Research Vision

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Predictive Tool for Oil, Gas and Chemical Industries Alireza Bahadori, PhD Research Scholar Department of Chemical Engineering Curtin University GPO Box U1987, Perth 6845 Australia [email protected] [email protected] Research Vision

Transcript of Research Vision

Page 1: Research Vision

Predictive Tool for Oil, Gas and Chemical Industries

Alireza Bahadori, PhD Research Scholar

Department of Chemical EngineeringCurtin University

GPO Box U1987, Perth 6845Australia

[email protected]@gmail.com

Research Vision

Page 2: Research Vision

Presentation Outline

• Why Predictive Approaches?

• Development of Novel Predictive Tools

• Typical Applications in Oil and Gas Industries

• Research Outcomes to Date

• Case study and examples

• Potential Areas of Collaboration

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Why Predictive Tools?• Predictive or modelling tools are useful

• to avoid unnecessary experimental trials• Resolve operational issues at low costs• optimisie the equipment or plant performance

• Conventional methods usually comprises of • unnecessarily complicated equations • not easy for for the purposes of practical importance with most simulations requiring

• simultaneous iterative solutions of many nonlinear and highly coupled sets of equations

• Development of predictive tools is therefore essential • to minimize the complex and time-consuming calculation steps

• Mathematically compact, simple, and reasonably accurate equations containing few tuned coefficients would be

• preferable for computationally intensive simulations.

• Development of practical correlations using a modified equation of well-known Vogel-Tammann-Fulcher (VTF)

• is the primary motivation of our efforts which yields correlations with accuracy comparable to the existing rigorous simulations.

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Example

• Predicting density of liquid water

• An equation of state approach taken by Wagner and Pruss (2002) required more than 56 constants model for representing the anomalous behaviour of the density of liquid water [1].

• Similar result can be achieved using only four empirical fitting constants based on the Vogel–Tammann–Fulcher–Hesse–Civan equation (VTFHC) [2].

• References:

• [1] Wagner, W.; Pruss, A. (2002) The IAPWS Formulation 1995 for the Thermodynamic Properties of Ordinary Water Substance for General and Scientific Use. J. Phys. Chem. Ref. Data, 31, 387-535.

• [2] Civan, F. (2007) Critical Modification to the Vogel-Tammann-Fulcher Equation for Temperature Effect on the Density of Water Ind. Eng. Chem. Res., 46, 5810-5814.

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Vogel-Tammann-Fulcher (VTF) Equation

VTF equation:

After revisions made by Civan (2007):

If Tc=0 it is converted to Arrhenius type function:

*CIVAN F.,, Critical modification to the Vogel–Tammann–Fulcher equation for temperature effect on the density of water, Industrial Engineering & Chemistry Research Journal 46 (17) ,2007, 5810–5814.

)()ln(ln

cTTRE

cff

3)(

d2)(

lnlncTTcTT

c

cTTb

cff

32lnlnTd

Tc

Tbff c

2lnlncc

c TTc

TTbff

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Vandermonde Matrix & Tuning of Coefficients

• Vandermonde matrix:

• Vandermonde matrix is a matrix with the terms of a geometric progression in each row, i.e., an m × n matrix*

• evaluates a polynomial at a set of points; formally, it transforms coefficients of a polynomial to the values the polynomial takes at the desired point.

• non-vanishing of the Vandermonde determinant for distinct points αi shows that, for distinct points,

• the map from coefficients to values at those points is a one-to-one correspondence, and thus that the polynomial interpolation problem is solvable with unique solution; this result is called the unisolvence theorem *

• They are thus useful in polynomial interpolation, since solving the system of linear equations Vu = y for u with V an m × n Vandermonde matrix is equivalent to finding the coefficients of the polynomial(s) *

• The Vandermonde matrix can easily be inverted in terms of Lagrange basis polynomials: each column is the coefficients of the Lagrange basis polynomial. *

*HORN, R. A. and JOHNSON C. R.,Topics in matrix analysis, Cambridge University Press. 1991 Section 6.1, UK. *FULTON, W.; HARRIS, J., , Representation theory. A first course, Graduate Texts in Mathematics, Readings in Mathematics, 129, 1991, New York:

Springer-Verlag, USA

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General Workflow of Algorithm for Tuning the Coefficients

Input data into model and M is maximum number of data set

If m<Mm=m+1Yes

Correlate F(X,Y) as a function of X for a given data set (m) using Vandermonde matrix and VFT equation

Correlate “a” as a function of Y using Vandermonde

Stop

Calculate F(X,Y) as a function of a, b, c and d

Correlate “b” as a function of Y using Vandermonde

Correlate “c” as a function of Y using Vandermonde

No

Correlate “d” as a function of Y using Vandermonde

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Our vision for Predictive Tool

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Selection of Independent Variables

Rules• Some theoretical and semi-theoretical correlations of parameters such as

thermal conductivity include other parameters such as density and therefore data or correlations of such additional parameters are also required when using these correlations.

• Consequently, in addition to creating an inconvenience, accuracy of correlations of physical properties expressed in terms of other physical properties inherits errors associated with additional properties included in such correlations.

• Fortunately, however, these problems can be alleviated readily because dependent quantities such as density should not be included at all in correlations of other dependent quantities such as viscosity or thermal conductivity which are both temperature dependent.

• The bottom-line is that correlations of physical properties and most of process engineering variables should be sought only in terms of independent variables such as temperature, pressure, molecular weight, concentration and so on.

