Production Analysis of Tight Gas and Shale Gas Reservoirs Using the Dynamic-Slippage Concept
Bibliographic record
Abstract
Abstract Shales and some tight gas reservoirs have complex, multi-model pore size distributions, including pore sizes in the nanopore range, causing gas to be transported via multiple flow mechanisms through the pore structure. In 1986, Ertekin et al. 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 multi-mechanism 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 multi-mechanism 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 multi-mechanistic flow in tight formations. Finally, we modify modern rate-transient 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 methods for reservoirs exhibiting multi-mechanistic (non-Darcy) flow. The approach is considered to be useful for analysis of production data from shale gas and tight gas formations as it captures the physics of flow in such formations realistically.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".