CO2 Accounting and Risk Analysis for CO2 Sequestration at

Article pubs.acs.org/est

CO2 Accounting and Risk Analysis for CO2 Sequestration at Enhanced Oil Recovery Sites Zhenxue Dai,*,† Hari Viswanathan,† Richard Middleton,† Feng Pan,‡ William Ampomah,§ Changbing Yang,∥ Wei Jia,‡ Ting Xiao,‡ Si-Yong Lee,⊥ Brian McPherson,‡ Robert Balch,§ Reid Grigg,§ and Mark White# †

Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States Energy and Geoscience Institute, The University of Utah, Salt Lake City, Utah 84108, United States § Petroleum Recovery Research Center, New Mexico Tech, Socorro, New Mexico 87801, United States ∥ Bureau of Economic Geology, The University of Texas at Austin, Austin, Texas 78713, United States ⊥ Schlumberger Carbon Services, Cambridge, Massachusetts 02139, United States # Pacific Northwest National Laboratory, Richland, Washington 99354, United States ‡

S Supporting Information *

ABSTRACT: Using CO2 in enhanced oil recovery (CO2-EOR) is a promising technology for emissions management because CO2-EOR can dramatically reduce sequestration costs in the absence of emissions policies that include incentives for carbon capture and storage. This study develops a multiscale statistical framework to perform CO2 accounting and risk analysis in an EOR environment at the Farnsworth Unit (FWU), Texas. A set of geostatisticalbased Monte Carlo simulations of CO2−oil/gas−water flow and transport in the Morrow formation are conducted for global sensitivity and statistical analysis of the major risk metrics: CO2/water injection/production rates, cumulative net CO2 storage, cumulative oil/gas productions, and CO2 breakthrough time. The median and confidence intervals are estimated for quantifying uncertainty ranges of the risk metrics. A response-surface-based economic model has been derived to calculate the CO2-EOR profitability for the FWU site with a current oil price, which suggests that approximately 31% of the 1000 realizations can be profitable. If government carbon-tax credits are available, or the oil price goes up or CO2 capture and operating expenses reduce, more realizations would be profitable. The results from this study provide valuable insights for understanding CO2 storage potential and the corresponding environmental and economic risks of commercial-scale CO2sequestration in depleted reservoirs.

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INTRODUCTION Geological carbon sequestration is a leading candidate for permanent storage of carbon dioxide or other forms of carbon to mitigate global warming and avoid extreme climate changes.1−9 Carbon dioxide is also an attractive displacing agent for enhanced oil recovery (CO2-EOR) because it has a relatively low minimum miscibility pressure in a wide range of crude oils. Since a large portion of the injected CO2 remains in place in depleted reservoirs after CO2-EOR, it is an option for permanently sequestering CO2 with reduced costs.10−16 However, with very low viscosity, CO2 tends to finger and break through to production wells and its mobility control is poor, which may leave large areas of the reservoir unswept. To overcome this disadvantage, current CO2-EOR projects alternatively inject gas and water (or brine) as slugs in what is known as water-alternating-gas (WAG) to control CO2 mobility and CO2 flood conformance.17−19 While WAG can be very effective, more detailed studies of CO2 interaction with © XXXX American Chemical Society

oil, formation water, and heterogeneous sediments are needed for understanding the mechanism of CO2 geological sequestration in oil/gas reservoirs and quantitatively evaluating the total amount of CO2 irreversibly stored in reservoirs. After reviewing the literature relevant to CO2-EOR, we find that a few operational and technical difficulties for commercialscale CO2-EOR still remain: (1) setting up guidelines for quantitatively determining time ratios of WAG injection are difficult;16−18 (2) highly heterogeneous reservoirs are difficult to characterize, and it is even more difficult to quantify the impact of reservoir heterogeneity on CO2 injectivity and oil/gas productivity;20−23 (3) lack of a systematic method for assessing the certainty in the fail-safe retention of CO2, as well as the CO2 trapping processes and possible leakage paths;24−27 and Received: April 7, 2016 Accepted: June 10, 2016

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DOI: 10.1021/acs.est.6b01744 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

Article

Environmental Science & Technology

Figure 1. Flowchart of a statistical framework of CO2 accounting and risk analysis for CO2 enhanced oil recovery (EOR).

Figure 2. Permeability and porosity in the regional Morrow reservoir. (a) The blue dots represent permeability and porosity data collected from medium to coarse grained sandstone. The pink dots represent the data from fine grained sandstone with mud drapes. The yellow triangles reflect data from cemented sandstone. The cross symbols show data from fine-grained cross-bedded sandstone. The stars represent data from transgressive lag. (b) Permeability statistics in the Farnsworth site. (c and d) The semivariogram in the horizontal (c) and vertical (d) directions.

