We present a methodology that uses pilot and anchor points with probability distributions for saturated hydraulic conductivity in a groundwater contaminant transport model. This approach directly links locations with calibration target data (e.g., water levels and drawdown at monitoring wells) to the most relevant physical parameter(s) that drive behavior, in a way that promotes model parsimony. Distributions for hydraulic conductivity are developed for monitoring well locations with pumping tests in order to reflect the state of uncertainty in the local estimates; these locations are called anchor points. Pilot points are placed between monitoring wells, and because they have more uncertainty these are generally assigned wider distributions that reflect plausible hydraulic conductivity values for the geologic material in which they are located. Scaling issues are considered in the development of these distributions. Pilot points are not randomly or uniformly distributed in the domain; rather they are considered connectors between locations with data (anchor points) and placed strategically between them. For a given model realization, hydraulic conductivity values at both pilot and anchor points are sampled from their respective distributions and all remaining locations are derived using an interpolation scheme (e.g., kriging). This approach to hydraulic conductivity assignment honors location-specific data, geologic heterogeneity, and spatial patterns. Given that inverse analysis of high-dimensional models tends to be ill-posed and thus sensitive to initialization of parameters, the distribution development process plays a critical role in driving the outcome of model calibration.
Despite significant progress over the past two decades, numerous economic, technical and non-technical challenges have hampered the deployment of carbon capture and storage (CCS) technologies. Recent advances in integrated assessment software have provided powerful new decision support tools to help overcome such challenges and to better evaluate investment and other risks in the integrated capture, transport and storage system. Specifically, the SimCCS software framework provides novel decision support capabilities for CCS project development. In this paper, we illustrate how the development of a new online science gateway platform of the SimCCS software termed SimCCS Gateway is now rapidly expanding the accessibility of this powerful tool. Applications in the SimCCS Gateway platform have been designed to facilitate engagement across the entire CCUS community, including commercial project developers, researchers in the technical and policy spheres, educational users (higher education and K-12) and the general public. The Gateway is providing an innovative new approach to technology transfer as well as outreach to communities in which CCS projects are being proposed and planned. Current applications include projects throughout the US and China, but nations such as Canada and Australia are poised for future implementation owing to availability of key datasets. In this way, the SimCCS Gateway software platform aims to provide a valuable new tool to facilitate the rapid deployment of CCS technologies around the globe.
CO2 capture and storage (CCS) infrastructure designs are traditionally generated with a static point of view, where it is assumed that construction of the full network happens instantaneously. This, of course, is an oversimplification of reality, where projects may take many years of construction before full operations commence. Modern CCS infrastructure design models only generate static deployments. In this research, we present a novel temporal model that allows (1) staged deployment of CCS infrastructure and (2) model parameters to change over time. This new model allows for more accurate infrastructure designs and their associated cost estimates.
CO2 capture and storage (CCS) needs to be deployed on a massive scale to have the impact required to limit global temperatures below 2°C of warming. Adoption at this scale will involve optimizing infrastructure deployments for hundreds of sources and sinks, and thousands of kilometres of pipeline networks. At their core, modern CCS infrastructure deployment models formulate a Mixed Integer Linear Program (MILP), the solution to which yields the most cost-effective locations and quantities of CO2 to capture, route via pipeline, and inject for storage given the scenario under consideration. Solving MILPs is an intractable computational problem, meaning that efficient algorithms do not exist that are guaranteed to solve MILPs in a reasonable time. As the number of sources and sinks grow, MILPs take disproportionately more time to solve, which makes an MILP-based optimization approach unreasonable for large, national-level scenarios. In this research, we develop custom optimization algorithms for the CCS infrastructure design problem that run faster than an MILP for large scenarios (i.e. tens of thousands of options for capture, storage, and pipeline routes) with minimal adverse cost to the quality of the solution.
