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Journal of Cleaner Production 200 (2018) 269e281
Contents lists available at ScienceDirect
Journal of Cleaner Production
journal homepage: www.elsevier.com/locate/jclepro
Lifetime oriented design of natural gas offshore processing for cleaner
production and sustainability: High carbon dioxide content
Luiz de Medeiros a, Giovani Cavalcanti Nunes b,
Alessandra de Carvalho Reis a, Jose
lia de Queiroz Fernandes Araújo a, *
Ofe
a
b
Federal University of Rio de Janeiro, Brazil
State University of Rio de Janeiro, Brazil
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 5 February 2018
Received in revised form
19 July 2018
Accepted 27 July 2018
Available online 30 July 2018
Production of natural gas in deepwaters with high gas-to-oil ratio and high carbon dioxide (CO2) content
challenges the design of offshore processing due to area and weight limitations. Furthermore, cleaner
production and process sustainability impose sending the separated CO2 to early enhanced oil recovery,
which has economic benefit but gradually increases %CO2 in raw gas, paralleled by decaying oil and gas
flowrates. These conditions favor CO2 capture by membrane permeation (MP) for bulk removal and
chemical absorption (CA) for polishing removal. Hybrid MP-CA has greater flexibility to face varying
production and %CO2, demanding lifetime-oriented process design. CO2 production profile is estimated
adopting %CO2 retained in source rock (0%, 60%) and gas flowrate predicted by empirical production
decline curves. Under transient gas production MP area and operational conditions are optimized via
non-linear programming at five points of process lifetime constrained by %CO2 in injected fluid above
75%mol. Treated gas reaches sale specification (%CO2 < 3%mol) in the CA unit placed downstream MP. The
obtained best design matched targets, but was more impacted by decreasing flowrate of raw gas than by
increasing %CO2.
© 2018 Elsevier Ltd. All rights reserved.
Keywords:
Lifetime-oriented design
Reservoir decline curve
CO2 capture
EOR
Process optimization
Membrane permeation
1. Introduction
The carbon budget is the cumulative amount of CO2 in the
Abbreviations: Bbl, Barrels of Petroleum Liquids (1 bbl ¼ 0.159 m3); BBL, Billion
Barrels of Petroleum Liquids; CO2, Carbon Dioxide; CA, Chemical Absorption; CAPEX,
Capital Expenditures; CEPCI, Chemical Engineering Plant Cost Index; COMP,
Compressor; E&P, Exploration and Production; ENG, Exported Natural Gas; EOR,
Enhanced Oil Recovery; EROI, Energy Return over Invested Energy; E-IG, Energy
fraction associated to injection gas; E-RNG, Energy fraction associated to raw natural gas; FPSO, Floating Production Storage and Offloading; GAMS, General Algebraic
Modeling System; GOR, Gas to Oil Ratio; HC, Hydrocarbon; HCDPA, Hydrocarbon
Dew-Point Adjustment; HRWH, Heat Recovery Water Heater; IG, Injection Gas; JT,
Joule-Thompson; LCC, Life-Cycle Cost; LHV, Lower Heating Value; MDEA, MethylDiethanolamine; MP, Membrane Permeation; MUSD, Million United States Dollars;
NG, Natural Gas; NLP, Non-Linear Programming; O&G, Oil & Gas; OPEX, Operational
Expenditures; PHW, Pressurized Hot Water; PZ, Piperazine; RNG, Raw Natural Gas;
RS, Response Surface; SG, Storage Gas; sm3, standard m3; USD, United States Dollar;
WDPA, Water Dew-Point Adjustment.
* Corresponding author.
E-mail addresses: alessandracr@eq.ufrj.br (A. de Carvalho Reis), jlm@eq.ufrj.br
(J.L. de Medeiros), giovani.nunes@uerj.br (G.C. Nunes), ofelia@eq.ufrj.br
(O.Q.F. Araújo).
https://doi.org/10.1016/j.jclepro.2018.07.271
0959-6526/© 2018 Elsevier Ltd. All rights reserved.
atmosphere corresponding to 450 ppm. For a 50% chance of keeping average global warming below 2 C by 2050, the budget is
estimated to be approximately 275 Gt of carbon (1008 Gt CO2)
(IPCC, 2014a). To limit emissions rate, carbon taxes and cap-andtrade mechanisms are expanding worldwide (Energy Institute at
Haas, 2016), intensifying the risk of “stranded assets” (Carbon
Tracker, 2017). Consequently, three quarters of proven reserves of
coal, oil, and natural gas may be unburnable (IPCC, 2014b),
contributing to reduce life expectancy of Oil & Gas (O&G) business.
The Organization of Petroleum Exporting Countries acknowledges that the O&G industry could be overinvesting, building
excess capacity (Musarra, 2017), while to achieve the expected return out of capital expenditure (CAPEX) production life needs to be
extended. Although proven oil reserves are expanding as offshore
exploration and production (E&P) is increasingly moving to remote
areas and deeper waters thanks to unstoppable development of
offshore E&P (Exploration and Production) technology, the easy oil
era has come to an end (ironically, not because reserves are drying).
Gerasimchuk et al. (2017) named “zombie energy” the production
from these fields that, although receiving strong government subsidies (“negative carbon taxes”), will remain unburned. However,
270
A. de Carvalho Reis et al. / Journal of Cleaner Production 200 (2018) 269e281
Nomenclature
A, A/FEED Permeation area (m2) and permeation area per feed
unit (m2/Nm3/h)
b
Arps' exponent
d
FPSO working days per y (d)
DI
Reservoir nominal decline rate (y1)
Ei
Annual average molar flow of ENG from each FPSO
(106 sm3/d)
f
Objective function in NLP optimization
F1
RS input factor #1 CH4 or CO2 feed partial pressure
F2
RS input factor #2 CO2 or CH4 feed partial pressure
F3
RS input factor #3 area per feed unit (m2/Nm3/h)
F3i
RS input factor #3 area per feed unit for each stage
(m2/Nm3/h)
F4
RS input factor #4 permeate pressure (bar)
F5
RS input factor #5 feed temperature (K)
FFEEDi
Feed molar flow rate for each stage (106 sm3/d)
Fi
Annual average molar flow of RNG fed to each FPSO
(106 sm3/d)
the installed capacity “locks in” fuel dependency, as they are
intensive in capital and long lived, hence production will last to
return investments, slowing the transition to lower-carbon energy
(Gerasimchuk et al., 2017).
Although the extension of subsidies can be argued, the fact is
that remote oil and gas reserves pose a general decline in energy
return of energy invested (EROI) (Hall et al., 2014), as more energy
is demanded for E&P operation, enforcing the need for
sustainability-oriented design of E&P. It is worth noting that the
Brazilian Pre-Salt oil reserves are distant from shore (>340 km), in
ultra-deepwaters (>2000 m) (Gaffney et al., 2010), with high Gasto-Oil Ratio (GOR) e greater than 400 standard m3 of gas/m3 of
oil (sm3/m3) e and with association to CO2-rich gas ~44 %mol
(Arinelli et al., 2017).