(4) lack of a general framework for evaluating feasibility and security of CO2-EOR sites to ensure that the pressure in reservoirs does not increase to a critical point which may induce fracturing or even seismic tremors.28−35 These operational and technical issues have served as motivation for this study. The goal of this study is to develop a multiscale statistical approach for CO2 accounting and risk analysis in CO2 enhanced oil recovery sites. Figure 1 shows the flowchart of the multiscale statistical framework, which starts from characterizing reservoir heterogeneity and defining the associated independent parameters (which are statistically independent from modeling results, or output variables, but

have a large impact on them). The independent parameters can be classified into three types: reservoir property, operational, and oil/gas property parameters. In most CO2-EOR sites such as in the Farnsworth Unit (FWU) site, the exact values of the independent parameters are not well-known, but we may obtain enough information to characterize or define the uncertainty distributions of these independent parameters. These distributions are used to sample the uncertain parameters and conduct geostatistical-based Monte Carlo (MC) simulations. This paper presents a multiscale statistical evaluation of the operational and technical risks of an active CO2-EOR project. A set of risk factor metrics is defined to postprocess the Monte B


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permeability, porosity, depth, thickness, and integral scale) are variable in the developed response surfaces, they can be applied in other CO2-EOR sites (with similar geological conditions) to calculate those risk factor metrics in the reservoir scale. The developed response surfaces also facilitate a statistical analysis for estimating the median (50th percentile) and confidence intervals (5th and 95th percentiles) of the risk factor metrics. Geostatistical Analysis of the Reservoir Heterogeneity. Regionally, Morrow reservoirs in the Anadarko Basin have produced more than 100 million barrels of oil and 14.2 billion m3 of gas since the 1950s including the primary and secondary recovery. Chaparral Energy LLC currently operates Farnsworth Unit and has been injecting CO2 through multiple five-spot patterns since December 2010 (tertiary recovery). Chaparral will be adding up to 3−5 new five-spot patterns each year for a total of 25 patterns for CO2-EOR (dependent on oil price). The CO2 used by Chaparral is sourced from a fertilizer plant in Borger, Texas, and an ethanol plant in Liberal, Kansas. The Southwest Regional Partnership on Carbon Sequestration (SWP) has partnered with Chaparral to study the results of CO2 injection at FWU for evaluation of long-term storage of CO2 and performance of EOR in the Morrow reservoir. The net CO2 injection is planned to be about 1.05 million tonnes in five years.36 In FWU, the Morrow reservoir lies at a depth around 2330 m, and its typical thicknesses are between 5 and 50 m. It is estimated that the CO2 storage capacity of the Morrow reservoir within the field may exceed 10 million tonnes based only on rough calculations of the formation volume and porosity. Previous studies of the Morrow reservoir at the Farnsworth Unit and nearby oil fields provide some information about the distributions of regional Morrow reservoir parameters, such as the depth, thickness, permeability, and porosity.36−39 Regionally, Morrow reservoirs mainly consist of Lower Pennsylvanian incised valley-fill sandstones that extend into eastern Colorado and western Kansas. Figure 2a shows the positive correlation between the measured permeability and porosity that is seen regionally in the Morrow formation (modified from the work of Bowen47,48). Current work at FWU site by SWP and Chaparral36 has provided additional measurements of important petrophysical properties. The distributions of recently measured porosity and permeability are plotted in Figure 2b, which are similar to the data points within the green circle in Figure 2a. Permeabilities range between 0.2 and 783.5 milli-Darcy (mD) and porosities between 3% and 25%. The computed Pearson’s correlation coefficient between porosity and log permeability is about 0.8, which means these two parameters are highly correlated. Note that the Pearson’s coefficient is a measure of the strength and direction of the linear relationship between two parameters. It is defined as the sample covariance of the variables divided by the product of their sample standard deviations. According to Bernabe et al.49 and Deng et al.,20 the relationship between permeability and porosity is expressed as

Carlo simulation results for statistical analysis. The risk factors are expressed as measurable quantities that can be used to gain insight into project risk (e.g., environmental and economic risks) without the need to generate a rigorous consequence structure, which include: (a) CO2 injection rate, (b) net CO2 injection rate, (c) cumulative CO2 storage, (d) cumulative water injection, (e) oil production rate, (f) cumulative oil production, (g) cumulative CH4 production, and (h) CO2 breakthrough time. The Morrow reservoir at the FWU site in the northeastern Texas panhandle is used as an example for studying the multiscale statistical approach for CO2 accounting and risk analysis.36−39

