Geologic Carbon Storage Sites Research Articles

Heuristic algorithms for design of integrated monitoring of geologic carbon storage sites

Designs for Risk Evaluation and Management (DREAM) is a tool developed under the National Risk Assessment Partnership (NRAP) to enhance geologic carbon storage safety and efficiency. Using potential leakage scenarios generated externally by the users preferred history-matching approach, DREAM constructs ideal combinations of sensor locations in the right place at the right time to detect as many leaks as possible, detect them as early as possible, and minimize cost. This user-friendly tool, developed in Java, features a window-based GUI for input and a 3D visualization tool for viewing the domain space and optimized monitoring plans. DREAM's latest version accommodates real-world usage by allowing for joint optimization of wellbore point sensor placements and surface geophysics survey geometries, and by using more efficient multi-objective optimization algorithms. In an example shown here, these two improvements combined allow us to support containment assurance and go from detecting 80–90 % of the potential CO2 leakage to +99.7 %, a step-change improvement that can make the deciding difference in whether a site is suitable for geologic carbon storage. Though developed for geologic carbon storage, this tool would be equally applicable in many surface or offshore environmental monitoring projects.

A quantitative risk assessment approach for developing contingency plans at a geologic carbon storage site

AbstractGeologic carbon storage (GCS) is an increasingly important technology for reducing carbon dioxide (CO2) emissions to the atmosphere. The leakage risks associated with GCS are an environmental and human health concern, however, and site operators must develop contingency plans that thoroughly consider leakage risks and identify potential mitigation strategies. Here, we use a GCS system model (the National Risk Assessment Partnership's Open‐Source Integrated Assessment Model, NRAP‐Open‐IAM) to evaluate different contingency plans for a hypothetical GCS site. In the scenario considered, an unplugged legacy well is discovered near the site after 5 years of CO2 injection. Our simulations show that the planned operation has a relatively high chance of causing brine leakage through the legacy well and into the two overlying aquifers, the shallower of which has potable water—an unacceptable outcome. To reduce this risk, we consider five remedial response scenarios that manipulate reservoir pressures through brine extraction, injection rate reduction, and early injection stopping. NRAP‐Open‐IAM is used to quantify the degree to which each scenario reduces the probability of brine leakage at the site amidst reservoir uncertainty. Evaluation of the different scenarios suggests that reduction of injection rates effectively reduces leakage risks while maintaining a substantial fraction of the initially intended cumulative CO2 storage. In the event of an emergency, the reservoir pressure management strategies considered here can provide operators more time while they pursue a more permanent solution. The analyses demonstrated here fit into a larger workflow we propose for evaluating the contingency plans of GCS sites. © 2023 Society of Chemical Industry and John Wiley & Sons, Ltd.

Effects of abiotic processes on the correlation between pH and pO2 in the Norwegian Sea: Implications for GCS monitoring

In the absence of abiotic sources of CO2, variation in pCO2 and pO2 is expected to be inversely correlated in the water column due to biogenic processes. It has previously been suggested to use this correlation for leakage monitoring of offshore geological carbon storage (GCS) sites. In this study the aim is to investigate the extent of this correlation in ocean water masses with different origin and history in the Norwegian Sea, as well as in water masses in the vicinity of an active hydrothermal vent field at Mohn's Ridge, where pH is used as a proxy for pCO2. Over a hydrothermal vent site, a strong correlation between pH and pO2 is observed from 0 to 1700 m, whereas at depths >1700 m there is no correlation, likely due to CO2 emissions from the hydrothermal vents. However, at a reference site nearly 200 km from the hydrothermal vents, the intermediate Arctic water masses (700 – 1600 m depth) also show pH-pO2 correlations that are inconsistent with biogenic processes, but less pronounced compared to the hydrothermal vent site. These findings show that the suitability of this monitoring strategy will depend on a thorough site-specific evaluation of pH/CO2 and O2 relationships of relevant water masses.

