P di ti T l f Oil & G I d t iPredictive Tool for Oil & Gas Industries

Mr Alireza Bahadori and A/Prof Hari Vuthaluru

Department of Chemical EngineeringDepartment of Chemical EngineeringCurtin University, GPO Box U1987

Perth 6845, Australia

Presentation Outline

• Why Predictive Tools?• Why Predictive Tools?

• Development of Novel Predictive Tools

• Typical Applications in Oil and Gas Industries

• Research Outcomes to Date

Case Study and Examples• Case Study and Examples

• Potential Areas of Collaboration

Why Predictive Tools?

• Predictive or modelling tools are useful • to avoid unnecessary experimental trials• resolve operational issues at low costs• optimise the equipment or plant performance

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

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

D l t f di ti t l i th f ti l• Development of predictive tools is therefore essential• to minimize the complex and time-consuming calculation steps

• Mathematically compact, simple, and reasonably accurateMathematically 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)

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

Example

• Predicting density of liquid water g y q

• 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]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.

Vogel-Tammann-Fulcher (VTF) Equation

VTF equation:E

After revisions made by Civan (2007):

)()ln(ln

cTTRE

cff−

−=

After revisions made by Civan (2007):

( )2lnlncc

c TTc

TTbff

−+

−+=

3)(

d2)(

lnlnTTTT

c

cTTb

cff−

+−

+−

+=

( )cc

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

)()( cTTcTTc

db

*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

32lnlnTd

Tc

Tbff c +++=

of water, Industrial Engineering & Chemistry Research Journal 46 (17) ,2007, 5810–5814.

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 thethe 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

General Workflow of Algorithm for Tuning Coefficients

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

If m<MYes

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

If m<Mm=m+1

Correlate “a” as a function of Y using Vandermonde

No

Correlate a as a function of Y using Vandermonde

Correlate “b” as a function of Y using Vandermonde

Correlate “c” as a function of Y using Vandermonde

Correlate “d” as a function of Y using Vandermonde

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

Correlate d as a function of Y using Vandermonde

Stop

Selection of Independent Variables

Rules• Some theoretical and semi-theoretical correlations of parameters such as 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• other physical properties inherits errors associated with additional properties included in such correlations.

• Fortunately, however, these problems can be alleviated readily becauseFortunately, 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 i d d t i bl hindependent variables such as

• temperature, pressure, molecular weight, concentration and so on.

Advantages of Predictive Tools

• Research efforts to date led to simple predictive tools which are

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

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

Developed tools are superior owing to their• 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 to have a quick check

• of 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

Typical Applications in Oil and Gas Industries

• Natural Gas Hydrate Prediction• Prediction of absorption/stripping factorsp / pp g• 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 E ti ti f di l t l f P t l d t t t i• 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 y• Prediction of aqueous solubility and density of carbon dioxide • A simple correlation for estimation of economic thickness of thermal insulation for

process piping and equipmentNew method accurately predicts carbon dioxide equilibrium adsorption isotherms• 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• Prediction of Transport Properties of Carbon Dioxide etc.

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• 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”, , , ( ) g p g ,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.

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-( p ) p g y gtype 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• 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• 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 ( ) pPiping 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" I t ti l J l f G h G C t l 3 (2009) 768 772International 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 330Technology, 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.

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.

h d i h l (2010) i i f C i fi i i C ll d G fi d S d f bli i i l i h l• 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)

0.6

16

18

20

b=0Datab=0.2Data

0 3

0.4

0.5

-GP 1)/(

tqi)

b=08

10

12

14

Di.t

Datab=0.4Datab=0.6Datab=0.8Datab=1

0.1

0.2

0.3

(GP 2-

b=1100 1010

2

4

6

Data

32)(

⎟⎟⎠

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⎟⎟⎠

⎞⎜⎜⎝

⎛ Δ+

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⎞⎜⎜⎝

⎛ Δ+=

Ppp

i

tqG

tG

tqGq

q θγβα

10 20 30 40 50 60 70 80 90 1000

qi/q

(qi/q) at any time on decline=4

10 10qi/qt

⎟⎠

⎜⎝

⎟⎠

⎜⎝⎠⎝ iii tqtqtq

31

2111 bDbCbBA +++=α

32

2222 bDbCbBA +++=β P 1)/(

tqi)

