Production Analysis of Tight-Gas and Shale-Gas Reservoirs Using the Dynamic-Slippage Concept
Bibliographic record
Abstract
Summary Shales and some tight-gas reservoirs have complex, multimodal pore-size distributions, including pore sizes in the nanopore range, causing gas to be transported by multiple flow mechanisms through the pore structure. Ertekin et al. (1986) developed a method to account for dual-mechanism (pressure- and concentration-driven) flow for tight formations that incorporated an apparent Klinkenberg gas-slippage factor that is not a constant, which is commonly assumed for tight gas reservoirs. In this work, we extend the dynamic-slippage concept to shale-gas reservoirs, for which it is postulated that multimechanism flow can occur. Inspired by recent studies that have demonstrated the complex pore structure of shale-gas reservoirs, which may include nanoporosity in kerogen, we first develop a numerical model that accounts for multimechanism flow in the inorganic- and organic-matter framework using the dynamic-slippage concept. In this formulation, unsteady-state desorption of gas from the kerogen is accounted for. We then generate a series of production forecasts using the numerical model to demonstrate the consequences of not rigorously accounting for multimechanism flow in tight formations. Finally, we modify modern rate-transient-analysis methods by altering pseudovariables to include dynamic-slippage and desorption effects and demonstrate the utility of this approach with simulated and field cases. The primary contribution of this work is therefore the demonstration of the use of modern rate-transient-analysis methods for reservoirs exhibiting multimechanism (non-Darcy) flow. The approach is considered to be useful for analysis of production data from shale-gas and tight-gas formations because it captures the physics of flow in such formations realistically.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".