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Carbon capture and storage (CCS) is being evaluated globally as a geoengineering measure for significantly reducing greenhouse emission. However, long-term liability associated with potential leakage from these geologic repositories is perceived as a main barrier of entry to site operators. Risk quantification and impact assessment help CCS operators to screen candidate sites for suitability of CO 2 storage. Leakage risks are highly site dependent, and a quantitative understanding and categorization of these risks can only be made possible through broad participation and deliberation of stakeholders, with the use of site-specific, process-based models as the decision basis. Online decision making, however, requires that scenarios be run in real time. In this work, a Python based, Leakage Assessment and Cost Estimation (PyLACE) web application was developed for quantifying financial risks associated with potential leakage from geologic carbon sequestration sites. PyLACE aims to assist a collaborative, analytic-deliberative decision making processes by automating metamodel creation, knowledge sharing, and online collaboration. In PyLACE, metamodeling, which is a process of developing faster-to-run surrogates of process-level models, is enabled using a special stochastic response surface method and the Gaussian process regression. Both methods allow consideration of model parameter uncertainties and the use of that information to generate confidence intervals on model outputs. Training of the metamodels is delegated to a high performance computing cluster and is orchestrated by a set of asynchronous job scheduling tools for job submission and result retrieval. As a case study, workflow and main features of PyLACE are demonstrated using a multilayer, carbon storage model.

The role of capillary forces during buoyant migration of CO2 is critical towards plume immobilization during post-injection of geological carbon sequestration. However, the inherent heterogeneity of most candidate reservoirs makes it very challenging to evaluate the effects of capillary forces on the storage capacity of these formations and to assess in-situ plume evolution. Given the technical challenges in characterizing deep reservoir's migration and trapping phenomena, the development of carefully controlled laboratory experiments allows recreating the interplay of governing forces that are expected in-situ. Visual observations, complemented by saturation measurements across homogeneous and heterogeneous sand packs, provide direct insight into capillary/buoyancy-dominated flow processes, allowing assessment of predictive ability of existing and new two-phase flow simulators.

The question of whether carbon dioxide enhanced oil recovery (CO 2 -EOR) constitutes a valid alternative for greenhouse gas emission reduction has been frequently asked by the general public and environmental sectors. Through this technology, operational since 1972, oil production is enhanced by injecting CO 2 into depleted oil reservoirs in order displace the residual oil toward production wells in a solvent/miscible process. For decades, the CO 2 utilized for EOR has been most commonly sourced from natural CO 2 accumulations. More recently, a few projects have emerged where anthropogenic CO 2 (A-CO 2 ) is captured at an industrial facility, transported to a depleted oil field, and utilized for EOR. If carbon geologic storage is one of the project objectives, all the CO 2 injected into the oil field for EOR could technically be stored in the formation. Even though the CO 2 is being prevented from entering the atmosphere, and permanently stored away in a secured geologic formation, a question arises as to whether the total CO 2 volumes stored in order to produce the incremental oil through EOR are larger than the CO 2 emitted throughout the entire CO 2 -EOR process, including the capture facility, the EOR site, and the refining and burning of the end product. We intend to answer some of these questions through a DOE-NETL funded study titled “Carbon Life Cycle Analysis of CO 2 -EOR for Net Carbon Negative Oil (NCNO) Classification”. NCNO is defined as oil whose carbon emissions to the atmosphere, when burned or otherwise used, are less than the amount of carbon permanently stored in the reservoir in order to produce the oil. In this paper, we focus on the EOR site in what is referred to as a gate-to-gate system, but are inclusive of the burning of the refined product, as this end member is explicitly stated in the definition of NCNO. Finally, we use Cranfield, Mississippi, as a case study and come to the conclusion that the incremental oil produced is net carbon negative.

© 2015, Springer International Publishing Switzerland. Successful large-scale implementation of geological CO2 sequestration (GCS) will require the preliminary assessment of multiple potential injection sites. Risk assessment and optimization tools used in this effort typically require large numbers of simulations. This makes it important to choose the appropriate level of complexity when selecting the type of simulation model. A promising multi-phase semi-analytical method proposed by Nordbotten et al. (Environ. Sci. Technol. 43, 743–749 2009) to estimate key system attributes (i.e., pressure distribution, CO2 plume extent, and fluid migration) has been found to reduce computational run times by three orders of magnitude when compared to other standard numerical techniques. The premise of the work presented herein is that the existing semi-analytical leakage algorithm proposed by Nordbotten et al. (Environ. Sci. Technol. 43, 743–749 2009) may be further improved in computational efficiency by applying a fixed-point-type iterative global pressure solution to eliminate the need to solve large sets of linear equations at each time step. Results show that significant gains in computational efficiency are obtained with this new methodology. In addition, this modification provides the same enhancement to similar semi-analytical algorithms that simulate single-phase injection into multi-layer domains.

Carbon Capture and Storage (CCS) represents a technology aimed to reduce atmospheric loading of CO2 from power plants and heavy industries by injecting it into deep geological formations, such as saline aquifers. A number of trapping mechanisms contribute to effective and secure storage of the injected CO2 in supercritical fluid phase (scCO2) in the formation over the long term. The primary trapping mechanisms are structural, residual, dissolution and mineralization. Knowledge gaps exist on how the heterogeneity of the formation manifested at all scales from the pore to the site scales affects trapping and parameterization of contributing mechanisms in models. An experimental and modeling study was conducted to fill these knowledge gaps. Experimental investigation of fundamental processes and mechanisms in field settings is not possible as it is not feasible to fully characterize the geologic heterogeneity at all relevant scales and gathering data on migration, trapping and dissolution of scCO2. Laboratory experiments using scCO2 under ambient conditions are also not feasible as it is technically challenging and cost prohibitive to develop large, two- or three-dimensional test systems with controlled high pressures to keep the scCO2 as a liquid. Hence, an innovative approach that used surrogate fluids in place of scCO2 andmore » formation brine in multi-scale, synthetic aquifers test systems ranging in scales from centimeter to meter scale developed used. New modeling algorithms were developed to capture the processes controlled by the formation heterogeneity, and they were tested using the data from the laboratory test systems. The results and findings are expected to contribute toward better conceptual models, future improvements to DOE numerical codes, more accurate assessment of storage capacities, and optimized placement strategies. This report presents the experimental and modeling methods and research results.« less