Bibliographic record
Abstract
Unconventional petroleum resources constitute an increasing frontier of reserves additions as conventional production declines globally. In this era of environmental conservation and sustainability concerns, new resource development efforts confront energy, emissions, and economic intensities. Clearer understanding of resource development choices and their implications can be gained by quantifying these intensities through a systematic approach which allows effective comparisons of alternative energy systems to be drawn in the context of policy and/or business decision-making. Yet, existing assessment studies often lack transparency or do not furnish detailed methodological descriptions of the approach needed for transferability or validation of results in subsequent studies which evaluate impacts of our existing and emerging energy systems design decisions. The combination of analytical and semi-analytical modelling holds great potential to address current methodological challenges in assessing impacts of unconventional resources development. Focusing on shale gas and oil sands resources, this thesis presents new modelling tools and assessment frameworks to quantify and compare impacts of operations and technologies needed during development and recovery of these energy resources. The first part of the contributions evaluated potential environmental impacts of flowback methane in the U.S. and Canada to be 2347 and 1859 Mg CO2e per completion, respectively. The second part assessed contributions of all preproduction activities to overall energy and environmental intensities, highlighting drilling and flowback intensities as major sources. The third and fourth contribution chapters investigated the role of innovation to improve oil sands production and demonstrated the application of carbon dioxide utilization to mitigate impacts of unconventional oil and gas production, respectively.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".