SPE 150822
Multiple EOS Fluid Characterization for Modeling Gas Condensate Reservoir
with Different Hydrodynamic System: A Case Study of Senoro Field
Sugiyanto Bin Suwono, SPE, JOB Pertamina–Medco E&P Tomori Sulawesi, Luky Hendraningrat, SPE, NTNU, PT
Medco E&P Indonesia, Dwi Hudya Febrianto, Medco LLC Oman, Bagus Nugroho, SPE, PT Medco E&P Indonesia,
and Taufan Marhaendrajana, SPE, Institut Teknologi Bandung (ITB)
Copyright 2012, Society of Petroleum Engineers
This paper was prepared for presentation at the North Africa Technical Conference and Exhibition held in Cairo, Egypt, 20–22 February 2012.
This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been
reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessar ily reflect any position of the Society of Petroleum Engineers, its
officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is p rohibited. Permission to
reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copie d. The abstract must contain conspicuous acknowledgment of SPE copyright.
Abstract
A proper analysis and fluid characterization is an essential key for successful modeling the behaviour of gas condensate
reservoir. This paper demonstrates a robust multiple equation of state (EOS) modeling process for gas condensate reservoir at
Senoro field. Senoro is a new major gas condensate field in East Indonesia with estimated IGIP greater than 2 Tcf and CGR
range from 3-80 STB/MMscf. Senoro field is divided into two structures: the northern part is a carbonate reefal build-up,
namely Mentawa, member of Minahaki formation, and the southern part is a platform carbonate Minahaki formation. The
hydrodynamic condition in both formations poses a challenge to fluid characterization, where Mentawa member has both oil
and gas with active aquifer, while Minahaki formation only has gas bearing rock with aquifer.
Senoro field has collected 36 samples, measured from down-hole and surface. The samples also cover composition analysis for
surface recombined fluid. The required laboratory experiment such as CCE, DL and CVD have also been measured. The
mathematical recombination was performed as a quality check to measure well-stream composition.
Two EOS models have been developed successfully to determine physical properties and to predict the fluid behaviour of
Senoro. The heptanes-plus fraction is split into three pseudo-components to characterize fluid using Gamma distribution
model. The fine-tuned fluid properties from all available data match both EOS models satisfactorily. These EOS models have
also been matched with historical single radial welltest model. Compositional grading has also been developed to generate
compositional map.
These established EOS models are used for compositional simulation. The gas and condensate profiles now could be predicted
for optimizing field development plan. The use of EOS models can lead not only to a further field development strategy, but
also to optimize the surface processing facilities.
Introduction
Senoro is a new major gas-condensate field in East Indonesia with estimated Initial Gas in Place (IGIP) greater than 2 Tcf and
Condensate Gas Ratio (CGR) range from 3-80 STB/MMscf. The gas sales agreement has been made to several buyers in East
Indonesia in the next couple of years. This field is located onshore, at the northeastern coastal region of Senoro-Toili Block on
the eastern arm of Sulawesi Island, Indonesia. The Senoro structure trends from northeast to southwest and encompasses an
area of approximately 90 km2. This structure was identified as carbonate rocks, whereas the northern part is a carbonate reefal
build-up, namely Mentawa, member of Minahaki formation, and the southern part is a platform carbonate Minahaki formation
(see Fig. 1).
The first exploration well has been drilled in April 1999. Currently there are six wells drilled in Senoro Field and all of them
have been tested and sampled but currently being temporarily plugged and abandoned (see Table 1). The gross pay thickness
found is up to 686 ft at North and 487 ft at South. The Well#1, #2, and #5 are located in the northern part while Well#3 and
Well#6 are located in the southern part of Senoro field. The southern part is located in a different hydrodynamic system with
northern part. The Southern part consists of only gas bearing rock with aquifer in Minahaki formation. Meanwhile Northern
part has both oil and gas with active aquifer. Consequently, it becomes the essential reason to divide the PVT of Senoro into
two regions.
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SPE 150822
Based on experimental laboratory test and DST data, this reservoir is indicated to be a gas condensate reservoir (see Fig. 2).