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Advantages of Predictive Tools• Research efforts to date led to simple predictive tools

• novel and most of them are theoretically meaningful• based on Arrhenius-type asymptotic exponential function

• easier than current available models• less complicated with fewer computations

• Developed tools are superior owing to their • accuracy and clear numerical background• the relevant coefficients can be retuned quickly with more data

• Tools are of immense practical value for Process, Petroleum, Oil and Gas engineers• the engineers and scientists to have a quick check

• on the various engineering and design parameters without opting for any experimental measurements and pilot plant set up

• In particular, practice engineers would find the approach to be user-friendly with transparent calculations involving no complex expressions

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Typical Applications in Oil and Gas Industries

• Natural Gas Hydrate Prediction• Prediction of absorption/stripping factors• Prediction of Methanol Loss in Vapor Phase During Gas Hydrate Inhibition • Rapid estimation of equilibrium water dew point of natural gas in TEG dehydration

systems • Estimation of displacement losses from Petroleum products storage containers • Simple equations to correlate theoretical stages and operating reflux in

fractionators • Simple methodology for sizing of absorbers for TEG (triethylene glycol) gas

dehydration systems • Prediction of aqueous solubility and density of carbon dioxide • A simple correlation for estimation of economic thickness of thermal insulation for

process piping and equipment• New method accurately predicts carbon dioxide equilibrium adsorption isotherms.• Prediction of bulk modulus and volumetric expansion coefficient of water for leak

tightness test of pipelines • Predicting Solubilities of Hydrocarbons in Hydrate Inhibitors• Determining Appropriate Mono-Ethylene Glycol Injection Rate to Avoid Gas Hydrate

Formation • Prediction of Temperature Drops in Natural Gas Production Systems for Black-Oil

Models • Prediction of Transport Properties of Carbon Dioxide etc.

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List of Recent Journal Articles (2008-Present)

• A. Bahadori and H. B. Vuthaluru, (2010),Prediction of Salinity of Salty Crude Oil Using Arrhenius-type Asymptotic Exponential Function and Vandermonde Matrix, accepted for publication in SPE Projects, Facilities & Construction Journal, SPE-132324-PA, (accepted 14, July, 2010).

• A. Bahadori and H. B. Vuthaluru, (2010), Simple Method for Estimation of Unsteady State Conduction Heat Flow with Variable Surface Temperature in Slabs and Spheres, accepted for publication in International Journal of Heat and Mass Transfer.

• A. Bahadori, and H. B. Vuthaluru, (2010), Estimation of Potential Savings from Reducing Unburned Combustible Losses in Coal-Fired Systems, accepted for publication in Applied Energy (Available online 3 July, 2010).

• A. Bahadori, and H. B. Vuthaluru, (2010) Estimation of Saturated Air Water Content at Elevated Pressures Using Simple Predictive Tool, accepted for publication in Chemical Engineering Research and Design (Available online 4 June 2010).

• A. Bahadori, and H. B. Vuthaluru, (2010), Estimation of Steam losses using a Predictive Tool, Petroleum Technology Quarterly, 15 (Q3), pp. 133-136.

• A. Bahadori, and H. B. Vuthaluru, (2010)” Simple Arrhenius-Type Function Accurately Predicts Dissolved Oxygen Saturation Concentrations in Aquatic Systems,” accepted for publication in Process Safety and Environmental Protection (Available online 27 May 2010).

• A. Bahadori, and H. B. Vuthaluru, (2010) A method for estimation of recoverable heat from blowdown systems during steam generation” Energy (Available online 3 June 2010).

• A. Bahadori, and H. B. Vuthaluru, (2010) Estimation of critical oil rate for bottom water coning in anisotropic and homogeneous formations”, accepted for publication in World Oil.

• A. Bahadori and H. B. Vuthaluru, (2010), Estimation of theoretical flame temperatures for Claus sulfur recovery unit using simple method, accepted for publication in Journal of Energy Sources, Part A: Recovery, Utilization, and Environmental Effects.

• A. Bahadori and H. B. Vuthaluru, (2010), Estimation of performance of steam turbines using a simple predictive tool" accepted for publication in Applied Thermal Engineering 30 (2010) 1832-1838.

• A. Bahadori, and H. B. Vuthaluru, (2010)”Predictive Tool for the Estimation of Methanol Loss in Condensate Phase during Gas Hydrate Inhibition” Energy & Fuels, 24, 2999–3002.

• A. Bahadori, and H. B. Vuthaluru, (2010) “Estimation of maximum shell-side vapour velocities through heat exchangers” accepted for publication in Chemical Engineering Research and Design. (Available online 9 April 2010).

• A. Bahadori, and H. B. Vuthaluru, (2010) “A Method for Prediction of Scale Formation in Calcium Carbonate Aqueous Phase for Water Treatment and Distribution Systems” accepted for publication in Water Quality Research Journal of Canada.

• A. Bahadori, and H. B. Vuthaluru, (2010) “Estimation of Energy Conservation Benefits in Excess Air Controlled Gas-fired Systems” accepted for publication in Fuel Processing Technology. (Available online 21 April 2010).

• A. Bahadori, and H. B. Vuthaluru, (2010) “Estimation of Steam Losses in Stream Traps”, accepted for publication in Chemical Processing. • A. Bahadori, and H. B. Vuthaluru, (2010) “Simple method for prediction of densities and vapour pressures of aqueous methanol solutions”,

OIL GAS European Magazine, 36(2), pp. 84-88.• A. Bahadori, and H. B. Vuthaluru, (accepted) “Predictive Tool for Estimation of Convection Heat Transfer coefficients and Efficiencies for

Finned Tubular Sections” accepted for publication in International Journal of Thermal Sciences.

