Position PaperNRAP-open-IAM: A flexible open-source integrated-assessment-model for geologic carbon storage risk assessment and management
Introduction
Geologic carbon storage (GCS) is a promising approach for reducing anthropogenic carbon dioxide (CO2) emissions and mitigating the effects of climate change (IPCC, 2005; National Petroleum Council, 2019). In GCS, CO2 recovered from the emissions of industrial sources (e.g., from ethanol production, conversion of fossil fuels to electricity and other energy products) or captured from the atmosphere (Fasihi et al., 2019; Keith et al., 2006) is compressed to a dense-phase fluid, transported to an approved storage facility, and injected deep underground for safe, long-term storage. While GCS is considered technically feasible, remaining hurdles to the successful large-scale deployment of GCS are the costs and energy demand associated with CO2 capture, and stakeholder concerns about the potential risk of fluid leakage through natural (e.g., fractures and faults) and man-made (e.g., compromised wellbores) pathways located in the vicinity of a storage complex (Pawar et al., 2015). The storage complex is defined as a subsurface geological system including a storage reservoir and primary and possibly secondary seal(s), extending laterally to the defined limits of the CO2 storage operation(s) (Canadian Standards Association Group, 2012). CO2 and brine that migrate upward from the storage reservoir(s) into potable groundwater aquifers may pose a risk to human health and the environment. In addition, CO2 that migrates upward into the atmosphere negates the utility of the GCS operation (Apps et al., 2010; Keating et al., 2016; Zheng et al., 2009). These risks must be appropriately characterized, assessed, and managed at each GCS site to ensure the safety and success of the project.
While numerous risk assessment methods are used to characterize potential (e.g., leakage) risks at GCS sites (Condor et al., 2011), there has been a noticeable trend towards quantitative approaches (Bourne et al., 2014; Pawar et al., 2015; Dean and Tucker, 2017). Quantitative leakage risk assessment relies on physics-based models that forecast long-term site behavior (Celia et al., 2011; Metcalfe et al., 2013; Stauffer et al., 2009; Zhang et al., 2007). Parameters of these models are assumed to be stochastic and to have a predefined probability distribution. The models are run for multiple realizations of the parameters values to predict the range of hypothetical risk scenarios at the site and account for the potentially significant uncertainties associated with GCS operations, which are often difficult and expensive to characterize (Rutqvist, 2012). Developing stochastic models of a GCS system is challenging (Pawar et al., 2015, White and Foxall, 2016). The physical processes associated with GCS involve non-isothermal, transient flows of multicomponent, multiphase fluids, and reactive geochemistry. These complex phenomena are often highly non-linear and take place in three-dimensional porous and fractured media over large scales, which is computationally expensive to simulate.
Over the past decade, the development of computational tools that facilitate quantitative leakage risk assessment for GCS has been a major focus of the National Risk Assessment Partnership (NRAP), a multi-year collaborative research partnership between five national laboratories (Los Alamos National Laboratory, Lawrence Livermore National Laboratory, Lawrence Berkeley National Laboratory, Pacific Northwest National Laboratory, and the National Energy Technology Laboratory) sponsored by the U.S. Department of Energy (U.S. DOE). In 2016, NRAP released their first leakage risk forecasting tool for GCS site operators, the NRAP Integrated Assessment Model for Carbon Storage (NRAP-IAM-CS) (Pawar et al., 2016). NRAP-IAM-CS is built on the systems-modeling approach established by CO2-PENS (Stauffer et al., 2009), which separates a GCS operation into its key components (e.g., reservoir, leakage pathway, receptor) and simulates the physical processes within each component separately. Component models are linked in a one-way forward manner, with the outputs from one component informing the inputs of the next. The component models within NRAP-IAM-CS were developed with computational efficiency in mind and are either reduced-order approximations of high-fidelity models, lookup tables of high-fidelity model outputs, or analytical/semi-analytical models. This approach allows for a sophisticated representation of the complex processes that occur within each component of a GCS operation but eases computational demand, which is necessary to enable stochastic quantification of potential risks.
While the release of NRAP-IAM-CS marked a substantial improvement in GCS system modeling, the program was developed with proprietary software that limited the ability of users to create new (or tailor existing) component models to represent the unique characteristics of their GCS site. Additionally, the NRAP team identified several new features and capabilities that could be added to the model to aid with risk-based decision-making at GCS sites and improve the decision support functionality of the tool. For these reasons, a new version of the integrated assessment model, NRAP Open-Source Integrated Assessment Model (NRAP-Open-IAM), was developed.
As its name implies, NRAP-Open-IAM was intentionally developed to be open source and customizable by its users (researchers, site operators, regulators, and other GCS stakeholders). The open-source nature of NRAP-Open-IAM allows users to create fit-for-purpose functionality that captures the unique aspects of their GCS site and addresses specific site performance questions of interest. NRAP-Open-IAM is written in the Python programming language and requires no special dependencies beyond popular and widely used Python libraries (e.g., NumPy, SciPy, or matplotlib). In this study, we describe the structure and function of NRAP-Open-IAM, highlight the new capabilities and features included in the tool, and demonstrate the use and applicability of these new features for GCS.
Section snippets
Core functionality
NRAP-Open-IAM is written in the Python 3 programming which provides 1) cross-platform capabilities; 2) an extensive number of packages for data handling, analysis, and visualization; and 3) flexibility to include libraries and code written in other programming languages that may be used for the development of embedded component models (e.g., FORTRAN, C++, etc.). The NRAP-Open-IAM tool is built upon the functionality of the Model Analysis ToolKit (MATK) Python package (Harp, 2015; //matk.lanl.gov
Illustrative use case
Example use cases (example applications described above) have been developed to illustrate how the NRAP-Open-IAM tool can be used to help address risk assessment, performance, and decision-making questions at GCS sites. Details of these use cases are provided below and in the supplementary materials.
Summary
NRAP-Open-IAM is an open-source integrated assessment model developed by the U.S. DOE's National Risk Assessment Partnership to characterize GCS risks. The NRAP-Open-IAM incorporates developments from its predecessor (NRAP-IAM-CS) into an open-source framework facilitating community involvement in software development and adds capabilities in uncertainty reduction and risk management. These new capabilities can help inform monitoring design, assess model concordance to measured field data, and
Computer code availability
The NRAP-Open-IAM tool and examples can be downloaded at https://gitlab.com/NRAP/OpenIAM. A copy of the tool can also be obtained through NETL's Energy Data Exchange website: https://edx.netl.doe.gov/dataset/nrap-open-source-iam by a request through an e-mail to [email protected]. NRAP-Open-IAM has a 3-clause BSD License (BSD-3-Clause).
Disclaimer
This project was funded by the United States Department of Energy, National Energy Technology Laboratory, in part, through a site support contract. Neither the United States Government nor any agency thereof, nor any of their employees, nor the support contractor, 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
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgement
This work was completed as part of the National Risk Assessment Partnership (NRAP) project. We would like to acknowledge the support of the U.S. Department of Energy, Office of Fossil Energy's Carbon Storage Program, the Director for the Division of Carbon Capture and Storage Research and Development Mr. Mark Ackiewicz, Carbon Storage Program Manager Mr. Darin Damiani, acting Carbon Storage Technology Manager Mr. Mark McKoy, former Carbon Storage Technology Manager Ms. Traci Rodosta, and NRAP
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