Most gas condensate reservoir and volatile oil reservoir fluid behaviour are not only a function of pressure but also dependant
of composition. A foremost challenge in developing gas-condensate reservoir is condensate blockage phenomena, sooner or
later gas production performance could undergo. The final objective of this study is to develop full-field compositional
simulation model for predicting the reliable and accurate performance of oil/condensate and gas recovery methods when liquid
and vapour undergo vigorous mass transfer during the recovery process. Therefore, providing the established EOS models is
compulsory and becomes the first EOS modeling for field operated under JOB Pertamina – Medco E&P Tomori Sulawesi. For
this purpose, we studied using Winprop, the state-of-art fluid characterization, and GEM, the compositional simulator.
Quality Check of PVT Data
The Senoro field has 36 fluid samples from existing 6 wells. Those samples were taken either from bottomhole or surface (see
Table 2). However, there are only 4 DST tests that were also detailed analyzed using CCE and CVD test, which are DST-1 of
Well#1, DST-1 of Well#5, DST-2 of Well#5, and DST-1 of Well#6. In favour of Well#5, it has two recombination reports,
DST-1 and DST-2. However, only data from DST-2 that is chosen to represent Well#5. There are 2 reasons why DST-1 is not
quite representative. First, the compositional analysis of separator liquid shows very small Methane (C1) mole fraction (2.98
% mole) compared to other reports (about 15.30 % mole). The DST-1 of Well#5 was tested around 6 ft below GOC, in such
that the GOR was too high due to gas coning. Second, the plus fraction of DST-1 was only reported up to C12+. Meanwhile,
DST-2 analyzed up to C20+. Therefore, the degree of accuracy DST-1 is smaller than DST-2. Based on those preliminary
screening, finally there are only three samples to be used for EOS modeling which are DST-1 of Well#1(bottomhole oil
sampling), DST-2 of Well#5 and DST-1 of Well#6.
In order to make representative fluid in reservoir condition, the surface gas samples should be recombined with its liquid at the
specific producing gas-oil ratio (GOR) during sampling. To ensure that the quality of recombined sample is reliable, the result
of lab recombination should be cross-check with calculated recombination result. A data with maximum of 2% error should be
considered as having a good consistency between lab and calculation result.
The data quality check (QC) for the recombined fluid can also be done by using Hoffman plot. The Hoffman plot is fast and
reliable technique for evaluating the consistency of the data through a graphical technique (CMG 2010) which is created by
plotting the logarithm of K-value times pressure versus a component characteristic factor (F). If the data is good, in such that
the liquid and vapour samples are reasonably in equilibrium at the separator conditions and the measurement of the liquid and
vapour compositions generally error free, then the points of components C1-C6 should fall on a straight line. Nitrogen (N2) and
Heptane-Plus (C7+) is not included in Hoffman consideration since it is generally not measured accurately. The equation of
characteristic factor (F) is given below.
F
log PC  log Pa   1
1 1
  
 Tb Tc 
1
   ........................................................................................................................................................... (1)
 Tb T 
Where Pc is critical pressure, Pa is atmospheric pressure, T is temperature, Tb is normal boiling point temperature, and Tc is
critical temperature.
The fluid recombination quality check as shown in Table 3 and Table 4 gives acceptable matched between lab and calculated
wellstream data with less than 2% of deviation. Figure 3 and Figure 4 also show that both north and south region data give a
good match in Hoffman plot.
Fluid Characterization
In doing fluid characterization, this study prefer to refers to procedures recommended by Whitson (1992). The first step is
splitting and characterizing the C7+ into a reasonable number of fractions (3 to 20). This number of fraction should depend on
the simulation process, the characterization procedure and machine specification. Second step is modifying the C7+ properties
such as: critical properties (pressure, volume, and temperature), acentric factor, shifting volume, molecular weight, constants
omega A & B. The pure component properties for Methane and Non-hydrocarbons may also be modified. Finally, it needs to
reduce the total number of components to as few as possible while still maintaining the accuracy of the phase behavior.
