Meeting the Challenge To Extend Success at the Pikes Peak Steam Project to Areas With Bottomwater
Bibliographic record
Abstract
Summary This paper provides a field review of the Pikes Peak steam project, showing key performance indicators of cyclic steam stimulation (CSS) and steamdrive in non-bottomwater. To test development over relatively thin bottomwater (less than 5 m), various steam processes were given field trials. Field pilot results from vertical-well CSS, dual horizontal well gravity drainage, and a combination of vertical injectors/horizontal well producers are presented for comparison. Based on field experience and numerical simulation input, CSS has been conducted successfully with economic steam/oil ratios (SORs) in areas with up to 4 m of bottomwater by injecting significantly larger steam slugs in what is termed a drive, block, and drain process. In thicker bottomwater, the ability to operate at constant pressure to prevent bottomwater influx confers an advantage on the horizontal well approach. Follow-up field-scale developments of some bottomwater areas are described. Numerical simulation results indicate that pressuring up of a depleted steamflooded zone is an optimum strategy for maximizing offset flank recovery. This is being implemented in the field by reinjecting produced vent gases.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".