Integrating the Key Learnings from Laboratory, Simulation, and Field Tests to Assess the Potential for Solvent Assisted - Steam Assisted Gravity Drainage
Bibliographic record
Abstract
Abstract ExxonMobil and its Canadian affiliate, Imperial Oil Resources, are actively developing the next generation of solvent-aided and solvent-dominated heavy oil recovery processes. While these new recovery processes possess multiple environmental and technical advantages relative to traditional heavy oil recovery processes, there are a variety of challenges that must be addressed and overcome before commercial application. One especially promising technology is the Solvent Assisted – Steam Assisted Gravity Drainage (SA-SAGD) process. In the SA-SAGD process, a light hydrocarbon solvent (diluent) is injected along with dry steam in a dual horizontal well SAGD configuration. An integrated research program has been implemented in order to progress the SA-SAGD technology from the laboratory to the field and to better quantify the benefits of SA-SAGD over SAGD. This integrated research program includes fundamental laboratory work, advanced numerical simulation studies, scaled physical laboratory models, and a two well-pair field pilot. In this paper, we review the scope, technical challenges, and key learnings from the laboratory, numerical modeling efforts, and the field pilot. Each individual component of the research program is important and provides unique and useful information concerning the SA-SAGD process. Given the technical and economic challenges of solvent-assisted thermal heavy-oil processes, these types of fully integrated research programs are essential in order to successfully progress new technologies from the laboratory to the pilot scale and ultimately to the commercial scale. Ultimately, the program is targeted at developing reliable commercial predictive capabilities that have been validated against both laboratory and field data, for application to a wide range of heavy oil reservoirs, operating conditions, and development plans.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".