Floating Production Storage and Offloading (FPSO) platforms
have been utilized in remote offshore areas without infrastructure
for many years but grew in importance with the push by offshore
industry into ever deeper waters (Shimamura, 2002). FPSO are
preferred for being mobile, self-sufficient, with high storage capacity and without the need of local piping infrastructure for oil
transport. Compared to fixed platforms, FPSOs offer the advantages
of being more rapidly developed, requiring lower initial investment, and keeping their aggregate value for longer time, since they
can be reallocated to other fields, and having lower abandonment
costs (Yu et al., 2015).
With huge deepwater reservoirs, Brazilian Pre-salt impacted the
FPSO scenario. Currently, 178 FPSOs (90 contractor owned and 88
operator owned) are operating worldwide (44 in Brazil) being 126
converted vessels. Additionally, 19 are not working but are available
for redeployment (2 in Brazil) and 12 are on order (8 in Brazil),
totaling 209 FPSOs (Barton et al., 2017). These FPSO and deep
offshore projects demand very sizeable investments that must be
optimized and protected (Thiabaud et al., 2011). In fact, for
extending use of fossil energy beyond 2050, increased energy efficiency is sought in E&P along with minimization of CO2 emissions.
It is relevant to the present study that oil production is dependent on the capacity of processing the associated high CO2-rich
natural gas (NG). In offshore processing of CO2-rich NG, the main
destination for the separated CO2 is Enhanced Oil Recovery (CO2EOR). The CO2 storage potential in EOR is high: 60% of injected CO2
can be retained in the reservoir (Gazalpour et al., 2005). CO2
Equipment foot-print (m2)
Annual gas hold-up in the reservoir (106 sm3)
Annual average molar flow of IG from each FPSO (106
sm3/d)
L
MP permeate phase
n
Number of FPSOs connected to the reservoir
PPCH4, PPCO2 Feed CH4 and CO2 partial pressures (bar)
PPERM
Permeate pressure (bar)
qI
Initial production flow rate (106 sm3/d)
q(t)
Production flow rate (106 sm3/d)
RECLCH4
RS response #1 CH4 %recovery in permeate
RECLCO2
RS response #2 CO2 %recovery in permeate
Si
Annual average molar flow of SG (106 sm3/d)
t
Time
TFEED
Feed temperature (K)
x0CO2, xi
Reservoir CO2 molar fractions: initial and for y i
xENGCO2, xIGCO2 Annual average CO2 molar fractions: for ENG and
for IG
y
year
FP
Hi
Ii
reinjection reduces oil density and viscosity, improving its fluidity
and increasing reservoir production, monetizing CO2. CO2-EOR recovers 1e3 bbl (barrels) of oil per injected ton of CO2, increasing,
thus, the reservoir economic lifetime (Luu et al., 2016).
High gas-to-oil ratio (GOR), associated with high CO2 content
(Gaffney et al., 2010), challenges the design of FPSOs, due to the
impact in area and weight required by the NG processing plant
(Andrade et al., 2015). In this case, uncommon steps are needed,
such as Water Dew Point Adjustment (WDPA) and Hydrocarbon
Dew Point Adjustment (HCDPA), efficient H2S and CO2 removal and
high-pressure CO2 reinjection (Formigli Filho et al., 2009).
Fig. 1 shows the gas processing steps on the topside of Brazilian
Pre-Salt deepwaters FPSO, highlighting CO2 separation while
illustrating the flow profile and increasing CO2 content in the
reservoir along operating lifetime. The mixed oil, gas and water
stream arrives in the FPSO through risers and proceeds to threephase separation, from where each fraction is directed to its
treatment. The gas stream is compressed and dehydrated for WDPA
avoiding hydrate formation in the transportation pipeline. Next, NG
is sent to HCDPA to remove heavier fractions. The gas then proceeds
to CO2 separation via membrane permeation (MP), where it is split
into permeate e a CO2-rich stream compressed to be dispatched as
Injection Gas (IG) e and retentate, a CO2-poor stream compressed
and exported to onshore facilities as NG for sale. Compression for
dispatching treated NG and CO2-rich fluid for EOR challenge energy
and area availability on FPSO topside. Composition specification of
the CO2-rich stream to be reinjected is also an important design
premise.
From an environmental point of view CO2-EOR is beneficial
because it allows for the storage of part of the CO2 injected while
increasing oil recovery (Kwak et al., 2014) - 50% according to the
authors. Gazalpour et al. (2005) suggests a “gross” CO2eretention
efficiency of approximately 60% at CO2 breakthrough if separation
and reinjection is not considered after the breakthrough. The
unretained CO2 is responsible for a steady rise in CO2 content of
produced NG along reservoir operation lifetime, concomitantly to
the decrease in NG production due to depletion. This extreme
scenario challenges the design of offshore NG processing plants,
mainly concerning CO2 separation, demanding advancements in
FPSO design for enhanced production (Islam et al., 2012).
In fact, sustainability of NG processing e and its survival as
A. de Carvalho Reis et al. / Journal of Cleaner Production 200 (2018) 269e281
271
Fig. 1. Typical NG processing on FPSO topside in Brazilian Pre-Salt.
energy source in a foreseen low carbon future e depends heavily on
CO2 management technologies (Araujo et al., 2017), especially the
ones related to its separation and destination. In this sense, optimized design of NG processing plants plays a central role in utilization of NG under stringent specifications. Most studies reported
in the literature propose plants designed at specific operating point
(sizing case) of the production curve, i.e., at one single period of the
process lifetime.
For instance, Araújo et al. (2017) compared CO2 separation alternatives in ultra-deep waters in technical, economic and environmental terms, facing constant gas flow rate feeds (6,000,000
sm3/d) with three CO2 content scenarios (10%, 30% and 50% mol),
considering EOR as CO2 destination. Lifetime was used solely in the
economic analysis (20 y). For feeds with higher CO2 content (50%
CO2), Araújo et al. (2017) indicated technical advantages of hybrid
MP þ CA process consisting of MP for bulk CO2 removal and
Chemical Absorption (CA) for polishing. The authors highlighted
that, even in drastic conditions, MP þ CA overcome pure CA and
pure MP designs.
Reis et al. (2017) targeted optimization of MP þ CA under two
types of constraints. The first forced treated gas to comply with sale
specification (%CO2 < 3% mol) resulting in single MP process with
significant hydrocarbon loss (HC Losses) in the injected gas. The
second imposed injected gas to a minimum of 75% mol CO2,
requiring a CA polishing unit. The authors showed that the hybrid
MP þ CA offers more flexibility than MP alternatives. Reis et al.
