A Theoretical Model for Optimizing Surfactant Usage in a Gas Well Dewatering Process
Bibliographic record
Abstract
Abstract Liquid loading significantly affects the production performance of a gas well. The available dewatering techniques include velocity string, foaming, gas lift, plunger lift and other artificial lift methods, among which the foam-aided dewatering method is the simplest and most economical one by effectively utilizing the natural reservoir energy. It has always been a challenging task to determine the surfactant usage due to its cost and continuous consumption during the dewatering process. In this paper, a method is developed and applied to optimize the surfactant usage in a gas well dewatering process. More specifically, surface tension and foam density are measured at different surfactant concentrations. With a pre-determine a calibration curve of the surface tension and foam density versus the surfactant concentration, the minimum critical velocity is determined as a function of the surfactant concentration by using the modified Turner's droplet model. Then, a mathematical model is formulated to optimize the foam dewatering performance by maximizing the gas production rate per unit rate of the foaming agents. A field case is presented to illustrate the optimization procedure of the model developed in this paper. In comparison with the existing empirical methods, it is found that the surfactant usage can be optimized and that the foam dewatering technique performs better in wells with high productivity, high gas-liquid ratio and small size tubing. Introduction Liquid loading is one of the severe problems encountered in a mature gas reservoir. As pressure in the reservoir depletes, liquid tends to accumulate in the wellbore due to the inability of the gas to lift the reservoir liquid to the surface. The accumulated liquid column, which imposes a backpressure to the reservoir, significantly reduces gas production rate. Once the gas production rate is reduced, it becomes more difficult to remove the liquid by the gas itself in a gas well. Eventually, the liquid loading will kill the well and affect the ultimate gas recovery in the gas reservoir. For example, in the Sichuan Gas Field, China, the average gas recovery is 40 ∼60% in a water-drive gas reservoir, whereas the recovery is up to 90% in a dry gas reservoir[1]. Different measures have been adopted to solve the liquid loading problem in a gas well[2, 3]. In practice, at the early stage of the liquid loading, a small tubing or coiled-tubing is used to increase the gas velocity and surfactants can be injected to unload a gas well by foam. It should be noted that these techniques take full advantages of the natural reservoir energy. However, if the reservoir energy declines to a certain value at which it is not sufficient to unload the produced liquid out of the well, artificial lifting methods must be utilized. These methods include the wellhead boosting, gas lift, plunger lift, jet pump, and the combination of the above-mentioned lifting methods[4, 5]. Obviously, the liquid unloading methods that utilize the reservoir energy are more economical, among which the foaming method is the simplest one.
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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) | 0.000 | 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".