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Exploring safe disposal of CO2 and wastewater in saline aquifers
Journal of Petroleum Science and Engineering 170 (2018) 197–205
Contents lists available at ScienceDirect
Journal of Petroleum Science and Engineering journal homepage: www.elsevier.com/locate/petrol
Exploring safe disposal of CO2 and wastewater in saline aquifers a
a
b,∗
T.N. Phan , M.P. Gogri , C.S. Kabir a b
, Z.A. Reza
T
a
University of Oklahoma, United States University of Houston, United States
A R T I C LE I N FO
A B S T R A C T
Keywords: Safe disposal of CO2 & wastewater In saline aquifers Use of injection diagnostic tool Assessment of injected pore volume Probabilistic modeling approach
Process safety has taken the spotlight in recent years for both carbon sequestration and wastewater disposal operations. We present a systematic approach to monitoring injection-well performance in saline aquifers for both wastewater disposal and CO2 injection with required well surveillance and injection data. Uncertainty assessment formed the cornerstone of understanding the fundamental variables impacting the injection performance with numerical modeling. Among the variables studied, aquifer heterogeneity and injection parameters, rock compressibility, and fracture gradient have a significant influence on total cumulative injection and safe-volume injection. The modified-Hall analysis turned out to be the critical diagnostic tool for identifying the abnormal injection behavior and real-time disposal process for both fluids. We estimated the safe pore-volume injection for disposal of wastewater and CO2 to be 1.6% and 1.5%, respectively, in saline aquifers at 80% probability. Of the CO2 trapping mechanisms studied here, critical gas saturation turned out to be the most dominant in residual trapping mechanism, whereas porosity and brine salinity appeared dominant in solubility trapping mechanism.
1. Introduction According to IEA GHG (2008), saline aquifers are the most important among three main options for CO2 sequestration and storage capacity to reduce greenhouse gas emissions compared to the other two options, such as those in depleted hydrocarbon reservoirs and unminable coal seams. Safe sequestration and storage of greenhouse gases, such as CO2 to mitigate their adverse impact on the environment are gaining increased attention in all industries. At the same time, to sustain oil and gas exploitation from tight unconventional reservoirs, we have to ensure safe disposal of a significant volume of completion and produced wastewater. Most studies in carbon sequestration focus on how to efficiently store and sequester carbon in a sustained manner emphasizing on the trapping mechanisms. In this study, we emphasize on how much we can safely inject without any adverse effect. The other objective of the study is to draw a parallel between carbon sequestration and wastewater disposal operations concerning injectivity diagnostics. Numerous studies have appeared on CO2 storage efficiency in deep saline aquifers. Bachu (2015) conducted a comprehensive review of CO2 storage capacity in saline aquifers. He summarized the storage efficiency published in the literature to vary from less than 1% to greater than 10%. A multitude of factors, such as characteristics of the storage aquifer (pressure, temperature, salinity, porosity, permeability,
∗
Corresponding author. E-mail address: [email protected] (C.S. Kabir).
https://doi.org/10.1016/j.petrol.2018.06.065 Received 4 February 2018; Received in revised form 11 May 2018; Accepted 21 June 2018 Available online 22 June 2018 0920-4105/ © 2018 Elsevier B.V. All rights reserved.
heterogeneity and anisotropy), confining aquitards, attributes of a CO2 storage operation and regulatory constraints all contribute to this uncertainty range. Song et al. (2014) revealed that high heterogeneity coefficient in the reservoir and high CO2 injection rates lead to low sweep efficiency and impair CO2 storage potential in a reservoir. Hongjun et al. (2010) studied the effects of main reservoir parameters including vertical to horizontal permeability ratio (kv/kh), salinity, and residual-phase saturation of CO2 sequestration and storage in saline aquifers. The results from his model showed that dissolution of CO2 in aquifers decreases with salinity increase, but the low kv/kh increases the dissolution of CO2 because of lateral flow in the injection interval of CO2. Also, the CO2 dissolution increases with the decrease in critical gas saturation. Nghiem et al. (2009) examined the two most important mechanisms for CO2 storage in saline aquifers, namely residual trapping, and solubility trapping. Their results showed a significant increase of residual-gas trapping with the use of water injection in the low-permeability aquifers. Ide et al. (2007) investigated the effects of gravity, viscous and capillary forces on amount and timing of trapping. In closed aquifers, storage efficiency depends on the rock and fluid compressibility (Bachu, 2015). Despite the prolific studies of CO2 storage efficiency in saline aquifers, real-time monitoring of well injection performance appears very limited. Hall (1963) initiated the diagnostic analysis of water injection wells. More recently, Izgec and Kabir (2009, 2011) suggested a new
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formulation of the Hall analysis. Their modified-Hall analysis involves analysis of the separation of the derivative of the Hall-integral (DHI) from the Hall-integral (IH) curve. This separation helps to identify the characteristics of a high-permeability channel as the DHI separates lower than the IH, and indicates formation plugging when the DHI curve rises above the IH. In simple terms, the IH curve provides information on the time evolution of pressure buildup around the disposal well due to injection. In contrast, DHI indicates the rate at which the pressure buildup develops. When the well struggles to inject, the pressure buildup happens rapidly with little effective injection. This situation gives rise to high DHI, but IH remains stable. On the other hand, when the well experiences fracture or any fluid flow path of low resistance, the pressure does not increase but the injection volume will increase. This situation points to a small DHI value with stable IH. We use this change or separation in the two curves (DHI and IH) to identify abnormal injection behavior. Recently, Gogri et al. (2018) demonstrated the usefulness of the modified-Hall approach for safe water-disposal operations by ascertaining the point of departure from normal injection behavior. Given the need for disposal of both fluids in some production-operation settings, this study explores various factors in a probabilistic framework to understand their relative importance in disposal operations of both CO2 and waste-water to obtain a comparative picture. These variables include aquifer heterogeneity, vertical anisotropy, porosity, permeability, salinity, rock and fluid compressibility. Thereafter, safe-volume injection for both scenarios of waste-water and CO2 injection is derived from abnormal behavior injection, identified from the modified-Hall plot. Finally, we evaluate the two primary CO2 trapping mechanisms contributing to overall CO2 storage capacity, namely residual-gas trapping and solubility trapping.
Fig. 1. Workflow of injection-well performance monitoring (after Gogri et al., 2018).
3. Injection-well monitoring workflow and model setup Studying the injection-zone reservoir dynamics under various conditions becomes critical for designing and implementing the safe injection operations. Fig. 1 presents the proposed workflow for investigating the injection process for both water-disposal and CO2 injection in saline aquifers. First, we constructed a 3D reservoir model containing the aquifer zone separated by a barrier. Second, we geostatistically distributed petrophysical properties (porosity and permeability) constrained to synthetic well-logs and information based on Elsayed et al., 1993 and Wegelin 1985. Fluids and rock-physics models are created using data from Elsayed et al. (1993), Firoozabadi et al., 1992, Riding and Rochelle 2005 and Wegelin 1985. Logs are not shown in this paper as 3D reservoir modeling is not the primary focus of this study. Subsequently, we designed and completed injection well for disposal zone. In this paper, we considered single injector vertical well models only. The well completions included casing, injection tubing, isolation packers and the perforation interval of about 40 ft. The well operational conditions were set via allocation of injection rates with appropriate bottomhole-pressure limits under the fracture gradient pressure conditions. Then, the uncertainty analysis followed using the Design of Experiments (DoE) to capture the complexity of saline aquifer's heterogeneity and injection-well performance. Following the above workflow, we examined the saline aquifer with characteristics of the Weyburn field, located in southeastern Saskatchewan, Canada. Fig. 2 presents the corner-point grid model involving 50 × 50 × 42 grid cells for the 320-acre aquifer at a depth of 4500 ft. Table 1 specifies the typical input parameters with its appropriate uncertainty ranges used to set up the simulation runs for both water-disposal and CO2-injection scenarios. Fig. 3 displays the normal and lognormal distribution of porosity and permeability of this heterogeneous aquifer model. Fig. 4 shows 3D views of porosity and permeability from one of the realizations. The porosity ranges from 0.08 to 0.25, with an average value of about 0.16. Permeability within this aquifer ranges from 0.5 to 30 md, with the average of 5 md. Furthermore, several factors, such as pore volume multiplier and permeability multiplier are considered in the Design of Experiment (DoE) matrix to capture the wide range of petrophysical parameters and heterogeneous aquifer. As a result, porosity can range from 0.06 to 0.3 and permeability ranges less than 0.1 to greater than 100 md for the Weyburn formation. These values conform to those
2. Comparison of CO2 sequestration and waste-water injection A comprehensive comparative investigation of injectivity diagnostics in carbon sequestration and waste-water disposal operations is rare in the literature. Notwithstanding, standard and more advanced injection diagnostics will apply to both these operations. In the following, we note several studies alluding to the comparison of water and CO2 injection. Maxwell et al. (2008) compared the effect of using water-based gel and liquid CO2 in supercritical state as fracturing fluids. They show the seismic efficiency (that is, the ability to induce acoustic events) for both the fracturing fluids was the same. Verdon et al. (2010) also compared passive seismic monitoring results using water-based gel and supercritical CO2 as hydraulic fracturing fluids at similar conditions. Both the fluids induced events with similar magnitudes and occurred at comparable rates. He also suggested that the event magnitudes correlate to the injection pressures. Verdon (2014) studied similarities between wastewater injection and CO2 storage. He emphasized that the change in stress acting on the faults plays the key role in reactivating the faults. Because the amount of pore space occupied during injection determines the pore pressure, the seismicity imprint does not change during injection of CO2 as opposed to water despite the difference in their physical properties. The study also inferred the role the injected fluid density plays in determining the location of induced earthquakes. This study shows the similarity or lack thereof of injectivity diagnostics of carbon sequestration and wastewater disposal operations. We aim to devise a safe injection monitoring scheme for both the processes. To reach our goals, we developed numerical flow models in realistic aquifer settings, learned from the injection-well performance strategy, identified independent variables impacting such performance using the statistical design of experiments, and explored significant trapping mechanisms for CO2.
