Bibliographic record
Abstract
Abstract In a typical gas lift system, slippage between gaseous and liquid phases results in increased water cut and decreased reservoir pressure which reduces lifting efficiency and causes low oil recovery. Pig lift is a novel artificial lift technique proposed in recent years for effectively reducing liquid accumulation and increasing two-phase flow stability in certain special cases, such as high gas-liquid ratio, low reservoir pressure, horizontal and/or rather deep wells, highly viscous or waxy oil, sand production, etc. In this paper, a theoretical study is conducted to simulate the dynamic gas-liquid flow behaviour and optimize the operating parameters of a continuous pig lift system. First, two new flow theories, i.e., non-instantaneous separation and three segment flow, are proposed to simulate the transient two-phase upward flow process during which the pig is launched periodically into the injecting gas. Then, a two-phase flow model, which comprises a set of one-dimensional mass, momentum, energy balance equations, and the equation of state for real gas, is developed to accurately predict gas-liquid flow behaviour in the wellbore. An algorithm to solve this transient flow problem by coupling the gas-liquid flow model and the flow model with the pig is developed and implemented. Finally, a method for the design and optimization of continuous pig lift wells is presented by using nodal system analysis. The detailed simulation results show that, compared with conventional gas lift, pig lift decreases the pressure drop in the wellbore and reduces the slippage loss significantly. The pig launching frequency is the most important parameter in the pig lift system. Increasing the pig launching frequency decreases slippage loss; when the pig launching frequency reaches a certain value, which is the so-called optimal pig launching frequency in this paper, the pressure drop tends towards a non-slippage pressure drop. At the given bottomhole pressure and production rate, the wellhead flow pressure increases with the increase of time, and finally, is close to non-slippage wellhead flow pressure under the optimal pig launching frequency. The gas lift performance curve can be used as an optimization tool to select the optimal operating parameters for the pig lift system. Introduction In a typical gas lift system, gas is continuously or discontinuously injected in the production well to modify the mixture density of producing fluids, decrease the pressure gradient in the liquid and then provide sufficient energy to produce fluid flows. However, during production, the slippage between gaseous and liquid phases results in increased water cut and decreased reservoir pressure, which causes liquid loading, reduces lifting efficiency and causes low oil recovery, especially for some special cases, such as high gas-liquid ratio, low reservoir pressure, horizontal and/or rather deep wells, highly viscous or waxy oil and sand production, etc. Pig lift is a novel gas lift technique proposed by Lima et al.(1) in recent years for effectively reducing liquid accumulation and increasing two-phase flow stability in those cases. They developed a model for the intermittent pig lift system operation and incorporated it into software.
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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 | 0.002 | 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".