Techno-economic analysis of intermediate hydrocarbon injection on coupled CO 2 storage and enhanced oil recovery

Abstract

This study proposes economic evaluation of CO₂ geological storage with enhanced oil recovery. The procedures consider capital expenditures and operating costs of infrastructures and revenues from oil recovery and carbon tax credits. Extensive CO₂ geological storage with enhanced oil recovery simulations was conducted to determine the most promising scenario among cases, where miscibility was controlled by the addition of liquefied petroleum gas. The addition of liquefied petroleum gas into a CO₂ injection stream can accelerate reduction of oil viscosity, interfacial tension, and oil density, which cause improved displacement efficiency. The larger was the amount of liquefied petroleum gas injected, the greater was the miscibility due to minimum miscibility pressure reduction, resulting in higher oil recovery and less CO₂ sequestration. Although liquefied petroleum gas addition enhances the performance of CO₂ enhanced oil recovery, economic analysis should be conducted for CO₂ geological storage with enhanced oil recovery due to the higher price of liquefied petroleum gas than that of CO₂. Net present value decreased from liquefied petroleum gas mole fraction of 0–2% and started to increase from mole fraction 2–13% due to the miscibility effect. Then, net present value started to decrease, because the purchasing and injecting prices of the required liquefied petroleum gas exceeded that of the oil produced. Economic evaluation showed that addition of 13% liquefied petroleum gas was the most promising scenario, with a net present value of 91 MM$. Thus, we confirmed an optimum liquefied petroleum gas concentration in the CO₂ geological storage with enhanced oil recovery process.

Keywords

CCUS LPG economic feasibility miscibility

Introduction

It is expected that world gross domestic product (GDP) will double from 2016 to 2040 because of the economic expansion and development of non-Organisation for Economic Co-operation and Development (OECD) and OECD countries; more dominant from developing countries (ExxonMobil, 2018). According to the report, the energy consumption will increase in every sector of transportation, industries, and electricity generation mainly due to the development in non-OECD. However, because of the environmental effect, the portion of coal use in global energy mix will decrease, and the demand of oil in transportation sector and gas and renewables in electricity generation sector will increase. Because of the complexity of the relationship from economic growth to energy, many researches have been conducted (Appiah, 2018; Tiba and Omri, 2017; Walheer, 2018). Showing how economies react to an unexpected reduction in oil production, Bastianin et al. (2017) emphasized the importance of energy security with oil.

Worldwide oil demand and carbon emissions are predicted to increase gradually until 2040, and good solutions for this issue are CO₂ enhanced oil recovery (EOR) and CO₂ geological storage (Alvarado and Manrique, 2010; BP, 2018). The main drawback of CO₂ storage in an aquifer is lack of commercial value; CO₂ EOR can resolve this problem by enhancement of oil production as part of the carbon capture, utilization, and storage processes (Leung et al., 2014). Coupled CO₂ storage and EOR (CCS–EOR) can propose commercial opportunities for oil producers and ensure the geological storage of large quantities of CO₂. Nearly, 80 MtCO₂ is already being used for CCS–EOR each year (IEA, 2019). CO₂-EOR is one of the most effective EOR methods and is performed by injecting CO₂, which acts as a solvent to reduce oil viscosity, interfacial tension (IFT), and oil density. This can extend the production period by 15–20 years, with oil recovered at 15–25% of the original oil in place (Dong et al., 2000; Yongmao et al., 2004).

There have been a number of attempts to combine CCS and EOR. Ettehadtavakkol et al. (2014) focused on the sensitivity of economic parameters with various water alternating gas (WAG) ratios during CCS–EOR. However, the effects of reservoir in-situ condition controls, such as miscibility adjustment, impurities in the CO₂ stream, and IFT change-triggered relative permeability curve alteration, on net present value (NPV) have not been considered. Hasan et al. (2015) designed a CO₂ utilization through CO₂ EOR considering economic parameters, not considering fluid flow mechanisms and the operating cost for miscibility while the process. Rognmo et al. (2019) provided an experimental approach to test and upscale a CO₂ foam injection as one of injection schemes for CCS–EOR without the economic analysis. Nunez-Lopez et al. (2019) performed reservoir simulation studies with various CO₂ injection scenarios for CCS–EOR. However, they only focus the oil production and CO₂ storage without economic analysis and constant miscibility mechanisms. Those studies focused only on in-situ mechanisms and performances and neglected the economics of field operation including CO₂ capture, transportation, and injection.