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cases, the bottomhole pressure, cumulative injection and injectant/ native fluid interface pressure, which is designed as pe, were extracted. The workflow started with the generation of injection rate and pressure profiles. Thereafter, we calculated the Hall Integral (IH) and its derivative with respect to cumulative injection (DHI) to plot against the cumulative injection. As Fig. 6 shows, the departure point occurs when the DHI curve starts deviating from its IH counterpart. The safe cumulative injection during both the wastewater disposal and CO2 injection correspond to the departure points, and further analysis of injection behavior using this information follows. The wastewater-disposal scenario yields smooth lines for IH and DHI at normal injection condition, while the higher compressibility CO2 results in DHI fluctuations. Despite that different behavior in the modified-Hall analysis plots, the departure point can be obtained in both scenarios for the safe cumulative injection volume without any ambiguity. Our analysis involves calculation of percent safe pore-volume injection (PVID%) using the cumulative injection at the departure point (CID). This variable is defined as the cumulative injected pore-volume divided by the total pore volume of the injection compartment. This metric constitutes the primary dependent variable in discerning the safe, operable limits. The underlying objective is to identify the limiting conditions of variables for safe operating conditions.
Fig. 2. Schematic of 3D aquifer model with characteristics of the Weyburn field.
suggested by Elsayed et al. (1993). The vertical anisotropy (k v / kh ) ranged from 0.01 to 0.1. Appropriate ranges of aquifer characteristics, such as formation salinity, formation water density, formation-water compressibility, and rock compressibility originate from available sources (Riding and Rochelle, 2005; Wegelin, 1985; Firoozabadi et al., 1992). The parameters related to the gas relative-permeability curve and hysteresis effects, such as critical gas saturation, connate-gas saturation, and the gas relative permeability at connate liquid and residual liquid conditions are also examined to study the impact of CO2 injection on the overall injection behavior and the consequent storage capacity. Thereafter, several hundreds of DoE simulation runs followed to generate the reservoir behavior for evaluating the impact of the array of input variables. These runs allowed generation of performance plots and Pareto charts to highlight the most influential dependent variables affecting the injection-well performance, namely cumulative injection volume and safe pore-volume injection. We defer our discussion on the injection-well performance of carbon sequestration and waste-water disposal wells under various subsurface and operational conditions until we present the safe injection-well condition and monitoring of injection wells.
5. Impact of key variables for well-injection monitoring in saline aquifers We define safe cumulative injection as the maximum allowable volume of fluid into the aquifer at normal injection conditions. Initially, we investigated the total cumulative injection for both wastewater disposal and CO2 injection in saline aquifers under various subsurface and operational conditions. Fig. 7 shows the Pareto charts of the primary uncertain variables affecting cumulative injection for both scenarios of wastewater disposal (Fig. 7a) and CO2 injection (Fig. 7b). Three major uncertain variables impacting the total cumulative injection are fracture gradient (or the limiting bottomhole pressure gradient), formation compressibility, and formation porosity for both wastewater disposal and CO2 injection. Fig. 8 shows examples of departure-point determination for both scenarios of water disposal (Fig. 8a) and CO2 injection (Fig. 8b). For brevity, we show only four randomly-selected simulation cases from the DoE runs in the figure. In these plots, x -axis is cumulative water injection (MRB) in Fig. 8a and cumulative CO2 injection (MRB) in Fig. 8b, respectively, while y -axis represents (psi-MRB) IH (blue) and DHI (orange). The black dots represent the departure points in Fig. 8. Fig. 9 showing the Pareto charts indicate the major uncertain variables affecting safe-volume injection for both scenarios of water disposal (Fig. 9a) and CO2 injection (Fig. 9b). In these Pareto plots, we
4. Diagnostics for injection-well monitoring We used the modified-Hall plot suggested by Izgec and Kabir (2009, 2011) to monitor disposal of both wastewater and CO2. In this study, safe water-disposal and CO2-injection operations can be achieved by continuous monitoring of the injection process and check the injection behavior. Fig. 5 presents a workflow to monitor and identify the abnormal injection behavior, which was applied to the previous section for both scenarios. Upon completion of the simulation runs for both Table 1 Range of independent variables used in the study. Variables
Low
Likely
High
Water Disposal
CO2 Injection
Aquifer porosity, fraction Aquifer permeability, mD Pore volume multiplier Permeability multiplier Vertical anisotropy Aquifer formation compressibility, psi−1 Formation water compressibility, psi−1 Formation water density, lb/ft3 Formation water salinity, ppm Fracture gradient, psi/ft Connate gas saturation Critical gas saturation Gas relative permeability at liquid saturation Gas relative permeability at residual liquid saturation
0.06 0.1 0.75 0.2 0.01 5E-6 2E-06 63 40,000 0.6 0 0.05 0.8 0.35
0.16 5 1 1 0.05 8E-06 3E-06 70 90,000 0.7 0.025 0.1 0.9 0.45
0.3 150 1.25 5 0.1 2E-05 4E-06 75 120,000 0.8 0.05 0.2 1 0.65
✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
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60000
14000
50000
10000
Frequency
Frequency
12000
8000 6000
40000 30000 20000
4000 10000
2000 0
0 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
0.08 0.1 0.12 0.14 0.16 0.18 0.2 0.22 0.24
Porosity
Permeability (md)
(a)
(b)
Fig. 3. Porosity (a) and permeability (b) distribution of heterogeneous 3D reservoir system.
show only those uncertain variables with more than 2% significance level. For waste-water disposal in saline aquifers, only two uncertain variables (as evident in Fig. 9a) turn out to be significant for the safevolume injection. These are fracture gradient (or the limiting bottomhole pressure gradient) and formation compressibility. We note that uncertainty characterization is a dynamic process and very much dependent on the ranges of the uncertain variables used. For the safevolume CO2 injection in saline aquifers, the significant variables are formation porosity, formation compressibility, fracture gradient (or the limiting bottom-hole pressure gradient), formation permeability and to some extent the vertical anisotropy. The significance of some of these variables can be justified by different trapping phenomena present in the case of CO2 injection, namely residual-gas trapping (or hydrodynamic trapping) and solubility trapping. These aspects will be explored later in more detail. Furthermore, in the wastewater disposal scenario, fracture gradient turned out to be the most influential variable impacting the cumulative injection and safe pore-volume injection, as exhibited in Figs. 7a and 9a, respectively. The rock compressibility also plays an important role. In contrast, rock compressibility plays a dominant role in the CO2 injection performance. In closed aquifers, the storage efficiency depends mostly on the compressibility of the system, as discussed by Bachu (2015). As Figs. 7b and 9b suggest porosity, rock compressibility, and fracture gradient all have a relatively high impact on the CO2 injection performance. In other words, different characteristics of the two fluids produce somewhat different outcome based on the underlying physics. Fig. 10 displays the histogram of PVID% (that is, percent safe porevolume injection) obtained from the analysis of more than 250 simulation runs for each scenario of both fluids. We identified with 80% probability that safe pore-volume injection occurs at 1.6% PV for wastewater disposal and 1.5% PV for CO2 injection at the trigger point of the abnormal behavior.
Fig. 5. Workflow for diagnostics of injection-well monitoring (after Gogri, 2018).
