Simulation of Leakage Scenarios for CO2 Storage at Gordon Creek, Utah
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract CO2 Capture and Storage (CCS) has been suggested as a key component of an effective climate strategy. Hence, a significant amount of research in the United States is aimed at capturing and storing CO2. As a part of a near-commercial scale demonstration by the Southwest Regional Partnership on Carbon Sequestration (SWP), a net of ~ 2.9 million tons of CO2 will be injected into the Navajo formation at Gordon Creek, Utah over a period of 4 years, starting in 2013, for permanent sequestration. The Navajo formation is an aquifer that is currently used for disposal of produced water from Gordon Creek natural gas production. In order to achieve CO2 sequestration, it is important to ensure that there is no significant leakage to the surface or underground sources of drinking water (USDW). Leakage can occur by a variety of mechanisms such as high permeability pathways i.e. faults, failure of an existing plugged well and exceeding formation fracture pressure. Incomplete characterization of the field may result in undetected transmissive or non-transmissive faults in the aquifer. Transmissive faults create a permeability pathway for the CO2 to leak back to the surface or into overlying formations while non-transmissive faults limit the CO2 storage volume. Additionally, the presence of faults affect the hydrodynamic and geochemical trapping mechanisms in the aquifer. This work investigates the impact of faults on the storage of CO2 in an aquifer. INTRODUCTION Atmospheric levels of CO2 have been increasing due to anthropogenic activities. In order to mitigate the increasing CO2 levels in the atmosphere, the United Nations Intergovernmental Panel on Climate Change (IPCC) suggested CCS as one of the best practices (Metz, Davidson, Coninck, Loos and Meyer, 2005). Other mitigation options include improving energy efficiency, switching to less carbon-intensive fuels, nuclear energy, renewable energy, enhancement of biological sinks, and reduction of non-CO2 greenhouse gas emissions (Metz, Davidson, Coninck, Loos and Meyer, 2005). A significant amount of research in the United States is directed at CCS. Potential areas for capture and storage have been identified: Potential capture sites for CO2 are power generation plants, cement production facilities, refineries, iron and steel industries, and petrochemical indus-tries. The potential storage sites identified include depleted oil and gas reservoirs, un-minable coal seams and deep saline aqui-fers. The captured CO2 could be transferred from the source to the storage site by means of pipelines or shipping. The technol-ogy for transferring CO2 into subsurface formations is mature and has been used by the petroleum industry for Enhanced Oil Recovery for many years. In order to investigate the best solution for capture and storage of CO2, The US Department of Energy (DOE), has funded a network of seven regional partnerships that include 350+ state agencies, universities and private companies, spanning 43 states, three Native American organizations, and four Canadian provinces. Researchers of each partnership will investigate best solutions for capturing and storing CO2 in their region. The seven partnerships include Big Sky Regional Carbon Seques-tration Partnership (Big Sky), Plains CO2 Reduction Partnership (PCOR), Midwest Geological Sequestration Consortium (MGSC), Midwest Regional Carbon Sequestration Partnership (MRCSP), Southeast Regional Carbon Sequestration Partner-ship (SECARB), Southwest Regional Partnership on Carbon Sequestration (SWP) and the West Coast Regional Carbon Se-questration Partnership (WESTCARB).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it