Predictive Field-Scale Simulations for the Design of a Solvent Injection Pilot
Bibliographic record
Abstract
Abstract This paper summarizes a study that was part of the $40 million Joint Implementation Vapor Extraction Program and was in support of a planned solvent (60% CH4 – 40% C3H8) injection field pilot at Fort Kent, Alberta where CHOPS wells are prolific oil producers. The pilot design was for a horizontal well between and below four vertical wells. The vertical wells were to be on CHOPS before becoming solvent injectors with the horizontal well as a producer. CHOPS simulations were based on matching specified oil production. The simulations utilized an AITF CHOPS model (with CMG STARS™ as a platform) to design CHOPS operating strategies. The CHOPS simulations determined: (a) wormhole penetration length required to maintain the specified oil rate, (b) when to stop CHOPS and start solvent injection, and (c) the initial conditions (wormhole occupied region, fluid saturations, etc.) for solvent injection simulations. The predicted post-CHOPS reservoir properties were transferred to a reservoir model that represented part of the proposed well arrangement and was used in the solvent injection simulations. Conclusions from the post-CHOPS solvent injection simulations included: During the post-CHOPS re-pressurization period, solvent should be injected in the horizontal well as well as the vertical wells. This strategy reduces subsequent channeling between the vertical and horizontal wells.Solvent injection performance depends on: (a) lateral offset between vertical injectors and horizontal producer, (b) layers in which vertical wells are perforated, (c) maintained pressure difference between injectors and producer, (d) operating pressure, and (e) gas production rate.Oil rates were significantly reduced by spreading of injected solvent in the high permeability region created by wormholes and by displacement of oil in this region away from the wells.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.005 | 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".