An Overview of Methods to Mitigate Condensate Banking in Retrograde Gas Reservoirs
Bibliographic record
Abstract
Condensate blockage is one of the major problems that have been addressed in the industry for many decades. When the reservoir fluid pressure drops below the dew point pressure during the production process, the liquid drops out of the gas phase and forms condensate in the formation. There are two scenarios that can result in a pressure drop. The first one is the pressure drop due to the flow of the reservoir fluid. The reservoir fluid flows from a high pressure of the reservoir to a lower pressure of the separators at the surface. The second scenario is the drop in reservoir pressure due to pressure depletion. During the production of gas and condensate, the reservoir pressure will decrease with time and when it drops below the dew point pressure, condensate forms everywhere inside the reservoir. The condensate dramatically reduces the gas permeability. Hence, it decreases the gas productivity. Several methods have been suggested to solve this problem such as gas injection, CO 2 Huff-n-Puff, wettability alteration, interfacial tension reduction, hydraulic fracturing, and nonconventional wells. Some of these methods have been implemented in the field and showed positive results, but each method has its own advantages and disadvantages that need to be studied further in order to improve its efficiency. This paper will give a general review of all these methods and their effectiveness in mitigating condensate banking. The decision of using a proper treatment of condensate banking can then be made based on different scenarios that are described in this paper. Key words: Mitigate condensate banking; Retrograde gas reservoirs; CO 2 Huff-n-Puff
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".