Heptanes-Plus Characterization
The C7+ fraction characterization was conducted by using gamma distribution model (Whitson 1983). This method is widely
used for describing the heptanes plus fraction in reservoir fluid if partial extended analysis such as MW, mole fraction and SG,
is specified. The gamma distribution parameters for northern and southern parts are shown in Table 5. The C7+ fraction was
divided into single carbon number (SCN) up to C31+. The result from several wells give satisfaction matched with deviation
between measured and calculated of Z+, MW+ and SG+ below 0.4% (see Table 6 and 7). Both of EOS now has 36components (EOS36). The SCN mole fraction of both EOS model are showed in Figure 5.
SPE 150822
3
In order to utilize the established multiple EOS in full-field compositional reservoir simulation study, the EOS36 should be
lumped into smaller number of components. It is important since EOS36 will take much longer CPU time compared to smaller
number of components. The lumping method used Gaussian quadrature as defaulted in Winprop to reduce from EOS36 to
EOS14 by grouping into three pseudo or hypothetical component (HYP) as shown in Table 8. In northern part, HYP01
consists of C7 to C12, HYP02 consists of C13 to C30, and HYP03 consists of C31+. Whereas in southern part, HYP01 consists of
C7 to C9, HYP02 consists of C10 to C17, and HYP03 consists of C18-C31+.
Equation of State (EOS)
To describe the fluid phase behavior, Peng-Robinson 1978 EOS was used in this study. The Peng-Robinson EOS is
recommended to be used for highly volatile oils or liquid rich gas condensates near to critical point. The critical properties
were calculated using Twu correlation (Twu 1984) as defaulted in Winprop. For viscosity modeling, there are only Pedersen
correlation and Jossi-Stiel-Thodos (JST) correlation in WinProp. The Pedersen correlation is expected to give better liquid
viscosity predictions for light and medium gravity oils than the JST model (CMG 2010). Therefore, modifed Pedersen
(Pedersen and Fredenslund 1987) is expected to have better estimation for Senoro viscosity modeling.
Tuning EOS parameters
The tuning or regression of the EOS parameters should be performed if the EOS model does not match with fluid properties
from experimental data. Refering to Whitson (1992), parameters in viscosity correlation for calculating viscosity model might
be tuned during regression to match experimental viscosity data. It needs trial and error in setting regression parameters and
data weight factors such as saturation pressure weight, exponential ROV, liquid saturation, gas viscosity, density and z-factor.
Figure 6 – 10 shows result of final EOS tuning for all Senoro samples. The fine-tuned EOS14 in all fluid models are
consistently matches with the experimental fluid properties i.e. saturation pressure (minor deviation ~0.57%), relative volume
(deviation less than 10%), z-factor (minor deviation ~1.0%), gas viscosity, gas density (deviation less than 10%), liquid
volume (deviation less than 7%), vapour produced (deviation less than 5%), and GOR (deviation less than 10%). Therefore, all
of the properties are considered to be of acceptable in quality.
Compositional Gradient
Compositional gradient phenomena represent variation of fluid composition as a function of reservoir depth. It should consist
of information about compositions variation, PVT properties and GOC. The determination of GOC is a tricky problem
(Whitson 1992). The GOC is defined as depth where the fluid changed from having a dewpoint to having a bubble point at
constant reservoir temperature. The GOC can represent a saturated condition where the reservoir pressure equals the saturation
pressure. In other hand, composition has a transition from a dewpoint to a bubble point at undersaturated condition (P r > Psat).
The only way this can occur is that the saturation pressure of the fluid at the point of transition is a critical point (called
undersaturated GOC). The cross-plot between saturation pressure and reservoir pressure with depth would determine the GOC.
Since the oil rim was found only in the Nortern part of reservoir, compositional gradient was generated for Northern reservoir
only. Therefore, only samples from Well#1 and Well#5 can be calculated and compared each other. Comparing with measured
GOC from RFT data which is found at depth of 6,496 ft-ss, calculated GOC from Well#1 sampling is located higher at 6,466
ft-ss (deviation 0.46%). Otherwise, calculated GOC from sample Well#5 is located deeper at 8,324 ft-ss (deviation 28%). The
possibility of high deviation in Well#5 could be a result of liquid or condensate flow back in the wellbore in such that the
heavier component did’nt flow-up to surface sampling separator during sampling. That hypothesis is actually supported by fact
that the calculated GOR is matched by increasing the plus fraction molecular weight 3.5 times from existing data at Well#5.