Physics-informed machine learning for fault-leakage reduced-order modeling

Geologic carbon storage (GCS) is a promising technology for mitigating CO2 emissions. The overall success of GCS depends on safe operations that are informed by risk assessment and have proper mitigation plans in place. Performing quantitative probabilistic risk assessment for a GCS site using traditional reservoir simulators can be challenging due to the high computational costs. To overcome this challenge, the US Department of Energy’s National Risk Assessment Partnership (NRAP) project has developed an integrated assessment modeling approach that utilizes computationally efficient reduced-order models (ROM) for simulating various parts of a GCS storage site to quantify uncertainty. In this study, we develop a reduced-order model for fault leakage risk assessment. We use a deep learning approach to build the reduced-order model. We perform a sensitivity analysis and find that the deep learning model yields high accuracy with a much smaller computational cost than full-physics simulation. We also evaluate the performance of the model in scenarios where simulations are not possible to run, providing analysis not previously performed in fault-leakage ROM analyses. Based on a sensitivity analysis of the model, we suggest a simplified conceptual model for fault leakage and site monitoring.

A Project Lifetime Approach to the Management of Induced Seismicity Risk at Geologic Carbon Storage Sites

Abstract The geologic storage of carbon dioxide (CO2) is one method that can help reduce atmospheric CO2 by sequestering it into the subsurface. Large-scale deployment of geologic carbon storage, however, may be accompanied by induced seismicity. We present a project lifetime approach to address the induced seismicity risk at these geologic storage sites. This approach encompasses both technical and nontechnical stakeholder issues related to induced seismicity and spans the time period from the initial consideration phase to postclosure. These recommendations are envisioned to serve as general guidelines, setting expectations for operators, regulators, and the public. They contain a set of seven actionable focus areas, the purpose of which are to deal proactively with induced seismicity issues. Although each geologic carbon storage site will be unique and will require a custom approach, these general best practice recommendations can be used as a starting point to any site-specific plan for how to systematically evaluate, communicate about, and mitigate induced seismicity at a particular reservoir.

Geologic Carbon Storage of Anthropogenic CO2 under the Colorado Plateau in Emery County, Utah

Geologic Carbon Storage (GCS) is a promising technology for storing large volumes of anthropogenic CO2 effectively and permanently. Numerical simulations are an integral part of site selection and characterization for any potential GCS site. As part of the DOE-funded CarbonSAFE Rocky Mountains Phase I project, a regional GCS analysis was undertaken to understand the efficacy of storing CO2 emissions from the power generation and heavy industry in central Utah’s favorable geology. In this study, the injection of CO2 for geologic storage was simulated in the Navajo Sandstone Formation in Emery County, Utah. Carbon dioxide was sourced from regional power generation stations and heavy industries throughout Utah, with an emphasis on emissions reduction at the Hunter Power Plant near Castle Dale, Utah. A simulation grid was extracted from the project’s geological model encompassing an area around Price, Huntington, and Castle Dale in central Utah. The Navajo Sandstone Member of the Glen Canyon Group was the target of CO2 injection with the overlying Carmel formation providing the primary seal. A suite of simulations was performed assessing the viability of this area for permanent CO2 storage. Results indicate that the area can not only store 46 million metric tons of anthropogenic CO2, meeting the project goals, but this area has the capacity to securely store at least 1.3 billion tons of CO2, suggesting the injection site and surrounding geology are suitable locations for commercial-scale GCS.

