A Sensitivity Analysis of Cyclic Solvent Stimulation for Post-CHOPS EOR: Application on an Actual Field Case
Bibliographic record
Abstract
Summary An uncertainty-screening procedure was performed to assess the feasibility of cyclic solvent stimulation as a post-cold heavy-oil production with sands (CHOPS) enhanced-oil-recovery (EOR) method. To achieve this, three cycles of cyclic solvent stimulation were modeled on a previously history-matched Albertan CHOPS field with 15 wells. The results were compared with a base model at each cycle in terms of oil production, gas production, and gas-injection volumes. The emphasis was on both uncertain parameters (wormhole vertical location, strength of foamy oil, time-dependent gas dissolution/exsolution) and operational input (injection pattern, injection rate, injection/soaking time). It was shown that bottom-located wormholes and nonequilibrium gas dissolution/exsolution could highly affect incremental oil production and project net present values (NPVs), but unfortunately, they cannot be controlled in practice. On the other hand, injection pattern, injection rate, and injection/soaking time, as operational inputs, can contribute to project profitability. Next, an economic model was developed, and after-tax NPV of the field was introduced as an economic indicator. This uncertainty analysis revealed that an increase in operational input such as injection rate may enhance oil recovery from a technical point of view, but does not necessarily increase project profitability (NPVs), yet an optimum value exists. It was shown that such an economic model had the priority to oil-recovery factor or cumulative oil production because it could incorporate costs and sales simultaneously by performing continuous discounting, and allow the asset holder to maximize NPVs by selecting the best development strategy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".