Investigation of Liquid Loading in Tight Gas Horizontal Wells with a Transient Multiphase Flow Simulator
Bibliographic record
Abstract
Abstract Liquid loading occurs when gas production declines to a rate that is insufficient to lift the associated liquids to surface. At lower rates, gas production becomes intermittent and eventually stops entirely. However, liquid holdup in the horizontal section may impair production before loading in the production tubing becomes evident. Holdup in the horizontal section can lead to slug flow from the horizontal wellbore to the tubing and to an earlier onset of liquid loading in the tubing. This paper presents liquid holdup data from a single onshore horizontal tight gas well, obtained through video-logging. A transient multiphase flow model is then used to match the observed conditions. The results from the transient multiphase flow model were found to be consistent with the measured data acquired from the video-logging. Sensitivity analyses were performed with normalized trajectories representing toe-up, toe-down, undulating, and complex drilling profiles. Sensitivities to variations in the liquid-gas ratio and the distribution of the reservoir inflow were also investigated. The results of the transient multiphase flow modelling support the conclusion that complex trajectories are more prone to production losses caused by liquid holdup. The implications of this conclusion for trajectory optimization and tubing landing depth selection are explored. Modelling liquid holdup can lead to improvements in planning new drilling projects, mitigating the impact of liquid loading on long-term performance.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".