Monitoring and Production of a Heavy-Oil Extraction Process at Peace River, Canada
Bibliographic record
Abstract
This reference is for an abstract only. A full paper was not submitted for this conference. Abstract Peace River is a 100% Shell-owned heavy oil property located in Northern Alberta, Canada. A total of 7 billion barrels of 7 API oil is trapped in a 30 m thick semi-consolidated sand layer buried at a depth of about 600 m. At present, a "huff-and-puff" approach is employed to extract the oil, using closely spaced multi-lateral horizontal wells drilled from a central pad. As part of a strategy to gain a better understanding of the extraction process at Peace River, Shell Canada designed and implemented a monitoring program over the most recently drilled production pads. This program included microseismic monitoring, surface time-lapse (2D and sparse 3D), a timelapse 3DVSP, and continuous tiltmeter monitoring. Shell Canada's Peace River asset team and EP Research and Development have been engaged in a collaborative, multidisciplinary effort to understand this data in terms of the implications for heat distribution, fluid flow, well and cap rock integrity, and other operational issues. We have learned that our initial model of the extraction process at Peace River was incorrect, and that drilling and operational strategies based on this incorrect model are suboptimal. Our strategy for developing the field has now fundamentally changed based on the information gleaned from this monitoring project. Best practices include the integrated use of microseismic and other monitoring techniques for optimizing the drilling and operational strategy for a heavy oil field.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".