Global Optimization of Gas Lifting Operations: A Comparative Study of Piecewise Linear Formulations
Bibliographic record
Abstract
Continuous gas lifting is the process of increasing oil well production by injecting compressed natural gas, called “lift gas”, into the production tubing of an oil well [ Presented at the SPE Gas Technology Symposium, Calgary, Alberta, Canada, 1996 ]. This paper considers the problem of optimizing the distribution of a limited supply of lift gas to wells in an oil field using piecewise linearization techniques. Four modeling approaches, proposed by Nemhauser and Woolsey [ Integer and Combinatorial Optimization; J. Wiley: New York, 1988 ], Foudas [ Nonlinear and Mixed-Integer Optimization: Fundamentals and Applications; Oxford University Press: New York, 1995 ], Sherali [ Oper. Res. Lett. 2001, 28, 155.], and Keha et al. [ Oper. Res. Lett. 2004, 32, 44.], are presented and the gas lifting problem is solved using each method. Each of the four frameworks is sufficient to solve the problem to global optimality and the method presented by Keha et al. has the best computational performance. The gas lifting problem is used within the context of larger problems such as well scheduling in oil fields [ Comput. Chem. Eng. 2005, 29, 1523.], and this proposed work can be used to make one of the key parts of the well scheduling problem more efficient.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".