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 reduce lifting efficiency and cause 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 some 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 behavior and optimize the operating parameters of a continuous pig lift system. More specifically, 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 behavior in the wellbore. An algorithm to solve this transient flow problem by coupling the gas-liquid flow model and the flow model with pig is developed and implemented. Finally, a method for design and optimization of continuous pig lift wells is presented by using nodal system analysis. The detailed simulation results show that comparing 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 pig-launching frequency decreases slippage loss; when the pig-launching frequency reaches a certain value, which is so-called optimal pig-launching frequency in this paper, the pressure drop tends to non-slippage pressure drop. At the given bottomhole pressure and production rate, the wellhead flowing pressure increases with the increase of time, and finally is close to non-slippage wellhead flowing 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, a 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 fluids flow. However, during the production, the slippage between gaseous and liquid phases results in increased water cut and decreased reservoir pressure, which cause liquid loading, reduce lifting efficiency and cause 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. Later on, Lima(2) published a U.S.A. patent related to the method and apparatus for intermittent pig lift. Lima(3) presented the pig lift installation and operation.
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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 | 0.000 | 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) | 0.001 | 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 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".