(2017) also used constant gas flowrate with three cases of CO2
content.
Kim et al. (2017) approached topside equipment for optimum
use of space, but process engineering was an a priori task outside
their scope. Thiabaud et al. (2011) focused on key engineering issues in FPSO design and claimed the relevance of lifecycle simulator
e a virtual plant used throughout engineering and operational
phases, for transients associated to slug flow. The slow lifecycle
transient due to production decline (Arps, 1945) and its impact in
the topside plant was not covered by the authors.
Gallo et al. (2017) studied the energy use of a Brazilian Pre-salt
FPSO platform and concluded that the need for gas compression
represents the main use of fossil energy (between 38% and 50% of
the total energy consumption). Due to the high %CO2 in the gas, it
requires CO2 removal, compression and reinjection in the oil field,
increasing energy needs. The authors estimated a total production
curve, with production peak at 7.5 y and with a total life time of
25 y. Their aim was to evaluate power usage along lifecycle (at
chosen points in the production curve), for fixed FPSO design,
concluding that, for most part of the operation, the power generation system was oversized. The work did not explore process
design.
Barrera et al. (2015) presented another exergy analysis of a
Brazilian FPSO. Their findings indicated the highest energy loss
associated to the exhaust gases from gas turbines and the gas injection system as the second highest exergy sink.
Araújo et al. (2017) explored the process design gap in the
literature and the loss of thermal energy from gas turbines to
supply heat for solvent regeneration in amine-based CO2 separation. A set of performance indicators, under the premises adopted,
favored hybrid capture e membrane permeation (MP) for bulk CO2
separation followed by chemical absorption (CA) as a CO2 removal
polishing step. However, the service split between MP and CA was
not explored to improve process performance. Filling this gap, Reis
et al. (2017) optimized the hybrid arrangement of Araújo et al.
(2017), with a fixed gas production (6,000,000 sm3/d) and
different content of CO2 in the raw gas (10%, 30% and 50% mol CO2).
Optimization used a response surface (RS) model to represent MP
separation under two types of constraint e a threshold on
maximum CO2 content in treated gas, and a threshold of minimum
CO2 content in the injected CO2-rich gas. The later type provided
the optimum split of CO2 separation service between MP and CA
units. Reis et al. (2017) did not include in their analysis the production curve, which presents variations in gas production and, due
to CO2 injection, CO2 content increases along production lifecycle.
The present work targets sustainability of long lived FPSO,
aiming at designs with CO2 reinjection, which impacts CO2 content
in produced gas along project lifetime. Furthermore, production
cycle is accelerated, reducing project expectancy to 20 y, to face
carbon budget constraints, differently from the 25 y production
lifetime adopted by Gallo et al. (2017). For the relevant technology
niche of offshore processing of CO2-rich NG, the main literature gap
targeted by this work consists in evaluating process performance in
a dynamic scenario of gas production decline, along with increasing
CO2 content in the produced gas resulting from CO2-EOR. Specifically, this work approaches the impact of varying feed conditions
(flowrate and composition) on FPSO design. Due to the flexibility of
the hybrid process composed by MP and CA units, as defended by
Reis et al. (2017), this technology route was selected as CO2 separation pathway. It was evaluated in terms of Capital Expenditures
(CAPEX), footprint and HC losses on a time-varying scenario of feed
flow rate and CO2 content.
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A. de Carvalho Reis et al. / Journal of Cleaner Production 200 (2018) 269e281
Finally, CO2 management is the road for sustainability of fossilbased energy. Processing CO2-rich NG demands capital and energy
intensive upstream processes that lower EROI of NG exported to
onshore facilities, reinforcing the need of careful gas process engineering to face the stringent offshore scenario and move towards
sustainability with constrained carbon budget. Although the FPSO
produces oil besides associated NG, the focus is on the gas processing plant, which, in the Pre-salt applications occupy nearly 60%
of the deck area and manages large amount of CO2. For instance, in
the period 2010e2016, 4,600,000 t of CO2 was reinjected in Lula
and Sapinho
a fields (Petrobras, 2017).
2005). Fig. 3 is a simplified representation of the four streams,
considering the systems boundaries: raw NG (RNG), which is processed in FPSOs, generating NG stream to be exported (ENG), the
CO2-rich stream for EOR injection (IG) and storage gas stream (SG,
pure CO2 captured into source rocks).
The construction of the two CO2 content profile (for 0% and 60%
of injected CO2 stored in reservoir rock) adopts reservoir constant
pressure and GOR along process lifetime. Additionally, the raw NG
stream (RNG) is considered water and sulfur free, and the
condensate flow rate generated in the HCDPA unit (Joule-Thomson
unit) is added to the oil produced, which is not represented in Fig. 3.
2. Background information
2.2.1. Scenario 1e0% CO2 storage
The reservoir is modeled as a perfectly mixed tank. Hence, the
reservoir CO2 content in a given y corresponds to the CO2 content in
the raw NG produced in this same y. The approach assumes that the
variables used in the balances are constant throughout a y, and that
the average production molar flow for a given y is the arithmetic
average of two consecutive yearly values (present and previous y).
Eqs. (4) and (5) describe reservoir balances, while Eqs. (6) and (7)
refer to balances of a FPSO. Eqs. (4) and (5) correspond to annual
reservoir balances of gas and CO2, respectively.
Certain infra-structure concepts characteristic of oil and gas E&P
are necessary to develop the analysis in Sec. 3. These concepts are
defined and formalized in this section.
2.1. Gas production curve
Arps (1945) presents empirical production decline curves that
remain widely used in predicting production profile for oil and gas
E&P. Arps proposes three decline profiles depending on time (t, y),
initial production rate (qI, 106 sm3/d), reservoir nominal decline rate
(DI, y1) and Arps' exponent (b). According to this last parameter,
profiles are classified as exponential (b¼0), hyperbolic (0 < b<1)
and harmonic (b¼1). Eqs. (1)e(3) present, respectively, the rate
expressions for these three kinds of profiles.
q(t) ¼ qI ∙ exp (- DI ∙ t)
q(t) ¼ qI / ((1 þ b ∙ DI ∙ t)
(1)
1/b
)
q(t) ¼ qI / (1 þ DI ∙ t)
Hiþ1 ¼ Hi e Fi ∙ d ∙ n þ Ii. ∙ d ∙ n
(4)
xiþ1 ∙ Hiþ1 ¼ xi ∙ Hi e xi ∙ Fi ∙ d ∙ n þ xIGCO2 ∙ Ii ∙ d ∙ n
(5)
Fi ¼ Ei þ Ii
(6)
xi ∙ Fi ¼ xENGCO2 ∙ Ei þ xIGCO2 ∙ Ii
(7)
(2)
(3)
Decline curves are normally constructed to predict oil production. However, if gas to oil ratio (GOR) is considered constant along
the reservoir lifetime, it is expected that the gas production presents a profile proportional to the oil production profile. With this
premise, Fig. 2 illustrates Arps decline curves representing the gas
production decline of a FPSO.
