The Impact of Oil Prices on Oil Shale Development in the United States
Bibliographic record
Abstract
Abstract The United States has the largest oil shale resources in the world, in excess of 6 trillion barrels. Potential U.S. oil shale reserves could exceed 800 billion barrels, depending on oil price. New technologies may soon enable these resources to be produced efficiently and economically. Recently, increased oil demand, tightening supplies, and higher oil prices stimulated investment in energy projects around the globe, driving up the cost of materials, skilled labor, and other project inputs. A clear example can be seen in the rapid increase of capital and operating costs in Alberta's oil sands. Similar experiences could be expected during the development of a domestic oil shale industry. This raises questions about the effect of oil price on oil shale economics, including both capital and operating costs. To answer this question, an analysis was conducted using the oil price as a surrogate for energy costs, tightness of the labor market, the costs of materials, and other factors which impact the development of oil shale projects. After developing a relationship between the oil price and the cost of development, an analysis was conducted to determine the impact caused by increasing oil prices on the minimum economic price of four representative oil shale development technologies. This paper will discuss the relationship and impact of oil price and oil shale development costs. In addition, the paper will describe the impact of the cost relationship on production and other macro-economic benefits which can be realized.
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 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) | 0.000 | 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".