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Record W2000307716 · doi:10.2118/07-10-03

Simulating the Effects of Deep Saline Aquifer Properties for CO2 Sequestration

2007· article· en· W2000307716 on OpenAlexaff
B. Basbug, F. Gümrah, Bora Oz

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsShell (Canada)
FundersOrta Doğu Teknik ÜniversitesiU.S. Department of Energy
KeywordsSupercritical fluidAquiferCaprockDissolutionSaturation (graph theory)Carbon sequestrationGeologyPorous mediumPermeability (electromagnetism)Petroleum engineeringCarbon dioxideGreenhouse gasSoil sciencePorosityGroundwaterGeotechnical engineeringChemistry

Abstract

fetched live from OpenAlex

Abstract CO2 is one of the hazardous greenhouse gases causing significant changes to the environment. The sequestering of CO2 in a suitable geological media can be a feasible method to avoid the negative effects of CO2 emissions into the atmosphere. A numerical model was developed regarding CO2 sequestration in a deep saline aquifer. A compositional numerical model using CMG software (GEM) was employed to study the ability of the selected aquifer to accept and retain large quantities of CO2 injected in a supercritical state for long periods of time (up to 200 years). Supercritical CO2 is a one-state fluid which exhibits both gas- and liquid-like properties. In this study, supercritical CO2 was sequestered in three forms in a deep saline aquifer. It was assumed to be supplied in an isothermal condition during the injection and sequestration processes and we ignored porosity and permeability changes due to mineralization. Also, CO2 adsorption was not considered in our numerical model. Gas bubble formation, dissolution of CO2 in brine and precipitation of CO2 with calcite mineral in aquifers have been discussed. The CO2 gas bubble displaces the formation water with immiscible behaviour. During and after displacement, the gravitational effects cause the CO2 to rise and accumulate under the caprock. Both vertical and horizontal permeability ratios and initial pressure conditions were the most dominating parameters affecting CO2 saturation in the three layers, whereas the CO2 injection rate influenced CO2 saturation in layers two and three since CO2 was injected from layer three at the bottom of the reservoir. Introduction CO2 sequestration is the capture of, separation and long-term storage of CO2 in underground reservoirs for environmental purposes. CO2 is one of the hazardous greenhouse gases causing significant changes in global temperature and sea levels(1), which could have negative consequences for people in many parts of the world. Scenarios for stabilizing atmospheric CO2 at reasonable levels will eventually require substantial cuts in overall emissions over the next few decades(1,2). If usage of fossil fuels is to continue at current levels while avoiding undesirable climate change, technical means need to be found to reduce the carbon dioxide emitted to the atmosphere in the production and consumption of fossil fuels(3). CO2 sequestration can be regarded as one possible solution for reducing CO2 emissions in a form where they will not reach the atmosphere. Disposal environments for CO2 sequestration can be divided into four different categories. Oceans, terrestrial basins, a biological environment and geologic formations are the candidates for the disposal of CO2. Among these alternatives, geologic formations can be regarded as the best possible environment to sequester CO2 because of the fact that the storage of CO2 in geologic formations is a self-containing and volumetrically efficient process. In geologic formations, CO2 can be sequestered in porous or non-porous media. Depleted oil and gas reservoirs, aquifers and coal beds can be categorized as porous media, whereas salt caverns and lined rock caverns can be regarded as types of non-porous media.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.410
Threshold uncertainty score0.854

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.234
Teacher spread0.225 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations20
Published2007
Admission routes1
Has abstractyes

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