A Successful Methanol Treatment in a Gas-Condensate Reservoir: Field Application
Bibliographic record
Abstract
Abstract A field test was conducted to investigate the effectiveness of methanol as a solvent for removing condensate banks that form when pressure in the near wellbore region falls below the dewpoint. Core flood experiments on Texas Cream Limestone and Berea cores show that condensate accumulation can cause a severe decline in gas relative permeability, especially in the presence of high water saturation. This can result in well productivity declining by a factor of 3 to 5 as bottom hole pressure declines below the dewpoint. PVT analysis performed on field samples taken from the Hatter's Pond field in Alabama indicate retrograde condensate behavior. These high-temperature deep gas wells show low gas productivity and large skin. A preliminary analysis of the data indicated the possibility of condensate and water blocking due to the loss of water-based drilling fluids. Core samples were used to measure gas relative permeability. Compatibility tests were conducted to ensure that the injection of filtrate and methanol did not cause any damage to the core. Since the formation brine is very saline, tests were conducted to check for salt precipitation during methanol injection. Based on these laboratory results and a single-well numerical simulation, a field test was conducted. The well chosen for treatment was producing 250 MSCFPD with 87 BPD of condensate. A thousand barrels of methanol was pumped down the tubing at a rate of 5 to 8 B/min. Gas production increased by a factor of 3 initially and stabilized at about 500 MSCFPD. Condensate production doubled to 157 BPD. The well shows a skin of –1.9 after methanol treatment. The increase in gas and condensate production was observed to persist more than 10 months after the treatment. Several possible explanations are provided for the positive results obtained in this test. Some general conclusions are made for the design for future treatments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".