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MATERIALS AND METHODS A set of integrated multiscale Monte Carlo simulations of CO2−oil/gas−water flow and transport in reservoirs is Table 1. Uncertain Parameters and Dependent Variables for the Farnsworth Site variables names independent uncertain parameters

dependent variables or risk metrics

min

max

mean

thickness (m) 5.0 20 12.5 depth (km) 2.2 2.4 2.3 integral scale 0.2 0.5 0.35 (km) permeability 0.2 783.5 69.2 (mD) porosity 0.03 0.25 correlated well spacing 0.1 0.5 0.3 (km) inject pressure 33.8 44.8 39.3 (MPa) time ratio of 1.0 10 5.5 WAG initial water 0.31 0.77 0.54 saturation initial oil 0.23 0.69 correlated saturation CO2 injection rate (kt/d), net CO2 injection rate (kt/d) cumulative CO2 storage (Mt), cumul water injection (Mbbl) oil production rate (Mbbl/d), cumul oil production (MMbbl) cumul gas production (m3), CO2 breakthrough time (y)

developed by coupling the uncertainty quantification tool PSUADE,40 the Los Alamos developed geostatistical modeling tool GEOST41−43 modified from the Geostatistical Software Library,44 and the multiphase reservoir simulator SENSOR.45 PSUADE is used to sample 1000 realizations of the uncertain independent parameters with Latin Hypercube Sampling in the reservoir scale, to conduct global sensitivity analysis of the output variables in relation to the uncertain independent parameters, and to derive response surfaces or reduced order models (ROMs, see eqs S1−S3 in the Supporting Information) for understanding the relationships of the input parameters and the output variables.46 GEOST is used to analyze geostatistically the existing permeability data in the reservoir scale and to generate heterogeneous permeability distributions in reservoir local scale with a sequential Gauss method. The reservoir simulator SENSOR is used to model CO2−oil/gas−water flow and transport in a five-spot pattern (in a local scale) for each generated heterogeneous model under a Monte Carlo simulation framework. Since the reservoir parameters (such as

k = aøb

(1)

where k is permeability (m2), ø is porosity, a and b are sitespecific constants. Deng et al.17 estimated the constants to simulate correlations between permeability and porosity in the Rock Springs Uplift, Wyoming. Since eq 1 is highly nonlinear, the estimated constants are highly uncertain. Based on the data shown in Figure 2b, we modify the porosity−permeability relationship eq 1 as C


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Figure 3. Multiscale simulations of the CO2−oil−water flow and transport in the heterogeneous reservoir based on a five-spot EOR pattern. (a) Farnsworth site model for porosity. The red color indicates large porosity (0.25), and the blue color indicates small porosity (0.03). (b) Five-spot EOR model. (c) Generated permeability heterogeneity in a cell model.

ø = c + d log10 k

horizontal permeability) is 0.1. The relative permeability functions for CO2−oil/gas−water multiphase flow simulations were calculated on the basis of Stone’s approach to define the related coefficients.38,50−52 Table 1 also lists the range of time ratio of WAG for alternatively injecting CO2 gas and water within each time period or cycle (such as 10 days). This injection time ratio is calculated by dividing CO2 injection time (days) with water injection time (days) in each time period. A site-scale geological framework model of the Farnsworth site was constructed by Ampomah et al. (Figure 3a).53 Within the site-scale model several five-spot well patterns (Figure 3b) are designed for CO2 injection tests in this study, where the production well is located in the center surrounded by four injection wells at the corners of the pattern (Figure 3b). The heterogeneity in the model area is assumed to be similar in all four quadrants of the EOR pattern. This way, only one localscale model (one-quarter of the five-spot pattern) is modeled with one injection well and one-fourth of the production well (Figure 3c), which implies that all of the six boundaries (top, bottom, front, back, left, and right) are fixed as no-flow. The numerical model sizes and grid numbers for the local scale models are automatically calculated based on the sampled well spacing and reservoir thickness.