6. CO2 injection diagnostics using MHA and RTA The injection-well monitoring approach using Modified Hall Analysis (MHA) presented in the previous section can be further augmented using Rate Transient Analysis (RTA). Using RTA, we can characterize the compartment size of the disposal zone that the injection well is connected to. We note that the fundamental purpose of RTA as used in the reservoir-engineering discipline pivots on estimating the
Fig. 4. 3D representation of porosity (a) and permeability (b) models from one of the realizations used in the DoE simulation runs. 200
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Fig. 6. Departure point identification in the modified-Hall plot; (a) waste-water disposal and (b) CO2 injection.
connected pore volume in the presence of late-time boundary-dominated flow in producing wells. We attempt to replicate the same idea in the context of available and contiguous pore volume connected to the injection well. The knowledge of this connected compartment volume facilitates us to monitor if the injection is within the safe-injection limits in terms of percent pore-volume injected. In a previous article (Gogri et al., 2018), we proposed the same notion towards waste-water injection monitoring. In the following, we examined the efficacy of determining the compartment size of the closed bounded-aquifer using RTA. Fig. 11 shows the RTA results, including the pressure and injection-rate match, the log-log and the Blasingame type-curve plots, as detailed in Houze et al. (2017). Using RTA, the compartment volume was estimated to be 85 MMRB pore volume for a simulated case of 78 MMRB pore volume. The difference in the connected volume can be attributed to the very different nature of the analyses in the semi-analytical formulation of RTA and robust multi-physics formulation in a numerical reservoir simulator. We note that in RTA a homogenized reservoir model is conceptualized. Notwithstanding, these results validate that RTA technique can yield a reasonable estimate of the compartment size. In the following section, we discuss the trapping mechanisms present in CO2 injection in saline aquifers. We also investigated the importance of different uncertain variables on the major trapping mechanisms relevant to CO2 injection in saline aquifers.
is hydrodynamic trapping which is nothing but the extremely slow migration of the fluids under gravitational and buoyancy forces. Residual-gas trapping is a form of hydrodynamic trapping. On the geochemical trapping front, solubility trapping and mineral trapping are the major ones. In the solubility-trapping mechanism, brine dissolves supercritical CO2 making the aqueous phase denser and promoting migration of the aqueous phase into deeper formations. Mineral trapping is the slowest of all and involves chemical reaction with the host rock and forming solid minerals. In this section, we explore a few trapping mechanisms, such as residual-gas trapping and solubility trapping, and their consequential impact on the dependent variables of interest. Both wettability and capillary effects in porous media trigger the residual gas trapping, as discussed by Nghiem et al. (2009). Juanes et al. (2006) described that the snap-off dominates the trapping mechanism in pore space. During the drainage process engendered by CO2-injection, the nonwettingphase CO2 gas saturation increases and tends to move to the top of aquifers due to buoyancy forces, as depicted in Fig. 12a. Once the injection stops, water displaces gas in an imbibition process resulting in increased water saturation at the trailing edge. Thereby, trapped gas is formed by snap-off effect and residual, immobile CO2 gets left behind and remains in the aquifer (Fig. 12b). Mathematically, the gas hysteresis is commonly modeled by using Land and Carlson (CMG GEM, 2016) approach. The trapped-gas saturation Sgt is determined by:
7. Main trapping mechanisms for supercritical CO2 injection
Sgt =
Carbon sequestration efficacy strongly depends on several physical and geochemical trapping mechanisms (IPCC, 2005). Stratigraphic and structural trapping (which is a physical trapping mechanism) plays a significant role in the initial stage of the sequestration process. Essentially, low permeability formations trap the movement of the injected CO2. Capillary trapping is a very similar physical trapping process that involves rendering CO2 immobile due to the presence of strong capillary forces at pore-scale level. Another physical trapping phenomenon
Rock compressibility
C=
(2)
Rock Compressibility
1.00
Fracture gradient
0.26
Formation water density
1 1 − Sgtmax Sgmax
In Eq. (2), Sgtmax represents the maximum trapped gas saturation
0.61
Porosity
(1)
where Sgi represents the actual gas-saturation corresponding to flow reversal (drainage to imbibition or vice versa) and C is known as Land's parameter, which is given by the following expression:
1.00
Fracture gradient
Sgi 1 + CSgi
0.55
0.27
Porosity
0.02
Krg at residual liquid
(a)
0.03
(b)
Fig. 7. Impact of critical variables on total cumulative injection for (a) wastewater disposal and (b) CO2 injection. 201
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Fig. 8. Examples of departure-point (shown in black dots) determination for both scenarios of water disposal (a) and CO2 injection (b). x -axis is cumulative water injection (MRB) in (a) CO2 injection (MRB) (b) and y -axis represents (psi-MRB) Hall Integral (blue) and derivative of Hall Integral (orange). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
presented similar findings. We considered two saline aquifer scenarios by changing key aquifer parameters, namely depth, pressure, and temperature, as shown in Table 2, to address their impact on two foremost CO2 trapping mechanisms. The result shows a relative reduction in residual gas trapping, but with an increase in CO2 solubility, as illustrated in Fig. 14. CO2 solubility increases with increasing pressure but decreases with increasing temperature, as testified by other studies of Bachu (2015), Span and Wagner (1996), Enick and Klara (1990), and Wiebe and Gaddy (1940). In other words, pressure change contributes more impact than its temperature counterpart, leading to increased CO2 solubility in the deep aquifer case. Table 3 presents a summary of the results of both safe-injection volume and the attendant contributions of the trapping mechanisms in a probabilistic framework. Note that the safe injection volumes were derived from the principle shown earlier in Fig. 8, which used the departure point on the modified-Hall plots. Dominance of the residual trapping mechanism of the CO2 becomes readily transparent.
while Sgmax the maximum gas saturation associated with imbibition relative-permeability curve. Nghiem et al. (2009) and Juanes et al. (2006) provide details on trapping and relative-permeability hysteresis model. Given that CO2 is highly soluble in brine, solubility trapping turns out to be an essential process relevant to safe CO2 storage in saline aquifers. In the following, we consider residual-gas trapping and CO2 dissolution trapping to be the primary mechanisms for CO2 storage. From more than 250 flow simulation runs in 60 years, mass-mole of trapped CO2 of residual gas and solubility are tracked corresponding to specific uncertainty ranges identified in Table 1. Fig. 13 displays the Pareto charts identifying the principal variables impacting the two trapping mechanisms. Critical gas saturation Sgcrit is by far the most critical variable (as illustrated in Fig. 13a) for CO2 residual-trapping volume due to the hysteresis effect. Higher critical gas saturation allows more immobile gas to be trapped in the aquifer formation. Fluid-migration related parameters, such as gas relative permeability at the residual liquid saturation, horizontal permeability and vertical to horizontal permeability ratio k v / kh have a moderate impact on CO2 residual-trapping volume. For CO2 solubility volume (as evident in Fig. 13b), porosity and brine salinity are the dominant factors, followed by rock compressibility and fracture gradient. While ignoring the ionic and mineral trapping effect, CO2 solubility largely depends on brine volume and salinity concentration. Earlier, Hongjun et al. (2010)
We presented a systematic approach to monitoring the injectionwell behavior in saline aquifers using subsurface characteristics in 3D reservoir models, followed by diagnostic analysis of real-time injection
[VALUE]
Fracture gradient
1.00
Porosity
0.41
Rock compressibility
[VALUE]
Rock compressibility Formation water density
8. Discussion
0.23
Fracture gradient
0.02
Permeability Vertical anisotropy Krg at residual liquid
0.11 0.07 0.03
Fig. 9. Impact of critical parameters on safe-volume injection, (a) for waste-water disposal and (b) for CO2 injection. 202
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100
50
100
40
20
40
10
20
Frequency
60
Cumulative, %
Frequency
1.6% PV
30
80
35 30
1.5% PV
25 20
60 40
15 10
Cumulative, %
80
40
20
5 0
0
0
0
0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 2.7 3
0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 2.7 3 3.3
% Pore-Volume Water Injection
% Pore-Volume CO2 Injection
(a)
(b)
Fig. 10. Histograms of percent pore-volume injection for waste-water disposal (a) and CO2 injection (b).