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List of Recent Journal Articles (2008-Present)

A. Bahadori, and H. B. Vuthaluru, (2010) Rapid Estimation of Heat Losses From Oil and Gas Process Piping and Equipment Surfaces, accepted for publication in Petroleum Technology Quarterly.

A. Bahadori, and H. B. Vuthaluru, (accepted) “Predictive Tools for the Estimation of Downcomer Velocity and Vapor Capacity Factor in Fractionators, accepted for publication in Applied Energy ( Available online 5 March 2010).

A. Bahadori, and H. B. Vuthaluru, (accepted) “Prediction of Methanol Loss in Vapor Phase During Gas Hydrate Inhibition Using Arrhenius-type Functions” accepted for publication in Journal of Loss Prevention in the Process Industries.( Available online 14 January 2010).

A. Bahadori, and H. B. Vuthaluru, (accepted)” Predictive Tool for An Accurate Estimation of Carbon Dioxide Transport Properties, accepted for publication in International Journal of Greenhouse Gas Control.( Available online 18 January 2010).

A. Bahadori, and H. B. Vuthaluru, (accepted)” A new method for prediction of absorption/stripping factors, accepted for publication in Computers & Chemical Engineering.( Available online 15 January 2010).

A. Bahadori, and H. B. Vuthaluru, (2010), Simple Equations to Correlate Theoretical Stages and Operating Reflux in Fractionators, accepted for publication in Energy, 35 (2010) 1439–1446.

A. Bahadori, and H. B. Vuthaluru, (accepted), Novel Predictive Tools for Design of Radiant and Convective Sections of Direct Fired Heaters, accepted for publication in Applied Energy .( Available online 21 December 2009).

A. Bahadori and H. B. Vuthaluru (2010)" A Simple Method for the Estimation of Thermal Insulation Thickness" Applied Energy 87 (2010) pp.613–619

A. Bahadori, and H. B. Vuthaluru ( 2010), Accurate Prediction of Molten Sulfur Viscosity” Petroleum Technology Quarterly 15(1)pp. 13-14.

A. Bahadori, and H. B. Vuthaluru, ( 2010) ” Estimation of Displacement Losses From Storage Containers Using a Simple Method Journal of Loss Prevention in the Process Industries, 23 (2010) 367-372.

A. Bahadori, and H. B. Vuthaluru, ( 2010) " Novel predictive tool for accurate estimation of packed column size" Journal of Natural Gas Chemistry. 19(2), PP.

A. Bahadori and H. B. Vuthaluru (2010)" A Simple Correlation for Estimation of Economic Thickness of Thermal Insulation for Process Piping and Equipment" Applied Thermal Engineering, 30, 254–259

A. Bahadori and H. B. Vuthaluru (2009)" Rapid Estimation of Equilibrium Water Dew Point of Natural Gas in TEG Dehydration Systems" Journal of Natural Gas Science & Engineering, 1(3)(2009), pp. 68-71.

A. Bahadori and H. B. Vuthaluru (2009)" Simple Methodology for Sizing of Absorbers for TEG Gas Dehydration Systems", Energy 34 (2009) 1910–1916.

A. Bahadori and H. B. Vuthaluru (2009) " New Method Accurately Predicts Carbon Dioxide Equilibrium Adsorption Isotherms" International Journal of Greenhouse Gas Control 3 (2009) 768–772

A. Bahadori and H. B. Vuthaluru (2010)" Prediction of Silica Carry-over and Solubility in Steam of Boilers Using Simple Correlation" Applied Thermal Engineering, 30 (2010) 250–253.

A. Bahadori and H. B. Vuthaluru ( 2009 )" A Novel Correlation for Estimation of Hydrate Forming Condition of Natural Gases" Journal of Natural Gas Chemistry, 18(4)(2009) pp. 453-457.

A. Bahadori and S. Mokhatab (2009) " Simple Correlation Accurately Predicts Densities of Glycol Solutions” Petroleum Science and Technology, 27(3) pp. 325 – 330.

A. Bahadori and H. B. Vuthaluru ( 2010)" Rapid Prediction of Carbon Dioxide Adsorption Isotherms for Molecular Sieves Using Simple Correlation" SPE Projects, Facilities & Construction Journal 5(1), PP.17-21.

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List of Recent Journal Articles (2008-Present)

A. Bahadori, H. B. Vuthaluru and S. Mokhatab (2009) " Rapid Estimation of Water Content of Sour Natural Gases" Journal of the Japan Petroleum Institute. 52(5) pp. 270-274.

A. Bahadori, H. B. Vuthaluru ( 2009)" Explicit Numerical Method for Prediction of Transport Properties of Aqueous Glycol Solutions" Journal of the Energy Institute 82 (4), pp. 218-222.

A. Bahadori, H. B. Vuthaluru and S. Mokhatab, ( accepted)"Accurate Determination of Required Mono-Ethylene Glycol Injection Rate To Avoid Natural Gas Hydrate Formation" accepted for publication in the Journal of Energy Sources, Part A: Recovery, Utilization, and Environmental Effects.

A. Bahadori, H. B. Vuthaluru and S. Mokhatab (2009)" New Correlations Predict Aqueous Solubility and Density of Carbon Dioxide" International Journal of Greenhouse Gas Control (3), pp. 474–480.