(2)

where c and d are also site-specific constants. By using the permeability and porosity data collected in this site (Figure 2b), we estimated the two constants: c = 0.0518 and d = 0.0741. Equation 2 will be used to correlate porosity with sampled permeability when we conduct Monte Carlo simulations. The existing spatial-based permeability data are also collected from the Farnsworth site. Using the permeability data collected from 65 wells, we computed the log permeability semivariograms in the vertical and horizontal directions.44 The sample semivariograms are fitted with an exponential function as γψ (h) = σ 2(1 − e−h / λ)

(3)

where, γψ is the modeled semivariogram of log permeability in the direction of ψ, h is the lag distance, σ2 is the variance, and λ is the integral scale of log permeability. The results for fitting the exponential function (eq 3) are shown in Figures 2c and d. The estimated statistical parameters of the log permeability are a variance of 1.05, vertical integral scale of 3.5 m, and horizontal integral scale of 350 m. Monte Carlo Simulations. On the basis of the prior parameter information available for the regional Morrow reservoir and the geostatistical analysis of recently measured porosity−permeability data, we summarize the ranges and distributions of the uncertain parameters for simulating the heterogeneity of the Morrow reservoir at the FWU site in Table 1. Having been limited by the available data, we assume that the permeability anisotropy factor (or ratio of vertical and

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RESULTS AND DISCUSSION Global Sensitivity of Risk Factor Metrics to Independent Parameters. In order to determine the key flow and transport parameters that drive CO2−oil/gas−water migration behaviors in the reservoir, global sensitivity analysis techniques D


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Figure 4. Global sensitivity analysis with multivariate adaptive regression spline (MARS) method (Y-axis is the sensitivity index, and 100 indicates a maximum sensitivity).

controlled by the reservoir permeability, time ratio of WAG, thickness, and injection pressure (Figure 4a−c). Figure 4d shows that the cumulative water injection is also most sensitive to the reservoir permeability, thickness, time ratio of WAG, injection pressure, as well as well spacing. The oil production is most sensitive to the well spacing, reservoir thickness, permeability, and initial water/oil saturation (Figure 4e and f). The CH4 (gas) production is most sensitive to the well spacing, reservoir thickness, permeability and initial water/gas saturation (Figure 4g). Further correlation analysis indicates that the oil and gas (CH4) production rates are negatively correlated to the initial water saturation and positively correlated to the initial oil saturation since they are linearly correlated. Finally, The CO2 breakthrough time is most sensitive to the reservoir permeability, porosity, well spacing, and time ratio of WAG. Response Surface Analysis. Response surface analysis is an application of statistical and mathematical techniques useful for developing and reducing the orders of the multiphase and multicomponent process models. Note that different response surface models may perform quite differently.54,55 Here we use the rigorous MARS approach with bootstrap aggregating (bagging)40 to conduct a response surface analysis of the risk metrics.. The fitting results of the regression to generate the MARS response surfaces with the 1000 MC simulations are presented in Figure S1 in the Supporting Information. The corresponding R2 of these response surfaces are larger than

are used for investigating input−output sensitivities over the entire distributions of the uncertain parameters. The multivariate adaptive regression spline (MARS) method is applied to quantify the uncertainty and sensitivity of the risk factor metrics with normalized indices.46 MARS global sensitivity analysis is based on the concept of the variance of conditional expectation (VCE) as VCE(Xk) =

100 s

s

∑ (Yj̅ − Y ̅ )2 − j=1

1 sr 2

s

r

∑ ∑ (Yij − Yj̅ )2 j=1 i=1

(4)

where, VCE measures the variability in the conditional expected values of output variable or risk metrics Y when the input uncertain parameter Xk takes on different values. s is the number of distinct values of each input parameter and r is the number of replications. N = sr is the sample size. The sensitivity of the risk metrics to the input parameters is quantified by eq 4 and reranked with numbers from 0 to 100 to represent the importance of the input parameters. We evaluate the VCE analytically using the MARS response surface function (see the Supporting Information for detail) which is more computationally efficient. Our computations indicate that the MARS sensitivity analysis performs very well and it can significantly reduce the computational cost.40 The global sensitivity results plotted in Figure 4 show that different risk metrics are most sensitive to different parameters. The CO2 injection rate and net CO2 storage are mainly E


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Figure 5. Statistical analysis of net CO2/water injection (a−d), oil production (e−g), and CO2-EOR profit estimate (h).

Statistical Analysis of Risk Factor Metrics. By using the postprocessing results of the 1000 MC simulations, we conduct a statistical analysis of the risk metrics. The CO2 breakthrough time at the production well is a point value for each realization and its 5th, 50th, and 95th percentiles are 9.58, 1.92, and 0.41 y, respectively, which means about 5% of the 1000 MC runs have a CO2 breakthrough time larger than 9.58 years, 5% of them less than 0.41 years and the median value is 1.92 years. The 5th, 50th, and 95th percentiles of other risk metrics are computed at different time steps (Figure 5). The computed median CO2 injection rate reaches 2.5 × 10−4 kt/d at about 2.5 years (Figure 5a), while the net CO2 injection rate reaches its peak at 2.5 years and then decreases dramatically. After 5 years it is below 8 × 10−6 kt/d or close to zero (Figure 5b). Figure 5c shows that the net CO2 storage stays constant after 5 years, which means

