Conversion Feasibility of the KILnGAS Commercial Module (KCM) to a Hazardous Waste Facility

This study presents the results of a preliminary assessment of the feasibility of converting an existing kiln facility, the KILnGAS Commercial Module (KCM) in East Alton, lllinois, to a hazardous waste incinerator. The study examined the RCRA and Superfund waste volumes and characteristics as well as the treatment and disposal capacity of the State of Illinois to identify potential incineration capacity shortfalls. A centerline waste, soils contaminated with PCBs, was selected to provide a reference case to study the facility conversion. A conceptual facility design was developed using the technical and environmental criteria for the selected waste as a design basis. Major process equipment was identified, sized, and priced. A heat and material balance was developed for a centerline mode of operation to forecast performance. Economics for waste treatment were examined based upon a range of competitive tipping fees and other parameters impacting commercial viability. Finally, tentative conclusions regarding the feasibility of the facility conversion are presented and a "next step" action plan is outlined to corroborate the technical, economic, and regulatory assumptions and to examine design alternatives with the potential for reducing facility costs and/or enhancing its performance or siting potential.HWRIC Project No. 88-050published or submitted for publicationis peer reviewe

Probabilistic Assessment of Above Zone Pressure Predictions at a Geologic Carbon Storage Site.

<p>Carbon dioxide (CO2) storage into geological formations is regarded as an important mitigation strategy for anthropogenic CO2 emissions to the atmosphere. This study first simulates the leakage of CO2 and brine from a storage reservoir through the caprock. Then, we estimate the resulting pressure changes at the zone overlying the caprock also known as Above Zone Monitoring Interval (AZMI). A data-driven approach of arbitrary Polynomial Chaos (aPC) Expansion is then used to quantify the uncertainty in the above zone pressure prediction based on the uncertainties in different geologic parameters. Finally, a global sensitivity analysis is performed with Sobol indices based on the aPC technique to determine the relative importance of different parameters on pressure prediction. The results indicate that there can be uncertainty in pressure prediction locally around the leakage zones. The degree of such uncertainty in prediction depends on the quality of site specific information available for analysis. The scientific results from this study provide substantial insight that there is a need for site-specific data for efficient predictions of risks associated with storage activities. The presented approach can provide a basis of optimized pressure based monitoring network design at carbon storage sites.</p

IEAGHG Investigation of Extracted Water from CO2 Storage: Potential Benefits of Water Extraction and Lesson Learned

AbstractThe Energy & Environmental Research Center analyzed formation water extraction from carbon dioxide (CO2) storage reservoirs under a project jointly sponsored by the IEA Greenhouse Gas R&D Programme and the U.S. Department of Energy. This paper presents some of the results of this project, which included a study of the impacts of formation water extraction on CO2 storage as well as the potential for the beneficial use of the extracted water. This paper also identifies several beneficial use options for the extracted water and candidate treatment technologies to achieve the water quality required by these end uses

CO2 Enhanced Oil Recovery Life Cycle Analysis Model (Rev. 2)

In “How green is my oil?” by Azzolina et al., the authors presented an integrated life-cycle model for CO2-EOR where the CO2 is sourced from a coal-fired power plant. The model was developed entirely in Microsoft Excel® to improve transparency and provide a useful tool for other practitioners. This model is an updated version of the model from the article. The cells have been unlocked so they can be modified. Azzolina, N.A., Peck, W.D., Hamling, J.A., Gorecki, C.D., Ayash, S.C., Doll, T.E., Nakles, D.V., and Melzer, L.S., 2016, How green is my oil? a detailed look at greenhouse gas accounting for CO2-enhanced oil recovery (CO2-EOR) sites: International Journal of Greenhouse Gas Control, v. 51, p. 369–379. DOI: /10.1016/j.ijggc.2016.06.008. Acknowledgment: This material is based upon work supported by the U.S. Department of Energy National Energy Technology Laboratory under Award Number DE-FC26-05NT42592.https://commons.und.edu/eerc-publications/1000/thumbnail.jp

Quantifying the Benefit of Wellbore Leakage Potential Estimates for Prioritizing Long-Term MVA Well Sampling at a CO₂ Storage Site

This work uses probabilistic methods to simulate a hypothetical geologic CO₂ storage site in a depleted oil and gas field, where the large number of legacy wells would make it cost-prohibitive to sample all wells for all measurements as part of the postinjection site care. Deep well leakage potential scores were assigned to the wells using a random subsample of 100 wells from a detailed study of 826 legacy wells that penetrate the basal Cambrian formation on the U.S. side of the U.S./Canadian border. Analytical solutions and Monte Carlo simulations were used to quantify the statistical power of selecting a leaking well. Power curves were developed as a function of (1) the number of leaking wells within the Area of Review; (2) the sampling design (random or judgmental, choosing first the wells with the highest deep leakage potential scores); (3) the number of wells included in the monitoring sampling plan; and (4) the relationship between a well’s leakage potential score and its relative probability of leakage. Cases where the deep well leakage potential scores are fully or partially informative of the relative leakage probability are compared to a noninformative base case in which leakage is equiprobable across all wells in the Area of Review. The results show that accurate prior knowledge about the probability of well leakage adds measurable value to the ability to detect a leaking well during the monitoring program, and that the loss in detection ability due to imperfect knowledge of the leakage probability can be quantified. This work underscores the importance of a data-driven, risk-based monitoring program that incorporates uncertainty quantification into long-term monitoring sampling plans at geologic CO₂ storage sites