•  A. Bahadori and H. B. Vuthaluru(2009) " Prediction of Bulk modulus and Volumetric Expansion Coefficient of Water for Leak Tightness Test of Pipelines" International Journal of Pressure Vessels and Piping, (86)pp. 550–554

A. Bahadori (2009)"New Model Predicts HC Emissions from TEG Plants" Petroleum Chemistry, 49(2), pp. 171–179 . A. Bahadori, (2009)"Estimating Water-Adsorption Isotherms" Hydrocarbon Processing , 88(1) pp. 55-56. A. Bahadori, H.B. Vuthaluru and S. Mokhatab (2009)“ Simple Correlation Accurately Predicts Aqueous Solubility of Light Alkanes” Journal of Energy Sources, Part A:

Recovery, Utilization, and Environmental Effects, 31:(9), 761—766. A. Bahadori, H.B. Vuthaluru and S. Mokhatab (2009)“ Method Accurately Predicts Water Content of Natural Gases” Journal of Energy Sources, Part A: Recovery,

Utilization, and Environmental Effects, 31 (9) 754 — 760. A. Bahadori and S. Mokhatab (2009)" Correlation rapidly estimates pure hydrocarbons’ surface tension" Journal of the Energy Institute 82 (2)pp. 118-119.

A. Bahadori, H. B. Vuthaluru, and S. Mokhatab (2009)" Determining appropriate Size of Inlet Scrubber and Contactor in TEG Gas Dehydration Systems", Petroleum Science & Technology, 27(16) 1894 — 1904.

A. Bahadori and H. B. Vuthaluru (2009)" Predicting Emissivity of Combustion Gases" Chemical Engineering Progress, 105, (6), 38-41. A. Bahadori (2009)"Minimize vaporization and displacement losses from storage containers" Hydrocarbon Processing 88(6) pp. 83-84. A. Bahadori, (2009)" Estimation of Hydrate Inhibitor Loss in Hydrocarbon Liquid Phase", Petroleum Science & Technology (27) pp. 943–951. A. Bahadori (2009)"Predicting Storage Pressure of Gasolines in Uninsulated Tanks" Journal of the Energy Institute 82 (1) p. 61. A. Bahadori and H. B. Vuthaluru (2008)" Simplified Method for Calculating Hydrocarbons Solubilities in Hydrate Inhibitors", Chemical Engineering and Technology ,31 (9)

pp. 1369-1375. A. Bahadori, H. B. Vuthaluru, M. O. Tade and S. Mokhatab (2008)" Predicting Water-Hydrocarbon Systems Mutual Solubility" Chemical Engineering & Technology 31,

(12)pp. 1743-1747. A. Bahadori, H. B. Vuthaluru and S. Mokhatab (2008)"Estimating Methanol Vaporization Loss and Its Solubility in Hydrocarbon Liquid Phase" OIL GAS European Magazine

34, (3) pp. 149-151. A. Bahadori, H.B. Vuthaluru, S. Mokhatab and M. O. Tade (2008) "Predicting Hydrate Forming Pressure of Pure Alkanes in the Presence of Inhibitors", Journal of Natural Gas

Chemistry Vol.17, No.3, pp. 249-255. A. Bahadori, H. B. Vuthaluru and S. Mokhatab (2008) "Optimizing Separators Pressures in the Multistage Crude Oil Production Unit ", Asia-Pacific Journal of Chemical

Engineering ,3, (4) pp. 380-386. A. Bahadori and S. Mokhatab (2008) "Estimating Thermal Conductivity of Hydrocarbons" Chemical Engineering 115, (13)pp. 52-54. A. Bahadori and S. Mokhatab, (2008)"Predicting Water Content of Compressed Air" Chemical Engineering Vol. 115, NO.9, pp. 56-57. A. Bahadori and S. Mokhatab (2008)" Predicting Physical Properties of Hydrocarbon Compounds" Chemical Engineering 115 (8) pp. 46-48.•   A. Bahadori, H. B. Vuthaluru and S. Mokhatab (2008)" Rapid Prediction of CO2 Solubility in Aqueous Solutions of DEA and MDEA" Chemical Engineering & Technology, 31,

(2) pp. 245-248. A. Bahadori, S. Mokhatab and B. F. Towler (2008)“ Predicting Hydrate Forming Conditions of Light Alkanes and Sweet Natural Gases” , Hydrocarbon Processing, 87 (1) pp.

65-68. A. Bahadori, (2008)" New Correlation Accurately Predicts Thermal Conductivity of Liquid Paraffin Hydrocarbons" Journal of the Energy Institute 81 (1) pp. 59-61. A. Bahadori, (2008)" Correlation Accurately Predicts Hydrate Forming Pressure of Pure Components" Journal of Canadian Petroleum Technology, 47, No.2, pp.13-16. A. Bahadori, H. B. Vuthaluru (2010)Estimation of Energy Conservation Benefits in Excess Air Controlled Gas-fired Systems, accepted for publication in Fuel Processing

Technology. Other articles are still under review…… Several papers presented in Society of Petroleum Engineers (SPE)

Page 15: Research Vision

In this study, a simple-to-use correlation is developed to predict the bulk modulus and volumetric expansion coefficient of both fresh and sea water as a function of temperature and pressure. The proposed correlation helps to cover the bulk modulus and volumetric expansion coefficient of both fresh and sea water for temperatures less than 50 °C (40 °C for sea water) as well as pressures up to 55,000 kPa (550 bar). The results can be used in follow-up calculations to determine whether any pressure variation in pipeline hydrostatic test is a result of temperature changes or the presence of leaks.

