Experimental Study on Water Shutoff Technology Using In-Situ Ion Precipitation for Gas Reservoirs
Bibliographic record
Abstract
Once a gas well begins to produce water, gas production will be seriously affected. If no effective measures are taken, the gas well will be shut down. Although some methods can be adopted to limit the production of unwanted water in gas reservoirs, they do not radically solve the problem of excessive water production, which may cause gas shutoff and dramatically increase the cost of dealing with disposing of the unwanted water. In this study, water shutoff technology with in-situ ion precipitation was tested for a gas well in southwest China, with results demonstrated through experiments of ion precipitation distribution and core displacement. The results of the ion precipitation distribution experiments show that it can be artificially controlled to produce ion precipitation blocking the water layer. The distribution of ion precipitation depends on influencing factors including injection flow rate, injection height, and ion concentration, which is generally hill-shaped. Dynamic displacement experiments through two types of cores (matrix core and fracture core) show that during the process of injecting gas-field water into the core, the ion precipitation caused by the in-situ reaction constantly blocked the seepage channel, resulting in a significant decrease in the injection rate. After injection of the gas-field water, the permeability of the core decreased, the starting pressure gradient increased, and the effect of water shutoff was remarkable. Given the experimental results, the in-situ ion precipitation water-plugging technology for gas reservoirs can directly solve the problem of water production in gas wells in the water layer because it can block the water layer by using formation water itself. This technology has promise for use in southwest China and can provide technical guidance for other gas reservoirs.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".