2.2. Curve of CO2 content in the reservoir
To represent the increase of CO2 content in a reservoir in which
the reinjection practice is used along its entire lifetime, global mass
balance and CO2 balance are herein proposed focusing solely on the
produced NG. Two scenarios are considered: (a) CO2 storage in the
source rock does not occur (0% CO2 storage); and (b) 60% of injected
CO2 is retained in the reservoir (60% CO2 storage) (Gazalpour et al.,
Fig. 2. Arps decline curves for gas production of a FPSO.
Fig. 3. Representation of a cluster of FPSOs with EOR: (a) 0% CO2 storage; (b) 60% CO2
storage [RNG ≡ Raw NG, IG ≡ Injection Gas, ENG ≡ Exported NG, SG ≡ Storage Gas (pure
CO2)].
A. de Carvalho Reis et al. / Journal of Cleaner Production 200 (2018) 269e281
where Hi is annual gas hold-up in the reservoir (106 sm3); Fi is
annual average molar flow (106 sm3/d) of raw NG (RNG) fed to each
FPSO; Ii is annual average molar flow (106 sm3/d) of injection gas
(IG) from each FPSO; Ei is annual average molar flow (106 sm3/d) of
exported NG (ENG) from each FPSO; xi is annual average CO2 molar
fraction in the reservoir; xIGCO2 is annual average CO2 molar fraction
in the injection gas (IG); xENGCO2 is annual average CO2 molar
fraction of exported NG (ENG); d is number of FPSO working days
per y (d); and n is the number of FPSOs connected to the reservoir.
2.2.2. Scenario 2e60% CO2 storage
The only differences between Scenario 2 and Scenario 1 are the
reservoir balances, which now have flow rates of CO2 captured into
the source rocks as shown in Eqs. (8) and (9). Si represents annual
average molar flow rate (106 sm3/d) of pure CO2 captured into
source rock (SG), where Si is proportional to Ii according to the
coefficient of injected CO2 retained in source rocks (60%).
Hiþ1 ¼ Hi e Fi ∙ d ∙ n þ Ii ∙ d ∙ n e Si ∙ d
3. Methods
This section presents the premises and equations used in this
work.
3.1. Gas production curve
The reservoir under analysis mimics the Libra field in the Brazilian Pre-Salt, whose E&P plan predicts nine FPSOs in operation
(Gaffney et al., 2010). The Replicant FPSO (a replicated standard
design) is considered, with maximum gas production capacity of
6,000,000 sm3/d (Andrade et al., 2015). For the construction of the
gas production curve shared by each FPSO, it is considered that the
maximum production is steeply reached by the end of the first y,
remaining constant for five y at the “Production Plateau” and
declining hyperbolically (0 < b < 1) from the sixth y onwards.
Table 1 presents the premises for constructing the gas production
profile of a FPSO.
3.2. Curve of reservoir CO2 content
Table 2 shows the parameters and values adopted for both
scenarios.
3.3. Process description
Raw NG from the dehydration unit is pressurized to 50 bar, sent
to the HCDPA and to the membrane permeation (MP) unit. The MP
unit produces a low-pressure CO2-rich permeate and a highTable 1
Premises for estimation of gas production curve.
Parameter
Value
b
DI (y1)
Maximum production (106 sm3/d)
Production plateau duration (y)
Project lifetime (y)
0.5a
0.17b
6
5
20
a
Arps' exponent, Eq. (2).
Reservoir nominal decline rate (DI), Eq. (2).
Table 2
Premises for estimation of reservoir profile of %CO2
content.
Parameter
Value
Hold-up (BBL)
GOR (sm3/m3)
Number of FPSOs
x0CO2
xIGCO2
xENGCO2
d
Lifetime (y)
3.65a
500b
9a
0.44c
0.9d
0.03e
350
20
a
Gaffney et al. (2010).
Pinto et al. (2014). GOR ¼ Gas to oil ratio.
Arinelli et al. (2015). x0CO2: CO2 molar fraction in
RNG at operation startup.
d
xIGCO2: annual CO2 molar fraction in the injection
gas (IG).
e
xENGCO2: annual CO2 molar fraction of the exported
natural gas (ENG).
b
c
(8)
xiþ1 ∙ Hiþ1 ¼ xi ∙ Hi e xi ∙ Fi ∙ d ∙ n þ xIGCO2 ∙ Ii ∙ d ∙ n - Si ∙ d(9)
b
273
pressure NG retentate, which is compressed to 50 bar and cooled
to 40 C to enter at the bottom of the chemical absorption (CA)
column. Lean aqueous methyldiethanolamine (MDEA) - piperazine
(PZ) is fed at the top, yielding a NG top stream (<3% mol CO2). The
bottom stream with CO2-rich solvent is expanded and heatintegrated with the hot lean solvent, before entering the stripper
for regeneration. The stripper top product is mixed with the
permeate from the MP to form a CO2-rich stream, sent to
compression and then injected into the reservoir for enhanced oil
recovery (EOR). The cooled lean solvent receives water makeup and
is pumped and cooled to 40 C returning to the CA absorber. The gas
product from the absorber (<3% mol CO2) is compressed to 150 bar,
cooled and sent as Exported NG (ENG).
The reboiler heat duty in the stripper is supplied by pressurized
hot water (PHW) at 190 C. PHW is produced by the Heat Recovery
Water Heater (HRWH), which recovers heat from the hot flue gas
exhausted by the gas turbine. Heat from the HRWH supplies the
heat demand of CA, which is considered cost-free up to the
maximum heat recovery capacity of the HRWH at about 75 MW per
100 MW of power. It is worth noting that the FPSOs presently
operating in the Brazilian Pre-Salt reservoirs have in average four
gas turbines, three in operation and one in standby mode, totaling
100 MW of installed capacity. The power generated in the gas turbines is used to drive the compressors of sale NG (ENG to pipeline)
and of CO2 to EOR (Araújo et al., 2017). Fig. 4a displays the conceptual diagram of the MP þ CA hybrid process.
Equipment sizing of the MP þ CA hybrid process uses mass and
energy balances obtained by simulations in Aspen HYSYS environment, except for MP module areas, which are obtained through
non-linear optimization (employing GAMS - General Algebraic
Modeling System), depicted in Fig. 4b.
