Economic Co-optimization of Enhanced Oil Recovery and Carbon Sequestration
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
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) | 0.010 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".