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A simple method to accurately predict the carbon dioxide adsorption isotherms for a microporous material as a function of temperature and partial pressure of carbon dioxide. The method appears promising and can be extended for CO2 capture as well as for separation of wide range of adsorbents and microporous materials including several molecular sieves merely by the quick readjustment of tuned coefficients.

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In the present work, simple-to-use predictive tool,, is formulated to arrive at an appropriate estimation of the transport properties (namely viscosity and thermal conductivity) of carbon dioxide (CO2) as a function of pressure and temperature.

Page 18: Research Vision

In this paper, new correlations for predicting density and the solubility of carbon dioxide in pure water as well as the aqueous sodium chloride solutions are developed, where using the generated interaction parameters, the solubility model is applied to correlate the carbon dioxide solubilities in aqueous solutions for temperatures between 300 and 400 K and pressures from 50 to 700 bar. The correlation developed for predicting density of carbon dioxide accurately works for pressures between 25 and 700 bar and temperatures between 293 and 433 K.

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A simple-to-use correlation, which is simpler than currently available models involving a large number of parameters, requiring less complicated and lshorter computations, has been developed for the rapid estimation of the water dew point of a natural gas stream in equilibrium with a TEG solution at various temperatures and TEG concentrations. This correlation can be used to estimate the required TEG concentration for a particular application or the theoretical dew point depression for a given TEG concentration and contactor temperature. Actual outlet dewpoints depend on the TEG circulation rate and number of equilibrium stages, but typical approaches to equilibrium are 6–11 °C. Equilibrium dewpoints are relatively insensitive to pressure and this correlation may be used up to 10 300 kPa (abs) with little error. The proposed correlation covers VLE data for TEG–water system for contactor temperatures between 10 °C and 80 °C and TEG concentrations ranging from 90.00 to 99.999 wt%.

Page 20: Research Vision

The primary parameters involved in the design of fractionators are the number of stages and the reflux ratio. The aim of this study is to develop easy-to-use equations, which are simpler than current available models involving a few number of parameters and requiring less complicated and shorter computations, for an appropriate prediction the operating reflux ratio for a given number of stages. Alternatively, for a given reflux ratio, number of stages can be determined. .

Page 21: Research Vision

The aim of this study is to develop a predictive tool, for accurate determination of downcomer velocity and vapor capacity factor as a function of tray spacing and density. The results can be used in follow-up design calculations to size fractionators without foaming formation.

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A predictive tool is formulated to accurately predict the absorption efficiency as a function of absorption factor and number of absorber stages. The proposed predictive tool also can be used to determine the number of trays required for a given lean oil rate or to calculate recoveries with a given oil rate and tray count

Page 23: Research Vision

A generalized pressure drop correlation (GPDC) is developed for sizing randomly packed fractionation columns for pressure drops up to 150 mm water per meter of packing. This correlation can be used to estimate pressure drop for a given loading and column diameter. Alternatively, for a given pressure drop the diameter can be determined.

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simple-to-use method, to correlate water removal efficiency as a function of TEG circulation rate and TEG purity for appropriate sizing of the absorber at wide range of operating conditions of TEG dehydration systems.

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In this work, a simple correlation, which is a mathematically compact and reasonably accurate equation containing few tuned coefficients, is presented here for the prediction of methanol vaporization loss and vapor pressures of aqueous methanol solutions as a function of temperature and methanol mass fraction in aqueous solutions using a novel and theoretically meaningful Arrhenius-type asymptotic exponential function and Vandermonde matrix.

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In this work, firstly, a simple-to-use correlation is developed to estimate the true vapor pressure of liquefied petroleum gas (LPG) and natural gasoline as a function of Reid Vapor pressure (RVP) and temperature as well as the vapor pressure of different mixtures of propane and butane are correlated as a function of ambient air temperature and propane volume percent. Secondly, the filling losses from storage containers are estimated in percentage of liquid pumped in tanks as a function of working pressure and vapor pressure at liquid temperature.

Page 28: Research Vision

The purpose of this study is to formulate a novel correlation for rapid estimation of hydrate formation condition of sweet natural gases. The developed correlation holds for wide range of temperatures (265–298 K), pressures (1200 to 40000 kPa) and molecular weights (16–29). New proposed correlation shows consistently accurate results across proposed pressure, temperature and molecular weight ranges.

Page 29: Research Vision

In this work, a method is developed to predict the viscosity and thermal conductivity of aqueous glycol solutions as a function of temperature and glycol mole fraction.

280 300 320 340 360 380 400 420 0.20.4

0.60.8

1

0

10

20

30

40

50

Triethylene Glycol, Mole fractionTemperature, K

Visc

osity

of T

rieth

ylen

e G

lyco

l ,(m

Pa.S

) [1m

Pa.s

=1cp

]

5 10 15 20 25 30 35 40 45

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20 40 60 80 100 120

103

104

Temperature, °C

Effe

ctiv

e W

ater

Con

tent

of C

O2

in N

atur

al G

as, m

g H

2O/(S

td. c

ubic

met

er)

P=1500 kPaDataP=2000 kPaDataP=3000 kPaDataP=4000 kPaDataP=6000 kPaDataP=13000 kPaData

20 30 40 50 60 70 80 90 100 110 120102

103

104

105

Temperature, °C

Effe

ctiv

e W

ater

Con

tent

of H

2S in

Nat

ural

Gas

, (m

g H

2O)/(

Std.

cub

ic m

eter

)

P=1500 kPaDataP=2000 kPaDataP=3000 kPaDataP=6000 kPaDataP=13000 kPaData

The aim of this study is to describe an accurate method for predicting water content of sweet and sour natural gases, where the obtained results by the proposed method show good agreements with the data in the literature.