3.4. Optimization of membrane permeation (MP) modules
The optimization procedure uses MP response surface (RS)
model and object function proposed by Reis et al. (2017). In this
work, CH4 %Recovery in permeate (RECLCH4, %) and CO2 %Recovery
in permeate (RECLCO2, %) were chosen as MP output responses
against five relevant independent input factors: (a) CH4 feed partial
pressure (PPCH4, bar); (b) CO2 feed partial pressure (PPCO2, bar); (c)
Permeation area per unit of gas feed (A/Feed, m2/Nm3/h); (d)
Permeate pressure (PPERM, bar), and (e) Gas feed temperature (TFEED,
K). It is worth mentioning that the factors were ordered according
to the modeled response. The RS formula for Y¼RECLCH4 (%), with Y
as dependent variable, is written as a function of the independent
274
A. de Carvalho Reis et al. / Journal of Cleaner Production 200 (2018) 269e281
Fig. 4. NG processing: (a) conceptual diagram; (b) solving steps; HC¼Hydrocarbon, CA¼Chemical Absorption, MP ¼ Membrane Permeation, DP ¼ dewpoint, MDEA ¼ MethylDiethanolamine, PZ¼Piperazine.
factors F1 ≡ PPCO2 (bar), F2 ≡ PPCH4 (bar), F3 ≡ A/Feed (m2/(Nm3/h)), F4
≡ PPERM (bar) and F5 ≡ TFEED (K) according to Eq. (10), using the
following parameter values: B0 ¼ 2.79258; B1 ¼ 0.958001; B2 ¼
0.06217; B3 ¼ 8.51175; B4 ¼ 1.60924; B5 ¼ 0.00596025; B6 ¼
0.00195653; B7 ¼ 0.00593021; B8 ¼ 0.849712; B9 ¼ 0.0119562;
B10 ¼ 0.00293719; B11 ¼ 0.274505; B12 ¼ 0.00304543;
B13 ¼ 0.588134; B14 ¼ 0.0355225; B15 ¼ 0.00455962. On the
other hand, the RS formula for Y¼RECLCO2 (%), with Y as dependent
variable, is written as a function of F1 ≡ PPCH4 (bar), F2 ≡ PPCO2 (bar),
F3 ≡ A/Feed (m2/(Nm3/h)), F4 ≡ PPERM (bar) and F5 ≡ TFEED (K) in Eq.
(11), with the following parameters: B0 ¼ 36.9348; B1 ¼ 3.45146;
B2 ¼ 39.4261; B3 ¼ 9.76926; B4 ¼ 0.00259308; B5 ¼ 0.0368232;
B6 ¼ 7.14655; B7 ¼ 0.0252322; B8 ¼ 0.0111323; B9 ¼ 0.0362618;
B10 ¼ 0.000736667;
B11 ¼ 0.482942;
B12 ¼ 0.108566;
B13 ¼ 1.18341; B14 ¼ 0.0242281; and B0 ¼ 0.00728997.
Y¼B0 þB1∙F1 þB2∙F2 þB3∙F3 þB4∙F4 þB5∙F21 þB6∙F24 þ
(B7∙F2 þB8∙F3 þB9∙F4 þB10∙F5)∙F1þ(B11∙F3 þ B12∙F4)∙F2 þ
(B13∙F4 þ B14∙F5)∙F3 þ B15∙F4∙F5
f ¼
n
X
ðF3 Þi $ðFFEED Þi
(12)
i¼1
3.5. Performance metrics
Design performances are evaluated with four metrics: lifecycle
cost (LCC), process footprint, carbon footprint and hydrocarbon
losses in the injection gas (IG).
3.5.1. Lifecycle cost (LCC)
Araújo et al. (2017) employed LLC as an economic indicator,
according to Eq. (13), where t is the project lifetime in y and E-ENG
is the equivalent amount of low heating value of ENG, in GJ,
exported per y by the FPSO. Since LCC is a function of CAPEX and
OPEX, an analysis of these parameters is necessary.
(10)
Y¼B0 þB1∙F2 þB2∙F3 þB3∙F4 þB4∙F21 þB5∙F22 þB6∙F23 þ(B7∙F2
þB8∙F3 þB9∙F4 þB10∙F5)∙F1þ(B11∙F3 þ B12∙F4)∙F2 þ (B13∙F4 þ
B14∙F5)∙F3 þ B15∙F4∙F5
(11)
The permeation of ethane, heavier hydrocarbons and nitrogen
were considered negligible. Another constraint was applied in the
injected gas, imposing the content of CO2 to be greater than 75%
mol. The decision variables are the values of factor F3 in the RS
models of all involved membrane stages. In order to force the NLP
optimization to remain within the range of adherence of RS models,
lower and upper bounds were imposed on the feasible range of
factor F3 in Eqs. (10) and (11) i.e. 0.4 m2/(Nm3/h) F3 2.1 m2/(Nm3/
h).
The objective function is defined in Eq. (12) as the total
permeation area of the MP train. This objective respond to decision
variables (F3)I, the permeation area per unit feed of all stages i,
where the feed flow rate of stage 1 (FFEED)1 (Nm3/h) is a fixed
parameter. More details about the optimization model are present
in Reis et al. (2017).
LCC ¼ (CAPEXOffshore þ t ∙ OPEX) / (t ∙ E-ENG)
(13)
3.5.1.1. CAPEX. Mass and energy balances obtained by process
simulation (Aspen HYSYS) are used for equipment sizing needed to
estimate CAPEX of six FPSOs processing conditions (five related to
the RNG evaluated feeds and the “Sizing FPSO” condition) for each
scenario. CAPEX is first estimated based on onshore plants, according to the procedure presented by Turton et al. (2012) with
CPCI corresponding to 2016 reference y and using a cost intensification factor of two applied due to the installation of facilities on
FPSOs e i.e. CAPEXOffshore ¼ 2∙ CAPEXOnshore e as described in Araújo
et al. (2017). Table 3 lists additional premises for economic analysis.
3.5.1.2. OPEX. OPEX was computed according to Araújo et al.
(2017), who state that, in the FPSO context, the main OPEX
component corresponds basically to the specified NG with 3% CO2
which is burnt for power generation to support the power demand
of CO2 separation technologies. In this work, OPEX evaluation also
A. de Carvalho Reis et al. / Journal of Cleaner Production 200 (2018) 269e281
275
4. Results and discussion
Table 3
Economic premises.
Item
Premise
CEPCI
NG Price
MDEA-PZ price
MP price
MP lifetime (y)
d
Project lifetime (y)
541.7 (2016)a
USD 6/Million BTUb
USD 1937.27/tb
USD 50/m2c
5c
350
20
Reservoir analysis results are used to obtain RNG flowrates and
compositions fed to each FPSO along 20 y of operation. Next, performance metrics results of each FPSO design are computed.
Detailed values of gas production curve, %CO2 of reservoir gas curve
and HYSYS process flow diagrams are available in Supplements A
and B (Supplementary Materials).
a
Value obtained from Jenkins (2017).