Rock Physics-Based Data Assimilation of Integrated Continuous Active-Source Seismic and Pressure Monitoring Data during Geological Carbon Storage

Summary There has been substantial controversy concerning the role of geological carbon storage (GCS) in sequestering anthropogenic carbon emissions to mitigate climate change and global warming. Arguments center on the inability to monitor a geological storage site precisely and continuously, especially highlighting the associated costs and spatiotemporal trade-offs when using conventional subsurface monitoring techniques (well logs, core samples, chemical tracers, and 4D seismics). Active surveillance of GCS sites is essential for managing and mitigating potential leaks but is also required by regulation. With the goal of enhancing the monitoring capability at GCS sites, we present a rock physics-based joint data assimilation model to study a popular GCS site at Cranfield, Mississippi, USA. Synthetic continuous active-source seismic monitoring (CASSM) data (in the form of Vp and Qp measurements) and wellbore pressure monitoring data are assimilated with an ensemble of reservoir realizations to monitor gas saturation and reservoir pressure changes over a period of 100 years. Synthetic seismic attributes are generated using rock physics models (RPMs) and wellbore pressure monitoring data are extracted from the ground truth. Two assimilation methods, ensemble Kalman filter (EnKF) and ensemble Kalman smoother (EnKS), are tested in an observation system simulation experiment (OSSE) environment to assess the prediction accuracy of the individual and composite observation systems. The joint monitoring system achieves more accurate estimates of gas saturation and pressure, across the time span from start of injection to end of forecast, as compared to a single type of monitoring tool and irrespective of data assimilation algorithm choice. These results indicate that jointly assimilated data from two types of sensors (in this case, crosswell seismic and downhole pressure) may lead to a more risk-reducing monitoring design. One would expect that more data, vis-à-vis inclusion of a new sensor type, will improve the accuracy of any GCS monitoring system. However, from a practical standpoint, one important question is whether such a gain in accuracy is worth the additional cost associated with the new sensor. This paper focuses on quantifying the gain in accuracy, such that a practitioner can answer this question.

A Quantitative Comparison of Risk-based Leak Mitigation Strategies at a Geologic Carbon Storage Site

A Quantitative Comparison of Risk-based Leak Mitigation Strategies at a Geologic Carbon Storage Site

Computational Tools and Workflows for Quantitative Risk Assessment and Decision Support for Geologic Carbon Storage Sites: Progress and Insights from the U.S. DOE’s National Risk Assessment Partnership

Computational Tools and Workflows for Quantitative Risk Assessment and Decision Support for Geologic Carbon Storage Sites: Progress and Insights from the U.S. DOE’s National Risk Assessment Partnership

NRAP-open-IAM: A flexible open-source integrated-assessment-model for geologic carbon storage risk assessment and management

Large-scale implementation of geologic carbon storage (GCS) to help reduce atmospheric greenhouse gas emissions requires stakeholder confidence that injected CO2 will remain contained and that potential subsurface environmental risks are acceptably small and manageable. The U.S. Department of Energy's National Risk Assessment Partnership (NRAP) has developed an open-source integrated assessment model (NRAP-Open-IAM) to help address questions about a potential GCS site's ability to effectively contain injected CO2 and protect groundwater and other overlying environmentally sensitive receptors. NRAP-Open-IAM allows a user to: (1) incorporate relevant site geologic and injection scenario data; (2) characterize important site features and events; (3) couple fast prediction models of various system components of the engineered geologic system; and (4) execute stochastic, dynamic simulation of whole GCS system performance, leakage risk assessment, and uncertainty quantification. NRAP-Open-IAM is available on GitLab (https://gitlab.com/NRAP/OpenIAM), and is accompanied by multiple application examples and detailed user and developer guides.

Time-lapse electromagnetic and gravity methods in carbon storage monitoring

For geologic carbon storage (GCS), monitoring of the storage reservoir and detection of secondary plumes if they accumulate outside of the reservoir are important to confirm that the injected CO2 stays where intended. Seismic methods are most often applied but are expensive. Due to cost considerations, especially for long-term monitoring, less expensive techniques play a role when designing monitoring networks. In this article, the merits of gravity and electromagnetic (EM) methods as monitoring tools for GCS are presented. Many of the technologies are well established, and several new technologies are on the horizon. EM and gravity techniques are complementary to seismic methods and together provide better subsurface monitoring. Time-lapse multiphysics joint inversion, including seismic, EM, and gravity, could be a game changer for carbon storage monitoring. The trade-off between the sensitivity or resolution to a given plume size and the associated costs will be an important factor in selecting efficient and reliable monitoring arrays at GCS sites. Complex digital models representing geology encountered at storage sites can be used for this purpose and present another cost savings.