0 5 10 15x 104

102

103

104

105

106

Pressure, kPa

Swee

t Gas

Wat

er C

onte

nt, m

g/(S

td. C

ubic

met

er)

T=0°CT=30°CT=60°CT=90°CT=120°CT=150°CT=180°C

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In this study, a simple correlation is developed to predict paraffin hydrocarbon surface tension as a function of molecular weight and temperature.

50 100 150 200 2500

5

10

15

20

25

30

Molecular weight

Sur

face

Ten

sion

, dyn

e/cm

Temperature= -20°C

Temperature= 200°C

220 240 260 280 300 320 340 360 380 400 4200

5

10

15

20

25

30

Temperature,K

Sur

face

Ten

sion

, dyn

e/cm

Molecularweight=20

Molecular weight=240

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230 240 250 260 270 280 290 300 310 320 330100

101

102

Evaporator Temperature, K

Gas

Pow

er p

er M

W R

efer

iger

atio

n D

uty,

kW

Refrigerant-Condensing Temperature, 15°C (288.15K)

Refrigerant-Condensing Temperature, 60°C (333.15K)

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280 290 300 310 320 330 340 350 360 370 380

10-7

10-6

Temperature, K

Hydr

ocar

bon

Solu

bilit

y in

Wat

er, M

ole

Frac

tion

HeptaneDataOctaneDataNonaneDataDecaneData

280 290 300 310 320 330 340 350 360 370 380

10-2

10-1

Temperature, K

Wat

er S

olub

ility

in H

ydro

carb

on, (

kg W

ater

/(100

Kg

of H

ydro

carb

on))

PropaneDataButaneDataPentaneDataHexaneData

270 280 290 300 310 320 330 340 350 360 370 380

10-2

10-1

Temperature, K

Wat

er S

olub

ility

in H

ydro

carb

on, (

kg W

ater

/(100

Kg

of H

ydro

carb

on))

HexaneDataHeptaneDataOctaneDataDecaneData

This work presents an easy-to-use correlation for an excellent prediction of the mutual solubility of water-hydrocarbon systems in a broad range of temperatures between 0 and 120 °C, and heavy hydrocarbons between C3 and C10.

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This article presents a simple-to-use correlation for better prediction of aqueous solubility of light alkanes, where the obtained results of the proposed method have been compared with two activity coefficient models (NRTL and UNIQUAC) and showed good agreements with the observed values.

104

10-3

Methane Partial Pressure, KPa

Sol

ubili

ty o

f Met

hane

in W

ater

, Mol

e Fr

actio

n

T=0°CT=2°CT=4°CT=6°CT=8°CT=10°CT=12°CT=14°C

103

10-3.9

10-3.7

10-3.5

10-3.3

10-3.1

Ethane Partial Pressure, KPa

Sol

ubili

ty o

f Eth

ane

in W

ater

, Mol

e Fr

actio

n

T=0°CT=2°CT=4°CT=6°CT=8°CT=10°CT=12°CT=14°C

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Flagship publication of American Institute of Chemical Engineers, AIChE

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Firstly, a simple correlation is developed to provide an accurate and rapid prediction of the absorbed heat in the radiant section of a fired heater, expressed as a fraction of the total net heat liberation, in terms of the average heat flux to the tubes, the arrangement of the tubes, and the air to fuel mass ratio. Secondly, another simple correlation is developed to approximate external heat transfer coefficients for 75, 100, and 150 mm nominal pipe size (NPS) steel pipes arranged in staggered rows and surrounded by combustion gases. Finally, a simple correlation is presented to predict the gross thermal efficiency as a function of percent excess air and stack gas temperature.

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In this study, a simple method is developed to estimate the thickness of thermal insulation required to arrive at a desired heat flow or surface temperature for flat surfaces, ducts and pipes. The proposed simple method covers the temperature difference between ambient and outside temperatures up to 250 C and the temperature drop through insulation up to 1000 C. The proposed correlation calculates the thermal thickness up to 250 mm for flat surfaces and estimates the thermal thickness for ducts and pipes with outside diameters up to 2400 mm.

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In this work, a simple correlation is presented to predict silica (SiO2) solubility in steam of boilers as a function of pressure and water silica content. The solubility of silica in steam directly depends on both the density and temperature of steam. With decreasing temperature and density, solubility of silica reduces. As the pressure affects steam density which has a strong bearing on steam temperature, it has an important effect on the solubility of silica in steam. The proposed correlation predicts the solubility of silica (SiO2) in steam for pressure up to 22,000 kPa and boiler water silica contents up to 500 mg/kg.

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In this work, simple-to-use correlation, is formulated to arrive at the economic thickness of thermal insulation suitable for process piping and equipment. The correlation is as a function of steel pipe diameter and thermal conductivity of insulation for surface temperatures at 100 C, 300 C, 500 C and 700 C. A simple interpolation formula generalizes this correlation for wide range of surface temperatures.

