co2 injection into depleted gas reservoirs

8/3/2019 CO2 Injection Into Depleted Gas Reservoirs

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SPE 123788

CO2 Injection Into Depleted Gas ReservoirsHrvoje Galic, Steve Cawley, and Simon Bishop, BP, and Steve Todman and Frederic Gas, Petroleum Experts Ltd.

Copyright 2009, Society of Petroleum Engineers

This paper was prepared for presentation at the 2009 SPE Offshore Europe Oil & Gas Conference & Exhibition held in Aberdeen, UK, 8–11 September 2009.

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 beenreviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, itsofficers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission toreproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.

Abstract

The paper presents:

• the challenges associated with modelling CO2 injection into depleted reservoirs and

• the application of Integrated Asset Modelling in planning CO2 injection into depleted reservoirs.

Depleted gas fields represent an opportunity for CO2 storage. However, low reservoir pressure, as advantageous as it may

seem, will present a significant challenge for CO2 injection due to CO2 phase behaviour issues. This behaviour will

significantly constrain the operating injection parameters during the early stages of CO2 injection.

It has been recognised that an efficient way of understanding and possibly resolving these flow assurance problems is to adopt

an Integrated Asset Modelling (IAM) approach. The benefits of this approach are that it recognises the interaction between allthe system elements and enables the user to observe the effects of many parameter changes within the whole system.

The PETEX IAM suite of tools was selected to complete this task as follows:• PVTP to characterise reservoir fluids and CO2.

• REVEAL to model the reservoir

• PROSPER to model well performance

• GAP to model the injection system

• RESOLVE as an overall controller and integrator.

An integrated asset model was built for CO2 injection into a UK Southern North Sea depleted gas field, allowing various

scenarios to be run and providing important data on CO2 injection. This comprised a full field reservoir description and well

distribution, injection rates and time frame, trunkline (main CO2 delivery pipeline) and injection hardware requirements, and

the impact of external parameters such as seasonal ambient temperature changes etc..

The methodology, workflow, benefits and challenges of this novel approach will be presented in this paper, giving a deeper

insight into the CO2 injection planning process.

Introduction

The case study modelled a BP-operated asset located in the offshore UK Southern North Sea (UK SNS) for CO2 injection.

The main project objectives were to:

1. understand the CO2 injectivity and storage potential of the asset

2. identify any additions or modifications required to the existing infrastructures for CO2 service

3. test different injection scenarios to maintain a plateau injection rate governed by a CO2 delivery schedule

4. understand what operational issues might pertain, particularly during the early period of injection.



2 SPE 123788

In the overall project scenario, the modelled CO2 supply would come from burning fossil fuels at a power plant. The resulting

CO2 would be captured, dehydrated, compressed and transported to an onshore pumping facility, typically at approximately

70-80bar and at ambient temperature (0-20°C). Under these conditions CO2 is in dense, liquid phase.An onshore pump would

then be used to transport the CO2 via a subsea-pipeline to an offshore injection facility. From there it would be distributed to

several injection wells, most likely converted from existing production service, and injected into a depleted gas reservoir

which had initial conditions of perhaps around 27bar and 90°C. The model assumed an approximately constant CO2 delivery

rate of ~2MMt/year (~100MMscf/d) for a nominal project life of 20 years, with injection beginning in 2013.

To achieve the project objectives, a toolkit was needed to allow:

• access to a detailed description of every component of the system network (pipeline, wells, reservoir)

• an understanding of the physical and dynamic interactions between these components to be built.

A key concern to be addressed was around how would a change in the dynamics of one of these components affect the rest of

the system today and in the future?

Ultimately, all the components of the system have to be integrated to optimise the injection project design, combined with

forecasting capabilities able to describe injectivity potential and dynamic changes through time. This should lead to the

identification of the most suitable operating scenario to achieve the required injection targets. For the case study describedbelow, this was achieved through the use of an Integrated Asset Model approach using the Petroleum Experts Ltd suite of

tools.

