Bibliographic record
Abstract
Abstract With the prospect of sustained low oil prices, several planned SAGD projects have been postponed because their supply cost exceeds oil price forecasts. A consortium of Harris Corporation, Nexen Energy ULC, Suncor Energy, Inc., Devon Canada Corporation, and Alberta's Climate Change and Emissions Management Corporation (CCEMC) are developing Effective Solvent Extraction Incorporating Electromagnetic Heating (ESEIEH™). The ESEIEH process integrates electromagnetic heating with solvents to reduce energy consumption and to eliminate the need for added water (steam) when compared to SAGD. Further to the operational benefits are the reduction of surface facilities and the option to economically export undiluted bitumen without the added cost of diluent recovery units. This paper summarizes an initial case study aimed at forecasting the supply cost of a notional 10,000 barrel per day (bpd) ESEIEH demonstration project in Alberta's oil sands. Utilizing Harris' Coupled Electromagnetic Reservoir Simulator (CEMRS), the ESEIEH process is first simulated as a single well pair. Applying a field scheduling tool, the resulting reservoir run is then duplicated in time until sufficient wells are in place to establish a sustained field level oil production rate of 10,000 bpd. Various scenarios will be assessed and their corresponding capital requirements estimated; these scenarios include a green field with dilbit export, a green field with undiluted rail export, and an ESEIEH pad added adjacent to an existing SAGD cogeneration facility with available low water cut produced fluids handling capacity. For each of these scenarios a cash flow analysis is performed, supply cost estimated, and basic sensitives performed. The technology, which began development in 2010 and recently commenced the second technical demonstration phase in July 2015 is expected to be economically viable in a sustained sub-$60 WTI (USD) oil price environment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".