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Record W2030932721 · doi:10.7122/151483-ms

Simulation of Leakage Scenarios for CO2 Storage at Gordon Creek, Utah

2012· article· en· W2030932721 on OpenAlexaboutno aff
Durga Sharat Achanta, Reid B. Grigg

Bibliographic record

VenueCarbon Management Technology Conference · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsnot available
FundersNational Energy Technology LaboratoryU.S. Department of Energy
KeywordsLeakage (economics)Computer scienceEnvironmental science

Abstract

fetched live from OpenAlex

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).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.155
Threshold uncertainty score0.308

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.024
GPT teacher head0.269
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2012
Admission routes1
Has abstractyes

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