Prices of NG, Methyl Diethanolamine (MDEA) and Piperazine (PZ)
obtained from Reis et al. (2017) and Araujo et al. (2017) as
(0.464*1900USD/t MDEA þ 0.036*2419USD/t PZ)/0.5 for aqueous solution of 46.4% w/w MDEA and 3.6% w/w PZ.
c
Value obtained from H€
agg et al. (2017).
4.1. Gas production curve
b
Fig. 5 shows a FPSO production profile along 20 y of operation,
with Arps hyperbolic decline from the sixth y onwards, which
provides RNG flowrates along operation lifetime.
considered MP replacement (5 y) and was calculated for each scenario and their 5 feed conditions, over a y of production.
3.5.1.3. Exported natural gas (ENG). To complete the LCC calculation, it is necessary to obtain the amount of ENG generated during
the project lifetime (20 y). ENG was also computed in PJ (1015 J)
using LHV (Lower Heating Value) for each scenario and their five
feed conditions over a y of production.
3.5.2. Process footprint
Calculation of footprints for the MP, CA and Compressor (COMP)
skids were performed using the correlations presented in Araújo
et al. (2007), displayed in Table 4.
3.5.3. Carbon footprint
To compare the environmental impact caused by each scenario
for the five feed conditions, the Carbon Footprint parameter was
defined, which evaluates the amount of CO2 emitted (t) to generate
1 MW-e of ENG over a y of production, as presented in Eq. (14). The
calculation is based on data present in Araújo et al. (2017) that
establishes the emission of 517 g of CO2 for the production of 1
kWe-h.
Carbon Footprint (tCO2 / MWe ENG) ¼ (CO2 emitted / EeENG)
4.2. Curve of reservoir CO2 content
Fig. 6 shows the CO2 content profile in the reservoir along its
lifetime (20 y) for the two scenarios. The first scenario considers no
occurrence of CO2 retention in the source rocks from the injected
CO2. That is, all injected CO2 is added to the gas hold-up in the
reservoir. For the second scenario, it is assumed that 60% of the
injected CO2 is trapped in the source rock not reaching the reservoir
gas hold-up. Consequently, Scenario 2 generates a profile with
lower CO2 content values when compared to values of Scenario 1.
4.3. RNG flow rate to FPSO
RNG flow rate is computed at five points of the production
curve: 1, 5, 10, 15 and 20 y. Figs. 5 and 6 provide FPSO feed flowrates
and respective CO2 content for the five points. It is considered that
the increase in CO2 content occurs at the expense of equivalent
reduction in the methane fraction of the RNG while the remaining
components are maintained at constant fractions. Additional RNG
conditions are from Reis et al. (2017) and Araújo et al. (2017).
Table 5 shows RNG conditions for the five evaluated points.
(14)
3.5.4. HC losses
Hydrocarbon Losses (HC Losses) are indirectly measured by the
energy fraction associated to raw NG (E-RNG) stream that is wasted
in the injected gas (E-IG), according to Eq. (15). The LHV of the IG is
used to compute HC Losses.
HC Losses (%) ¼ (E-IG / EeRNG) ∙ 100%
(15)
Fig. 5. Gas production curve of a FPSO.
Table 4
Footprints (FP) correlations of main gas processing equipment.
Skid
Correlation (FP in m2)
Nomenclature
a
Skid ¼ 0:00296,A
FPMP
MP
AMP ¼ MP area (m2)
MP
Compressora
CAa
a
Araújo et al. (2017).
Skid
FPCompressor
¼ 0:8017,ðPowerÞ2=3
pffiffiffi
Skid
FPCA ¼ 18 ððDAbs þ DReg Þ= 2Þ2
Power ¼ compressor power (kW)
DAbs ¼ diameter (m) of CA absorber
DReg ¼ diameter (m) of CA regenerator
276
A. de Carvalho Reis et al. / Journal of Cleaner Production 200 (2018) 269e281
4.5.1. LCC
The calculation of LCC requires the values of CAPEX (“Sizing”
FPSO), OPEX and ENG, those last two obtained for the lifetime of the
project.
Fig. 6. Reservoir %CO2 versus time profile.
4.4. FPSO designs
Five FPSO designs were performed for each of the two scenarios,
accordingly to RNG conditions presented in Table 5, totaling ten
designs for gas processing on the FPSO. Each design includes skids
for HCDPA, CO2 separation (MP and CA) and gas compression (IG
and ENG). In both scenarios, the final FPSO design is the assemblage
of the largest equipment pieces for every skid along the five RNG
feed conditions (Table 5) in each scenario: MP modules, CA units,
compression skids and respective auxiliary equipment (heat exchangers and vessels included in the skids). This resulting FPSO
design, for each scenario (0% and 60% CO2 storage) is denominated
“Sizing FPSO” and is a flexible platform able to operate at the most
stringent feed flow rate (y 1 to 6 in the Production Plateau) and CO2
composition condition, covering the five evaluated RNG feeds.
Supplements C and D (Supplementary Materials) present
simulation results for equipment sizing. Tables E12 and E13 of
Supplement E (Supplementary Materials) show equipment sizing
results for each feed condition and for 0% and 60% CO2 storage
cases. It is worth noting that, for both cases, “Sizing FPSOs” consist
of equipment sized at Points 1 and 2 at which production flow rate
is at its maximum value (i.e., first and fifth y). Since production flow
rates at these points are equal, as they define the Production
Plateau, it can be concluded that “Sizing FPSO” is dictated by the
largest flowrate at the Production Plateau with 6,000,000 sm3/
d and CO2 content between 44.72% and 46.53% mol.
4.5. Performance metrics
FPSOs performances are analyzed according to four metrics:
Lifecycle Cost (LCC), process footprint, carbon footprint and hydrocarbon losses. Detailed results of performance metrics are
available in Supplement F (Supplementary Materials).
4.5.1.1. CAPEX. From equipment sizing at six FPSO conditions (five
related to the RNG evaluated feeds listed in Table 5 and the “Sizing
FPSO”) for each scenario (0% and 60% CO2 storage), their respective
CAPEX were calculated with the procedure proposed by Turton
et al. (2012) including the inventories of MDEA/PZ for CA solvent.
Fig. 7a shows the decreasing profiles of CAPEX resulting from
changing conditions of flow rate and CO2 content in RNG. CAPEX
decline is justified by the reduction in equipment size due to the
decrease of RNG flow rate. To cope with this, a specific CAPEX-1
index is proposed as the CAPEX divided by the energy content of
RNG, USD/MW RNG. However, since a single design is to be chosen
for the FPSO operating along the entire lifetime (the “Sizing FPSO”),
the implemented CAPEX is constant, independently of the CO2
content in RNG. Therefore, a specific CAPEX-2 index is proposed
dividing the CAPEX of the “Sizing FPSO” (MUSD 235 for 0% CO2
storage, and MUSD 233 for 60% CO2 storage) by RNG energy content
of each point in gas production curve.