0.95, respectively, which means the generated response surfaces can represent the process models very well. Figure S2 shows the two- and three-dimensional plots of the MARS response surfaces for CO2 injection and oil and gas (CH4) production in relation to the most sensitive parameters. The net CO2 injection rates and oil/gas production rates are positively correlated to reservoir porosity, permeability and thickness. Note that for the 1000 MC runs the CO2 injection pressures are assumed to be 70% of the hydrostatic pressures at the reservoir tops, which causes the CO2 injection rates and oil/gas production rates to be positively correlated to the reservoir depths. The developed response surfaces are utilized in the framework of CO2−PENS,56 for evaluating CO2, oil, gas (CH4), and brine water interactions under the CO2-EOR environment at the Farnsworth site. F



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that after that time almost all of the injected CO2 is produced from the production well. At the same time, although the WAG injection still keeps the same rates (Figure 5d), the oil production rate decreases to below 5 × 10−2 Mbbl/d (Figure 5e). The cumulative oil/gas production also stays constant after 5 y (Figures 5f and g). Therefore, the 5 y time is a stop point for our CO2-EOR in the Farnsworth site. This result is based on the permeability/porosity data of the Farnsworth site. If in other sites the permeability is less than that in this site, it may take a longer time to reach the stop point. The well spacing (or distance between the injection and production wells) is another factor which impacts the stop point. The developed response surfaces can be used in other sites with similar geological and geophysical conditions to predict the stop point based on sitespecific reservoir and operational parameters. Some basic analog analysis should also be conducted to test applicability of the response surfaces for the sites. CO2 Accounting and Risk Analysis. This study captures the complex multiphase flow and transport processes of CO2− oil/gas−water in the reservoir by generating computationally efficient response surfaces. These response surfaces are functions of the most sensitive independent parameters with reduced order forms. The developed response surfaces are utilized in economic analysis and risk assessment within the framework of CO2−PENS.56 We calculate 1000 separate reservoir simulationsincluding injected CO2 and water as well as produced CO2, water, oil, and gasusing the response surfaces and combinations on input parameters including reservoir permeability, porosity, and thickness for the Morrow formation. First, we calculate the net CO2 stored in the depleted reservoir. At the stop point (5 years), about 40% of the injected CO2 is permanently stored in the reservoir and 60% of the CO2 is produced from the production wells. We assume that this produced CO2 is separated, recycled, and reinjected into this (or another) reservoir. The CO2-to-oilproduced ratio for our 1000 realizations is 1:2.2; 1 ton of gross CO2 injection produces 2.2 barrels of oil. When we account for CO2 recycling and storage, the net ratio is 1:5.5; 5.5 barrels of oil is produced for each ton of CO2 stored. Then, we develop a straightforward economic model to calculate the profitability of CO2-enhaned oil recovery for the FWU site. Total profit of each realization is estimated by using a current oil price ($38/ bbl) and other cost parameters from the literature including CO2 capture and transportation cost ($30/tCO2), CO2 separation/recycling cost ($10/tCO2), water treatment cost ($2/tWater), operating expense, capital expense, royalties, and production tax.23,56−58 Outputs from the economic model suggest that approximately 31% of the 1000 realizations can be profitable. Profitable realizations tend to consist of some cases with relatively larger reservoir permeability and thickness (Figure 5h, note that negative profit indicates unprofitable operations). If government carbon-tax credits are available, the oil price goes up, the CO2-EOR processes can be further optimized or CO2 capture and operating expenses reduce, more realizations would be profitable. This result provides valuable insights for us to understand the impact of the reservoir heterogeneity and other operational parameters on the critical economic decision making and the cost-effectiveness of CO2 sequestration through EOR at other depleted reservoirs.

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ASSOCIATED CONTENT

* Supporting Information S

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.6b01744. Further details on calcuations and Figures S1−S3 (PDF)

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AUTHOR INFORMATION

Corresponding Author

*Telephone: 505-665-6387. E-mail: [email protected]. Notes

This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. The authors declare no competing financial interest.

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ACKNOWLEDGMENTS Funding for this work is provided by the U.S. Department of Energy’s (DOE), National Energy Technology Laboratory (NETL) through the Southwest Partnership on Carbon Sequestration (SWP) under Award No. DE-FC2605NT42591. We gratefully acknowledge the assistance of George Guthrie, Rajesh Pawar, Martha Cather, and Julianna Fessenden-Rahn for providing guidance and constructive comments on our work. We are also grateful to Brian Coats of Coats Engineering, Inc., for providing the multiphase flow and transport modeling code.

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REFERENCES

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