Development of robust pressure management strategies for geologic CO2 sequestration

Harp, Dylan R.; Stauffer, Philip H.; O’Malley, Daniel; Jiao, Zunsheng; Egenolf, Evan; Miller, Terry A.; Martinez, Daniella; Hunter, Kelsey; Middleton, Richard S.; Bielicki, Jeffrey M.; Pawar, Rajesh J.

doi:10.1016/j.ijggc.2017.06.012

Cited by 30 publications

(26 citation statements)

References 45 publications

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“…Researchers in various disciplines are working on ways to mitigate the atmospheric carbon problem. One of the options currently discussed is the capture of CO 2 from large point sources such as fossil‐fueled power plants and subsequent storage of captured CO 2 in geological formations . Carbon capture, utilization and storage (CCUS) is considered one of a suite of technology alternatives recognized by the Intergovernmental Panel on Climate Change (IPCC) and other organizations as a technically viable approach to reduce anthropogenic CO 2 emissions .…”

Section: Introductionmentioning

confidence: 99%

“…One of the options currently discussed is the capture of CO 2 from large point sources such as fossil-fueled power plants and subsequent storage of captured CO 2 in geological formations. [4][5][6] Carbon capture, utilization and storage (CCUS) is considered one of a suite of technology alternatives recognized by the Intergovernmental Panel on Climate Change (IPCC) and other organizations as a technically viable approach to reduce anthropogenic CO 2 emissions. [7][8][9] International Energy Agency (IEA) projections indicate that a least-cost pathway to achieve a 2°C scenario (limiting average global warming to well below 2°C above preindustrial times) would require the capture and storage of almost 4000 million tonnes of CO 2 per annum in 2040.…”

Section: Introductionmentioning

confidence: 99%

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Application of a new reduced‐complexity assessment tool to estimate CO₂ and brine leakage from reservoir and above‐zone monitoring interval (AZMI) through an abandoned well under geologic carbon storage conditions

et al. 2018

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This study employs a combination of detailed simulations and reduced‐complexity models in the Well Leakage Analysis Tool (WLAT), developed by the National Risk Assessment Partnership (NRAP), US Department of Energy, to investigate how the rates of CO2 and brine leakage through an abandoned well under geologic CO2 storage conditions are affected by various factors. The factors considered in this study include depth of well penetration into the storage system, location relative to the point of injection, and effective permeability of the abandoned well. Location is the primary factor used to identify high‐ and low‐risk abandoned wells. For the typical CO2 injection scenario considered in this study, the potential impact of CO2 and brine leakage through an abandoned well is very low if the abandoned well is 6.2 km or further away from the CO2 injector. Due to the buoyancy of CO2 relative to formation brine, wells intersecting (i.e., accepting flow from) the top of the above‐zone monitoring interval (AZMI), which is the interval immediately above the seal, have a higher chance to become a pathway for CO2 than wells intersecting the bottom of the AZMI. If the permeability of an abandoned well is 10−14 m2 or less, the risk of CO2 and brine leakage through the well becomes very low. Due to the combined effect of pressure increase and buoyancy, the CO2 leakage rate is higher than brine leakage rate within the CO2 plume extent. Specific values reported in this study are site‐specific results and cannot be regarded as general findings. © 2018 Society of Chemical Industry and John Wiley & Sons, Ltd.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of a new reduced‐complexity assessment tool to estimate CO₂ and brine leakage from reservoir and above‐zone monitoring interval (AZMI) through an abandoned well under geologic carbon storage conditions

et al. 2018

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“…However, it can be achieved if CCS is added as a routine technology to any process that uses fossil fuels. Thus far, geological reservoirs, such as depleted oil or gas fields, or deep saline aquifers, have been considered as appropriate geologic formations for storing CO 2 emissions at a depth of several thousand meters [1][2][3]. Saline aquifers provide large storage capacities, are broadly distributed geographically, and are more accessible to capture sites as they facilitate the entire CO 2 transport process [4].…”

Section: Introductionmentioning

confidence: 99%

Managing Uncertainty in Geological CO2 Storage Using Bayesian Evidential Learning

Tadjer

Bratvold

2021

Energies

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Carbon capture and storage (CCS) has been increasingly looking like a promising strategy to reduce CO2 emissions and meet the Paris agreement’s climate target. To ensure that CCS is safe and successful, an efficient monitoring program that will prevent storage reservoir leakage and drinking water contamination in groundwater aquifers must be implemented. However, geologic CO2 sequestration (GCS) sites are not completely certain about the geological properties, which makes it difficult to predict the behavior of the injected gases, CO2 brine leakage rates through wellbores, and CO2 plume migration. Significant effort is required to observe how CO2 behaves in reservoirs. A key question is: Will the CO2 injection and storage behave as expected, and can we anticipate leakages? History matching of reservoir models can mitigate uncertainty towards a predictive strategy. It could prove challenging to develop a set of history matching models that preserve geological realism. A new Bayesian evidential learning (BEL) protocol for uncertainty quantification was released through literature, as an alternative to the model-space inversion in the history-matching approach. Consequently, an ensemble of previous geological models was developed using a prior distribution’s Monte Carlo simulation, followed by direct forecasting (DF) for joint uncertainty quantification. The goal of this work is to use prior models to identify a statistical relationship between data prediction, ensemble models, and data variables, without any explicit model inversion. The paper also introduces a new DF implementation using an ensemble smoother and shows that the new implementation can make the computation more robust than the standard method. The Utsira saline aquifer west of Norway is used to exemplify BEL’s ability to predict the CO2 mass and leakages and improve decision support regarding CO2 storage projects.

