Prediction of Slug-to-Annular Flow Pattern Transition (SAT) for Reducing the Risk of Gas Lift Instabilities and Effective Gas/Liquid Transport From Low-Pressure Reservoirs
Bibliographic record
Abstract
Abstract Slug to annular flow pattern transition (SAT) taking place during the upward gas-liquid well transportation is a source of flow instabilities often experienced with conventional gas lifting as well as with unloading operations of water accumulated at the bottom level of gas wells in low-pressure gas or coalbed reservoirs. In order to minimize the pressure drop and gas compression work, gas lifting of relatively large volumes of fluid (oil and water) uses mainly a slug flow pattern while the production of gas with relatively small amounts of condensate or water (unloading operation) uses an annular flow pattern. In both situations, significant decreasing of tubing pressure from perforation to wellhead levels, is associated to significant increase of superficial gas velocity, may induce flow pattern transitions (usually from bubble to slugs and, further from slugs to annular). This paper uses field data and laboratory measurements to suggest that SAT can be a source of flow instabilities and should be avoided. Understanding and proper prediction of SAT is particularly essential for developing suitable production operations and for designing effective gas lifting or unloading strategies from low-pressure gas and oil reservoirs (including upward transportation of hot fluids resulting from steam-assisted heavy oil operations). With depletion of existing gas reservoirs trend the need for effective gas well deliquification is in great demand. Transportation of water produced at the perforation level (usually between 10-60 m3/d) over a vertical depth of 200 to 2000 m under low (often variable) reservoir pressure (< 50 m of water) ask for finding un-conventional and effective artificial lifting strategies. Improving the understanding of gas-liquid upward transportation mechanisms including the avoidance of instabilities induced by flow pattern transitions is essential. This paper addresses this problem through laboratory measurements of steady and oscillatory components of flow-pressure under a broad range of gas injected rate and simulated reservoir pressures. Comparison of laboratory data with existing STA models is performed first; selected models are then tested for field situations. Effective field strategies for avoiding the SAT occurrence using either a slug or an annular flow pattern regime under low-pressure and standard (IPR) reservoir conditions are discussed in view of practical field applications and selection of a suitable gas lifting 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".