Evaluating probability of containment effectiveness at a GCS site using integrated assessment modeling approach with Bayesian decision network

AbstractImproved scientific and engineering understanding of the behavior of geologic CO2 storage together with established regulatory framework and incentive structures raise the prospects for accelerated, large‐scale deployment of this greenhouse gas emissions reduction approach. Incentive structures call for the establishment of appropriate verification and accounting approaches to support claims of the integrity of a geologic storage complex and to justify taking credit for long‐term storage. In this study, we present a framework for assessing the probability of containment effectiveness over the lifetime of a geologic carbon storage site (e.g., after 70 years of injection and postinjection site performance) using forward stochastic model realizations based on site characterization data and using a monitoring‐informed Bayesian network based on hypothetical detectability from surface seismic surveys over the site injection and post‐injection phases. The National Risk Assessment Partnership's open‐source Integrated Assessment Model (NRAP‐Open‐IAM) was utilized to develop an ensemble of 10,000 a priori stochastic forecasts of CO2 containment. Those simulations were used to train the Bayesian network model to estimate the prior probabilities of the CO2 leakage mass into overlying, monitorable aquifers considering the uncertainties in the reservoir properties, permeability of potentially leaky wells and the overlying aquifers. The conditional probabilities in the Bayesian network were either learned from the NRAP‐Open‐IAM simulations or derived from the predefined detection thresholds for the monitoring method. Observations obtained from monitoring, over time during the site operation phases were then used to generate updated posterior probabilities of containment (and any loss from containment) in the Bayesian network by propagating the prior probabilities through the conditional probabilities. We demonstrate how to construct and use the Bayesian network for verifying the long‐term storage complex effectiveness informed by monitoring based on the NRAP‐Open‐IAM simulations previously developed for the FutureGen 2.0 site. This approach may have relevance for stake holders to demonstrate secure geologic storage, provide a defensible, probabilistic approach to claim credit for geologic storage, and to estimate the likelihood that any fraction of the claimed credit may need to be refunded to the creditor based on available monitoring information. © 2021 Society of Chemical Industry and John Wiley & Sons, Ltd.

Detection and quantification of underground CO2 leakage into the soil using a fiber-optic sensor

• The fiber-optic sensor was implemented in the field for underground CO 2 gas leakage monitoring. • Hollow-core photonic crystal fiber was used to improve the sensitivity of the sensor. • The integrated sensor package was used to cancel complex crosstalk due to the temperature and humidity changes. • The proposed fiber-optic sensor has the potential to monitor the gas leakage continuously and remotely. This study presents a field demonstration of underground CO 2 leakage detection using a fiber-optic interferometric sensor at an artificial leakage experiment site. CO 2 gas was injected for a couple of days into the unsaturated soil zone and CO 2 leakage monitoring was continuously performed for nine days using the fiber-optic sensor buried in the soil, using a hollow core photonic crystal fiber (HC-PCF) based sensor. Temperature and relative humidity in the soil were simultaneously measured by the commercial sensors. The measurement results of the HC-PCF sensor were evaluated by comparing with those by the commercial non-dispersive infrared sensor. This comparison illustrated that the proposed HC–PCF sensor was able to in-situ monitor CO 2 gas concentration with a range of 0–100%. Accordingly, the proposed fiber-optic sensor has the potentials to provide a reliable assessment of CO 2 gas leakage in geologic carbon storage sites and other gas leaking sites.