Since the optimum operating design and condition to enhance the performance of CCS–EOR at each reservoir is different, it is necessary to develop site-specific design based on economic analysis for the field application. To overcome the limitations of previous studies, CCS–EOR was performed from the viewpoint of economics to derive the most promising scenario with variable miscibility conditions with liquefied petroleum gas (LPG) and economic parameters. Capital expenditures (CAPEX) and operating costs (OPEX) of operation and revenues from oil recovery and carbon tax credits were adopted to evaluate NPV, because it is one of the most practical tools used in investment analysis for evaluating profitability, also known as economic decision criteria (Abdel-Aal and Al Sahlawi, 2013).

First, a numerical study for CCS–EOR was conducted. Oil recovery and sequestered CO₂ by each mechanism of solubility trapping, residual trapping, and free CO₂ under different miscibility conditions were examined. The results of the fluid model matched the experimental data. Then, minimum miscibility pressure (MMP) was calculated from a slim-tube simulation. The amount of LPG added into the injection gas was controlled to change miscibility conditions. Addition of LPG into the CO₂ stream can lower the MMP, which accelerates the reduction of oil viscosity, IFT, and oil density. The effects of IFT change due to miscibility increase were included by modification of liquid–gas relative permeability curves, which led to different flow regimes and performances under different miscibility conditions. Finally, NPVs of various miscibility conditions, which were adjusted by pressure control and LPG addition, were analyzed.

Numerical model

Residual and solubility trappings

The relative permeability curve follows different paths with imbibition or drainage processes, which causes the hysteresis effect. For the WAG process, CO₂ can be stored as water imbibes into a reservoir. Numerous studies have established equations to explain this phenomenon (Blunt, 2000; Jerauld, 1997; Killough, 1976; Land, 1968; Larsen and Skauge, 1998; Lenhard and Oostrom, 1998; Lenhard and Parker, 1987). The Land model, which is the most widely used empirical trapping model and base for subsequent hysteresis models, was used in this study. The hysteresis effect on gas relative to permeability is calculated as follows (equations (1) to (3))

k_{r g} (S_{g}) = k_{r g} (S_{g}), during drainage

(1)

k_{r g} (S_{g}) = k_{r g} (S_{g, shifted}), during imbibition, and

(2)

S_{g,shifted} = S_{g r} + \frac{(S_{g} - S_{grh}) (S_{g h} - S_{g r})}{S_{g h} - S_{grh}}

(3)

where

$k_{r g}$ = relative permeability of gas, and

$S_{g h}$ = $S_{g}$ at the shift to imbibition.

$S_{grh}$ is the value of $S_{g r}$ corresponding to $S_{g h}$ via Land’s equation (Land, 1968) as follows

\frac{1}{S_{g r max} - S_{g r}} - \frac{1}{S_{g max} - S_{g r}} = \frac{1}{S_{grh} - S_{g r}} - \frac{1}{S_{g h} - S_{g r}} and

(4)

S_{g max} = 1 - S_{w r}

(5)

where

$S_{w r}$ = connate water saturation.

$S_{g r max}$ is calculated using the Holtz equation as shown below (Holtz, 2002)

S_{g r max} = - 0.9696 ϕ + 0.5473

(6)

where

ϕ

is porosity.

The Peng–Robinson EOS (1976) was applied to calculate the fugacities of components in the oil and gas phases and to determine CO₂ solubility and phase behavior of the fluid model with the reservoir oil and CO₂. To calculate the fugacity of CO₂ in the aqueous phase, Henry’s law was used (Li and Nghiem, 1986)

f_{i w} = y_{i w} H_{i} and

(7)

ln H_{i} = ln H_{i}^{*} + \frac{{\bar{v}}_{i} (p - p^{*})}{R T}

(8)

where

$H_{i}$ =Henry’s constant of component i (kPa);

$y_{i w}$ = mole fraction of component i in the aqueous phase;

$p$ = pressure (kPa);

$p^{*}$ = reference pressure (kPa);

$H_{i}^{*}$ = Henry’s constant for component i at pressure $p^{*}$ and temperature T (kPa);

$T$ = temperature, (K); and

${\bar{v}}_{i}$ = partial molar volume of component i at infinite dilution as computed by Harvey’s correlation (1996) (cm³/mol).