2222

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2333 bDbCbBA +++=γ

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2444 bDbCbBA +++=θ

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P

(qi/q) at any time on decline=100

)(D ηϕφ

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Arps’ decline-curve exponent,"b"

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Case Study

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

• Methanol Loss During Gas Hydrate Inhibition

(Classic example demonstrating the benefits)

Methanol Losses During Gas Hydrate Inhibition

• Methanol • is the most commonly used hydrate inhibitor• only effective as a hydrate inhibitor in the aqueous phasey y q p

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

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

• Methanol as a hydrate inhibitor• 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 pipelinep p p p

• 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

Thi t l b d t ti t th l l i ffi i h d b• 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

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

leaves an offshore platformt 38°C and 8300 kPa (abs). The gascomes onshore at 4°C and 6200 kPa (abs). The hydratetemperature of the gas is 18°C Methanol mass percent intemperature of the gas is 18 C. Methanol mass percent inliquid phase is 27.5%. Calculate the amount of vaporizedmethanol?

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 ~ $3000 per day

Prediction of Methanol Loss

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 massis 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:

• 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%.

Methnol Loss in Condensate Phase

Potential Areas of Collaboration

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

• Support for the development of hand-held predictive tool comprising of

• parameters of interest to Oil & Gas industries• compatible with strategic needs of SPT• compatible with strategic needs of SPT

• Integrate our efforts into• Integrate our efforts into

• OLGA or any other software and expand the capabilities of y p pcurrently owned programs of SPT

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;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

d f ddesign 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 ; andBalance Equation, Predicting Oil Reservoir Performance ; and• Become familiar with fundamental aspects of Gas Reservoirs, Principles of Water

flooding, Analysis of Decline and Type Curves

O i i f PREDICTIVE TOOL (P TOG) i• Our vision for PREDICTIVE TOOL (PreTOG) is .....

Our vision for Predictive Tool (PreTOG)

Development Plan of Curtin’s PreTOG Software

Graphical U

All developed predictive models (>100)

VandemondeMatrix_basedMethodologyUser

User Interface

predictive models (>100) gyUser

MATLAB ToolboxesMATLAB Toolboxes

Strategy for Curtin’s Tool Integration

C ti ’Curtin’s Predictive

tool

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

Strategy for Curtin’s Tool Integration

Curtin’sCurtin s Predictive

tool

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

Integration into OLGA

Curtin’s P TOGPreTOG

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

Feedback on our predictive tools

Feedback 1

Feedback on our predictive tools

Feedback 2

Feedback on our predictive tools

Feedback 3

Feedback on our predictive tools

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Feedback on our predictive tools

From: Joe Aiken <JAiken@transfieldworley.co.nz>Date: Fri, Sep 17, 2010 at 6:29 AM Subject: 550034 Stage 2A - Rapid Estimation of Water Content of Sour Natural Gasesj g pTo: Alireza Bahadori <alireza.bahadori@postgrad.curtin.edu.au> I just came across your paper which was published in 2009 and it looks very relevant for my current requirement (dew point measurement at the outlet of a TEG Dehydration plant forcurrent requirement (dew point measurement, at the outlet of a TEG Dehydration plant, for process control) and I wonder what further aspects may have developed since publication. Specifically: • Have you performed any comparison of the accuracy and applicable range relative to the ‘standard’ correlation defined in ISO‐18453:2004? • Is there a spreadsheet or similar available which incorporates your correlation? Regards, Joe Aiken

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Let’s find a way forward to make use of our PreTOGLet s find a way forward to make use of our PreTOG

Special thanks to Andrew Paterson (SPT Group) for providing us the opportunity to share our efforts to dateproviding us the opportunity to share our efforts to date

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