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Record W2300777651

Economic Co-optimization of Enhanced Oil Recovery and Carbon Sequestration

2011· article· en· W2300777651 on OpenAlexaffabout
Andrew Leach, Charles F. Mason, Klaas T. van ’t Veld

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCarbon sequestrationNatural resource economicsFossil fuelGovernment (linguistics)Enhanced oil recoveryCarbon capture and storage (timeline)Software deploymentOil reservesGreenhouse gasBusinessCoalProduction (economics)Environmental scienceEnvironmental economicsWaste managementEconomicsEngineeringClimate changePetroleumChemistryCarbon dioxideGeology
DOInot available

Abstract

fetched live from OpenAlex

There is a growing consensus in both policy circles and the energy industry that within the next few years, the US Federal government will adopt some form of regulation of CO2 emissions. At the same time, it is widely believed that much of the nation’s energy supply over the coming decades will continue to come from fossil fuels, coal in particular. Many analysts believe the only way to reconcile the anticipated growth in the use of coal with anticipated limits on CO2 emissions is through the development and deployment of carbon capture and geological sequestration (CCS). However, many key players are hesitant to undertake CCS. The recent cancellation of a next-generation power plant in Tampa that would have been CCS-capable speaks to this hesitation. While a number of problems must be resolved before CCS will be widely deployed, a concern of particular importance is the uncertainty that sequestered carbon will escape. One likely way out of this conundrum is expanded use of CO2-based enhanced oil recovery (EOR). This technique, which has been used successfully in a number of oil plays (notably in West Texas, Wyoming, and Alberta), entails injection of CO2 into mature oil fields in a manner that causes the CO2 to mix with some fraction of the oil that still remains underground. Doing so reduces the oil’s viscosity, thereby making it possible to extract additional, otherwise unrecoverable oil. Although some of the CO2 resurfaces with the oil, it can be separated from the output stream, recompressed, and reinjected. Eventually, when the EOR project is terminated, all the injected CO2 is sequestered. Currently, such sequestration yields no economic benefits. In fact, any sequestration over the course of a project is a negative from the point of view of EOR operators, as it results in the need for additional CO2 purchases from some outside source. However, future regulations of CO2 emissions in the context of climate-change policies may generate such benefits, as EOR projects should be able to earn credits for units of CO2 sequestered. Moreover, the enhanced oil revenues that come with EOR make it the economically most attractive sequestration option in the short run, Evaluation of EOR when the potential for CCS is entertained is therefore likely to be of considerable importance in the coming years. Our paper provides such an evaluation. We start by developing a theoretical framework that analyzes the dynamic co-optimization of EOR and CO2 sequestration. This framework explicitly considers both the physical and economic tradeoffs between oil recovery and sequestration. Our model allows for oil extraction via one of two methods: water flooding (so-called secondary extraction) and CO2 flooding (tertiary extraction, or enhanced oil recovery). The decision to commence with tertiary extraction requires payment of a substantial one-time cost, and so firms choose the switching time subject to a transversality condition. Once tertiary extraction has started, some of the injected CO2 displaces the extracted oil; this adds to the amount of sequestered CO2. The decision to cease extraction is also determined by a transversality condition. Given the starting and ending times, and the amount of oil produced during the tertiary phase, an amount of sequestered CO2 can be calculated. Each of the various decisions depends in part on the anticipated price of oil, the cost of the input, and the carbon tax or credit price (which will partially offset the input price). The paper concludes with an example based on an ongoing project. Data from this project are used to predict time paths of extraction under secondary and tertiary production; from these time paths once can estimate the additional volume of oil produced under EOR and the amount of CO2 that is ultimately sequestered.

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)0.001
metaresearch head score (Gemma)0.003
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: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.016
GPT teacher head0.229
Teacher spread0.213 · 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

Citations1
Published2011
Admission routes2
Has abstractyes

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