Integrating Nuclear Energy to Oilfield Operations -- Two Case Studies
Bibliographic record
Abstract
Abstract Fossil fuel resources that require large energy inputs for extraction, such as the Canadian oil sands and the Green River oil shale resource in the western USA, could benefit from the use of nuclear power instead of power generated by natural gas combustion. This paper discusses the technical and economic aspects of integrating nuclear energy with oil sands operations and the development of oil shale resources. A high temperature gas-cooled reactor (HTGR) that produces heat in the form of high pressure steam (no electricity production) was selected as the nuclear power source for both fossil fuel resources. Both cases were based on 50,000 bbl/day output. The oil sands case was a steam-assisted, gravity-drainage (SAGD) operation located in the Canadian oil sands belt. The oil shale development was an in-situ oil shale retorting operation located in western Colorado, USA. The technical feasibility of the integrating nuclear power was assessed. Preliminary economic analyses of each case were evaluated using a discounted cash flow, rate of return approach. Integrating an HTGR to both the SAGD oil sands operation and the oil shale development was found to be technically feasible for both cases. In the oil sands case, integrating an HTGR eliminated natural gas combustion and associated CO2 emissions, although there were still some emissions associated with imported electrical power. In the in situ oil shale case, integrating an HTGR reduced CO2 emissions by 88% and increased natural gas production by over 400%. Economic analyses included parametric evaluations on taxes imposed on CO2 emissions, royalty rates, capital costs, etc. The magnitude of the tax on CO2 emissions was found to have a major impact on the economic results of the cases considered. As the world moves toward limiting CO2 emissions, integrating non-CO2-emitting energy sources to the development of energy-intense fossil fuel resources is becoming increasingly important. This paper attempts to reduce the barriers that have traditionally separated fossil fuel development and application of nuclear power and to promote serious discussion of ideas about hybrid energy systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".