Unconventional Resource Development and the Role of the Regulator
Bibliographic record
Abstract
Abstract Alberta's Energy Resources Conservation Board (ERCB) has regulated oil and gas resource development for almost 75 years. Throughout most of that time the industry and the regulator were concerned with conventional oil and gas resources. In the last decade or so the emphasis has shifted, for several reasons, towards unconventional resources—in situ bitumen, tight oil, tight gas, deep- or basin-centred gas, coalbed methane (CBM), shale gas, and in situ coal gasification. The characteristics of these resources, as well as the technologies required to develop them, create the need to modify the ERCB's regulatory framework. This is integral to reaching the ERCB's vision of being The Best NonConventional Regulator in the World, capable of dealing with the pressing issues of the future energy development in Alberta. Given the realities of the changing resource base, unconventional resources are a priority, and the ERCB needs to adopt non-conventional ways of regulating their development. The ERCB has already adapted the regulatory framework to deep/basin-centred/tight gas, coal bed methane, and in situ bitumen. The ERCB responded with new regulatory structures—Development Entities for CBM and Deep Basin gas; control wells for CBM; commingling of production streams from coal-sand sequences—to more effectively manage the development of some of those resources. After only a few years of operational experience, and with industry feedback, we have already improved the efficacy of some of these approaches and continues this effort as an ongoing practice. Moving to the next level, however, requires some substantively different approaches. The ERCB's Unconventional Gas Regulatory Framework Project is intended to identify new ways of regulating and facilitating such resource development. It is being done with the consultative input of knowledgable stakeholders. We have already determined the major regulatory risks, and are focusing on developing ways to mitigate them with the appropriate level of necessary regulation or other, non-regulatory means.
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 | 0.013 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
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".