Bibliographic record
Abstract
Abstract This paper summarizes a project that was part of the $40 million 2006-2010 Joint Implementation of Vapour Extraction (JIVE) pilot program managed by the Petroleum Technology Research Centre and including Husky Oil, CNRL, NEXEN, Alberta Innovates–Technology Futures, and the Saskatchewan Research Council. The project was in support of a cyclic solvent injection (CSI) field pilot in the Lloydminster region of Saskatchewan that was evaluating the potential of CSI to exploit reservoirs following cold heavy oil production with sand (CHOPS). History matches were performed for two Edam CHOPS wells in the Colony formation and they determined initial reservoir conditions (e.g. pressure, effective permeability, porosity, fluid saturations, and gas and oil phase mole fractions) for subsequent CSI simulations. Thin formation layers (~15 cm) were used in the CHOPS simulations to improve representation of wormhole generation and advance. One well had rapid sand production that quickly declined whereas the other well had continuous sand production due to wormhole propagation and scouring and resulted in sustained oil production. The reservoir model for the application of CSI contained a number of wells including the CSI well and two communicating offset wells (Figure 1). Using an Alberta Innovates–Technology Futures (AITF) CSI model, a history match was obtained for CSI Cycle 1. Eleven different potential injection/production strategies were then evaluated for Cycle 2 and the simulation results were used in the design of this cycle. One conclusion was that expanding the solvent injection period from 1 to 2 months increased the combined oil production for the three wells by 29% but resulted in a 46% increase in net solvent to oil ratio. Figure 1Conversion from post-CHOPS radial geometry to Cartesian geometry for CSI simulations
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".