Relative permeability alteration

Decrease of IFT between oil and gas improves relative permeability for gas and oil (Araujo et al., 2001; Bardon and Longeron, 1980; Jamaloei, 2015; McDougall et al., 1997). The relative curves for oil and gas are linear functions of oil and gas saturations in miscible conditions. The modified relative permeability curves for oil and gas are denoted as $k_{rot}$ and $k_{rgt}$ , respectively

k_{rot} = f k_{r o} + (1 - f) k_{r h} \frac{S_{o}}{1 - S_{w}}

(9)

k_{rgt} = f k_{r g} + (1 - f) k_{r h} \frac{S_{g}}{1 - S_{w}} and

(10)

k_{r h} = 0.5 [k_{row} (S_{w}) + k_{r g} (S_{g})]

(11)

where

f = 1 {\begin{cases} 1, & where σ > σ_{o} \\ {(\frac{σ}{σ_{o}})}^{n}, & where σ \leq σ_{o} \end{cases}

where

σ

is the IFT between oil and gas in dynes per centimeter calculated using the MacLeod–Sugden correlation (Reid et al., 1977), while

σ_{o}

is the reference IFT for relative permeability calculations and n is the exponent for gas-oil relative permeabilities, which was set as 0.1.

Economic parameters calculation

For CCS–EOR, it is important to examine not only reservoir performances, but also infrastructure-related costs that critically affect economic feasibility. Figure 1 shows a brief schematic of CCS–EOR. Here, CO₂ is captured from facilities, such as a pulverized coal and integrated gasification combined cycle (IGCC) plant, boosted for transport to the reservoir site through a pipeline, and injected with the help of surface facilities, such as a pump. Every step needs economic evaluation for successful development in the oil field.

Figure 1.

Schematic of CCS–EOR infrastructures (Sheng et al., 2015).

There are several other costs that affect the cost calculation, such as surface equipment lease fee and minor costs for screening, contracts, insurance, and monitoring. The equipment lease fee and other costs, which are dependent on reservoir site, were adopted from the work of Ettehadtavakkol et al. (2014).

Compressor and pump

The compression procedure is significant for transporting CO₂ from the capture site to the oil field. The compressor and pump are two main facilities that boost CO₂ pressure. The compressor is initially used to boost CO₂ pressure to its supercritical pressure for transport. Then, CO₂ can be delivered through the pipeline as a supercritical dense fluid. The pump is used to further increase the CO₂ pressure to injection pressure.

To adopt the compression procedure, equations (12) to (17) are referred from Mohitpour et al. (2000) and McCollum and Ogden (2006). They proposed the compression ratio (CR) as follows

C R = {(\frac{p_{discharge}}{p_{inlet}})}^{\frac{1}{N}}, C R \leq 6

(12)

where

$N$ = number of compression stages;

$p_{discharge}$ = discharge pressure (MPa); and

$p_{inlet}$ = inlet pressure (MPa).

CAPEX and OPEX concepts were considered in this study. CAPEX for the compressor is calculated through the following equation

C_{comp .} = m_{train} N_{train} [\begin{array}{l} (0.13 \times 10^{6}) {(m_{train})}^{- 0.71} + \\ (1.40 \times 10^{6}) {(m_{train})}^{- 0.60} ln (\frac{p_{cut - off}}{p_{initial}}) \end{array}]

(13)

where

$C_{comp .}$ = CAPEX of compressor ($);

$m_{train}$ = CO₂ mass flow rate through each compressor train (kg/s);

$N_{train}$ = number of parallel compressor trains;

$p_{cut-off}$ = pressure at which compression switches to pumping (MPa); and

$p_{initial}$ = initial pressure of CO₂ directly from the capture system (MPa).

Like the compressor, CAPEX of the pump is calculated as

C_{pump} = 1.11 \times 10^{6} (\frac{w_{pump}}{1000}) + 0.07 \times 10^{6}

(14)

where

$C_{pump}$ = CAPEX of pump ($) and

$w_{pump}$ = pumping power requirement (kW).

