Practical Surveillance Analysis on Thermal Heavy Oil Projects: Integrating Seismic Data with Production Case Studies
Bibliographic record
Abstract
Abstract Enhanced oil recovery methods for heavy oil are growing at a fast pace. In Canada alone, approximately 53% of the nation's crude oil production (~2.8 MMBbl/day) comes from Alberta's Oil Sands. Remote sensing and monitoring technologies developed for thermal methods are providing the industry with an immense amount of data that will aid in the improvement and optimization of production performance. This paper details how to integrate seismic, tiltmeter, temperature observation well data with field production data, first with simple surveillance techniques, then with flow simulation. Currently, heavy oil is experiencing significant growth in reserves whereas conventional light oil reserves are essentially fully tapped and are diminishing: heavy oil is becoming key to meeting growing energy demands worldwide. Current analytical approaches to heavy oil reservoir studies are too general, and tend to not include rich surveillance data such as seismic, temperature and pressure. This general approach over-simplifies the dynamics present in these reservoirs and does not give an accurate depiction of behavior and performance estimation. On the other hand large simulation studies can be cumbersome and not adaptive to new surveillance data. This paper focuses on a hybrid approach to analyzing various heavy oil fields in Canada, and outlines newly developed surveillance methods which better characterize heavy oil reservoirs. These field cases include geological data, pressure readings, seismic analysis, temperature observations, and historical production, all of which are used to enable the optimization of field production. The proposed analytical techniques allow for a more precise estimate of recovery and sweep efficiency so that an efficient optimization strategy can be developed. Applying these analytical techniques to the Surmont and Christina Lake projects in the Athabasca area has given particularly important insight into SAGD steam chamber development and has shown to accurately estimate steam chamber volume and shape.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".