Fig. 7a evidences that CO2 content does not impact FPSO design,
which is dominated by RNG flowrate: the farther in the production
curve is the sizing point used for FPSO design, the smaller the
production flow rate and the smaller the calculated CAPEX. Fig. 7b
indicates that specific CAPEX-1 index suffers mild impact by CO2
content in RNG, confirming that equipment size is mainly affected
by RNG flowrate. A minimum specific CAPEX-1 value is presented at
the third sizing point (10 y of operation). Fig. 7c shows that the
specific CAPEX-2 index is practically the same for the first production points, since the “Sizing FPSO” is mainly composed by the
FPSOs design associated to these feed conditions as the largest
pieces of equipment resulted from the conditions of largest feed
flow rates. However, specific CAPEX-2 increases significantly with
RNG flow reduction after the Production Plateau, indicating that
the plant is oversized for flow rates past the Production Plateau
period. Differently from CAPEX and specific CAPEX-1, at the end of
lifetime specific CAPEX-2 presents z14% reduction for Scenario 2
(52.68% CO2 in RNG, 1,260,000 sm3/d) in comparison to Scenario 1
(64.16% CO2 in RNG, 1,260,000 sm3/d). Specific CAPEX-2 is nearly
invariant for the two scenarios at the Production Plateau
(6,000,000 sm3/d, % CO2). This behavior confirms that CAPEX is
more impacted by feed flow rate than by the CO2 content in the
RNG feed.
Fig. 8 illustrates the investment costs related to MP, CA and
COMP skids as components of the Total CAPEX. Skid costs
comprehend the equipment present: (a) MP skid includes total MP
area; (b) CA skid assembles absorption and stripping columns, heat
exchangers, vessels and pumps; and (c) COMP skid comprises
Table 5
RNG Conditions at evaluated gas production points.
Parameter
Value
Temperature (ºC)
Pressure (bar)
Point Number
y
RNG
Flow Rate (106 sm3/d)
RNG composition (%mol)
50
15
1
1
6.00
2
5
6.00
N2 0.24, C2 4.90, C3 3.44, i-C4 1.25, n-C4 2.65, i-C5 0.94, nC5 1.59, n-C6 1.56
Scenario 1
CO2
44.72
51.01
CH4
38.71
32.42
Scenario 2
CO2
44.25
46.53
CH4
39.18
36.90
3
10
3.35
4
15
1.93
5
20
1.26
58.00
25.43
49.43
34.00
61.83
21.60
51.34
32.09
64.16
19.27
52.68
30.75
A. de Carvalho Reis et al. / Journal of Cleaner Production 200 (2018) 269e281
Fig. 7. CAPEX profiles for five FPSO designs: (a) total CAPEX; (b) specific CAPEX-1; and
(c) specific CAPEX-2 [Two scenarios: (1) 0% CO2 storage; (2) 60% CO2 storage; Specific
CAPEX-1: Total CAPEX at corresponding RNG feed conditions/RNG energy content;
Specific CAPEX-2: “Sizing FPSO” CAPEX (MUSD 235 for 0% CO2 storage, and MUSD 233
for 60% CO2 storage)/RNG energy content; 1 ¼1st y, 2 ¼ 5th y, 3 ¼ 10th y, 4 ¼ 15th y and
5 ¼ 20th y].
vessels, heat exchangers, drivers and compressors. Fig. 8a and c
shows that COMP is the dominant CAPEX, which explain invariance
of Total CAPEX upon CO2 reduction observed in Fig. 7a, while
compressor power (and hence CAPEX) decreases steeply with
decreasing gas flowrate. In the first y, MP and CA CAPEX's are in the
same order of magnitude and correspond only to z10% of COMP
CAPEX in both scenarios. MP CAPEX, similarly to COMP, is nearly
invariant with CO2 content in RNG, being dominated by RNG
flowrate.
However, as shown in Fig. 8c, CAPEX of CA is affected by both
flow rate and CO2 composition. CA cost decreases with increasing
CO2 content in RNG feed for a given production point. This behavior
results from MP, whose retentate is poorer in CO2 with increasing
CO2 content in RNG as seen in the GAMS optimization results in
Supplement D (Supplementary Materials). Hence, CA has lower CO2
content in its feed, demanding smaller columns and less costs. For
points 4 and 5 in Fig. 8c (respectively, 61.83% and 64.16% CO2 in
277
Fig. 8. CAPEX of skids for five FPSO designs: (a) COMP CAPEX; (b) MP CAPEX; and (c)
CA CAPEX [Two scenarios: (1) 0% CO2 storage; (2) 60% CO2 storage, where 1 ¼1st y,
2 ¼ 5th y, 3 ¼ 10th y, 4 ¼ 15th y and 5 ¼ 20th y, Design stands for the “Sizing FPSO”].
RNG), in Scenario 1, MP already specifies NG, dismissing the need of
the CA polishing step. The MP and CA costs in the “Sizing FPSO” are
equal to those of Point 1 in both scenarios, since MP area and CA
size are the same in the two FPSOs sized on the Production Plateau.
Since this work adopts hybrid MP þ CA CO2 removal, it must be
highlighted that Fig. 8b and c do not aim to compare costs of MP
and CA because they perform different services, processing feeds
with different %CO2. These figures also present the %CO2 of each
skid feed. MP and CA are hence complementary and do not
compete in this study. It is worth mentioning that, in all cases, the
added costs of MP and CA represent less than 20% of Total CAPEX,
with COMP imposing the main capital costs.
4.5.1.2. OPEX. OPEX as a function of time (Fig. 9), for each feed
condition, shows profile similar to that observed for gas production.
To find the value of OPEX over the 20 y of production, a linear
regression was performed yielding OPEX as a function of gas production in Eq. (16). The parameters of the linear regression are
given in Table 6.
278
A. de Carvalho Reis et al. / Journal of Cleaner Production 200 (2018) 269e281
Fig. 9. OPEX time profile [Two scenarios: (1) 0% CO2 storage; (2) 60% CO2 storage; 1 ¼ 1st y, 2 ¼ 5th y, 3 ¼ 10th y, 4 ¼ 15th y and 5 ¼ 20th y].
Table 6
Regression parameters for OPEX profile in Eq. (16).
Table 7
Regression parameters for ENG and E-ENG profiles in Eq. (17).
Parameter
Scenario 1
Scenario 2
Parameter
Scenario 1
Scenario 2
a
b
0.260
8.521
0.407
8.406
a
b
5.413
7.266
2.396
7.205
OPEX ¼ a þ b ∙ gas production
(16)
4.5.1.3. Exported natural gas - ENG. The calculated profiles of ENG
and E-ENG (Fig. 10) followed the same procedure for estimating
OPEX, obtaining the linear regression of Eq. (17) for ENG and E-ENG
with parameters shown in Table 7.
