Experimental and Numerical Evaluation of Water Control and Production Increase in a Tight Gas Formation With Polymer
Bibliographic record
Abstract
An integrated technique has been developed to experimentally and numerically evaluate water control and production increase in a tight gas formation with polymer. Experimentally, polymer has been appropriately selected and formulated to form a preferentially blocking membrane on the surface of pore and throat in core plugs collected from a tight gas reservoir. The unsteady-state experiments at high temperatures and confining pressures are then conducted to not only measure gas and water relative permeability but also to evaluate the performance of water control and gas production with and without such formulated polymers. The inlet and outlet pressure of the coreholder and flow rates of water and gas are measured throughout the displacement experiments. Theoretically, numerical simulations have been performed to history match the coreflooding experiments and then extended to evaluate well performance in gas fields with and without polymer treatment. Due to the good agreement between the simulated relative permeability and the measured values, the formulated polymer is found to simultaneously control water and increase gas production. Also, it is found from simulation that, after 10 years of production, gas wells after polymer injection show a higher recovery of 10.8% with a lower water-to-gas ratio and a higher formation pressure.
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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) | 0.000 | 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 | 0.000 |
| 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".