Annual OPEX for the compressor and pump, $O_{comp . and pump}$ , consists of annual operating and maintenance costs, $O & M_{annual}$ ; and electric power costs, HP and $w_{pump}$ . $O & M_{annual}$ is calculated as follows

O & M_{annual} = O & M_{factor} (C_{comp .} + C_{pump})

(15)

where

O & M_{factor} = 0.04

Electric power costs for the compressor and pump are also considered. Electricity power cost is calculated as the product of power use and electricity cost. Compressor power use, HP, can be obtained from the following equation

H P = 0.0857 \frac{K}{K - 1} T_{1} [{(\frac{p_{2}}{p_{1}})}^{\frac{K - 1}{K}} - 1]

(16)

where

$H P$ = adiabatic power requirement (HP/MMSCFD);

$K$ = adiabatic gas exponent;

$T_{1}$ = suction temperature (R);

$p_{1}$ = suction pressure (psia);

$p_{2}$ = discharge pressure (psia); and

$\frac{p_{2}}{p_{1}}$ = compression ratio.

Pump power use can be expressed as follows

w_{pump} = \frac{(1000) (10)}{(24) (36)} [\frac{m (p_{final} - p_{cut - off})}{ρ η_{pump}}]

(17)

where

$m$ = CO₂ mass flow rate (tons/day);

$ρ$ = gas density (kg/m³); and

$η_{pump}$ = compressor efficiency.

Transportation

In CO₂ flooding, CO₂ flows through a pipeline. In this study, it was assumed that only OPEX for the pipeline was the operator’s responsibility, as was true in a previous study (Ettehadtavakkol et al., 2014). McCollum and Ogden (2006) suggested equations for estimating onshore pipeline annual OPEX, $O_{pipeline}$ , as follows

C_{pipeline} = 9970 F_{L} F_{T} m^{0.35} L^{1.13} and

(18)

O_{pipeline} = O & M_{factor} C_{pipeline}

(19)

where

$F_{L}$ = location factor, UK = 1.2, USA/Canada/Europe/Japan/Australia = 1.0;

$F_{T}$ = terrain factor, cultivated land = 1.1, grassland = 1.0, wooded = 1.05;

$C_{pipeline}$ = pipeline CAPEX ($);

O & M_{factor} = 0.025

$m$ = mass flow rate (kg/s); and

$L$ = pipeline length (km).

For ease of calculation, $F_{L}$ , $F_{T}$ , and $L$ were assumed to be 1.0, 1.0, and 100 km, respectively.

NPV calculation

Equations (20) to (22) show the calculation of CAPEX and OPEX of overall economical consideration

{CAPEX}_{total} = (\begin{array}{l} C_{comp .} + C_{pump} + C_{minor cost} \\ + C_{surface equipment} + C_{{CO}_{2}} + C_{LPG} \end{array})

(20)

{OPEX}_{annual} = (\begin{array}{l} O_{comp . and pump} + O_{minor cost} \\ + O_{surface equipment} + O_{pipeline} \end{array}), and

(21)

{OPEX}_{total} = \sum_{i} \frac{{OPEX}_{annual}}{{(1 + r)}^{i}}

(22)

where

${CAPEX}_{total}$ = total CAPEX ($);

$C_{comp .}$ = compressor CAPEX ($);

$C_{pump}$ = pump CAPEX ($);

$C_{minor cost}$ = minor cost CAPEX ($);

$C_{surface equipment}$ = surface facility CAPEX ($);

$C_{{CO}_{2}}$ = CO₂ purchase charge ($);

$C_{LPG}$ = LPG purchase charge ($);

${OPEX}_{annual}$ = annual OPEX ($);

${OPEX}_{total}$ = total OPEX ($);

$O_{comp . and pump}$ = compressor and pump annual OPEX ($);

$O_{minor cost}$ = minor annual OPEX cost ($);

$O_{surface facility}$ = surface facility annual OPEX ($); and

$O_{pipeline}$ = pipeline annual OPEX ($).

Revenue in this study was obtained from two major sources: oil recovery and CO₂ geological storage carbon tax credit. Discounted revenue from oil recovery is calculated as follows

{Discounted revenue}_{oil recovery} = \sum_{i} \frac{q_{oil, month}}{{(1 + r)}^{i}}

(23)

where

$i$ = monthly period (month);

$r$ = monthly discount rate; and

$q_{oil, month}$ = monthly oil production (bbl/month).

Discounted revenue from CCS carbon credit is expressed as follows

{Discounted revenue}_{CCS} = \frac{S_{{CO}_{2}} (tax credit)}{{(1 + r)}^{(month) (year)}}

(24)

where

$S_{{CO}_{2}}$ = stored CO₂ at the end of the project (ton).