However this method is not recommended but only for concluding the issue. Thus, this final data QC concludes that EOS14
model to be used for further compositional simulation are only from Well#1 for Northern part and Well#6 for Southern part.
Single Well Radial Simulation
In order to have a good deliverability forecast, history matching process is needed to match the reservoir model with available
DST data. Since the DST test was conducted only for very short time period, the history matching was conducted in single
well radial matching. The single radial homogenous well model was developed in GEM. It contains 100-200 active grid cells
with external radius depends on the well testing interpretation. The input parameters such as porosities, permeability, skin,
thickness were based on well testing reports. Several DST wells data could not represent the well testing condition due to
bottomhole leakage while testing. Hence only Well#2 and Well#5 for Northern part and Well#6 for Southern part to be
matched with fine-tuned EOS each region (See Figs. 12, 13 and 14). In this history matching, once again the previous finetuned EOS should be re-tuned to get the best match of bottomhole pressure (BHP) and oil-gas ratio (OGR). These two
parameters are very important in modeling gas-condensate reservoir. It should be noted that the best fine-tuned EOS model
resulted from this history matching also need to be re-confirmed in Winprop to ensure that the model is still consistent with lab
experimental data as it did in the tuning EOS parameter step.
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SPE 150822
Full-field Compositional Simulation Model
The full-field compositional simulation modeling has been constructed in GEM. The reservoir properties were obtained from
the results of geo-statistical analysis using commercial geo-statistical modeller that were exported into numerical
compositional reservoir simulation. The properties modeling were developed by inputting all relevant geological and reservoir
parameters, such as top structure map, isopach map, iso-porosity map, iso-permeability map, well data, formation tests data,
reservoir rock, as well as reservoir pressure data. The model was developed in single porosity system since there is no
evidence of fracture existence in Accoustic-Impedance interpretation. The reservoir geometry structure is showed in Table 11.
The established multiple EOS14 with compositional gradient then also imported to numerical compositional simulation. Each
region has been set with different initial reservoir temperature, 217oF in Northern part and 212 oF in southern part. The GOC in
Northern part is similar depth with GWC in southern part at 6,496 ft-ss. The OWC in northern part is located at 6,522 ft-ss.
Therefore, the thin oil column in Northern is about 26 ft-ss.
Initialization
The initial hydrocarbon in-place matching between the volumetric (geo-statistical results) and reservoir simulation models
should be performed to ensure the consistency of simulator model, before the simulator model used for further reservoir
performance predictions. The equilibrium method is used for initialization. The difference of hydrocarbon in-place between
geo-statistical and simulator model below 0.6% was considered good match and acceptable in the engineering practice.
Comparison between the volumetric and reservoir simulation to estimate the initial hydrocarbon in-place for Senoro field is
listed in Table 12.
Forecasting
The formulation option method of Adaptive Implicit is used in compositional simulation. Surface separator data in simulation
is set at single-stage separator with pressure and temperature at surface condition. The vertical flow performance (VFP) tables
were generated and used based on well testing result. A scenario had been performed with gas plateau rate at 319 MMscfd
(Figure 17) with several proposed wells as part of field development. One of the important things is to minimize error or
warning message related to convergence problem. The use of the EOS models can lead not only to a further field development
strategy, but also for optimization of the surface processing facilities.
Conclusions
1. The robust multiple equation of state (EOS) has been successfully developed for Northern and Southern part of Senoro
Field which has different hydrodynamic system. Both of EOS14 models have been fine-tuned the parameters and have a
good acceptable matched.
2. The multiple EOS14 models have also been matched with historical single radial welltest model.
3. The established multiple EOS14 has successfully input in developed full-field compositional reservoir model and run in
numerical compositional simulation for initialization with minor error (below 0.6%) compare to volumetric calculation and
provides molar rate of SCN for optimizing the surface processing facilities.