Quantitative assessment of deep-seated CO2 leakage around CO2-rich springs with low soil CO2 efflux using end-member mixing analysis and carbon isotopes

This study examined a mountainous area with two hydrochemically distinct CO2-rich springs to understand the origin, flow, and leakage of CO2, which may provide implications for precise monitoring of CO2 leakage in geological carbon storage (GCS) sites. The carbon isotopic compositions of dissolved inorganic carbon (DIC) in CO2-rich water (δ13CDIC) and those of soil CO2 (δ13CCO2) indicated a deep-seated CO2 supply to the near-surface environment in the study area. The hydrochemical difference (e.g. pH, total dissolved solids) for the two CO2-rich springs separated by 7 m, despite similar δ13CDIC and partial pressure of CO2, was considered as the result of different evolution of shallow groundwater affected by deep-seated CO2 preferentially rising along fracture zones. Electrical resistivity tomography also suggested flow through fracture zones beneath the CO2-rich springs, showing low resistivity compared to other surveyed zones. However, soil CO2 efflux was low compared to that in other natural CO2 emission sites, and in particular it was noticeably low near the CO2-rich springs, whereas δ13CCO2 was high close the CO2-rich springs. The dissolution of CO2 in the near-surface water body seemed to decrease the deep-seated CO2 leakage through the soil layer, while δ13CCO2 imprinted the source. End-member mixing analysis was performed to assess the contribution of deep-seated CO2 to the low soil CO2 efflux by assuming that atmospheric CO2 and soil CO2 (by respiration) as well as deep-seated CO2 contribute to the soil CO2 efflux. For each end-member, characteristic δ13CCO2 and CO2 concentrations were defined, and then their apportionment to soil CO2 efflux was estimated. The resultant proportion of deep-seated CO2 was up to 8.8%. Unlike the spatial distribution of high soil CO2 efflux, high proportions exceeding 3% were found around the CO2-rich springs along the east-west valley. The study results indicate that soil CO2 efflux measurement should be combined with carbon isotopic analysis in GCS sites for CO2 leakage detection because CO2 dissolution in the underground water body may blur leakage detection on the surface. The implication of this study is the need to quantitatively assess the contribution of deep-seated CO2 using the soil CO2 concentration, soil CO2 efflux, and δ13CCO2 at each measurement site.

Hydraulic fracturing to enhance injectivity and storage capacity of CO2 storage reservoirs: Benefits and risks

Several potential CO2 storage reservoirs have been found to have insufficient porosity and permeability to support cost effective commercial-scale injection. As a result, the use of hydraulic fracturing to enhance injectivity and storage capacity of CO2 storage reservoirs was explored. Previous modeling studies indicate that fracturing can increase storage capacity by modest to significant amounts (10%–35%), depending on model assumptions. Simulations completed as part of this study confirm that for a range of horizontal well lengths, number of fractures, fracture geometries, and fracture properties, injectivity is improved and capacity increases by 13%–71% over the base case unfractured vertical well. Intuitively, increasing the well length and number of fractures had a corresponding impact on increased capacity. Fracture area (i.e., the fracture height multiplied by its width) was an important parameter for increasing capacity but the specific geometry (e.g., the ratio of height-to-width) was unimportant. The most important aspect that affected capacity was the ability of a fracture to connect high permeability horizontal zones in the reservoir. This would allow a single well to access both layers, thereby maximizing total storage capacity of the reservoir while likely leading to an overall increase of the CO2 footprint, which is an important consideration for Class VI UIC permitting. The results of this work demonstrate that hydraulic fracturing is an attractive option to consider when faced with an underperforming geologic carbon storage site that is at risk of causing a project to fail.