Another aspect of CO2-injection in saline aquifers is a chemical reaction of the injected CO2 with surrounding rock matrix (mineral trapping). Chemical reaction likely occurs over a long timescale, especially after the injection period (Bachu, 2015; IPCC, 2005). Therefore, mineral trapping does not contribute significantly to CO2 storage during the injection period (Bachu, 2015). Our approach did not take account of this phenomenon into modeling CO2-injection in saline aquifers. Instead, we focused on residual gas and CO2 solubility as the principal trapping mechanisms for CO2 storage efficiency. Overall, the results pointed to 1.6% and 1.5% pore volume as the maximum injection container for water-disposal and CO2-injection, respectively, with 80% probability. These allowable injection volumes are in substantial alignment with CO2 storage efficiency figures for limestone formations discussed in previous studies. In this context, Gogri et al. (2018) found 2% PV injection with 80% probability for wastewater disposal in sandstone formations. Earlier, Ehlig-Economides and Economides (2010) suggested 1% of supercritical CO2 injection, while Zhou et al. (2008) proposed 0.5% for the same fluid with simplified analytical modeling. More recently, Peck et al. (2018) explored
data. In fact, this method can be pursued to obtain the limits of safevolume injection for both wastewater disposal and CO2 injection in saline aquifers to avoid over-limit of injected volumes and maintain seal integrity of the target reservoir segment. Real-time injection monitoring can be achieved efficiently by setting alerts to cease injection once the safe-volume approaches, as indicated in the modified-Hall plot. Of course, as shown elsewhere by Gogri et al. (2018), periodic analysis of the connected-pore volume by way of rate-transient analysis will be particularly useful. Moreover, additional surveillance data; for example, temperature survey and shut-in tests will provide a complimentary evaluation of the well-injection monitoring process. For cost implication of the injection-monitoring process using the proposed approach, as mentioned above we simply need pressure and injection rate data (through surveillance or monitoring) and the initialreservoir pressure around the injector. Wellhead pressure and injection rate are usually measured for most modern injection wells by regulation. Also, the initial pressure is determined for most wells. That means there is no additional cost to implement this monitoring approach provided pressure, rates and initial pressure data are available.
Fig. 11. RTA results - injection-rate (a) and pressure (b) match, log-log diagnostic plot (c) and Blasingame type-curve (d) plot. 203
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Fig. 12. Residual gas trapping due to hysteresis effect: (a) drainage process and (b) imbibition process.
the CO2 storage efficiency factor in the context of enhanced recovery processes. Collectively, the prior studies appear to suggest that the saline aquifers offer somewhat limited scope in overall storage capacity. Perhaps depleted hydrocarbon reservoirs provide more substantial opportunity. To determine abnormal injection behavior in this study, we used a percentage threshold to determine the departure points. For CO2 injection cases, we note the DHI curves may sometimes reveal an initial upward concavity curvature due to the compressibility effect. We ignore this deviation as this does not reflect the state of injection. In most scenarios, the compressibility effect dissipates at high pressures. Additionally, we have performed DoE with more than 250 simulation runs. The subjectivity in the departure point identification will not have a meaningful effect on the 80-percent probability values or on P-10, P50 and P-90 values that we are reporting. We must add that any computational or numerical algorithm is not immune from this level of subjectivity. It must be pointed out that in this study, we have used synthetic models to provide the proof of concept. However, there is no reason why the injection diagnostics and monitoring cannot be applied in field pilots or live wells. Working with real data (having lots of noise) is always challenging. However, one great characteristic of the modifiedHall Integral is the fact that we are dealing with an integral, which should have a smooth profile. We note that field experiences show that the derivative of the Hall Integral does not create any obstacle, as shown by Izgec and Kabir (2009, 2011), among others, published since that time. Based on our experience using the statistical DoE that we investigated in this study, we observed the timing of the departure from normal injection occurs about 10 years in an average sense, for the ranges of the variables examined in this study. We are currently working on the uncertainty aspect of the time to depart from normal injection behavior, which will be the focus of a future article.
Critical gas saturation
Scenarios
Depth (ft)
Pressure (psia)
Temperature (oF)
Shallow aquifer Deep aquifer
4500 9000
2400 3900
160 215
8
CO2 trapped mass moles, 1E8
7 6 5 4 3 Solubility Shallow Aquifer Solubility Deep Aquifer Residual Shallow Aquifer Residual Deep Aquifer
2 1 0 0
10
Horizontal permeability
30
40
50
60
Fig. 14. CO2 trapped mole profiles for shallow and deep aquifers.
9. Conclusions This study provided a systematic approach to monitor well-injection operation in saline aquifers for both wastewater and CO2 disposal. Both 3D numerical reservoir modeling and a diagnostic approach for monitoring surveillance data paved the way for the following findings: Porosity
1.0
Brine salinity
0.21
0.65
Rock compressibility
0.15
Fracture gradient Vertical anisotropy
20
Years
1.0
Krg at residual liquid condition Rock compressibility
Table 2 Shallow and deep aquifer investigation for carbon sequestration.
0.10 0.05
(a)
0.39 0.24
Krg at residual liquid condition
0.11
Vertical anisotropy
0.07
(b)
Fig. 13. Effect of independent variables on CO2 residual trapping (a) and CO2 solubility trapping (b). 204
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Table 3 Probabilistic projections of CO2 safe injection volumes and contributions of trapping mechanisms. Probability
P-90 P-50 P-10
CO2 Safe Injection Volume
CO2 Trapping Mechanisms
ton/acre
ton/acre/year
Residual, ton/ acre
Solubility, ton/acre
148 284 500
2.5 4.7 8.3
123 223 300
20 37 51
Enick, R.M., Klara, S.M., 1990. CO2 solubility in water and brine under reservoir conditions. Chem. Eng. Commun. 90 (1), 23–33. Firoozabadi, A., Ottesen, B., Mikkelsen, M., 1992. Measurements of supersaturation and critical gas saturation. SPE Form. Eval. 7 (4), 337–344. Gogri, M.P., Rohleder, J., Kabir, C.S., et al., 2018. Prognosis for safe water-disposal-well operations and practices that are based on reservoir flow modeling and real-time performance analysis. SPE Res Eval & Eng. http://dx.doi.org/10.2118/187083-PA. SPE-187083-PA. Gogri, M., 2018. Investigation and Real-time Monitoring for Safe Waste-water Disposal with a Focus on Arbuckle Group, Oklahoma. MS thesis. University of Oklahoma, Norman, Oklahoma (January 2018). Hall, H.N., 1963. How to analyze waterflood injection well performance. World Oil (October) 128–130. Hongjun, Z., Xinwei, L., Yangfang, C., et al., 2010. Sensitivity analysis of co2 sequestration in saline aquifers. Petrol. Sci. 7 (3) 372–278. Houze, O., Viturat, D., Fjaere, O.S., 2017. Dynamic Data Analysis. v. 5.10. Chap. 4. https://www.kappaeng.com/documents/flip/dda512/. Ide, S., Jessen, K., Franklin, M., 2007. Storage of CO2 in saline aquifers: effects of gravity, viscous, and cappilary forces on amount and timing of trapping. Int J Greenh Gas Contr 1, 481–491. IEA Greenhouse Gas R&D (IEA GHG, November 2008. Aquifer Storage-development Issues, 2008/12. http://ieaghg.org/docs/General_Docs/Reports/2008-12.pdf. IPCC (Intergovernmental Panel on Climate Change), 2005. Special Report on Carbon Dioxide Capture and Storage. Cambridge University Press, Cambridge, UK, New York, NY, USA. Izgec, B., Kabir, C.S., 2009. Real-time performance analysis of water-injection wells. SPE Reservoir Eval. Eng. 12 (1), 116–123. Izgec, B., Kabir, C.S., 2011. Identification and classification of high-conductive layers in waterfloods. SPE Reservoir Eval. Eng. 14 (1), 113–119. Juanes, R., Spiteri, E.J., Orr Jr., F.M., Blunt, M., 2006. Impact of relative permeability hysteresis on geological CO2 storage. Water Resour. Res. 42 (12), W12418. Maxwell, S.C., Shemeta, J., Campbell, E., et al., 2008. Microseismic deformation rate monitoring. In: Paper SPE-116596-MS Presented at the SPE Annual Conference and Exhibition in Denver, Colorado, 21–24 September 2008. Nghiem, L., Yang, C., Shrivastava, V., et al., 2009. Risk mitigation through the optimization of residual gas and solubility trapping for CO2 storage in saline aquifers. Energy Procedia 1 (1), 3015–3022. Peck, W.D., Azzolina, N.A., Ge, J., et al., 2018. Quantifying CO2 storage efficiency factors in hydrocarbon reservoirs: a detailed look at CO2 enhanced oil recovery. Int. J. Greenh. Gas Control 69, 41–51. Riding, J., Rochelle, C., 2005. The IEA Weyburn CO2 Monitoring and Storage Project. Final Report of the European Research Team. British Geological Survey, pp. 54 RR/ 05/03. Song, H., Huang, G., Li, T., et al., 2014. Analytical model of CO2 storage efficiency in saline aquifer with vertical heterogeneity. J. Nat. Gas Sci. Eng. 18, 77–89. Span, P., Wagner, W., 1996. Equation of state for carbon dioxide covering the fluid region from the triple point temperature to 1100K at pressures up to 800 MPa. J. Chem. Ref. Data 25 (6), 1509–1596. Verdon, J.P., Kendall, J.M., Maxwell, S.C., 2010. A comparison of passive seismic monitoring of fracture stimulation from water and CO2 injection. Geophysics 75 (3), MA1–MA7. Verdon, J.P., 2014. Significance for secure CO2 storage of earthquakes induced by fluid injection. Environ. Res. Lett. 9 (6), 064022. Wegelin, A., 1985. Reservoir characteristics of the Weyburn field, southeastern saskatchewan. In: Presented at the First Annual Technical Meeting of the South Saskatchewan Section, Petroleum Society of CIM, Regina, September 15-17, 1985. PETSOC-SS-85-17. Wiebe, R., Gaddy, V., 1940. The Solubility of Carbon Dioxide in Water at Various Temperatures from 12 to 40o and at Pressures to 500 Atmospheres. Critical Phenomena. Zhou, Q., Birkholzer, J.T., Tsang, C.F., et al., 2008. A method for quick assessment of CO2 storage capacity in closed and semi-closed saline formations. Int J Greenh Gas Control 2 (4), 626–639.
