Determining the Optimal Artificial Lift Strategy When Operating a Mature CO2 Flood in the Real World
Bibliographic record
Abstract
Abstract Effectively operating artificial lift systems can be a very challenging endeavor when implementing tertiary recovery on a mature oil field. The desire to produce a maximum amount of oil must be balanced with the physical limitations of the artificial lift equipment. The traditional operating limitations of artificial lift equipment may be too liberal in a CO2 flood. Being too aggressive when attempting to reduce fluid levels can lead to excess failures and thus, reduced overall production and excessive costs. Attempting to determine the optimal artificial lift strategy in this environment can be a daunting task. Traditional models for determining operational policy may not capture all of the dynamics that affect the performance of the system. An empirical approach can be helpful in setting artificial lift guidelines. This paper discusses the results of an empirical analysis of an artificial lift system's performance in a mature CO2 flood vs. the performance in a mature water flood. For the analysis, data from two oil fields in southeastern Utah, the McElmo Creek Unit (mature CO2 flood) and the Ratherford Unit (mature water flood) was compared and contrasted. The data was analyzed using statistical modeling tools to determine the appropriate strategy for the system. The results from the review gave significant insights to the optimal strategy for operating the system and will be discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".