Toolkit and Fluid Phase Challenges

The injection modelling toolkit comprised:

• a full field thermal reservoir model using REVEAL

• wells and well network descriptions using PROSPER

• surface network descriptions using GAP

• dynamic connections between the reservoir and well/surface network models using RESOLVE, the tool acting as a

data transfer system and master controller for the integrated full-field model at each forecast timestep

• a consistent set of fluid PVT descriptions performed in PVTP (see below), between the different models.

In addition to presenting the behaviour of the system under specific conditions, this approach allowed sensitivity runs to

compare different injection setups. Although not undertaken in this study, the injection system could be linked directly to theexisting model of the gas production system so that scenarios involving concurrent hydrocarbon production and CO2 injection

could be evaluated.

One of the major technical challenges considered in project relates to the phase behaviour of CO 2. Pure CO2 has a triple point

of 5.18bar at -56.6°C and a critical point of 73.8bar at 31.1°C as shown in Figure 1, enabling three different phase regions to

be defined under real-life reservoir conditions: liquid, vapour and supercritical fluid. Keeping the CO2 in a liquid or

supercritical phase is essential to realize the benefits from the high density of the fluid in terms of :

• bulk transportation efficiency

• injectivity potential

• stored CO2 mass.

The initial low reservoir pressure in the depleted gas field will be a major challenge to injection operations. If a maximum

injection rate constraint is defined, the well(s) might have to be choked back via a surface choke (as illustrated in Figure 2) toobtain a wellhead pressure low enough to respect that constraint. But this low wellhead pressure will force the wellhead

flowing conditions towards CO2 vaporisation. A key modelling objective was to design, monitor and control the injection

system to avoid the flowing pressure and temperature conditions crossing the vaporisation curve, thereby avoiding phase

changes in the injection network and wells. Should flowing conditions cross the vaporisation curve, CO2 will change from

liquid to vapour phase, resulting in a change of pressure gradient in the system. This may lead to well instability and

significant reduction in injection rates, as shown in Figure 3.

Finally, although not undertaken in this case study, thermal and chemical interactions between the injected CO 2 and reservoir

could be incorporated into the evaluation of the injection system. CO2 would probably be injected at a temperature

significantly lower than the reservoir temperature, creating a thermal front around the injection wellbore. This thermal front

would affect the well injectivity potential by modifying the viscosity of the injected fluid, as well as the stress field around the

injection wellbore. Thermal stress reduction around the wellbore associated with liquid injection could lead to thermal

fracturing, which may affect well injectivity and reservoir integrity.



SPE 123788 3

Chemical interactions between the reservoir matrix, connate water and CO2 could occur, leading, for instance, to the

dissolution of part of the reservoir matrix (dependent on reservoir mineralogy). This could weaken the reservoir fabric, which,

combined with the flow around the wellbore, could lead to sand failure, solids transport and reservoir integrity issues.

However, at this early stage of learning how to model CO2 injection into a depleted gas field, the project focussed on getting

the basic model correct. A key outcome of the study was to be able to understand the operational implications of the injection

scenarios run: reservoir fracturing, chemical and sand failure aspects were therefore not considered at this time.

In summary, when considering CO2 injection, the complete injection system should be designed, monitored and controlled to:

• keep the CO2 under stable dense / liquid phase and avoid any phase changes in the injection system

• maximize the CO2 storage potential of the reservoir; and also

• ensure that the CO2 will be kept safely contained within the reservoir for the long-term by understanding and

monitoring the impact of stress variations and chemical interactions between the CO 2 and the reservoir on its

integrity.

Workflow

The discussion below describes the modelling workflow via description of the:

1. Fluid properties

2. Reservoir behaviour

3. Well and surface networks; and

4. Integration of the different components into a single, full-system study .

1. Describing the fluid properties – PVT Modelling Approach

Modelling a potentially multi-phase fluid such as CO2 requires a good knowledge of its PVT properties over the range of

operating conditions likely to be encountered. It is important that the full-field integrated study respects the fact that the fluid

cat the injection manifold is the same fluid that is injected into the reservoir, only at different pressure and temperature

conditions. Therefore, all the individual components where the fluid occurs (i.e. reservoir, wells, surface network) must have aconsistent PVT description. This was achieved using an Equation of State (EOS) approach as it provided a more accurate

description of the fluid properties than the black oil models typically designed for hydrocarbon fluids.