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Predictive Tool for Estimation of Convection Heat Transfer Coefficients and Efficiencies for Finned Tubular Sections

In this paper, firstly, an attempt has been made to formulate a novel and simple-to-use correlation for the prediction of efficiencies for uniform thickness finned tubular sections as well as fin tip temperature for wide range of conditions (covering finned pipe diameter to pipe diameter ratios of up to 3). Secondly, another simple correlation is developed to approximate external convection heat transfer coefficients for nominal pipe size (NPS) steel pipes of 75, 100, and 150 mm arranged in staggered rows surrounded by combustion gases

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

50

60

70

80

90

100

Coefficient

Fin

Effi

cien

cy, P

erce

nt

df/do=1df/do=1.2df/do=1.4df/do=1.6df/do=1.8df/do=2df/do=2.2df/do=2.4df/do=2.6df/do=2.8df/do=3

0.5 1 1.5 2 2.5 310

15

20

25

30

35

40

45

Mass Velocity, kg/(Square meter.S)Con

vect

ion

Coe

ffici

ent,

W/(S

quar

e m

eter

.°C)

T=100°CDataT=200°CDataT=300°CDataT=400°CDataT=500°CDataT=600°CData

tkh

HCf

ofo 045.0

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100 101 102

101

Temperaturte of Surface Less Temperature of Air, °C Coe

ffici

ent f

or C

ombi

ned

Con

vect

ion

and

Rad

iatio

n, W

/(Squ

are

met

er.°C

)

Wind Velocity=0 m/s (Still air)

Wind Velocity=17 m/s

In this work, an attempt has been made to formulate a simple-to-use predictive tool for the rapid estimation of heat losses in terms of the wind velocity and the temperature difference between the process piping and equipment surfaces and the surrounding air.

Petroleum Technology Quarterly (2010)

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100 150 200 250 300 3500.65

0.7

0.75

0.8

0.85

0.9

Stack temperature rise,°C

Com

bust

ion

effic

ienc

y, fr

actio

n

Excess air=100%

Excess air=0%

Combustion efficiency term commonly used for boilers and other fired systems and the information on either carbon dioxide (CO) or oxygen (O) in the exhaust gas can be used The aim of this study is to develop a simple-to-use predictive tool which is easier than existing approaches less complicated with fewer computations and suitable for combustion engineers for predicting the natural gas combustion efficiency as a function of excess air fraction and stack temperature rise (the difference between the flue gas temperature and the combustion air inlet temperature). The results of proposed predictive tool can be used in follow-up calculations to determine relative operating efficiency and to establish energy conservation benefits for an excess-air control program.

Estimation of Energy Conservation Benefits in Excess Air Controlled Gas-fired Systems

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In this paper, a simple-to-use method which is easier than current available models, is presented to predict accurately the appropriate temperature drop accompanying a given pressure drop in natural gas production wells based on the black-oil model to get a quick approximate solution for the temperature drop of a natural gas streams in gas production wells. The obtained results illustrate that excellent agreement is observed between the reported data and the values calculated using the new developed method. Considering the results, the new developed correlation is recommended for rapid estimation of wellbore temperature drops in gas production wells for pressures up to 45 MPa and pressure drops up to 25 MPa.

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The aim of this study is to present a numerical method for accurate prediction of solubilities of light hydrocarbons in methanol and ethylene glycol (at different weight percantages in water) as a function of reduced partial pressure and reduced temperature. The

predictions from the proposed method have been compared with reported experimental data and found good agreement with average absolute deviation being less than 1.6%. It was also found that this method is more accurate than conventional thermodynamical

approaches (in particular, the NRTL model with average deviations of 20 percent) in predicting the solubilities of light alkanes in hydrate inhibitors.

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In this paper, an attempt has been made to formulate a simple-to-use method which is easier than existing approaches, less complicated and with fewer computations for accurate and rapid estimation of crude oil salinity as a function of brine quantity that remains in the oil, its salinity (in vol% of sodium chloride concentration) and temperature using an Arrhenius-type asymptotic exponential function and Vandermonde matrix. The proposed method predicts the Salinity of Salty Crude Oil for temperatures up to 373 K and sodium chloride concentrations up to 250,000 ppm (25% by volume).

100106 ppm

32)ln(Td

Tc

Tba

31

211

1aDCB

A

32

222

2AbDCB

33

233

3AcDCB

34

244

4dDCB

A

100100

10001698.997C

R

Rs W

W

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In this work, an easy-to-use predictive tool which is simpler than existing approaches, less complicated with fewer computations, is formulated to arrive at an appropriate estimation of the water content of carbon dioxide-rich phase. The new developed method works for pressures ranging from 5 to 70 MPa (which covers the pressure that is widely considered in CO2 sequestration) and temperatures from 20 to 75°C. Comparison of predicted results with the reliable experimental data indicated excellent agreement with the average absolute deviations being less than 1.5%.

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In this work, a simple predictive tool which is easier than existing approaches involving a fewer number of parameters, requiring less complicated and shorter computations, is presented to accurately predict carbon dioxide compressibility factor as a function of reduced temperature and reduced pressure. The proposed predictive tool shows consistently accurate results across the proposed pressure and temperature ranges. Predictions show an average absolute deviation of 0.55 %

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In this work, a newly developed method is applied to accurately predict the carbon dioxide and nitrogen adsorption isotherms at low temperatures and pressures up to saturation for a commercial carbon molecular sieve as a function of temperature and partial pressure of these components. Accurate prediction of such data is useful in evaluating the feasibility of using pressure swing adsorption to separate nitrogen and carbon dioxide from natural gases at cryogenic temperatures for carbon dioxide and nitrogen.

