Performance Evaluation of Gas Production With Consideration of Dynamic Capillary Pressure in Tight Sandstone Reservoirs
Bibliographic record
Abstract
In this paper, a pragmatic and consistent framework has been developed and validated to accurately predict reservoir performance in tight sandstone reservoirs by coupling the dynamic capillary pressure with gas production models. Theoretically, the concept of pseudo-mobile water saturation, which is defined as the water saturation between irreducible water saturation and cutoff water saturation, is proposed to couple dynamic capillary pressure and stress-induced permeability to form an equation matrix that is solved by using the implicit pressure and explicit saturations (IMPES) method. Compared with the conventional methods, the newly developed model predicts a lower cumulative gas production but a higher reservoir pressure and a higher flowing bottomhole pressure at the end of the stable period. Physically, a higher gas production rate induces a greater dynamic capillary pressure, while both cutoff water saturation and stress-induced permeability impose a similar impact on the dynamic capillary pressure, though the corresponding degrees are varied. Due to the dynamic capillary pressure, pseudo-mobile water saturation controlled by the displacement pressure drop also affects the gas production. The higher the gas production rate is, the greater the effect of dynamic capillary pressure on the cumulative gas production, formation pressure, and flowing bottomhole pressure will be. By taking the dynamic capillary pressure into account, it can be more accurate to predict the performance of a gas reservoir and the length of stable production period, allowing for making more reasonable development schemes and thus improving the gas recovery in a tight sandstone reservoir.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".