ENG ¼ a þ b ∙ gas production
(17)
Table 8 summarizes economic analysis values, making it
possible to calculate LCC, according to Eq. (13). It is observed that
Scenario 2 presents a lower cost. LCC values found in this work are
compatible with those of Reis et al. (2017) for MP þ CA hybrid CO2
removal, which ranged from 1.5 to 1.8 USD/GJ-ENG. The authors,
however, considered in their analysis the maximum production of
6,000,000 sm3/d along the entire project lifetime.
4.5.2. Process footprint
Using MP total area in the MP þ CA process, the diameter of the
Table 8
Economic analysis values.
Parameter
Scenario 1
Scenario 2
“Sizing FPSO” CAPEX (MUSD)
OPEX (MUSD)
ENG (PJ)
LCC (USD/GJ-ENG)
235
801
373
2.15
233
794
428
1.86
CA columns and compressor power for the six FPSO designs in each
scenario, the footprints associated to these skids were calculated by
correlations in Table 4 as proposed in Araújo et al. (2017) and are
shown in Fig. 11a. Total Footprint decays mainly from the decrease
in equipment size resulting from reduction of RNG flowrate. Since
Footprint is directly related to equipment size, the Footprint profiles behave like the CAPEX profile: footprints decrease with
decreasing RNG flowrates. However, Scenario 2 (lower CO2 content
in RNG) shows slightly higher footprint after the Production
Plateau, being more pronounced after the 10th y of operation.
Similarly to the specific CAPEX-1 and specific CAPEX-2 indexes,
Fig. 11b and c presents specific Footprint-1 (Total Footprint divided
Fig. 10. ENG time profile [Two scenarios: (1) 0% CO2 storage; (2) 60% CO2 storage; 1 ¼1st y, 2 ¼ 5th y, 3 ¼ 10th y, 4 ¼ 15th y and 5 ¼ 20th y; PJ ¼ 1015 J].
A. de Carvalho Reis et al. / Journal of Cleaner Production 200 (2018) 269e281
Fig. 11. Footprint profiles for five FPSO designs: (a) total Footprint; (b) specific
Footprint-1; and (c) specific Footprint-2 [Two scenarios: (1) 0% CO2 storage; (2) 60%
CO2 storage; specific Footprint-1: Total Footprint/RNG energy content; specific
Footprint-2: Sizing FPSO Footprint (1582 m2 for 0% CO2 storage and 1558 m2 for 60%
CO2 storage)/RNG energy content; 1 ¼1st y, 2 ¼ 5th y, 3 ¼ 10th y, 4 ¼ 15th y and
5 ¼ 20th y; Design stands for the “Sizing FPSO”].
by RNG energy content) and specific Footprint-2 (“Sizing FPSO”
Total Footprint - 1582 m2 for 0% CO2 storage and 1558 m2 for 60%
CO2 storage, divided by RNG energy content). After the end of the
Production Plateau, Scenario 2 implies lower specific footprints
than Scenario 1 (~10%).
Footprint is more influenced by RNG feed flowrate than by its %
CO2, and specific Footprint-2 is most impacted by the production
curve, corroborating the conclusion that the gas processing plant
will be oversized for flow rates below 6,000,000 sm3/d (i.e., after
the Production Plateau). Fig. 12a, b and 12c decompose the footprint by equipment assemblage. MP and COMP skids are slightly
sensitive to RNG CO2 content, being mainly impacted by its flow
rate, while CA skid is influenced by both variables.
Although CAPEX of CA is larger than CAPEX of MP (Fig. 11b and
c), the profiles are reversed for Footprints (Fig. 12b and c). The
“Sizing FPSO” has the same sizes of MP and CA skids of Point 1 in
both scenarios, so their respective footprints are also the same. MP
279
Fig. 12. Footprint values for five FPSO designs: (a) total Footprint; (b) specific
Footprint-1; and (c) specific Footprint-2 [Two scenarios: (1) 0% CO2 storage; (2) 60%
CO2 storage; 1 ¼1st y, 2 ¼ 5th y, 3 ¼ 10th y, 4 ¼ 15th y and 5 ¼ 20th y; Design stands for
the “Sizing FPSO”].
and CA skids together represented only 30% of Total Footprint, with
COMP skids being responsible for the main footprint. Similarly to
CAPEX, Fig. 12b and c do not aim to compare the footprints of MP
and CA skids, as they perform different and complementary services, processing feeds with different CO2 contents. Fig. 12b and c
also present the %CO2 of each skid feed.
4.5.3. Carbon footprint
Dividing FPSO CO2 emissions by ENG energy is a way of
smoothing the impact of feed flow rate variation, evidencing the
huge influence of CO2 content in the feed. This, in fact, occurs, since
the point-to-point analysis reveals that higher %CO2 feeds (Scenario
1) generate larger CO2 emissions to achieve ENG specifications (3%
CO2), as can be seen in Fig. 13.
4.5.4. HC losses
Fig. 14 presents HC losses. For Scenario 1 (higher %CO2 in RNG),
280
A. de Carvalho Reis et al. / Journal of Cleaner Production 200 (2018) 269e281
and intermediate CO2 content. Both specific CAPEX and footprint
have similar behaviors: constant at the Production Plateau of gas
producing curve and increasing with reduction in RNG flowrate
beyond the fifth y. MP and COMP are mildly sensitive to %CO2 of
RNG but are highly impacted by RNG flowrate. CA is affected by
both variables. On entire production curve, HC losses were low e a
benefit of hybrid MP þ CA e from 4% to 5% for all FPSO designs.
Acknowledgements
OQF Araújo and JL de Medeiros acknowledge financial support
from PETROBRAS S.A. (0050.0096933.15.9) and CNPq-Brazil
(309640/2016-4 and 311076/2017-3).
Fig. 13. Carbon Footprint [1 ¼1st y, 2 ¼ 5th y, 3 ¼ 10th y, 4 ¼ 15th y and 5 ¼ 20th y].
Appendix A. Supplementary data
Supplementary data related to this article can be found at
https://doi.org/10.1016/j.jclepro.2018.07.271
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Fig. 14. HC losses in injection gas (IG) [1 ¼1st y, 2 ¼ 5th y, 3 ¼ 10th y, 4 ¼ 15th y and
5 ¼ 20th y].
HC losses are higher in Points 2 and 3. However, for Points 4 ad 5
(final period of the production curve, i.e., smaller RNG flowrates),
Scenario 2 shows higher HC losses than Scenario 1 (~5% higher).
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benefit granted by the hybrid MP þ CA technology, as presented by
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5. Conclusions
This work evaluated the design of FPSOs oriented by lifetime
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