Feasibility of electromagnetic methods to detect and image steam-assisted gravity drainage steam chambers
Bibliographic record
Abstract
ABSTRACT We have investigated the use of electric and electromagnetic (EM) methods to monitor the growth of steam-assisted gravity drainage (SAGD) steam chambers. SAGD has proven to be a successful method for extracting bitumen from the Athabasca oil sands in Alberta, Canada. However, complexity and heterogeneity within the reservoir could impede steam chamber growth, thereby limiting oil recovery and increase production costs. Using seismic data collected over an existing SAGD project, we have generated a synthetic steam chamber and modeled it as a conductive body within the bitumen-rich McMurray Formation. Simulated data from standard crosswell electrical surveys, when inverted in three dimensions, show existence of the chamber but lack the resolution necessary to determine the shape and size. By expanding to EM surveys, our ability to recover and resolve the steam chamber is significantly enhanced. We use a simplified survey design procedure to design a variety of field surveys that include surface and borehole transmitters operating in the frequency or time domain. Each survey is inverted in three dimensions, and the results are compared. Importantly, despite the shielding effects of the highly conductive cap rock over the McMurray Formation, we have determined that it is possible to electromagnetically excite the steam chamber using a large-loop surface transmitter. This motivates a synthetic example, constructed using the geology and resistivity logging data of a future SAGD site, where we simulate data from single and multiple surface loop transmitters. We have found that even when measurements are restricted to the vertical component of the electric field in standard observation wells, if multiple transmitters are used, the inversion recovers three steam chambers and discerns an area of limited steam growth that results from a blockage in the reservoir. The effectiveness of the survey shows that this EM methodology is worthy of future investigation and field deployment.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".