NPV is calculated by comparing revenue and cost and is briefly expressed as follows

NPV = [\begin{array}{l} ({Discounted revenue}_{CCS} + {Discounted revenue}_{oil}) \\ - ({CAPEX}_{total} {+OPEX}_{total}) \end{array}]

(25)

Results

Fluid modeling

Fluid properties and relative permeability curves were adopted from the work of Ghomian (2008). EOS parameters for pseudo-components are listed in Table 1, and relative permeability curves are presented in Figures 1 and 2. Once the fluid model was developed and MMP was estimated by slim-tube simulation, the results were matched with given MMP data of 1500 psia at a given temperature. Results and interpretation of the slim-tube simulation are shown in Figure 3, with a slope change around 1500 psia and exact MMP was of 1478 psia. These results are acceptable in comparison to the given MMP data of 1500 psia.

Table 1.

EOS parameters for pseudo-components (Ghomian, 2008).

ComponentProperties	CO₂	C₁	C_2–3	C_4–6	C_7–16	C_17–29	C₃₀₊
Initial composition	0.0192	0.0693	0.1742	0.1944	0.3138	0.1549	0.0742
p_c (atm)	72.799	45.4	44.932	33.238	20.676	15.675	15.636
T_c (K)	303.89	166.67	338.34	466.12	611.12	777.78	972.23
Acentric factor	0.225	0.008	0.126	0.244	0.639	1.000	1.281
M_w (g/gmol)	44.010	16.043	36.013	70.520	147.18	301.48	562.81
Volume shift	0.14	–0.15	–0.01	–0.04	0.06	0.18	0.30
Parachor	49.00	71.000	135.00	231.62	439.15	788.22	1,112.5

Figure 2.

Water-oil relative permeability curves (Ghomian, 2008).

Figure 3.

Gas-liquid relative permeability curves (Ghomian, 2008).

Model description

CCS–EOR was applied to a single-layer, two-dimensional reservoir model. A five-spot model was chosen with a 720 ft × 720 ft × 100 ft size of one quarter and reservoir properties as shown in Table 2. Since the reservoir was deeper than 1800 ft, it was applicable to CCS–EOR (Klins, 1984; Kovscek, 2002; Shaw and Bachu, 2002; Taber et al., 1997). The injection scenario was planned to combine five years of pre-water flooding and 20 years of WAG injection with a cycle of six months. For pre-water injection, water was injected at a rate of 300 bbl/day; for the WAG process, water and gas were injected at a rate of 300 bbl/day and 35,760 ft³/day of CO₂, respectively, with 0–0.15 mol fraction of additional LPG in each case. One ^PV of water was injected for 10 years during pre-water injection. Since CO₂ is a highly compressible fluid, an injection rate of 1 PV for 10 years under near-miscible conditions was chosen. The injector and producer were located at the corners of the model, which were diagonally positioned.

Table 2.

Reservoir model properties.

Properties	Values
Reservoir size (ft³)	51,840,000
Porosity	0.3
Horizontal permeability (md)	70
Initial pressure (psia)	1500
Depth to reservoir top (ft)	3000

Miscibility adjustment through LPG addition

The miscibility condition was also adjusted through the addition of LPG, which was composed of half propane and half butane. Propane and butane prices were introduced from the OPIS North America LPG Report (OPIS, 2017). Based on the pressure-controlled immiscible CCS–EOR model, LPG was injected at different mole fractions from 0 to 15% with CO₂ as a solvent. The amount of injected CO₂ remained constant, while additional LPG produced a larger slug for a higher LPG mole percent. Economic evaluation was then carried out for the models with different LPG mole fractions in injection gas.

Since LPG acts as an intermediate component in the CO₂-oil phase diagram, adding LPG into the CO₂ stream resulted in a lower MMP between oil and CO₂-LPG gas than that of oil and CO₂ only. MMP data obtained by slim-tube simulations between oil and CO₂-LPG gas are listed in Table 3 with respective mole fraction of LPG. As more LPG is added into CO₂ stream, the MMP becomes low. For LPG-CO₂ injection, it was easier to achieve miscible conditions at the same pressure and temperature conditions as for CO₂ injection. The case of 15% LPG in injection gas had an MMP of 1283 psia, which was 195 psia lower than that of the 0% LPG injection case. The MMP change must be considered since it affects not only oil recovery and CO₂ sequestration but also NPV.

Table 3.

MMPs derived from slim-tube simulations depending on amount of LPG added (psia).

