NET ZERO BASIN Pilot field development - SPE London
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NET ZERO BASIN
Pilot eld development
1
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Pilot field summary West 21/27a-6 East
● Fully appraised, seven reservoir penetrations,
5 Reserves Summary
three tested wells, core and fluid samples held
by Orcadian, two high quality 3D seismic
Pilot and
Pilot South 21/27a-6
Volumetric calculations have been undertaken for the Pilot discovery. A most likely
STOIIP for Pilot and Pilot South combined of 263 MMstb was estimated, uncertainty
assessments suggested a range of +/-8%. This is a relatively narrow range, and
survey reflects the fact the discovery is well appraised and seismic imaging is of high
quality. A hydrocarbon pore thickness map was created as part of the volumetric
calculations, see Fig. 5.1.
● Very high quality sandstone turbidite reservoir,
50 mSec TWT
34% porosity, 2 to 10 darcies of permeabilit HCPV
Data provided courtesy of WesternGeco
● Significant oil in place: 263 MMbbls; audited 2P
500m
reserve of 79 MMbbls, based on a low salinity
polymer flood of the reservoi
● Variable quality oil from 12º to 17º API, 160 cP
to 1,200 c
● Shallow water (c. 80m), 140 kms due East
from Aberdeen, c. 40 kms from Gannet et al
2
s
P
r
y
Pilot field development plan
FPP Jack-up
FPSO
WHP
3
A project delivering on the Government’s Net Zero
agenda
● Many opportunities to 35
reduce emissions identifie Scope 1 Scope 2 Benchmarks
30
● Integration of aggressive
process heat management Pilot field development
25
with high efficiency back-
up power generation and
Kg CO2/bbl
20
electrification has the Shorter field life
and lower fluid
potential to drive handling
15 30
emissions down by over
24.7
80 21 Heat pumps and
10
reciprocating gas FPP wind and wave
● Local wind farm power, 14.4 engines power unit
with highly efficient back- 5
2.3
up gas engines, reduces 5.9
0.4 2.6 1.4
emissions as low as 0
Typical North Sea Pilot Polymer Process & Grid Single Two
connection to a future grid viscous oil 2018 water ood ood power from wind wind
with half of today’s CO2 average generation year 2 turbine turbines
optimised
intensity
Estimates from recently submitted CSR Addendum prepared with Crondall Energy. These numbers are CO2e 4
fl
%
fl
d
100000
150000
200000
250000
0
100000
150000
200000
250000
0
50000
50000
fi
fl
y
Water Flood
Polymer Flood
J182638 DOI: 10.2118/182638-PA Date: 8-March-18 Stage: Page: 4 Total Pages: 20
J182638 DOI: 10.2118/182638-PA Date: 8-March-18 Stage: Page: 4 Total Pages: 20
fl
Water Flood
Polymer Flood
Close to end WF End WF
End WF Close
PVI to end WF
= 0.15 PVI = 0.5 End WF
End WF PVI = 0.15 PVI = 0.5
0.9
0.9
0.8 0.8
0.8 0.8
0.8
0.8
0.7 0.7
0.7 0.7 0.7
PVI = 0.15 PVI = 0.19 0.7
PVI = 0.15 PVI = 0.19
0.6
0.6 0.6 0.6
0.6 0.6
0.5 0.5
0.5 0.5
0.5
0.5
PVI = 0.49 0.4 PVI = 0.58 0.4
PVI = 0.49 0.4 PVI = 0.58 0.4
r
0.4
0.4
0.3
0.3 0.3
0.3
0.3
0.2
0.3
0.2
(a) (b) (c)
(a) (b) (c)
Fig. 4—Oil-saturation maps at the end of waterflooding (WF) and during polymer flooding for (a) Experiment No. 2, (b) Experiment
No. 6, and (c) Experiment No. 7. Fig. 4—Oil-saturation maps at the end of waterflooding (WF) and during polymer flooding for (a) Experiment No. 2, (b) Experiment
Why polymer reduces CO2
No. 6, and (c) Experiment No. 7.