Evaluation of Long-Term Impacts of CO2 Leakage on Groundwater Quality Using Hydrochemical Data from a Natural Analogue Site in South Korea

Three hydrochemical types of CO2-rich water (i.e., Na-HCO3, Ca-Na-HCO3 and Ca-HCO3) occur together in the silicate bedrock (granite and gneiss) of Gangwon Province in South Korea. As a natural analogue of geological carbon storage (GCS), this can provide implications for the environmental impacts of the leakage of CO2 from deep GCS sites. By using hydrochemical and isotopic datasets that were collected for previous and current studies, this study aimed to carefully scrutinize the hydrochemical differences in the three water types with an emphasis on providing a better understanding of the impacts of long-term CO2 leakage on groundwater quality (especially the enrichments of minor and trace metals). As a result, the Na-HCO3 type CO2-rich water contained higher Li, Rb and Cs than the Ca-HCO3 type, whereas Fe, Mn and Sr were higher in the Ca-HCO3 type than in the Na-HCO3 type despite the similar geological setting, which indicate that the hydrochemical differences were caused during different geochemical evolutionary processes. The δ18O and δD values and tritium concentrations indicated that the Na-HCO3 type was circulated through a deep and long pathway for a relatively long residence time in the subsurface, while the Ca-HCO3 type was strongly influenced by mixing with recently recharged water. These results were supported by the results of principal component analysis (PCA), whose second component showed that the Na-HCO3 type had a significant relation with alkali metals such as Li, Rb and Cs as well as Na and K and also had a strong relationship with Al, F and U, indicating an extensive water-rock interaction, while the Ca-HCO3 type was highly correlated with Ca, Mg, Sr, Fe and Mn, indicating mixing and reverse cation exchange during its ascent with hydrogeochemical evolution. In particular, the concentrations of Fe, Mn, U and Al in the CO2-rich water, the result of long-term water-rock interaction and cation exchange that was enhanced by CO2 leakage into silicate bedrock, exceeded drinking water standards. The study results show that the leakage of CO2 gas and CO2-rich fluid into aquifers and the subsequent hydrogeochemical processes can degrade groundwater quality by mobilizing trace elements in rocks and consequently may pose a health risk.

Mineral carbonate dissolution with increasing CO2 pressure measured by underwater laser induced breakdown spectroscopy and its application in carbon sequestration

In this study, the ability of laser-induced breakdown spectroscopy (LIBS) to measure the in situ aqueous dissolution of various mineral carbonates with increasing CO2 pressure was examined. Dissolution experiments included four geologically common mineral carbonates (CaCO3, MgCO3, MnCO3, SrCO3) and the CO2 pressure ranged from ambient to 250 bar. The ensuing plasma emission was spectrally analyzed, and the intensities of Ca, Mg, Mn, and Sr emission lines were used to monitor the respective metal cations released to the aqueous solution. The strong emission lines of Ca (Ca II 393.36, Ca II 396.84, Ca I 422.67 nm), Mg (unresolved magnesium doublet: Mg I 383.230, Mg I 383.829 nm), Mn (unresolved manganese triplet: Mn I 403.076, Mn I 403.307, Mn I 403.449 nm), and Sr (Sr II 407.77, Sr II 421.55, Sr I 460.73 nm) were identified in the spectra. The amounts of metals released from their respective carbonates were estimated at different time intervals following the CO2 injection (5 m, 1, 2, 3, 4, 24 h) and at different pressures (50, 100, 150, 200, 250 bar) using calibration models developed at corresponding pressure settings. The results demonstrated that the pressure-induced dissolution of all carbonates was consistent with their expected and selective pH-dependent solubility. The dissolution rate of CaCO3, MgCO3, and SrCO3 was found to be higher than that of MnCO3. The dissolution of constituents in a Mt. Simon sandstone associated with a deep saline reservoir at elevated CO2 pressure was also studied and Ca release was quantified. The results demonstrated that real-time monitoring of carbonate dissolution by LIBS may provide a useful indirect detection system indicative of CO2 leakage from geologic carbon storage sites.