All the tools used in the setup of the full-field integrated study are compatible with this type of PVT model, with RESOLVE

coordinating the transfer of the relevant PVT data from the reservoir to the well/surface network model. The EOS model was

set up using PVTP with two different compositional scenarios:

• “Pure CO2” (i.e. 99.97% CO2 and 0.03% N2)

• “Contaminated CO2” (i.e. 98% CO2 and 2% N2).

This change in composition did not noticeably affect the fluid properties and generated very similar results in terms of the

behaviour of the injection system for both types of model runs. Therefore only the “Pure CO2” option was subsequently used,

as this was more consistent with the expected composition of the CO2 expected to be captured from the power station.

The appropriate fluid model should allow for either liquid or gas to be present in the wellbore and pipeline network. In this

respect, CO2 was considered as a Dew Point system (i.e. “Retrograde Condensate” fluid model) rather than a “Dry and Wet

Gas” in the asset wells and surface network model. The “Dry and Wet Gas” model is not suitable in this case as it assumesthat all the fluid in the well is in gas phase and that liquid dropout only occurs at the surface.

2. Describing the reservoir behaviour – Reservoir Modelling Approach

In this study, REVEAL was the numerical reservoir simulator used in modelling the CO2 injection. The simulation grid,

reservoir petrophysical properties and the initial reservoir equilibration status were imported into REVEAL from a VIP model.

The VIP model had been history-matched from production start-up to 2007 and had a forward production prediction until the

commencement of CO2 injection in 2013. This (isothermal) model provided the starting point distributions of temperature,

pressure and gas saturation in the reservoir. The REVEAL CO2 injection model allowed the critical effect of temperaturevariation on fluid phase and injectivity to be fully captured. As noted above, rock mechanics, water chemistry and solid

transports aspects were not modelled at this early stage in the asset evaluation, but the IAM modelling capability would allow

these aspects to be added if or when required.



4 SPE 123788

3. Describing the CO2 injection network - Wells and Surface Network Modelling Aspects

When modelling the surface pipeline and wells networks, a major challenge was to accurately predict the CO2 fluid

temperature variations. These variations will be function of key parameters such as:

A) Pipeline Network:

• pipeline characteristics (i.e. length, diameter, type)

• topography encountered by the pipeline which will affect pressure losses

• ambient sea temperature around the pipeline, (which in this area may vary seasonally from 6°C to 16°C; British

Oceanographic Data Centre: www.bodc.ac.uk ) which will impact the flowing temperature of CO2 and hence its

density, as well as the heat exchange characteristics between the fluid and its surroundings.

The seasonal effects of temperature variations in the pipeline surroundings will have a potentially significant impact on the

fluid density and therefore on the pressure gradient in the injection system. These variations were taken into account in the

model by scheduling the ambient temperature value from data sources noted above.

B) Well Models:

• downhole equipment which will generate pressure losses

• parameters impacting the fluid temperature in the well such as the geological facies encountered along the

wellbore• presence of drilling mud in the annular space.

A model that accurately predicts temperature exchanges between the injected fluid and the surroundings by conduction,

convection, radiation and enthalpy variations should be used to predict fluid phase behaviour. Initially, the case study tested

the requirement for using a full “Enthalpy Balance” model to estimate the heat exchanges occurring along a selected individual

injector wellbore. This explicitly calculated conduction, convection, radiation and enthalpy variations heat exchanges based

on the type of surrounding formation, casing setup, drilling mud present in the well, etc..

An “Improved Approximation” approach was also tested, where the explicit calculation of convection, conduction and

radiation was replaced by the use of an overall heat transfer coefficient which is variable with depth. This is combined to a full

enthalpy balance calculation to estimate the heat exchanges occurring between the fluid and the wellbore. This approach

estimates the relevant heat exchanges associated with a full enthalpy balance calculation and is computationally much faster.

When applied, it provided well temperature predictions highly consistent with the “Enthalpy Balance” model, i.e. there wasless than 4% difference in the predicted bottom hole flowing temperatures. The “Improved Approximation” thermal approach

was therefore used in both the wells and the surface networks in the full asset model.