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In this work, a novel and simple predictive tool is presented to estimate the formation of calcium carbonate scaling as a function of pH, temperature, ionic strength of the solution, calcium cation concentration, bicarbonate anion concentration and carbon dioxide mole fraction when the water mixture is saturated with a gas containing CO 2to evaluate the effect of solution conditions on the tendency and extent of the precipitation. The proposed simple method covers concentration for calcium cation concentration, or bicarbonate anion concentrations up to 10000 mg/L, temperatures up to 90°C, total ionic strength up to 3.6 and pH values ranging between 5.5 and 8.

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Case Study

• A typical case study to illustrate the benefits for oil and has practitioners:

• Methanol Loss During Gas Hydrate Inhibition

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Methanol Losses During Gas Hydrate Inhibition

• Methanol • is the most commonly used hydrate inhibitor• only effective as a hydrate inhibitor in the aqueous phase

• Methanol is dissolved in hydrocarbon liquid phases and vaporized form in gas phase must be considered as losses

• for subsea pipeline, natural gas transmission and processing system applications

• Methanol as a hydrate inhibitor• significant expense associated with the cost of lost methanol• important to know methanol lost to the hydrocarbon liquid phase and the rate of

losses into vapor phase in the pipeline

• In this work, a simple Arrhenius-type function• which is easier than existing approaches less complicated with fewer computations

and suitable for process engineers is developed• This tool can be used to estimate methanol loss in paraffinic hydrocarbons as a function of temperatures and

methanol concentrations in water phase as well as methanol loss in vapor phase

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Example calculations• 2.83 million Standard cubic meter per day of natural gas

leaves an offshore platformt 38°C and 8300 kPa (abs). The gas comes onshore at 4°C and 6200 kPa (abs). The hydrate temperature of the gas is 18°C. Methanol mass percent in liquid phase is 27.5%. Calculate the amount of vaporized methanol?Solution:

• x=0.275 mass fraction of methanol• T=277.15 K

We calculate the adjusted parameters:• a= -3.4124821400054 *10^3• b= 2.8663831990333 *10^6 • c= -8.004219677314*10^8 • d= 7.437011549752*10^10

Methanol Loss in vapor phase which is around $3000 per day

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Prediction of Methanol Loss

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Example• 2.83 million Standard cubic meter per day of natural gas leaves an offshore platform at

38°C and 8300 kPa (abs) (Water content 850 mg/Sm3 ). The gas comes onshore at 4°C and 6200 kPa (abs) (Water content 152 m/ Sm3 ).The hydrate temperature of the gas is 18°C. Associated condensate production is 56 m3 / (million standard m3 ). The condensate has a density of 778 kg/ m3 and a molecular mass of 140. The required methanol inhibitor concentration in water phase to avoid hydrate formation is 27.5%. Calculate the mass rate of inhibitor in water phase and the amount of methanol loss in hydrocarbon liquid phase.

• Calculate condensate water:

• Calculate mass rate of inhibitor in water phase:

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• Estimate losses to hydrocarbon liquid phase from proposed method at 4°C and 27.5 wt% methanol:

• The solubility of methanol in hydrocarbon phase is estimated to be around 0.0011 or 0.11 mol%.

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Methnol Loss in Condensate Phase

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Potential Areas of Collaboration

• Use of SPT’s soft-wares such as OLGA and PIPEFLO for Education Purposes (for oil and gas units from Semester 1, 2011 – next slide)

• Support for the development of hand-held tool comprising of• Parameters of interest to Oil & Gas industries• Compatible with strategic needs of SPT

• Integrate our efforts into OLGA or any other software and expand the capabilities of currently owned programs of SPT

• Our vision for PREDICTIVE TOOL is .....

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Oil and Gas Units in Chemical Engineering Department

• Oil-field Processing:• Understanding basic theories of measurement, instrumentation, relief and

storage systems, fired equipments and heat exchangers;• Understanding of operational problems in oilfield processing including hydrate

formation, dehydration, desalting, hydrocarbon treating, wax and asphalt formation;

• Basic knowledge of multiphase flow in pipe lines, sizing of flow lines and accessories and design of separation equipments; and

• Become familiar with fundamental design aspects of flow lines, separators, pumps and compressors design, expanders and refrigeration systems including multiphase flow calculations.

• Oil and Gas Reservoir Engineering• Understanding basic theories of Fundamentals of Reservoir Fluid Behavior,

Reservoir-Fluid Properties • Understanding of Relative Permeability Concepts • Fundamentals of Reservoir Fluid Flow, Oil Well Performances and Gas Well

Performances• Basic knowledge of Gas and Water Coning, Oil Recovery Mechanisms and the

Material Balance Equation, Predicting Oil Reservoir Performance ; and• Become familiar with fundamental aspects of Gas Reservoirs, Principles of

Water flooding, Analysis of Decline and Type Curves

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Our vision for Predictive Tool

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Strategy for Curtin’s Tool Integration

Curtin’s Predictive Tool can be embedded in any of the above components as shown above

Curtin’s Predictiv

e tool

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Strategy for Curtin’s Tool Integration

Curtin’s Predictive Tool can be embedded in any of the above components as shown above

Curtin’s Predictiv

e tool

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Integration into OLGA

Curtin’s Predictive Tool (PreTOG) can be embedded in any of the above components or as a checking tool as a sub-tool

Curtin’s PreTOG