LPG addition (%)	MMP
0	1478
1	1429
2	1427
3	1420
4	1412
5	1402
6	1388
7	1373
8	1359
9	1347
10	1339
11	1330
12	1319
13	1307
14	1292
15	1283

Enhanced oil recovery

Once CO₂ was injected into the reservoir, it started to interact with oil. Mixing with CO₂ resulted in lighter, easier flowing, and swelled oil. With greater miscibility between oil and injected gas, the IFT decreased. Increased reservoir pressure as well as decreased MMP by LPG injection produced miscible conditions. Figure 4 shows IFT change of cases with LPG fractions of 0, 5, 10, and 15% at the CO₂ swept zone. First, IFT remained zero since injected gas had not yet arrived and only oil existed at this location. Once CO₂ affected the oil at this location, the IFT started to increase. The IFT between oil and gas was lower in miscibility at earlier stages but higher at later stages due to influx of heavy fractions left behind when the gas removed intermediate fractions of oil through vaporization. The IFT change affected the shape of the relative permeability curves, as presented in Figure 5. Modification of the liquid–gas relative permeability curve due to the decreased IFT between oil and gas allowed easier flow.

Figure 4.

Results of MMP measurements using a slim-tube simulation.

Figure 5.

IFT changes at the CO₂-swept zone with LPG addition into the injection gas.

Viscosity reduction is one of the major mechanisms of CO₂-EOR. With increasing addition of LPG to injection gas, oil viscosity decreased. Figure 6 shows viscosity changes for cases with LPG fractions of 0, 5, 10, and 15% at the CO₂ swept zone. Oil viscosity remained constant at the earlier stage since no gas flow had arrived. Once the gas reached this point, oil viscosity decreased to the minimum. The case of 15% LPG had the lowest oil viscosity of 0.08 cp, while the case of 0% LPG mole faction had a value of 0.14 cp. After this reduction, oil viscosity started to increase due to influx of heavy fractions. The lowest IFT and viscosity values at this location are listed in Table 4.

Figure 6.

Gradual transition of relative permeability curves due to decreased IFT.

Table 4.

Lowest IFT and oil viscosity values at the CO₂ swept zone depending on LPG addition.

LPG addition (%)	IFT (dyne/cm)	Viscosity (cp)
0	3.85	0.14
1	3.20	0.13
2	2.89	0.13
3	2.38	0.12
4	2.01	0.12
5	1.69	0.11
6	1.45	0.11
7	1.18	0.11
8	0.93	0.10
9	0.71	0.09
10	0.65	0.09
11	0.56	0.09
12	0.36	0.09
13	0.21	0.08
14	0.14	0.08
15	0.08	0.08

Figure 7 shows the oil recovery factor of every case. Increased addition of LPG injection led to higher miscibility and lower viscosity and IFT, which resulted in higher oil recovery. Since producer BHPs were fixed for every case, oil recovery factor curves showed an increasing tendency with the addition of LPG. The case of 14% LPG injection produced 15% more oil than the 0% LPG injection case.

Figure 7.

Oil viscosity changes at the CO₂-swept zone with LPG concentration in the injection gas.

CO₂ sequestration

Figure 8 shows the amount of sequestrated CO₂ at the last moment as a function of LPG mole fraction, showing a different tendency than the pressure-controlled CCS–EOR cases. Since the producer bottom hole pressure was fixed, earlier breakthrough, which caused major differences in sequestered CO₂ in pressure-controlled miscibility adjustment cases, had minor effects on these cases. However, the greater was the miscibility, the greater was the mixing of gaseous CO₂, which had the potential to be trapped by trapping mechanisms, with oil.

Figure 8.

Oil recovery with LPG concentration in the injection gas.

Amount of dissolved CO₂ into water had a decreasing tendency as miscibility increased since the fugacity of gaseous CO₂, which acts as a major parameter in Henry’s law, decreased. Residually trapped CO₂ was primarily dependent on saturation of gaseous CO₂. However, the amount of gaseous CO₂ was reduced, resulting in reduction of residually trapped CO₂.

NPV calculation

Propane and butane prices were assumed to be $507.324 and $462.05/ton, respectively (OPIS, 2017). Since LPG was very expensive compared to CO₂, produced LPG was recycled by separation in a separator, transportation to a compression site, transportation back to a reservoir, and re-injection. Pipeline to transport LPG from reservoir to compression site was constructed and considered in NPV calculation as CAPEX and OPEX. In compression, the adiabatic gas exponent of LPG was determined to be 1.28, which was the same as that of CO₂. Also, the discount rate is 2.3% (Advanced Resources International, 2006). Table 5 shows the CAPEX and OPEX of every case. Since WHPs of gas injectors in every case were around 1100 psia, a pump was not installed. As mole fraction of LPG increased, injection mass rate increased, which led to increase in CAPEX and OPEX.