Adaptive moment-tensor joint inversion of clustered microseismic events for monitoring geological carbon storage

SUMMARY Moment-tensor inversion of induced microseismic events can provide valuable information for tracking CO2 plumes at geological carbon storage sites, and study the physical mechanism of induced microseismicity. Accurate moment-tensor inversion requires a wide-azimuthal coverage of geophones. Cost-effective microseismic monitoring for geological carbon storage often uses only one geophone array within a borehole, leading to a large uncertainty in moment-tensor inversion. We develop a new adaptive moment-tensor joint inversion method to reduce the inversion uncertainty, when using limited but typical geophone receiver geometries. We first jointly invert a number of clustered microseismic events using a uniform focal mechanism to minimize the waveform misfit between observed and predicted P and S waveforms. We then invert the moment tensor for each event within a limited searching range around the joint inversion result. We apply our adaptive joint inversion method to microseismic data acquired using a single borehole geophone array at the CO2-Enhanced Oil Recovery field at Aneth, Utah. We demonstrate that our inversion method is capable of reducing the inversion uncertainty caused by the limited azimuthal coverage of geophones. Our inverted strikes of focal mechanisms of microseismic events are consistent with the event spatial distribution in subparallel pre-existing fractures or geological imperfections. The large values up to 40 per cent of the CLVD components might indicate crack opening induced by CO2/wastewater injection or rupture complexity.

CO2 leakage detection in the near-surface above natural CO2-rich water aquifer using soil gas monitoring

Conventional soil gas monitoring methods were applied above a natural low-level CO2 reservoir to locate natural CO2 leakage and assess effectiveness in monitoring geological carbon storage sites. The concentrations of the soil gas (N2, O2, and CO2) and carbon-13 isotopes of soil CO2 (δ13CCO2) were determined for ninety-four soil gas samples that were collected at a depth of ∼60 cm. The CO2 flux was also measured at the same sampling locations. The 93 soil gas samples were divided into two groups: Group A with low soil CO2 concentrations and high δ13CCO2, which was influenced by the atmospheric air, and Group B with high soil CO2 concentrations and low δ13CCO2, which originated from microbial processes. Sample M17, which was close to a CO2-rich water well, had an exceptionally high soil CO2 (36.0% v/v), δ13CCO2 (-5.7‰), and flux (546.2 g/m2/d), indicating geogenic CO2 inflow to the soil layer and discharge through the surface. This study shows that conventional soil gas monitoring methods are useful for locating CO2 leakage. A dense grid soil CO2 sampling near wells and periodic investigations are crucial for further understanding of the CO2 flow paths in the soil layer.

Managing well leakage risks at a geologic carbon storage site with many wells

Potential geologic carbon storage (GCS) sites with a history of oil and gas production have well-characterized injectivity and storage capacity, but the presence of legacy wells increases leakage risk. Thus, the success of GCS operations at sites with many wells will require a thorough leakage risk assessment and a robust strategy for managing well leakage risk over the lifetime of the project. In this study, we demonstrate a workflow that uses the National Risk Assessment Partnership’s open-source Integrated Assessment Model to quantify well leakage risks and test the performance of various leakage risk management strategies at a heavily drilled GCS site. Our model simulates a 50-year basin-scale injection of CO2 at a hypothetical site based on the Kimberlina Project Site in the Southern San Joaquin Valley of California. Brine and CO2 leakage through 1000 legacy wells into a USDW are stochastically simulated as a proxy for risk. We consider multiple scenarios that explore the efficacy of various well leakage risk management strategies with changes in well leakage behavior, reservoir behavior, and post-injection site care (PISC) length. Predicted leakage at the site after 100 years was small with a maximum CO2 leakage of 102.1 tonnes (4.08 × 10−5% of the 250 Mt injected) and a maximum brine leakage of 2.4 tonnes. Leakage risk management strategies based on accurate prior information about well leak probability reduced leakage risks more effectively at the modeled site and were more robust with respect to reservoir uncertainty than strategies based on the distance of the legacy well from the injector. The importance of the PISC period length was not clear as it had a negligible impact on CO2 leakage risk but a sizable impact on brine leakage risk in our model.