The well outflow performance in any integrated asset study is typically described using pre-generated Vertical Lift

Performance (VLP) curves which are dependent on fluid PVT properties, injection rate and pressure. But in the case of CO2

injection into a depleted gas reservoir it was essential that the injected fluid temperature was also considered as an additional

variable when generating VLP curves. When the wells models were dynamically connected to the reservoir model via

RESOLVE (see below), the bottom-hole flowing temperatures were transferred to the reservoir model enabling an accurate

representation the evolution of the reservoir temperature front around the injection wellbore.

Operating constraints such as injection rate, Trunkline (main CO2 delivery pipeline) Maximum Allowable Operating Pressure

(MAOP) and (original) reservoir pressure were set as limits in the model. The non-linear optimisation engine of GAP enabled

the optimisation of field settings (i.e. such as wellhead chokes for instance) to maximise the injection system capabilities whilerespecting the applied constraints.

4. Integrating the Reservoir, Well and Surface Networks – Full-Field Modelling Aspects

The reservoir, wells and surface network components of the injection system were fully modelled using REVEAL, PROSPER

and GAP respectively. In order to obtain the integrated full-field or asset model required, the network components weredynamically linked using RESOLVE, as shown in Figure 4 and described below.

At each forecast timestep, PVT and Inflow Performance Relationship data (i.e. reservoir pressure, temperature and IPR curve)

were exchanged between the reservoir and the well and surface network models. Well performance, based on the surface

network and well properties (i.e. prevailing pressures and, critically in this case, temperatures) could then be assessed. Once

the well performance had been calculated, the numerical reservoir simulation schedule was set up for the next forecast

timestep, controlling the well using a defined control parameter such as fixed rate, bottom-hole or tubing head pressure.



SPE 123788 5

Added-Value to the EngineerThis type of study helps understand the interactions between the different system components and answer questions, such

as:

• how does the seasonal variation of the pipeline surrounding temperature affect the bottom-hole injection temperature

and therefore the injected fluid density and injectivity?

• what will be the resulting impact in injection rate if, for instance, the injection manifold pressure varies?

Modelling the Depleted Gas Field - Scenarios

ScenariosThe integrated full-field workflow enabled multiple CO2 injection scenarios to be realised, the most likely of which is

presented here.

This UK SNS depleted gas field comprises four reservoir compartments A, B, C and D. The most likely injection scheduling

of a paired compartment scenario was based on an infrastructure availability model. This model suggested that the injection

would commence in the A and B compartments first, followed by the injection into the C and D compartments. Prior to

opening the first well in the C and D compartments the Trunkline pump discharge pressure must be 150bar, or all of the

injection wells in the A and B reservoir compartments must be at initial reservoir pressure.

ConstraintsThe model scenarios were constrained by a set of three operating parameters:-

1. Initial (pre-production) Reservoir Pressure

CO2 injection ceased when the average pressure of the modelled reservoir compartment reached its initial reservoir

pressure. In this way, the final reservoir “CO2 storage pressure” would not act as a pressure source in the field area,

thereby mitigating against potential geological leakage from the field. This constraint is shown in Table 1.

2. Trunkline and Pump Discharge Pressure

One of the objectives of this exercise was to establish:-

• when the injection system pressure exceeded the initial CO2 capture pressure (approx. 80bar); therefore

• when onshore pumping would be required; and

• when subsequent pump upgrades would be needed

to maintain a field-wide plateau injection rate of 100MMscf/d (see below). The pump discharge pressure wasdefined as a control variable between the initial CO2 capture pressure at 80bar and a maximum pump discharge

pressure of 150bar - this pressure limit was set lower than the Trunkline MAOP.

3. CO2 injection rate

• The well injection rates were optimised to try and maintain a field-wide plateau injection rate at an

equivalent of 2MMt/yr CO2 (approx. 100MMscf/d).

These operating constraints were applied using both the GAP optimiser, which controlled the wellhead chokes in order to

respect the field maximum CO2 injection rate, and the RESOLVE conditional scheduling capabilities. These enabled actions

to be triggered, such as a well opening or closing, based on the behaviour of the system itself. This allowed the model

optimisation to respect the system operating parameters.

