperature drop of natural gas streams. Genetic algorithm was used to optimise hyper-parameters (γ and σ²) which are embedded in the LSSVM model. Using this method is simple and it accurately determines the temperature drop through the natural gas stream with minimum uncertainty.