Investigation of Formation Damage Induced During Drill-In Process of Ultradeep Fractured Tight Sandstone Gas Reservoirs
Bibliographic record
Abstract
Ultradeep fractured tight sandstone gas reservoir is easy to suffer from severe formation damage during the drill-in process, yet few papers have been published on the corresponding formation damage mechanisms. This paper focuses on a typical ultradeep fractured tight sandstone reservoir in the Tarim Basin, China. Fluid sensitivity damage, phase trapping damage, and the formation damage induced by oil-based drill-in fluids were evaluated by a serious of modified experimental methods. As a supplement, the rock physics and surface property were analyzed deeply. Results showed that severe fluid sensitivity damage occurred with a decrease in fluid salinity (critical value: 3/4 formation water salinity (FWS)) and an increase in fluid pH value (critical value: pH = 7.5). The change in water film thickness, the enhancement of hydrophilia, particle detachment, and dissolution of quartz/albite under high formation temperature are the main damage mechanisms. Abnormal low water saturation, mixed wettability, abundant clay minerals, and complex pore structures are contributing to the severe phase trapping damage. The dynamic damage rate of oil-based drill-in fluids is 60.01%, and inadequate loading capacity is the main trigger of lost circulation. Finally, a formation damage control strategy was proposed, and a field test proved its feasibility.
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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 | 0.001 | 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 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".