Recommmendation for Further Work
A main challenge in developing gas-condensate full-field reservoir model is condensate blockage phenomenon where sooner
or later gas production performance could undergo. Fevang and Whitson (1996) provide how to model gas condensate well
deliverability more accurately using generalized pseudo-pressure (GPP). In the future, the GPP option should be added for loss
analysis and more reliable deliverability forecasting. In addition, more complex separator stage should be performed in
processing of gas condensate. The compositional reservoir model with history matching is also needed to be updated after this
field is produced in next couple of years
Acknowledgment
We would like to thank the management of JOB Pertamina–Medco E&P Tomori Sulawesi, PT Medco E&P Indonesia and
BPMIGAS for permission to publish this paper.
Nomenclature
CCE
CVD
CMG
DLE
DST
constant composition expansion
constant volume depletion
Computer Modelling Group Ltd, a company
differential liberation
drill stem test
SPE 150822
EOS
EOS14
EOS36
HYP
GEM
GWC
JOB
OWC
Pr
Psat
RFT
5
equation of state
equation of state model with 14-component
equation of state model with 36-component
hypothetical (pseudo) component
Equation of State Compositional Simulator, a product of CMG
gas-water contact
joint operating body
oil-water contact
reservoir pressure
saturation pressure
repeat formation testing
References
1. Whitson, C.H. and Brule, M.R. 2000. Phase Behaviour, Vol. 20, Henry L. Doherty series. Monograph Series, SPE. ISBN
1-55563-087-1.
2. Whitson, C.H. 1992. Phase Behaviour in Reservoir Simulation. Fourth International Forum on Reservoir Simulation.
Salzburg, Austria, August 31 – September 4.
3. Singh, K., Mantatzis, K., and Whitson, C.H. 2011. Reservoir Fluid Characterization and Application for Simulation Study.
Paper SPE 143612 presented at SPE EUROPEC/EAGE Annual Conference and Exhibition held in Vienna, Austria, 23-26
May.
4. Whitson, C.H. 1983. Characterizing Hydrocarbon Plus Fractions. SPEJ (August 1983) 683, Trans., AIME 275.
5. Whitson, C.H. 1984. Effect of C7+ Properties on Equation of State Prediciton. SPEJ (December 1984) 685, Trans., AIME
277.
6. Twu, C.H. 1984. An Internally Consistent Correlation for Prediciting the Critical Properties and Molecular Weight of
Petroleum and Coal-Tar Liquids. Fluid Phase Equilibria, No. 16, 137.
7. Computer Modelling Group. 2010. Winprop User’s Guide Version 2010.
8. Computer Modelling Group. 2010. GEM User’s Guide Version 2010.
9. Pedersen, K.S., and Fredenslund, A. 1987. An improved corresponding states model for the prediction of oil and gas
viscosities and thermal conductivities. Chemical Engineering Science, Vol. 42, No. 1, pp. 182-186.
10. Fevang, Ø. and Whitson, C.H. 1996. Modeling Gas-Condensate Well Deliverability. Paper SPE 30714, SPERE November
1996.