In summary, the conditional scheduling approach made it possible to:

• monitor the average reservoir pressure around each injection well and stop a well automatically when it exceeded the

initial reservoir pressure, switching injection to other well/s to maintain the plateau injection rate

• monitor the field-wide injection rate and automatically modify the pump pressure with time to meet the plateau

injection rate, i.e. predicting when a pump would need to be installed and when upgrades would be required.



6 SPE 123788

Modelling Results

1. Flow Assurance and Effects of Seasonal Temperature variation

The modelling obtained the distribution of pressure and temperature along the Trunkline, flowlines and wellbores for each

timestep of the prediction. This monitored whether the injection conditions were optimum (i.e. injecting dense phase CO2) or

whether phase changes were observed in the system.

This analysis was undertaken because the seabed temperature in the Southern North Sea has significant seasonal temperature

variations (British Oceanographic Data Centre: www.bodc.ac.uk ). In summer time the average seabed temperature is 16°C

whilst in winter time the temperature drops to 6°C; the winter period was nominally defined as 1st October to 31st April.

Figure 5 illustrates the pressure and temperature evolution in the Trunkline linking the onshore CO 2 source to the offshore

injection facility:-

• in both winter and summer conditions, at the beginning of the prediction forecast (i.e. 01/01/2013 and 12/06/2013),

• in the period when the pump discharge pressure is the lowest (i.e. 80bar) and

• where the risk of being close to the vapour pressure curve important.

In this case there appears to be no risk of CO2 vaporisation in the pipeline in either summer or winter flowing conditions (N.B.

the seabed topography traversed by the asset Trunkline has low relief and therefore only a small predicted pressure drop along

its length). In addition, analysing the temperature evolution in the Trunkline showed that the heat exchange between the pipe

and the CO2 fluid was such that the flowing CO2 temperature equalised with the surrounding near seabed temperature quite

quickly, within the first ~20% of the pipeline length seaward from the beach, in both winter and summer conditions.

Therefore the modelling showed that the typical temperature of CO2 arriving at the injector wellhead will generally be equal to

the average Trunkline or flowline surrounding temperature at any point in time.

Figure 6 shows the pressure and temperature evolution in one of the wellbores in both winter and summer conditions at the

beginning of the prediction forecast (i.e. 01/01/2013 and 12/06/2013) as a response to seasonal variation. At this point in the

simulation, the well shown was the only one open and was able to inject the full 100MMscf/d target rate. For both winter and

summer plots, the change in pressure gradient observed is due to the fluid density decrease with temperature becoming moreimportant than the fluid density increase with pressure.

Figure 6 also shows the impact of the wellhead flowing temperature and its evolution through the seasons:

• Wellhead flowing conditions in winter are very close to the vaporisation curve. This is characteristic of the behaviourof the wells at the beginning of the forecast: the reservoir pressure is low, forcing the wells to be choked at surface to

respect the maximum CO2 injection target rate. This leads to a low wellhead pressure which, combined with the low

injection temperature in winter times, brings the flowing conditions at the wellhead close to CO 2 vaporisation

conditions. This can indicate, for instance, that the injection target rate used and the number of wells injecting into

one compartment, need to be carefully defined to avoid lowering the wellhead pressure through the choke to respect

the injection target and mitigate phase changes at the wellhead.

• During the summer period the issue is less severe as both the wellhead temperature and pressure increase (i.e. the

fluid density is lower, requiring a higher wellhead pressure to inject at the same rate); this suggests that it might be

better to start CO2 injection in depleted UK SNS gas fields in the summertime.

Figure 7 shows the impact of seasonal temperature variations on the well injectivity capacity. During the winter period the

ambient near-seabed temperature of approximately 6°C gives a CO2 fluid density of 926Kg/m3 at 135bar. During the summer

period, an ambient temperature of 16°C, it is 866Kg/m3. Taking one specific well, this decrease in density between winter and

summer time resulted in an injection rate decrease at constant wellhead pressure which may be as high as 4MMscf/d,

corresponding to approximately 15% of the overall injection capacity of that well. So while it may be better to start CO2

injection in the summer, rates may be lower than expected.