Waterflood Modeling Using Darcy-Type Simulations Oil Recovered, MMbbls
Waterflood Modeling Using Darcy-Type Simulations
Methodology. The objective of this subsection is to verify whether the multiphase extension of Darcy’s law can predict the viscous-
Methodology.
fingering patterns observed experimentally. Clearly, a continuum Darcy-type formulation is not The objective
appropriate of this subsection
if viscous fingers areistrig-to verify whether the multiphase extension of Darcy’s law can predict the viscous-
gered below the scale of a few pore sizes, whereas it is more likely to be validfingering patterns
when fingers areobserved
generated experimentally.
at a macroscopic Clearly,
scale.a continuum Darcy-type formulation is not appropriate if viscous fingers are trig-
Nevertheless, very few validation studies are available in the literature. From the X-ray below the
gered images at early of a few
scaletimes, pore sizes,
the viscous whereas
fingers have ait is more likely to be valid when fingers are generated at a macroscopic scale.
20
40
60
80
0
100
fine dendritic aspect and a typical width of a few millimeters. Therefore, we do Nevertheless, very few validation
not expect a Darcy-type model tostudies
capturearetheavailable
experi- in the literature. From the X-ray images at early times, the viscous fingers have a
mental fingering patterns accurately, but we would like to verify how closely it can fine
reproduce aspect
dendriticthose and a typical width of a few millimeters. Therefore, we do not expect a Darcy-type model to capture the experi-
patterns.
mental
In this context, the most relevant study is that of Riaz et al. (2007), where several fingering patterns
experiments accurately,
were performed on but we would
a strongly like to verify how closely it can reproduce those patterns.
water-
0
wet Berea core sample, first to measure steady-state relative permeability functions In andthis context,
then the most
to measure saturation
relevantprofiles
study isand Riaz et al. (2007), where several experiments were performed on a strongly water-
thatoilofre-
covery during unsteady-state displacements. Different oil viscosities up to 303 cp wetand Berea
injection sample,
corerates up tofirst
3 cmto3 measure steady-state relative permeability functions and then to measure saturation profiles and oil re-
/min were used to iden- 3
tify the onset of instability, which was correctly predicted by the conventional covery
Darcy-type during unsteady-state
model dependentdisplacements.
on steady-stateDifferent
relative oil viscosities up to 303 cp and injection rates up to 3 cm /min were used to iden-
tify the onset of instability, which
permeabilities. However, the average saturation profiles of the unstable experiments could not be reproduced explicitly by high-resolu-was correctly predicted by the conventional Darcy-type model dependent on steady-state relative
permeabilities. However, the average
tion Darcy-type simulations modeling viscous fingers. Thus, it was concluded that the continuum Darcy-type model does not well pre- saturation profiles of the unstable experiments could not be reproduced explicitly by high-resolu-
tion Darcy-type
dict the flow regime with fully developed instabilities. In Sharma et al. (2012), another study was simulations
performedmodeling
on mildlyviscous
water-wet fingers.
2D Thus, it was concluded that the continuum Darcy-type model does not well pre-
micromodels. The average saturation profiles obtained from the waterflooding ofdict the flow
viscous oils regime
were better fully developed
withreproduced instabilities.
by a scaling law In Sharma et al. (2012), another study was performed on mildly water-wet 2D
micromodels. The average saturation profiles obtained
dependent on statistical theory rather than by a Buckley-Leverett profile, but no attempt was made to simulate the displacement with a from the waterflooding of viscous oils were better reproduced by a scaling law
2D Darcy-type model. dependent on statistical theory rather than by a Buckley-Leverett profile, but no attempt was made to simulate the displacement with a
In contrast with the previously discussed studies, the experiments considered in Darcy-type
2Dthis work were model.
performed under nonwater-wet con-
ditions and using even higher oil viscosities. As mentioned in the Introduction section, contrast
Inthis should with
leadthe
to previously
more severe discussed studies, the experiments considered in this work were performed under nonwater-wet con-
viscous fingering.
r
Nevertheless, a much lower injection velocity was used in these experiments compared ditionswithand those
using ofeven
Riazhigher
et al.oil viscosities.