Table 5.

Cost of infrastructure in cases with increasing LPG addition into the injection gas ($).

LPG addition (%)	Compressor		Pipeline
LPG addition (%)	CAPEX	OPEX	CAPEX	OPEX
0	4,340,817	264,714	6,230,677	155,767
1	4,358,190	265,410	7,625,368	190,634
2	4,375,809	266,114	8,018,026	200,451
3	4,393,682	266,829	8,304,671	207,616
4	4,411,814	267,555	8,541,264	213,532
5	4,430,213	268,290	8,747,724	218,693
6	4,448,884	269,037	8,934,100	223,353
7	4,467,834	269,795	9,105,440	227,636
8	4,487,072	270,565	9,265,237	231,631
9	4,506,605	271,346	9,379,256	234,482
10	4,526,439	272,140	9,499,693	237,492
11	4,546,585	272,945	9,619,392	240,485
12	4,567,049	273,764	9,725,242	243,131
13	4,587,841	274,596	9,822,970	245,574
14	4,608,970	275,441	9,892,626	247,316
15	4,630,446	276,300	9,964,710	249,117

Purchase of additional butane was not needed until 7% LPG mole fraction. Cases with LPG composition of 8% or more needed additional butane since the amount of injected butane exceeded the produced butane. Likewise, additional propane was needed in cases with LPG composition of 13% or more.

Figure 9 shows NPV as a function of mole fraction and of immiscible, near miscible, and miscible cases. Oil recovery increased and NPV decreased from LPG mole fractions of 0–2% since compression cost increased due to the higher mass flow rate and addition of a pipeline. From LPG mole fractions of 2–13%, NPV increased considerably due to the miscibility effect. NPV started to decrease from 13 to 15% since the purchasing and injecting prices of required LPG exceeded the price of oil produced. As a result, the highest NPV had the value of $90,572,302 when 13% of LPG is added.

Figure 9.

Amount of sequestrated CO₂ as a function of LPG concentration.

Figure 10.

NPV as a function of LPG concentration.

Conclusions

Comprehensive CCS–EOR models were developed and analyzed to evaluate economic feasibility. Additional oil was produced by IFT, viscosity reduction, and relative permeability change, while CO₂ was sequestrated through solubility trapping, residual trapping, and free CO₂. Economic evaluations were performed considering revenues and costs.

Immiscible, near miscible, and miscible cases were characterized through the addition of LPG. Further reduction of IFT and oil viscosity resulted in higher oil recovery with LPG. Since a larger amount of CO₂ was mixed with oil by LPG addition, eventually producing a mixture with oil, a smaller amount of CO₂ was sequestrated. Cases of 1–2% LPG addition had a lower NPV than 0% (immiscible case), since costs from the additional infrastructure for LPG injection exceeded additional revenue. However, LPG addition of 3–13% showed gradually increased NPV, with a maximum of 91 MM$. Finally, NPVs from LPG addition of 14–15% decreased due to costs exceeding revenues.

In this study, integrated CCS–EOR models based on economic analysis have been developed and applied for various operating conditions. It overcomes the limitations of previous studies that have not considered the effect of miscibility on economic feasibility of CCS–EOR. The developed model can be extended for the optimum field application with the optimization of various operating factors such as WAG ratio, injection scheme, and LPG composition.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Dr Jinhyung Cho was partially supported by the National Research Foundation of Korea (NRF) (Grant No. 2019R1C1C1002574) and Korea Gas Corporation (KOGAS).

ORCID iD

Kun S Lee

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Techno-economic analysis of intermediate hydrocarbon injection on coupled CO 2 storage and enhanced oil recovery

Abstract

Keywords

Introduction

Numerical model

Residual and solubility trappings

Relative permeability alteration

Economic parameters calculation

Compressor and pump

Transportation

NPV calculation

Results

Fluid modeling

Model description

Miscibility adjustment through LPG addition

Enhanced oil recovery

CO2 sequestration

NPV calculation

Conclusions

Footnotes

Declaration of conflicting interests

Funding

ORCID iD

References

CO₂ sequestration