Regulating Hydraulic Fracturing: Regulatory Recourse for Subsurface Communication
Bibliographic record
Abstract
This paper provides an overview of the legal framework for the regulation of hydraulic fracturing in Alberta and examines the potential regulatory options and liability for subsurface communication caused by hydraulic fracturing activities. Specifically, this paper examines the jurisdiction of the Alberta Energy Regulator (AER) to: (i) order that operations be shut-in or suspended due to communication, (ii) impose obligations on industry to provide notification of hydraulic fracturing activities, including subsurface communication, (iii) order mandatory commingling orders, (iv) encourage production sharing agreements, and (v) impose testing, monitoring, production controls and reporting obligations. With the widespread use of multistage horizontal hydraulic fracturing, disputes related to subsurface communication will continue to be raised with the AER and in the courts. Thus far the AER has taken a risk management approach through monitoring and testing requirements. Generally, the AER has permitted development to occur by endorsing an approach which relies on the known and inevitable consequences of mining and recovering the minerals. This approach has been justified by the AER on the basis that any production of another’s minerals does not result in irreparable harm. The harm or damaged caused can be identified, quantified and compensation paid.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".