The study therefore showed if, when and where there was a risk of observing a phase change in the injection system. In this

situation, sensitivities can be run to analyse how the system could be controlled to avoid them. It also showed the need toobtain high quality, seasonal environmental data to determine the likely arrival temperature of CO2 at the wellhead.



SPE 123788 7

2. Reservoir Pressure Evolution, Injection Feasibility and Duration

The main objective of this study was to assess the feasibility of injecting CO2 into a depleted gas field, using existing

infrastructure as the basis of the network model. The model assumed that there would be a requirement for 2MMT of CO2 to

be injected annually over a nominal project schedule of 20 years. The start of CO2 injection was scheduled to be in 2013 and

the evolving reservoir conditions (Reservoir Temperature, Pressure, Saturation etc.) after the start of injection were calculated

using the reservoir model in REVEAL.

The pressure in the modelled reservoir compartments at the start of injection was on average 27bar and then rose steadily as a

result of the CO2 injection. Figure 8 shows the gas injection rate vs. the reservoir pressure increase for one of the wells in the

field. This figure shows that injection into this well will stop in 2018 when the reservoir pressure reaches 280bar, which is the

initial reservoir pressure constraint as shown in Table 1. The optimization of the model then allowed a different injector to

become available to maintain the required field injection rate.

The full-field injection simulation was scheduled to run to 2033. However, as Figure 9 shows, effective injection stopped in

2028 due to the initial reservoir pressure constraint being reached in all but one of the injectors. The injection rate thereafter

dropped very rapidly from 91MMscf/d to 10MMscf/d. The model suggests that more injectors, in locations sited to access as

yet unused pore space, would be required to maintain continued injection after this time.

3. Pump Discharge Pressure

As a result of the CO2 injection, the reservoir pressure increase will lead to the initial CO2 capture pressure of approximately

70-80bar being insufficient to maintain the required injection rate of 100MMscf/d. At this point a pump would have to be

installed to allow higher discharge pressures from the Trunkline to the injection facility.

By coupling the reservoir with the surface network, the model estimated when a pump would be needed and when it would

require an upgrade. The pump itself was not modelled, but the Trunkline pressure was set to be identical to the pump discharge

pressure and hence this was used as a control variable in the study. Therefore by monitoring the evolution of the Trunklinepressure with time, the necessity for any required pump upgrades could be assessed.

As Figure 10 shows, the initial capture pressure condition would suffice from the start of injection in 2013 until mid-2019 at

which time a Trunkline pump would have to be installed. Subsequent upgrades would be needed, for example in mid-2020, or

a pump capable of the required maximum discharge pressure could be installed in mid-2019.

Conclusions

1. Modelling the injection of dense phase CO2 into a depleted gas field requires a workflow where the system, from delivery

pipeline through injection facility and wells to reservoir, can be dynamically linked. By analyzing the complete system in this

way, more reliable results should be achieved relative to an approach that models only the individual components. The

benefits and constraints of the integrated system also become more transparent.

2. The Integrated Asset Modelling (IAM) approach provided a toolkit and workflow to help understand some of the flow

assurance problems and potential operational issues related to injecting CO2 into a depleted gas field. The benefits of IAM are

that it works the interactions between the surface and sub-surface system components in a dynamically linked network, and

that the benefits themselves are accurately calculated.

3. The philosophy of applying an integrated modelling package such as IAM, and the capability of the software, were the key

tools in evaluating an asset for CO2 storage. They provided information essential to maintaining a plateau injection rate

governed by a CO2 delivery schedule, such as:-

• spatial and temporal distribution of active injectors

• optimisation of injection rates

• the duration of individual CO2 injectors, and field-wide injection

• the timing required for onshore pump installation and possible upgrades

• the need for environmental data which may have a direct impact on injection operations.

Such pieces of information are also critical inputs into the evaluation of project economics.

