Unconventional Imaging for Unconventional Reservoirs
Bibliographic record
Abstract
Abstract Thermal stimulation of bitumen in oil sands reservoirs is a critical requirement for the success of steam-based recovery processes. If the bitumen is not heated, it remains at its original viscosity, often in the millions of centipoise, and thus is not mobilized so that it can be moved to a production well. All oil sands reservoirs are heterogeneous; both with respect to geology and fluid composition, and thus steam conformance of steam in the reservoir is not uniform. At present, realtime monitoring of the steam conformance zone in the reservoir is not possible and thus the spatial distribution of heat delivery to the reservoir is uncertain. In this research, a new method for detecting heterogeneity and monitoring steam chambers has been developed and tested by detailed thermal-acoustic reservoir simulation. Here, a thermal fluid flow simulator was coupled to a wave propagation simulator to evaluate the potential of identifying rock and fluid discontinuities within a reservoir by using coded white noise reflection processes. Digital communication systems employ coded white noise processes to advantageously make use of unexpected reflections from environmental heterogeneities. The proposed theory and subsequent simulations reveal that it is possible to resolve features of an unconventional recovery process as well as imaging of heterogeneity within the reservoir as it evolves by using white noise reflection methods. The properties of the signals described provide an opportunity for property detection at lower power levels and higher frequencies than traditional sceismic methods. Furthermore the signals are such that the noise from recovery processes and the native reservoir environment do not interfere with the detection methods allowing for the monitoring method to be used concurrently with the recovery process. A SAGD model is tested and the results show that white noise reflections can be used to detect the edge of steam chambers.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".