(2007), which Asshouldmentioned
at in the Introduction section, this should lead to more severe viscous fingering.
Nevertheless,
least partially offset the effects of higher viscosity ratio and nonwater-wet conditions. much lower
Even ifa X-ray imagesinjection
suggest velocity was used in these experiments compared with those of Riaz et al. (2007), which should at
strong fingering
least
effects, it is worthwhile to test the validity of the generalized Darcy’s law on a set of partially
experiments offsetthat
thediffers of higher viscosity
effectssignificantly from other ratio and nonwater-wet conditions. Even if X-ray images suggest strong fingering
published studies. effects, it is worthwhile to test the validity of the generalized Darcy’s law on a set of experiments that differs significantly from other
published
In the following, we apply a similar methodology to that of Riaz et al. (2007), but because studies.
of the absence of steady-state relative per-
meability measurements, we use a 3D quasistatic PNM to infer relative permeability the following,
In and capillary pressure a similar
we applycurves. methodology
Because the rockto that of Riaz et al. (2007), but because of the absence of steady-state relative per-
meability measurements, we use
wettability is not known precisely, we assume that the aging process has led to a wettability state ranging from mildly water-wetPNMa 3D quasistatic to to infer relative permeability and capillary pressure curves. Because the rock
mildly oil-wet, including intermediate-wet and mixed-wet states. A screening study wettability
is performed is not known
with the quasistatic
precisely, PNMwe assume
code tothatgen- the aging process has led to a wettability state ranging from mildly water-wet to
200
erate multiple sets of relative permeability and capillary pressure curves. Each setmildly
of curvesoil-wet, including
is then provided intermediate-wet
as an input to our mixed-wet states. A screening study is performed with the quasistatic PNM code to gen-
andparallel
IHRRS (Moncorgé et al. 2012; Jauré et al. 2014) that is run in two dimensions eratewith amultiple sets of relative
high-resolution permeability
discretization. and capillary
Although direct pressure curves. Each set of curves is then provided as an input to our parallel
measurement of steady-state relative permeabilities is of course preferable, this IHRRS (Moncorgé
approach et al. 2012;
is deemed useful Jauré
whenetsuchal. 2014)
data that
are is run in two dimensions with a high-resolution discretization. Although direct
not available. measurement of steady-state relative permeabilities is of course preferable, this approach is deemed useful when such data are
not available.
Quasistatic PNM. The 3D PNM is directly extracted from 3D microcomputed-tomography images of a sample of Bentheimer sand-
stone. A maximal ball algorithm (Silin and Patzek 2006; Dong and Blunt 2009) isQuasistatic
applied to reconstruct
PNM. The a3D PNM is
network directly extracted
equivalent in terms from 3D microcomputed-tomography images of a sample of Bentheimer sand-
of topology and transmissibility. The reconstructed pore network contains 61,214 stone. A maximal
pores and 151,186ball algorithm
throats. (Silin and Patzek
Each pore/throat 2006; Dong and Blunt 2009) is applied to reconstruct a network equivalent in terms
is charac-
terized by an inscribed radius that controls the threshold entry pressure, a flow conductance
of topologythat transmissibility.
and defines the local The reconstructed
pressure drop caused pore network contains 61,214 pores and 151,186 throats. Each pore/throat is charac-
by viscous effect and flux, an effective length, and a volume. More details of theterized by an
extracted inscribed
pore networkradius
can bethat controls
found the threshold
in Table 2, and entry pressure, a flow conductance that defines the local pressure drop caused
by viscous effect and flux, an effective length, and a volume. More details of the extracted pore network can be found in Table 2, and
environmental impacts
an illustration is provided in Fig. 5.