4. As a 3D numerical simulator (REVEAL) was used to link the behaviour of the wells and surface networks, the resulting

dynamic model generated far more detailed data regarding the reservoir itself (e.g. CO 2 saturation through time, possible



8 SPE 123788

under-used or non-contacted pore space, etc.) than would have been available from a material balance approach. Such data

can be easily and effectively communicated to project stakeholders via 4D visualisation packages.

Acknowledgments

The Authors would like to thank BP and Petroleum Experts Ltd. for permission to publish this paper.

References

1. British Oceanographic Data Centre: www.bodc.ac.uk



SPE 123788 9

Figure1: CO2 Phase Diagram

Figure 2: CO2 Injection System

1. CO2 Pump and Pipeline2. Surface choke

3. Wellhead4. DHSV (Downhole Safety Valve)

5. Lock Nipple

6. Perforations

7. Near wellbore area

8. Reservoir

Operating area

Supercritical (dense phase)

Depleted conditions

350 psia, 24 bara

200°F, 93°C

Tubing head conditions1000 psia, 70 bara

40°F, 4°C

Temperature oC

P r

e s s u r e a t m

74 bara, 31°C

(1071 psia, 88°F)

Operating area

Supercritical (dense phase)

Depleted conditions

350 psia, 24 bara

200°F, 93°C

Tubing head conditions1000 psia, 70 bara

40°F, 4°C

Temperature oC

P r

e s s u r e a t m

74 bara, 31°C

(1071 psia, 88°F)

P

r e s s u r e

b a r



10 SPE 123788

Figure 3: CO2 Injection Rate vs. Injection Pressure

35 40 45 50 55

0

10

20

30

40

50

60

First Node Pressure (BARg)

G a s R a

t e

( M M s c f / d a y )

Phase

Change



SPE 123788 11

Figure 4: Resolve Snapshot



12 SPE 123788

Figure 5: Pipeline Temperature Changes

Figure 6: Wellbore Temperature Changes



SPE 123788 13

Figure 7: Seasonal Temperature Changes

Figure 8: Reservoir Pressure Constraint

Date

01/10/20101/04/201901/10/201801/04/201801/10/201701/04/201701/10/2016

C O 2 I n j e c t e d ( M M s c f / d a y )

28

26

24

22

20

18

16

14

12

10

8

6

4

2

0

WinterSummer

01/ 01/ 2013 01/ 01/ 2018 01/ 01/ 2023 01/ 01/ 2028 01/ 01/ 2033

0

5

10

15

20

25

30

35

0

40

80

120

160

200

240

280

Predi cti on Result s

Ti me (date )

G a s R a t e

( M M s c f / d a y )

R e s er v oi r P r e s s ur e

( B A R a )

M05

M05

CO2

In ected

Reservoir

Pressure



14 SPE 123788

Figure 9: CO2 Injection

Date333231302928272625242322212019181716151413

C O 2 I n j e c t e d : M M s c f / d a y

100

95

90

85

80

75

70

65

60

55

50

45

40

35

30

25

20

15

10

5

0

A02:Gas InjectedgfedcbA03:Gas InjectedgfedcbC01:Gas InjectedgfedcbC02:Gas InjectedgfedcbD02:Gas Injectedgfedcb

B01:Gas InjectedgfedcbB03:Gas InjectedgfedcbA01:Gas InjectedgfedcbD01:Gas InjectedgfedcbB02:Gas Injectedgfedcb



SPE 123788 15

Figure 10: Trunkline Pressure

Table 1: Initial Reservoir Pressure (Final Reservoir Pressure)

Well Pres

initial and

final (bar)

A01 280

A02 280

A03 288

B01 288

B02 280

B03 280

C01 281

C02 281

D01 281

D02 281

Year

333231302928272625242322212019181716151413

T r u n k l i n e : C O 2 ( M M s c f / d a y )

115

110

105

100

95

90

85

80

75

70

65

60

55

50

45

40

35

30

25

20

15

10

T r unk l i n e: Pr e s s ur e ( BAR a )

150

145

140

135

130

125

120

115

110

105

100

95

90

85

80

Trunkline:Gas Producedgfedcb

Trunkline:Pressuregfedcb

co2 injection into depleted gas reservoirs

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