1. Fracture gradient, rock compressibility, porosity, and permeability play dominant roles in CO2 injection monitoring in saline aquifers. Solubility and residual trapping are important to CO2 storage efficiency in saline aquifers. Critical gas saturation has a strong influence on residual CO2 trapping, while porosity and brine salinity in the formation plays a significant role in solubility trapping. 2. The modified-Hall plot aided in monitoring both wastewater and CO2 sequestration and provided the necessary diagnostics to identify the abnormal injection condition. 3. In sealed saline aquifers, the safe wastewater disposal volume turned out to be about 1.6% PV, and safe CO2 injection is 1.5% PV with 80% probability within the scope of this investigation. Nomenclature
DHI IH k v / kh C Sgt Sgi Sgtmax
derivative of Hall integral, psi-day Hall integral, psi-day vertical to horizontal permeability ratio, fraction Land's parameter, constant trapped-gas saturation, fraction actual gas saturation corresponding to flow reversal, fraction maximum trapped-gas saturation, fraction
Acknowledgment The authors thank Computer Modeling Group, Ltd. (CMG), Kappa Engineering and Schlumberger for providing their software used in this study. References Bachu, S., 2015. Review of CO2 storage efficiency in deep saline aquifers. Int. J. Greenh. Gas Control 40, 188–202. CMG Ltd, 2016. GEM Userguide. Compositional and Unconventional Reservoir Simulator, Version 2016. Ehlig-Economides, C., Economides, M.J., 2010. Sequestering carbon dioxide in a closed underground volume. J. Petrol. Sci. Eng. 70 (1–2), 123–130. Elsayed, S., Baker, R., Churcher, P., et al., 1993. Multidisciplinary reservoir characterization and simulation study of the Weyburn unit. J. Petrol. Technol. 45 (10), 930–934.
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Contents lists available at ScienceDirect
Journal of Petroleum Science and Engineering journal homepage: www.elsevier.com/locate/petrol
Exploring safe disposal of CO2 and wastewater in saline aquifers a
a
b,∗
T.N. Phan , M.P. Gogri , C.S. Kabir a b
, Z.A. Reza
T
a
University of Oklahoma, United States University of Houston, United States
A R T I C LE I N FO
A B S T R A C T
Keywords: Safe disposal of CO2 & wastewater In saline aquifers Use of injection diagnostic tool Assessment of injected pore volume Probabilistic modeling approach
Process safety has taken the spotlight in recent years for both carbon sequestration and wastewater disposal operations. We present a systematic approach to monitoring injection-well performance in saline aquifers for both wastewater disposal and CO2 injection with required well surveillance and injection data. Uncertainty assessment formed the cornerstone of understanding the fundamental variables impacting the injection performance with numerical modeling. Among the variables studied, aquifer heterogeneity and injection parameters, rock compressibility, and fracture gradient have a significant influence on total cumulative injection and safe-volume injection. The modified-Hall analysis turned out to be the critical diagnostic tool for identifying the abnormal injection behavior and real-time disposal process for both fluids. We estimated the safe pore-volume injection for disposal of wastewater and CO2 to be 1.6% and 1.5%, respectively, in saline aquifers at 80% probability. Of the CO2 trapping mechanisms studied here, critical gas saturation turned out to be the most dominant in residual trapping mechanism, whereas porosity and brine salinity appeared dominant in solubility trapping mechanism.
1. Introduction According to IEA GHG (2008), saline aquifers are the most important among three main options for CO2 sequestration and storage capacity to reduce greenhouse gas emissions compared to the other two options, such as those in depleted hydrocarbon reservoirs and unminable coal seams. Safe sequestration and storage of greenhouse gases, such as CO2 to mitigate their adverse impact on the environment are gaining increased attention in all industries. At the same time, to sustain oil and gas exploitation from tight unconventional reservoirs, we have to ensure safe disposal of a significant volume of completion and produced wastewater. Most studies in carbon sequestration focus on how to efficiently store and sequester carbon in a sustained manner emphasizing on the trapping mechanisms. In this study, we emphasize on how much we can safely inject without any adverse effect. The other objective of the study is to draw a parallel between carbon sequestration and wastewater disposal operations concerning injectivity diagnostics. Numerous studies have appeared on CO2 storage efficiency in deep saline aquifers. Bachu (2015) conducted a comprehensive review of CO2 storage capacity in saline aquifers. He summarized the storage efficiency published in the literature to vary from less than 1% to greater than 10%. A multitude of factors, such as characteristics of the storage aquifer (pressure, temperature, salinity, porosity, permeability,
∗
Corresponding author. E-mail address: [email protected] (C.S. Kabir).
https://doi.org/10.1016/j.petrol.2018.06.065 Received 4 February 2018; Received in revised form 11 May 2018; Accepted 21 June 2018 Available online 22 June 2018 0920-4105/ © 2018 Elsevier B.V. All rights reserved.
heterogeneity and anisotropy), confining aquitards, attributes of a CO2 storage operation and regulatory constraints all contribute to this uncertainty range. Song et al. (2014) revealed that high heterogeneity coefficient in the reservoir and high CO2 injection rates lead to low sweep efficiency and impair CO2 storage potential in a reservoir. Hongjun et al. (2010) studied the effects of main reservoir parameters including vertical to horizontal permeability ratio (kv/kh), salinity, and residual-phase saturation of CO2 sequestration and storage in saline aquifers. The results from his model showed that dissolution of CO2 in aquifers decreases with salinity increase, but the low kv/kh increases the dissolution of CO2 because of lateral flow in the injection interval of CO2. Also, the CO2 dissolution increases with the decrease in critical gas saturation. Nghiem et al. (2009) examined the two most important mechanisms for CO2 storage in saline aquifers, namely residual trapping, and solubility trapping. Their results showed a significant increase of residual-gas trapping with the use of water injection in the low-permeability aquifers. Ide et al. (2007) investigated the effects of gravity, viscous and capillary forces on amount and timing of trapping. In closed aquifers, storage efficiency depends on the rock and fluid compressibility (Bachu, 2015). Despite the prolific studies of CO2 storage efficiency in saline aquifers, real-time monitoring of well injection performance appears very limited. Hall (1963) initiated the diagnostic analysis of water injection wells. More recently, Izgec and Kabir (2009, 2011) suggested a new
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formulation of the Hall analysis. Their modified-Hall analysis involves analysis of the separation of the derivative of the Hall-integral (DHI) from the Hall-integral (IH) curve. This separation helps to identify the characteristics of a high-permeability channel as the DHI separates lower than the IH, and indicates formation plugging when the DHI curve rises above the IH. In simple terms, the IH curve provides information on the time evolution of pressure buildup around the disposal well due to injection. In contrast, DHI indicates the rate at which the pressure buildup develops. When the well struggles to inject, the pressure buildup happens rapidly with little effective injection. This situation gives rise to high DHI, but IH remains stable. On the other hand, when the well experiences fracture or any fluid flow path of low resistance, the pressure does not increase but the injection volume will increase. This situation points to a small DHI value with stable IH. We use this change or separation in the two curves (DHI and IH) to identify abnormal injection behavior. Recently, Gogri et al. (2018) demonstrated the usefulness of the modified-Hall approach for safe water-disposal operations by ascertaining the point of departure from normal injection behavior. Given the need for disposal of both fluids in some production-operation settings, this study explores various factors in a probabilistic framework to understand their relative importance in disposal operations of both CO2 and waste-water to obtain a comparative picture. These variables include aquifer heterogeneity, vertical anisotropy, porosity, permeability, salinity, rock and fluid compressibility. Thereafter, safe-volume injection for both scenarios of waste-water and CO2 injection is derived from abnormal behavior injection, identified from the modified-Hall plot. Finally, we evaluate the two primary CO2 trapping mechanisms contributing to overall CO2 storage capacity, namely residual-gas trapping and solubility trapping.