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“…Fully utilizing the information produced with a monitoring program requires a formal approach to integrate monitoring data into models in a manner that provides indications of the risks associated with the GCS operation. While there are many existing GCS related publications describing and demonstrating alternative monitoring techniques (Cheng et al, 2010;Dafflon et al, 2012;Doetsch et al, 2013;Lin and Huang, 2015), uncertainty quantification approaches (Sun et al, 2013;Trainor-Guitton et al, 2013;Harp et al, 2017), and data assimilation approaches (Kumar, 2010;Chen et al, 2018), there are few examples describing how to incorporate monitoring data as it becomes available during a GCS operation to support designations of conformance. Bielicki et al (2016) present an approach to quantify the monetized leakage risk of GCS operations, including the impact of remediating leaks, but the approach is intended to evaluate 2 potential sites, not assimilate monitoring data during a GCS operation.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we discuss how data and modeling can be coupled to support robust designations of conformance in GCS operations. The approach uses a similar decision analysis as in Harp et al (2017), which proposed an approach to select a GCS pressure management strategy, but in this case applies and extends the decision analysis to designations of conformance over the course of the GCS operation.…”

Section: Introductionmentioning

confidence: 99%

A metric for evaluating conformance robustness during geologic CO2 sequestration operations

Harp

Oldenburg

Pawar

2019

International Journal of Greenhouse Gas Control

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A metric for quantifying the robustness of a designation of conformance of a geologic CO 2 sequestration (GCS) project during its operational phase is developed and demonstrated. Conformance in this context is a measure of the degree to which the sequestration system is understood and can be accurately modeled along with the degree to which the storage system is performing as designed. The robustness of conformance quantifies the degree to which parameter values can deviate from their current nominal estimates and still produce model forecasts that meet the performance criteria for the GCS operation. We develop and demonstrate the approach on a simplified scenario to illustrate the concept using a single uncertain parameter (homogeneous reservoir permeability) and a single performance criterion (critical pressure at a monitoring well in the reservoir; i.e., one that may displace brine from the reservoir to an overlying drinking water aquifer for example). Increased confidence in conformance assessment as more monitoring data are obtained is incorporated through the standard error of the coefficient (reservoir permeability in the case presented here), which we designate as the concordance metric. As more monitoring data become available during the course of the GCS operation, the standard error of the coefficient decreases (in general), thereby leading to increased conformance robustness as a larger deviation from nominal is required to fail to meet performance criteria. Increasing conformance robustness over time builds confidence that a GCS project will continue to meet performance criteria during the life-span of the project, thereby supporting designations of conformance. A lack of conformance robustness provides a critical warning that the performance criteria of the GCS operation are not robust against probabilistic and non-probabilistic uncertainty in model conceptualization and/or model parameters.

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Development of robust pressure management strategies for geologic CO2 sequestration

Cited by 30 publications

References 45 publications

Application of a new reduced‐complexity assessment tool to estimate CO₂ and brine leakage from reservoir and above‐zone monitoring interval (AZMI) through an abandoned well under geologic carbon storage conditions

Application of a new reduced‐complexity assessment tool to estimate CO₂ and brine leakage from reservoir and above‐zone monitoring interval (AZMI) through an abandoned well under geologic carbon storage conditions

Managing Uncertainty in Geological CO2 Storage Using Bayesian Evidential Learning

A metric for evaluating conformance robustness during geologic CO2 sequestration operations

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Development of robust pressure management strategies for geologic CO2 sequestration

Cited by 30 publications

References 45 publications

Application of a new reduced‐complexity assessment tool to estimate CO2 and brine leakage from reservoir and above‐zone monitoring interval (AZMI) through an abandoned well under geologic carbon storage conditions

Application of a new reduced‐complexity assessment tool to estimate CO2 and brine leakage from reservoir and above‐zone monitoring interval (AZMI) through an abandoned well under geologic carbon storage conditions

Managing Uncertainty in Geological CO2 Storage Using Bayesian Evidential Learning

A metric for evaluating conformance robustness during geologic CO2 sequestration operations

Contact Info

Product

Resources

About

Application of a new reduced‐complexity assessment tool to estimate CO₂ and brine leakage from reservoir and above‐zone monitoring interval (AZMI) through an abandoned well under geologic carbon storage conditions

Application of a new reduced‐complexity assessment tool to estimate CO₂ and brine leakage from reservoir and above‐zone monitoring interval (AZMI) through an abandoned well under geologic carbon storage conditions