An Assessment of the Net Fluid Balance in the Alberta Basin
Bibliographic record
Abstract
Net fluid balance in the Alberta Basin has been negative over the last 60 years because extensive fluid production has consistently exceeded injection during this period. However, future gigaton-scale carbon sequestration, among other activities, can result in future cumulative fluid injection exceeding extraction (i.e., a positive net fluid balance). The in-situ net fluid balance (i.e., total fluids produced minus total fluids injected) in this basin over the period 1960–2020 shows that a liquids deficit of 4.53 × 109 m3 and a gas deficit of 6.05 × 1012 m3 currently exist. However, fluid deficits are more significant in the upper stratigraphic intervals (located more than 1 km above the Precambrian Basement) than in the stratigraphic intervals located within 1 km of the Precambrian Basement in most geographic regions. This observation indicates that greater sustainable injection capacity for large-scale fluid injection may exist in the upper stratigraphic intervals (located at more than 1 km above the Precambrian Basement), reducing the potential for generating induced seismicity of concern. Additionally, while fluid depletion rates consistently increased over most of the last 60 years in the Alberta Basin, this trend appears to have changed over the past few years. Such analysis of regional net fluid balance and trends may be useful in assessing regional sustainable fluid storage capacity and managing induced seismicity hazards.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".