For our quasistatic pore-network simulations, we use the model of Valvatne and an illustration
Blunt (2004),is provided
which is in Fig.
also used
5. to verify the qual-
ity of the generated network. To do this, we compute the capillary pressure and the For our quasistatic
relative permeabilitypore-network
curves that simulations,
we compare we withuse the model of Valvatne and Blunt (2004), which is also used to verify the qual-
the generated
experimental measurements. Ideally, we would like to use measurements made onitytheofsamples of Experiment To do
network.Nos. this,
2, 6, andwe7,compute
but such the capillary pressure and the relative permeability curves that we compare with
data are not available, and we therefore use measurements from the literature experimental measurements.
(Oren et al. 1998). Ideally,
A very good we would
agreement is like to use measurements made on the samples of Experiment Nos. 2, 6, and 7, but such
found
data are not available, and we therefore use measurements from the literature (Oren et al. 1998). A very good agreement is found
4 2018 SPE Journal
400
4 2018 SPE Journal
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time and for much less energ
600
Water Injected, MMbbls
● Field life significantly shortened with polyme
Water Flood
Polymer Flood
800
1000
● Fluid handling requirements massively reduced by using polyme
Production pro les for polymer ood and water ood from Orcadian Energy reservoir simulations; plot of recoverable oil vs. water injected based upon the same simulations.
● Polymer boosts recovery so there are more barrels produced in less
● Pumping wells and injecting fluid are the key drivers of CO2 emissions
● Polymer use significantly enhances project economics while minimising
1200
5
Emissions, by cause and scope
CSR initial polymer scenario
Power Demand kW
25,000 Power Demand kW Power Demand kW
20,000 20,000
Power Demand kW 18,000 18,000
20,000
20,000
16,000 16,000
18,000
Power demand kW
14,000 14,000
16,000
15,000
Power demand kW
Power demand kW
14,000 12,000 12,000
Power demand kW
12,000 10,000 10,000
10,000
10,000
8,000 8,000
8,000
6,000 6,000
6,000
5,000 4,000 4,000
4,000
2,000
2,000 2,000
00 0 0
Year
Year 11 Year
Year 2 2YearYear 3
3 Year 4Year 4
Year 5 Year
Year 56 Year7 6 Year
Year Year 7
8 Year Year 8
9 Year 10Year
Year 911 Year 1 Year 2 Year 3 Year 4 Year 5 Year 6 Year 7 Year 8 Year 9 Year 1 Year 2 Year 3 Year 4 Year 5 Year 6 Year 7 Year 8 Year 9
FPSO JACK UP WHP-A (South) WHP-B (North) Fluids Transfer Pump (WHP)
FPSO JACK UP WHP-A (South) WHP-B (North) Fluids Transfer Pump (WHP) FPSO JACK UP WHP-A (South) WHP-B (North) Fluids Transfer Pump (WHP)
FPSO Heat pumps Jack-up
WHP-A (South) WHP-B (North) Transfer Pump
Pilot East gas balance
7 ● Base case from September 2020 CSR submission, focus had been on quantifying rather than driving
/day) and Gas in Pilot East (bcf)
6 down emissions (included some worst case assumptions e.g. downhole pump power demands
5
4 ● Adoption of polymer flood had reduced both fluid handling and field life
3
2
1 6
)
Process optimisation
● Industrial heat pump
● Energy recovery
systems
7
s
Power Generation
● Wartsila, dual fuel gas
reciprocating engine
● “Smart heat recovery”
system
8
s
Emissions, by cause and scope
Process optimisations and recips
Power Demand kW
25,000 Power Demand kW Power Demand kW
20,000 20,000
18,000 18,000
20,000
16,000 16,000
Power demand kW
14,000 14,000
15,000
Power demand kW
Power demand kW
12,000 12,000
10,000 10,000
10,000 8,000