Fig. 1. Workflow of injection-well performance monitoring (after Gogri et al., 2018).
3. Injection-well monitoring workflow and model setup Studying the injection-zone reservoir dynamics under various conditions becomes critical for designing and implementing the safe injection operations. Fig. 1 presents the proposed workflow for investigating the injection process for both water-disposal and CO2 injection in saline aquifers. First, we constructed a 3D reservoir model containing the aquifer zone separated by a barrier. Second, we geostatistically distributed petrophysical properties (porosity and permeability) constrained to synthetic well-logs and information based on Elsayed et al., 1993 and Wegelin 1985. Fluids and rock-physics models are created using data from Elsayed et al. (1993), Firoozabadi et al., 1992, Riding and Rochelle 2005 and Wegelin 1985. Logs are not shown in this paper as 3D reservoir modeling is not the primary focus of this study. Subsequently, we designed and completed injection well for disposal zone. In this paper, we considered single injector vertical well models only. The well completions included casing, injection tubing, isolation packers and the perforation interval of about 40 ft. The well operational conditions were set via allocation of injection rates with appropriate bottomhole-pressure limits under the fracture gradient pressure conditions. Then, the uncertainty analysis followed using the Design of Experiments (DoE) to capture the complexity of saline aquifer's heterogeneity and injection-well performance. Following the above workflow, we examined the saline aquifer with characteristics of the Weyburn field, located in southeastern Saskatchewan, Canada. Fig. 2 presents the corner-point grid model involving 50 × 50 × 42 grid cells for the 320-acre aquifer at a depth of 4500 ft. Table 1 specifies the typical input parameters with its appropriate uncertainty ranges used to set up the simulation runs for both water-disposal and CO2-injection scenarios. Fig. 3 displays the normal and lognormal distribution of porosity and permeability of this heterogeneous aquifer model. Fig. 4 shows 3D views of porosity and permeability from one of the realizations. The porosity ranges from 0.08 to 0.25, with an average value of about 0.16. Permeability within this aquifer ranges from 0.5 to 30 md, with the average of 5 md. Furthermore, several factors, such as pore volume multiplier and permeability multiplier are considered in the Design of Experiment (DoE) matrix to capture the wide range of petrophysical parameters and heterogeneous aquifer. As a result, porosity can range from 0.06 to 0.3 and permeability ranges less than 0.1 to greater than 100 md for the Weyburn formation. These values conform to those
2. Comparison of CO2 sequestration and waste-water injection A comprehensive comparative investigation of injectivity diagnostics in carbon sequestration and waste-water disposal operations is rare in the literature. Notwithstanding, standard and more advanced injection diagnostics will apply to both these operations. In the following, we note several studies alluding to the comparison of water and CO2 injection. Maxwell et al. (2008) compared the effect of using water-based gel and liquid CO2 in supercritical state as fracturing fluids. They show the seismic efficiency (that is, the ability to induce acoustic events) for both the fracturing fluids was the same. Verdon et al. (2010) also compared passive seismic monitoring results using water-based gel and supercritical CO2 as hydraulic fracturing fluids at similar conditions. Both the fluids induced events with similar magnitudes and occurred at comparable rates. He also suggested that the event magnitudes correlate to the injection pressures. Verdon (2014) studied similarities between wastewater injection and CO2 storage. He emphasized that the change in stress acting on the faults plays the key role in reactivating the faults. Because the amount of pore space occupied during injection determines the pore pressure, the seismicity imprint does not change during injection of CO2 as opposed to water despite the difference in their physical properties. The study also inferred the role the injected fluid density plays in determining the location of induced earthquakes. This study shows the similarity or lack thereof of injectivity diagnostics of carbon sequestration and wastewater disposal operations. We aim to devise a safe injection monitoring scheme for both the processes. To reach our goals, we developed numerical flow models in realistic aquifer settings, learned from the injection-well performance strategy, identified independent variables impacting such performance using the statistical design of experiments, and explored significant trapping mechanisms for CO2.
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cases, the bottomhole pressure, cumulative injection and injectant/ native fluid interface pressure, which is designed as pe, were extracted. The workflow started with the generation of injection rate and pressure profiles. Thereafter, we calculated the Hall Integral (IH) and its derivative with respect to cumulative injection (DHI) to plot against the cumulative injection. As Fig. 6 shows, the departure point occurs when the DHI curve starts deviating from its IH counterpart. The safe cumulative injection during both the wastewater disposal and CO2 injection correspond to the departure points, and further analysis of injection behavior using this information follows. The wastewater-disposal scenario yields smooth lines for IH and DHI at normal injection condition, while the higher compressibility CO2 results in DHI fluctuations. Despite that different behavior in the modified-Hall analysis plots, the departure point can be obtained in both scenarios for the safe cumulative injection volume without any ambiguity. Our analysis involves calculation of percent safe pore-volume injection (PVID%) using the cumulative injection at the departure point (CID). This variable is defined as the cumulative injected pore-volume divided by the total pore volume of the injection compartment. This metric constitutes the primary dependent variable in discerning the safe, operable limits. The underlying objective is to identify the limiting conditions of variables for safe operating conditions.
Fig. 2. Schematic of 3D aquifer model with characteristics of the Weyburn field.
suggested by Elsayed et al. (1993). The vertical anisotropy (k v / kh ) ranged from 0.01 to 0.1. Appropriate ranges of aquifer characteristics, such as formation salinity, formation water density, formation-water compressibility, and rock compressibility originate from available sources (Riding and Rochelle, 2005; Wegelin, 1985; Firoozabadi et al., 1992). The parameters related to the gas relative-permeability curve and hysteresis effects, such as critical gas saturation, connate-gas saturation, and the gas relative permeability at connate liquid and residual liquid conditions are also examined to study the impact of CO2 injection on the overall injection behavior and the consequent storage capacity. Thereafter, several hundreds of DoE simulation runs followed to generate the reservoir behavior for evaluating the impact of the array of input variables. These runs allowed generation of performance plots and Pareto charts to highlight the most influential dependent variables affecting the injection-well performance, namely cumulative injection volume and safe pore-volume injection. We defer our discussion on the injection-well performance of carbon sequestration and waste-water disposal wells under various subsurface and operational conditions until we present the safe injection-well condition and monitoring of injection wells.
5. Impact of key variables for well-injection monitoring in saline aquifers We define safe cumulative injection as the maximum allowable volume of fluid into the aquifer at normal injection conditions. Initially, we investigated the total cumulative injection for both wastewater disposal and CO2 injection in saline aquifers under various subsurface and operational conditions. Fig. 7 shows the Pareto charts of the primary uncertain variables affecting cumulative injection for both scenarios of wastewater disposal (Fig. 7a) and CO2 injection (Fig. 7b). Three major uncertain variables impacting the total cumulative injection are fracture gradient (or the limiting bottomhole pressure gradient), formation compressibility, and formation porosity for both wastewater disposal and CO2 injection. Fig. 8 shows examples of departure-point determination for both scenarios of water disposal (Fig. 8a) and CO2 injection (Fig. 8b). For brevity, we show only four randomly-selected simulation cases from the DoE runs in the figure. In these plots, x -axis is cumulative water injection (MRB) in Fig. 8a and cumulative CO2 injection (MRB) in Fig. 8b, respectively, while y -axis represents (psi-MRB) IH (blue) and DHI (orange). The black dots represent the departure points in Fig. 8. Fig. 9 showing the Pareto charts indicate the major uncertain variables affecting safe-volume injection for both scenarios of water disposal (Fig. 9a) and CO2 injection (Fig. 9b). In these Pareto plots, we
4. Diagnostics for injection-well monitoring We used the modified-Hall plot suggested by Izgec and Kabir (2009, 2011) to monitor disposal of both wastewater and CO2. In this study, safe water-disposal and CO2-injection operations can be achieved by continuous monitoring of the injection process and check the injection behavior. Fig. 5 presents a workflow to monitor and identify the abnormal injection behavior, which was applied to the previous section for both scenarios. Upon completion of the simulation runs for both Table 1 Range of independent variables used in the study. Variables
Low
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High
Water Disposal
CO2 Injection
Aquifer porosity, fraction Aquifer permeability, mD Pore volume multiplier Permeability multiplier Vertical anisotropy Aquifer formation compressibility, psi−1 Formation water compressibility, psi−1 Formation water density, lb/ft3 Formation water salinity, ppm Fracture gradient, psi/ft Connate gas saturation Critical gas saturation Gas relative permeability at liquid saturation Gas relative permeability at residual liquid saturation
0.06 0.1 0.75 0.2 0.01 5E-6 2E-06 63 40,000 0.6 0 0.05 0.8 0.35
0.16 5 1 1 0.05 8E-06 3E-06 70 90,000 0.7 0.025 0.1 0.9 0.45
0.3 150 1.25 5 0.1 2E-05 4E-06 75 120,000 0.8 0.05 0.2 1 0.65
✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
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(b)
Fig. 3. Porosity (a) and permeability (b) distribution of heterogeneous 3D reservoir system.
