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Carbon dioxide (CO 2 ) storage into geological formations is regarded as an important mitigation strategy for anthropogenic CO 2 emissions to the atmosphere. This study first simulates the leakage of CO 2 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.

This work evaluates the detection sensitivity of deep subsurface pressure monitoring within an uncertain carbon dioxide sequestration system by linking the output of an analytical reduced-order model and first-order uncertainty analysis. A baseline (non-leaky) modeling run was compared against 10 different leakage scenarios, where the cap rock permeability was increased by factors of 2–100 (cap rock permeability from 10⁻³to 10⁻¹millidarcy). The uncertainty variance outputs were used to develop percentile estimates and detection sensitivity for pressure throughout the deep subsurface as a function of space (lateral distance from the injection wells and vertical orientation within the reservoir) and time (years since injection), or P(x, z, t). Conditional probabilities were computed for combinations of x, z, and t, which were then used to generate power curves for detecting leakage scenarios. The results suggest that measurements of the absolute change in pressure within the target injection aquifer would not be able to distinguish small leakage rates (i.e., less than 50 × baseline) from baseline conditions, and that only large leakage rates (i.e., >100 × baseline) would be discriminated with sufficient statistical power (>99 %). Combining measurements, for example by taking the ratio of formation pressure in Aquifer 2/Aquifer 1, provides better statistical power for distinguishing smaller leakage rates at earlier times in the injection program. Detection sensitivity for pressure is a function of space and time. Therefore, design of an adequate monitoring network for subsurface pressure should account for this space–time variability to ensure that the monitoring system performs to the necessary design criteria, e.g., specific false-negative and false-positive rates.

Cyanide is present at manufactured-gas plant (MGP) sites in oxide-box residuals, which were often managed on-site as fill during active operations. Cyanide can leach from these materials, causing groundwater contamination. Speciation, fate, and transport of cyanide in a sand-gravel aquifer underlying an MGP site in the upper Midwest region of the United States were studied through characterization, monitoring, and modeling of a plume of cyanide-contaminated groundwater emanating from the site. Results indicate that cyanide in the groundwater is primarily in the form of iron-cyanide complexes (>98%), that these complexes are stable under the conditions of the aquifer, and that they are transported as nonreactive solutes in the sand-gravel aquifer material. Weak-acid-dissociable cyanide, which represents a minute fraction of total cyanide in the site groundwater, may undergo chemical-biological degradation in the sand-gravel aquifer. It seems that dilution may be the only natural attenuation mechanism for iron-cyanide complexes in sand-gravel aquifers at MGP sites.

Remediation of soils at oil-gas manufactured gas plant (MGP) sites is driven primarily by the human health risks posed by the carcinogenic polycyclic aromatic hydrocarbons (PAHs), particularly benzo[a]pyrene (BaP), that are associated with lampblack residues. Although PAHs on lampblack are tightly sorbed, risk assessments do not account for this reduced availability. A multi-investigator study of 7 oil-gas MGP site soil samples demonstrated that the dermal and ingestion absorption factors are far lower than current default assumptions used in risk assessments. Using these sample-specific absorption factors in standard risk assessment equations increased risk-based cleanup levels by a factor of 72 on average (with a range from 23 to 142 times the default level). The rapidly released fraction of the BaP in each sample, as measured by supercritical fluid extraction, was closely correlated (r2 = 0.96) to these calculated cleanup levels. The weight of evidence developed during this research indicates that the risks posed by PAHs on lampblack are far less than assumed when using default absorption factors and that a tiered evaluation protocol employing chemical analyses, chemical release data, and in vitro bioassays can be used to establish more realistic site-specific criteria.

This work evaluates the detection sensitivity of deep subsurface pressure monitoring within an uncertain carbon dioxide sequestration system by linking the output of an analytical reduced-order model and first-order uncertainty analysis. A baseline (non-leaky) modeling run was compared against 10 different leakage scenarios, where the cap rock permeability was increased by factors of 2-100 (cap rock permeability from 10 super(-3) to 10 super(-1) millidarcy). The uncertainty variance outputs were used to develop percentile estimates and detection sensitivity for pressure throughout the deep subsurface as a function of space (lateral distance from the injection wells and vertical orientation within the reservoir) and time (years since injection), or P(x, z, t). Conditional probabilities were computed for combinations of x, z, and t, which were then used to generate power curves for detecting leakage scenarios. The results suggest that measurements of the absolute change in pressure within the target injection aquifer would not be able to distinguish small leakage rates (i.e., less than 50 baseline) from baseline conditions, and that only large leakage rates (i.e., >100 baseline) would be discriminated with sufficient statistical power (>99 %). Combining measurements, for example by taking the ratio of formation pressure in Aquifer 2/Aquifer 1, provides better statistical power for distinguishing smaller leakage rates at earlier times in the injection program. Detection sensitivity for pressure is a function of space and time. Therefore, design of an adequate monitoring network for subsurface pressure should account for this space-time variability to ensure that the monitoring system performs to the necessary design criteria, e.g., specific false-negative and false-positive rates.

Procedures for preservation and identification of cyanide and thiocyanate in coal gasification wastewater, possible pathways for aqueous-phase formation of thiocyanate, and reaction of cyanide and polysulfide-sulfur to produce thiocyanate were investigated. Cyanide preservation procedures require removal of sulfide and high levels of carbonate. An analytical procedure for thiocyanate determination based on a copper-pyridine colorimetric method with preextraction has been tested successfully on most samples. The reaction of cyanide with polysulfide-sulfur was of the order 1.54 ± 0.25 with a rate constant of about 0.24 $({\\rm M}/{\\rm l})^{-0.54}/\\text{min}$. Control of cyanide-polysulfide reaction requires selective control of sulfide oxidation kinetics.