SI Metric Conversion Factors
bbl
x
1.589873
ft
x
3.048
o
F
(oF -32)/1.8
MMscf x
2.831685
psi
x
6.894757
Tcf
x
2.831685
E-01
E-01
E+04
E+00
E+10
=
=
=
=
=
=
m3
m
o
C
m3
kPa
m3
6
SPE 150822
Table 1-Welltest Summary
Table 2-List of Sample
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7
Table 3-Wellstream Comparison of Well#2: Measured vs. Calculated
Table 4-Wellstream Comparison of Well#6: Measured vs. Calculated
Table 5-Gamma Distribution Parameters
Table 6-Heptane Plus Properties of Well#2 Recombination
Table 7-Heptane Plus Properties of Well#6 Recombination
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SPE 150822
Table 8-Single Carbon Number from EOS36 to EOS14 in Northern part
Lumped of C7+ Fraction/ EOS14
H2S
CO2
N2
C1
C2
C3
IC4
NC4
IC5
NC5
C6
HYP01
HYP02
HYP03
Single Carbon Number (SCN) / EOS36
H2S
CO2
N2
C1
C2
C3
IC4
NC4
IC5
NC5
C6
C7+C8+C9+C10+C11+C12
C13+C14+C15+C16+C17+C18+C19+C20+C21+C22+C23+C24+C25+C26+C27+C28+C29+C30
C31+
Table 9-Lumped Single Carbon Number from EOS36 to EOS14 in Southern part
Lumped of C7+ Fraction/ EOS14
H2S
CO2
N2
C1
C2
C3
IC4
NC4
IC5
NC5
C6
HYP01
HYP02
HYP03
Single Carbon Number (SCN) / EOS36
H2S
CO2
N2
C1
C2
C3
IC4
NC4
IC5
NC5
C6
C7+C8+C9
C10+C11+C12+C13+C14+C15+C16+C17
C18+C19+C20+C21+C22+C23+C24+C25+C26+C27+C28+C29+C30+C31+
Table 10-Saturation Pressure: Experiment vs. Simulation
Table 11-Simulation Model Summary
Model
Value (GEM)
Grid Cells
Grid Size
Calculation
Total Grid Cells
Active Grid
Total Wells
VFP Tables
Formulation
97 x 166 x 60
150 m x 150 m x 6 m
Corner Point
319 464
38 750
21
Yes
AIM
Table 12-Hydrocarbon Initialization Result for Senoro Field
Senoro Region
North
South
Total
Type
Gas Condensate
Solution Gas
Gas Condensate
Senoro Region
Type
North
Oil
IGIP (Unit Volume)
Volumetric
Comp. Simulation
157699
157698
9671
9673
55716
55783
223086
223155
Deviation
%
0.00 %
-0.02 %
-0.12 %
-0.03 %
IOIP (Unit Volume)
Volumetric
Comp. Simulation
1047
1050
Deviation
%
-0.54 %
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Well#6
Well#3
Well#4
Well#2
Well#1
Figure 1-Vertical Distribution of DST Samples
Figure 2-Senoro Properties and General Characteristics of Condensate Reservoir
Well#5
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Well#2
Figure 3-Hoffman Plot of Well#2
Well#6
Figure 4-Hoffman Plot of Well#6
Figure 5-Heptanes-plus fraction Splitting into Single Carbon Number (SCN)
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11
Well#1 DST-1
Well#1 DST-1
Figure 6-Well#1: Measured and calculated of EOS model from CCE
Well#1 DST-1
Well#1 DST-1
Figure 7-DLE experiment vs. Simulation of well Well#1:
Well#5 CCE
Well#5 CCE
Well#5 CCE
Well#5 CCE
Figure 8-Well#5: Measured and calculated of EOS model from CCE
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SPE 150822
Well#6 Retrograde Gas
Well#6 Retrograde Gas
Well#6 Retrograde Gas
Well#6 Retrograde Gas
Figure 9-Well#6 CCE comparison between Experimental and Simulation from CCE
Well#5 CVD Calc.
Well#6 CVD Calc.
Figure 10- CVD experiment vs. Simulation with Error 10%: Well#5 (Left) and Well#6
Well#1 DST-1
Well#5 Retrograde Gas
Bubblepoint Pressure
Dewpoint Pressure
Bubblepoint Pressure
Dewpoint Pressure
Figure 11-Calculated Compositional Gradient to determine GOC depth: Well#1 (Left) and Well#5 (Right)
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Well#2
Figure 12-Validation with Single Radial Well Model Well#2
Well#5
Figure 13-Validation with Single Radial Well Model Well#5
Well#6
Figure 14-Validation with Single Radial Well Model Well#6
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SPE 150822
EOS Set 2 (Well#6 DST-1)
EOS Set 1 (Well#1 DST-1)
Figure 15-Senoro Reservoir Model with 2 Regions
Figure 16-Senoro Pressure Profile
Figure 17-Senoro Production Profile
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Figure 18-Senoro Molar Rate Profile