show only those uncertain variables with more than 2% significance level. For waste-water disposal in saline aquifers, only two uncertain variables (as evident in Fig. 9a) turn out to be significant for the safevolume injection. These are fracture gradient (or the limiting bottomhole pressure gradient) and formation compressibility. We note that uncertainty characterization is a dynamic process and very much dependent on the ranges of the uncertain variables used. For the safevolume CO2 injection in saline aquifers, the significant variables are formation porosity, formation compressibility, fracture gradient (or the limiting bottom-hole pressure gradient), formation permeability and to some extent the vertical anisotropy. The significance of some of these variables can be justified by different trapping phenomena present in the case of CO2 injection, namely residual-gas trapping (or hydrodynamic trapping) and solubility trapping. These aspects will be explored later in more detail. Furthermore, in the wastewater disposal scenario, fracture gradient turned out to be the most influential variable impacting the cumulative injection and safe pore-volume injection, as exhibited in Figs. 7a and 9a, respectively. The rock compressibility also plays an important role. In contrast, rock compressibility plays a dominant role in the CO2 injection performance. In closed aquifers, the storage efficiency depends mostly on the compressibility of the system, as discussed by Bachu (2015). As Figs. 7b and 9b suggest porosity, rock compressibility, and fracture gradient all have a relatively high impact on the CO2 injection performance. In other words, different characteristics of the two fluids produce somewhat different outcome based on the underlying physics. Fig. 10 displays the histogram of PVID% (that is, percent safe porevolume injection) obtained from the analysis of more than 250 simulation runs for each scenario of both fluids. We identified with 80% probability that safe pore-volume injection occurs at 1.6% PV for wastewater disposal and 1.5% PV for CO2 injection at the trigger point of the abnormal behavior.
Fig. 5. Workflow for diagnostics of injection-well monitoring (after Gogri, 2018).
6. CO2 injection diagnostics using MHA and RTA The injection-well monitoring approach using Modified Hall Analysis (MHA) presented in the previous section can be further augmented using Rate Transient Analysis (RTA). Using RTA, we can characterize the compartment size of the disposal zone that the injection well is connected to. We note that the fundamental purpose of RTA as used in the reservoir-engineering discipline pivots on estimating the
Fig. 4. 3D representation of porosity (a) and permeability (b) models from one of the realizations used in the DoE simulation runs. 200
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Fig. 6. Departure point identification in the modified-Hall plot; (a) waste-water disposal and (b) CO2 injection.
connected pore volume in the presence of late-time boundary-dominated flow in producing wells. We attempt to replicate the same idea in the context of available and contiguous pore volume connected to the injection well. The knowledge of this connected compartment volume facilitates us to monitor if the injection is within the safe-injection limits in terms of percent pore-volume injected. In a previous article (Gogri et al., 2018), we proposed the same notion towards waste-water injection monitoring. In the following, we examined the efficacy of determining the compartment size of the closed bounded-aquifer using RTA. Fig. 11 shows the RTA results, including the pressure and injection-rate match, the log-log and the Blasingame type-curve plots, as detailed in Houze et al. (2017). Using RTA, the compartment volume was estimated to be 85 MMRB pore volume for a simulated case of 78 MMRB pore volume. The difference in the connected volume can be attributed to the very different nature of the analyses in the semi-analytical formulation of RTA and robust multi-physics formulation in a numerical reservoir simulator. We note that in RTA a homogenized reservoir model is conceptualized. Notwithstanding, these results validate that RTA technique can yield a reasonable estimate of the compartment size. In the following section, we discuss the trapping mechanisms present in CO2 injection in saline aquifers. We also investigated the importance of different uncertain variables on the major trapping mechanisms relevant to CO2 injection in saline aquifers.
is hydrodynamic trapping which is nothing but the extremely slow migration of the fluids under gravitational and buoyancy forces. Residual-gas trapping is a form of hydrodynamic trapping. On the geochemical trapping front, solubility trapping and mineral trapping are the major ones. In the solubility-trapping mechanism, brine dissolves supercritical CO2 making the aqueous phase denser and promoting migration of the aqueous phase into deeper formations. Mineral trapping is the slowest of all and involves chemical reaction with the host rock and forming solid minerals. In this section, we explore a few trapping mechanisms, such as residual-gas trapping and solubility trapping, and their consequential impact on the dependent variables of interest. Both wettability and capillary effects in porous media trigger the residual gas trapping, as discussed by Nghiem et al. (2009). Juanes et al. (2006) described that the snap-off dominates the trapping mechanism in pore space. During the drainage process engendered by CO2-injection, the nonwettingphase CO2 gas saturation increases and tends to move to the top of aquifers due to buoyancy forces, as depicted in Fig. 12a. Once the injection stops, water displaces gas in an imbibition process resulting in increased water saturation at the trailing edge. Thereby, trapped gas is formed by snap-off effect and residual, immobile CO2 gets left behind and remains in the aquifer (Fig. 12b). Mathematically, the gas hysteresis is commonly modeled by using Land and Carlson (CMG GEM, 2016) approach. The trapped-gas saturation Sgt is determined by:
7. Main trapping mechanisms for supercritical CO2 injection
Sgt =
Carbon sequestration efficacy strongly depends on several physical and geochemical trapping mechanisms (IPCC, 2005). Stratigraphic and structural trapping (which is a physical trapping mechanism) plays a significant role in the initial stage of the sequestration process. Essentially, low permeability formations trap the movement of the injected CO2. Capillary trapping is a very similar physical trapping process that involves rendering CO2 immobile due to the presence of strong capillary forces at pore-scale level. Another physical trapping phenomenon
Rock compressibility
C=
(2)
Rock Compressibility
1.00
Fracture gradient
0.26
Formation water density
1 1 − Sgtmax Sgmax
In Eq. (2), Sgtmax represents the maximum trapped gas saturation
0.61
Porosity
(1)
where Sgi represents the actual gas-saturation corresponding to flow reversal (drainage to imbibition or vice versa) and C is known as Land's parameter, which is given by the following expression:
1.00
Fracture gradient
Sgi 1 + CSgi
0.55
0.27
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0.02
Krg at residual liquid
(a)
0.03
(b)
Fig. 7. Impact of critical variables on total cumulative injection for (a) wastewater disposal and (b) CO2 injection. 201
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Fig. 8. Examples of departure-point (shown in black dots) determination for both scenarios of water disposal (a) and CO2 injection (b). x -axis is cumulative water injection (MRB) in (a) CO2 injection (MRB) (b) and y -axis represents (psi-MRB) Hall Integral (blue) and derivative of Hall Integral (orange). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
presented similar findings. We considered two saline aquifer scenarios by changing key aquifer parameters, namely depth, pressure, and temperature, as shown in Table 2, to address their impact on two foremost CO2 trapping mechanisms. The result shows a relative reduction in residual gas trapping, but with an increase in CO2 solubility, as illustrated in Fig. 14. CO2 solubility increases with increasing pressure but decreases with increasing temperature, as testified by other studies of Bachu (2015), Span and Wagner (1996), Enick and Klara (1990), and Wiebe and Gaddy (1940). In other words, pressure change contributes more impact than its temperature counterpart, leading to increased CO2 solubility in the deep aquifer case. Table 3 presents a summary of the results of both safe-injection volume and the attendant contributions of the trapping mechanisms in a probabilistic framework. Note that the safe injection volumes were derived from the principle shown earlier in Fig. 8, which used the departure point on the modified-Hall plots. Dominance of the residual trapping mechanism of the CO2 becomes readily transparent.
