Range of Operability of Gas-Assisted Gravity Drainage Process
Bibliographic record
Abstract
Abstract The gas-assisted gravity drainage (GAGD) process is being developed to overcome the limitations of, and as an alternative to, the conventional WAG process. In our recent paper (SPE 110132) we have presented the visual model results to demonstrate the feasibility of the GAGD process and the various mechanisms responsible for the high recoveries achieved. In this paper, we present visual and quantitative results from the physical model experiments to demonstrate the various modes of operability of GAGD, its applicability to fractured reservoirs, the effect of oil viscosity and a comparison of its performance with WAG and CGI processes. A Hele-Shaw type model - consisting of two parallel glass plates (23" × 13" × in size) with gap between them filled with Ottawa silica sand - has been used in all experiments with a perforated plastic tube serving as the horizontal production well placed at the bottom of the model. Vertical tubes were placed at different depths in the model to serve as gas injectors. The presence of fractures was simulated by placing cylindrical shaped fine wire mesh tubes into the sandpack. Separate models were built to study the effect of gas injection rate, depth, CGI, WAG, huff-and-puff, toe-to-heel, oil viscosity and wettability. This paper presents video images of the GAGD process in operation in various modes and discusses the quantitative results of these experiments that led us to conclude that, with the exception of toe-to-heel operation, the GAGD process yielded positive results in all the tests with oil recoveries ranging from 54% to 83% OOIP. This study demonstrates improved GAGD oil recoveries over CGI and WAG, in fractured model over homogeneous, in oil-wet media over water-wet, thereby signifying the potential for wide applicability of the process in both secondary and tertiary modes.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".