Successful Innovative Water-Shutoff Operations in Low-Permeability Gas Wells
Bibliographic record
Abstract
Abstract Water production from gas producing wells characterized by low productivity and low reservoir pressure zones can prematurely kill wells, leading to a considerable loss in recoverable reserves. In some cases, mechanical techniques provide a viable solution for shutting off water production, although often, such a solution will create a restriction inside the well, limiting access to deeper reservoir layers. Even though chemical water shutoff chemicals and techniques are improving significantly, not many options are available to treat high temperature and low permeability reservoirs. It may prove difficult to squeeze cement slurries and different types of gels into such formations owing to constraints of particle sizes or fluid viscosity. It is a challenge to get a squeezable fluid into low-permeability reservoirs that will be effective in sealing the near-wellbore area and be able to withstand high differential pressure while producing. Another challenge is to determine a placement technique to prevent excess displacement of the treatment, to minimize further intervention into the well, to clean any residual treatment from the tubing, and to minimize water damage to highly sensitive producing layers. This paper presents successful case histories of treatments that were performed to shut off water producing layers characterized by low permeability. It describes innovative techniques that were developed for this special project. The treatments were placed using coiled tubing, and only one run was required to shut off the zones in question.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".