while Sgmax the maximum gas saturation associated with imbibition relative-permeability curve. Nghiem et al. (2009) and Juanes et al. (2006) provide details on trapping and relative-permeability hysteresis model. Given that CO2 is highly soluble in brine, solubility trapping turns out to be an essential process relevant to safe CO2 storage in saline aquifers. In the following, we consider residual-gas trapping and CO2 dissolution trapping to be the primary mechanisms for CO2 storage. From more than 250 flow simulation runs in 60 years, mass-mole of trapped CO2 of residual gas and solubility are tracked corresponding to specific uncertainty ranges identified in Table 1. Fig. 13 displays the Pareto charts identifying the principal variables impacting the two trapping mechanisms. Critical gas saturation Sgcrit is by far the most critical variable (as illustrated in Fig. 13a) for CO2 residual-trapping volume due to the hysteresis effect. Higher critical gas saturation allows more immobile gas to be trapped in the aquifer formation. Fluid-migration related parameters, such as gas relative permeability at the residual liquid saturation, horizontal permeability and vertical to horizontal permeability ratio k v / kh have a moderate impact on CO2 residual-trapping volume. For CO2 solubility volume (as evident in Fig. 13b), porosity and brine salinity are the dominant factors, followed by rock compressibility and fracture gradient. While ignoring the ionic and mineral trapping effect, CO2 solubility largely depends on brine volume and salinity concentration. Earlier, Hongjun et al. (2010)
We presented a systematic approach to monitoring the injectionwell behavior in saline aquifers using subsurface characteristics in 3D reservoir models, followed by diagnostic analysis of real-time injection
[VALUE]
Fracture gradient
1.00
Porosity
0.41
Rock compressibility
[VALUE]
Rock compressibility Formation water density
8. Discussion
0.23
Fracture gradient
0.02
Permeability Vertical anisotropy Krg at residual liquid
0.11 0.07 0.03
Fig. 9. Impact of critical parameters on safe-volume injection, (a) for waste-water disposal and (b) for CO2 injection. 202
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100
50
100
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40
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0
0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 2.7 3
0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 2.7 3 3.3
% Pore-Volume Water Injection
% Pore-Volume CO2 Injection
(a)
(b)
Fig. 10. Histograms of percent pore-volume injection for waste-water disposal (a) and CO2 injection (b).
Another aspect of CO2-injection in saline aquifers is a chemical reaction of the injected CO2 with surrounding rock matrix (mineral trapping). Chemical reaction likely occurs over a long timescale, especially after the injection period (Bachu, 2015; IPCC, 2005). Therefore, mineral trapping does not contribute significantly to CO2 storage during the injection period (Bachu, 2015). Our approach did not take account of this phenomenon into modeling CO2-injection in saline aquifers. Instead, we focused on residual gas and CO2 solubility as the principal trapping mechanisms for CO2 storage efficiency. Overall, the results pointed to 1.6% and 1.5% pore volume as the maximum injection container for water-disposal and CO2-injection, respectively, with 80% probability. These allowable injection volumes are in substantial alignment with CO2 storage efficiency figures for limestone formations discussed in previous studies. In this context, Gogri et al. (2018) found 2% PV injection with 80% probability for wastewater disposal in sandstone formations. Earlier, Ehlig-Economides and Economides (2010) suggested 1% of supercritical CO2 injection, while Zhou et al. (2008) proposed 0.5% for the same fluid with simplified analytical modeling. More recently, Peck et al. (2018) explored
data. In fact, this method can be pursued to obtain the limits of safevolume injection for both wastewater disposal and CO2 injection in saline aquifers to avoid over-limit of injected volumes and maintain seal integrity of the target reservoir segment. Real-time injection monitoring can be achieved efficiently by setting alerts to cease injection once the safe-volume approaches, as indicated in the modified-Hall plot. Of course, as shown elsewhere by Gogri et al. (2018), periodic analysis of the connected-pore volume by way of rate-transient analysis will be particularly useful. Moreover, additional surveillance data; for example, temperature survey and shut-in tests will provide a complimentary evaluation of the well-injection monitoring process. For cost implication of the injection-monitoring process using the proposed approach, as mentioned above we simply need pressure and injection rate data (through surveillance or monitoring) and the initialreservoir pressure around the injector. Wellhead pressure and injection rate are usually measured for most modern injection wells by regulation. Also, the initial pressure is determined for most wells. That means there is no additional cost to implement this monitoring approach provided pressure, rates and initial pressure data are available.
Fig. 11. RTA results - injection-rate (a) and pressure (b) match, log-log diagnostic plot (c) and Blasingame type-curve (d) plot. 203
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Fig. 12. Residual gas trapping due to hysteresis effect: (a) drainage process and (b) imbibition process.
the CO2 storage efficiency factor in the context of enhanced recovery processes. Collectively, the prior studies appear to suggest that the saline aquifers offer somewhat limited scope in overall storage capacity. Perhaps depleted hydrocarbon reservoirs provide more substantial opportunity. To determine abnormal injection behavior in this study, we used a percentage threshold to determine the departure points. For CO2 injection cases, we note the DHI curves may sometimes reveal an initial upward concavity curvature due to the compressibility effect. We ignore this deviation as this does not reflect the state of injection. In most scenarios, the compressibility effect dissipates at high pressures. Additionally, we have performed DoE with more than 250 simulation runs. The subjectivity in the departure point identification will not have a meaningful effect on the 80-percent probability values or on P-10, P50 and P-90 values that we are reporting. We must add that any computational or numerical algorithm is not immune from this level of subjectivity. It must be pointed out that in this study, we have used synthetic models to provide the proof of concept. However, there is no reason why the injection diagnostics and monitoring cannot be applied in field pilots or live wells. Working with real data (having lots of noise) is always challenging. However, one great characteristic of the modifiedHall Integral is the fact that we are dealing with an integral, which should have a smooth profile. We note that field experiences show that the derivative of the Hall Integral does not create any obstacle, as shown by Izgec and Kabir (2009, 2011), among others, published since that time. Based on our experience using the statistical DoE that we investigated in this study, we observed the timing of the departure from normal injection occurs about 10 years in an average sense, for the ranges of the variables examined in this study. We are currently working on the uncertainty aspect of the time to depart from normal injection behavior, which will be the focus of a future article.
Critical gas saturation
Scenarios
Depth (ft)
Pressure (psia)
Temperature (oF)
Shallow aquifer Deep aquifer
4500 9000
2400 3900
160 215
8
CO2 trapped mass moles, 1E8
7 6 5 4 3 Solubility Shallow Aquifer Solubility Deep Aquifer Residual Shallow Aquifer Residual Deep Aquifer
2 1 0 0
10
Horizontal permeability
30
40
50
60
Fig. 14. CO2 trapped mole profiles for shallow and deep aquifers.
9. Conclusions This study provided a systematic approach to monitor well-injection operation in saline aquifers for both wastewater and CO2 disposal. Both 3D numerical reservoir modeling and a diagnostic approach for monitoring surveillance data paved the way for the following findings: Porosity
1.0
Brine salinity
0.21
0.65
Rock compressibility
0.15
Fracture gradient Vertical anisotropy
20
Years
1.0
Krg at residual liquid condition Rock compressibility
Table 2 Shallow and deep aquifer investigation for carbon sequestration.
0.10 0.05
(a)
0.39 0.24
Krg at residual liquid condition
0.11
Vertical anisotropy
0.07
(b)
Fig. 13. Effect of independent variables on CO2 residual trapping (a) and CO2 solubility trapping (b). 204
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Table 3 Probabilistic projections of CO2 safe injection volumes and contributions of trapping mechanisms. Probability
P-90 P-50 P-10
CO2 Safe Injection Volume
CO2 Trapping Mechanisms
ton/acre
ton/acre/year
Residual, ton/ acre
Solubility, ton/acre
148 284 500
2.5 4.7 8.3
123 223 300
20 37 51
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1. Fracture gradient, rock compressibility, porosity, and permeability play dominant roles in CO2 injection monitoring in saline aquifers. Solubility and residual trapping are important to CO2 storage efficiency in saline aquifers. Critical gas saturation has a strong influence on residual CO2 trapping, while porosity and brine salinity in the formation plays a significant role in solubility trapping. 2. The modified-Hall plot aided in monitoring both wastewater and CO2 sequestration and provided the necessary diagnostics to identify the abnormal injection condition. 3. In sealed saline aquifers, the safe wastewater disposal volume turned out to be about 1.6% PV, and safe CO2 injection is 1.5% PV with 80% probability within the scope of this investigation. Nomenclature
DHI IH k v / kh C Sgt Sgi Sgtmax
derivative of Hall integral, psi-day Hall integral, psi-day vertical to horizontal permeability ratio, fraction Land's parameter, constant trapped-gas saturation, fraction actual gas saturation corresponding to flow reversal, fraction maximum trapped-gas saturation, fraction
Acknowledgment The authors thank Computer Modeling Group, Ltd. (CMG), Kappa Engineering and Schlumberger for providing their software used in this study. References Bachu, S., 2015. Review of CO2 storage efficiency in deep saline aquifers. Int. J. Greenh. Gas Control 40, 188–202. CMG Ltd, 2016. GEM Userguide. Compositional and Unconventional Reservoir Simulator, Version 2016. Ehlig-Economides, C., Economides, M.J., 2010. Sequestering carbon dioxide in a closed underground volume. J. Petrol. Sci. Eng. 70 (1–2), 123–130. Elsayed, S., Baker, R., Churcher, P., et al., 1993. Multidisciplinary reservoir characterization and simulation study of the Weyburn unit. J. Petrol. Technol. 45 (10), 930–934.
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