8,000
6,000 6,000
5,000 4,000 4,000
2,000 2,000
0 0 0
Year 1 Year 2 Year 3 Year 4 Year 5 Year 6 Year 7 Year 8 Year 9 Year 10 Year 11 Year 1 Year 2 Year 3 Year 4 Year 5 Year 6 Year 7 Year 8 Year 9 Year 1 Year 2 Year 3 Year 4 Year 5 Year 6 Year 7 Year 8 Year 9
FPSO Heat pumps Jack-up FPSO JACK UP WHP-A (South) WHP-B (North) Fluids Transfer Pump (WHP) FPSO JACK UP WHP-A (South) WHP-B (North) Fluids Transfer Pump (WHP)
WHP-A (South) WHP-B (North) Transfer Pump
Pilot East gas balance
7
/day) and Gas in Pilot East (bcf)
6 ● Crondall reshaped process and power generation with the intention of driving down emission
5
4 ● The combination of heat pumps and high efficiency generation is the key to reducing emissions
3
2
1 9
s
SYSTEM OVERVIEW
• Platform
– North Sea design for 15m Hs sites
– Panel based semi submersible
– Makes use of harbour effect for transfer
– Hydrogen energy storage can be accommodated
• Turret Mooring System
– Vanes into wave direction
• WTG yaws independently
– Multi point catenary mooring system
– Disconnectable if required
• Wind Turbine Generator (WTG)
– Turbine agnostic
– 12 to 15MW (to be assessed)
• Wave Energy Convertor
– 4 off, from 500kW to 1MW each (to suit site)
– Wave energy converted to motion by absorber
– Mechanical motion converted to electricity via oil
hydraulic Power Take Off (PTO) system
Intro 10Emissions, by cause and scope
Local wind and wave power
Power Demand kW
Power Demand kW
25,000 Power Demand kW
20,000
20,000
18,000
18,000
20,000
16,000
16,000
Power demand kW
14,000
14,000
15,000
Power demand kW
12,000
Power demand kW
12,000
10,000
10,000
10,000 8,000
8,000
6,000
6,000
5,000 4,000
4,000
2,000
2,000
0 0
Year 1 Year 2 Year 3 Year 4 Year 5 Year 6 Year 7 Year 8 Year 9 Year 10 Year 11 0 Year 1 Year 2 Year 3 Year 4 Year 5 Year 6 Year 7 Year 8 Year 9
Year 1 Year 2 Year 3 Year 4 Year 5 Year 6 Year 7 Year 8 Year 9
FPSO Heat pumps Jack-up FPSO JACK UP WHP-A (South) WHP-B (North) Fluids Transfer Pump (WHP)
FPSO JACK UP WHP-A (South) WHP-B (North) Fluids Transfer Pump (WHP)
WHP-A (South) WHP-B (North) Transfer Pump
Pilot East gas balance
7 ● Local renewable power eliminates the need for a gas import pipeline and halves
f/day) and Gas in Pilot East (bcf)
6
5
emissions agai
4
3 ● Unit will be a 12MW wind turbine with a 2MW wave power generator from FPP
2
1 11
nComparison with global oil production emissions
Viscous oil doesn’t have to mean high emissions
● To be comparable with this Stanford University dataset, to our Scope 1 & 2 emissions we
have added
● Scope 3 emissions from offshore logistics, oil transportation to the refinery, and polymer productio
Pilot emissions
will lie in the ● Estimates of emissions during the exploration and development phases (done using the Stanford tool)
lowest 5% of
● Pilot field comparable emissions are 1.4 gCO2eq/MJ with a single wind turbine
global oil
production
Masnadi et al (2018). Global carbon intensity of crude oil production. Science. 361. 851-853. 10.1126/science.aar6859. 12
:
nAuditor: PKF Littlejohn Broker: WH Ireland Banker: Barclays
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1 Cornhill, President Kennedylaan 19 6th Floor 60 Gracechurch Street, London,
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email: steve.brown@orcadian.energ
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London, United Kingdom, EC3V 0HR twitter: @orcadian_energ
website: https://orcadian.energy
ORCADIAN ENERGY PLC 13
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