Non-Darcy Flow in Shale Nanopores: Do We Have a Final Answer?
Bibliographic record
Abstract
Abstract Many shale gas and ultra-low permeability tight gas reservoirs can have matrix permeability values in the range of tens to hundreds of nanodarcies. The ultra-fine pore structure of these rocks can cause violation of the basic assumptions behind usage of Darcy’s law. Depending on a combination of P-T conditions, pore structure and gas properties, non-Darcy flow mechanisms such as Knudsen diffusion and/or gas slippage effects could be important. Quantifying these effects is critical for correcting laboratory permeability measurements to obtain true (intrinsic) matrix permeability; several authors have also noted that corrections for these effects may also be important when analyzing field data. In order to make corrections for non-Darcy flow, numerous authors have quantified these effects as an apparent permeability that changes as a function of Knudsen number or gas pressure. There are now many correlations available for quantifying apparent permeability changes, but it is not known how much impact they really have on the long term production performance of shale gas wells. In this work, we summarize the various methods for quantifying non-Darcy flow in unconventional gas reservoirs, and compare the apparent permeability and slippage factor predictions for all the models we have compiled. For determining the impact of the model predictions on well performance, we incorporate the apparent permeability predictions of each model into a numerical simulator, and compare rate-time and cumulative gas-time forecasts for each scenario. The importance of dual porosity (which is usually assumed necessary for shale gas) is also examined. The results of this work are important in several aspects. First, the differences between various formulations of the apparent matrix permeability in shales are illustrated. Secondly, the impact of non-Darcy effects on history matching and recovery forecasting is demonstrated. Finally, it is shown that there may not be a need for dual porosity modeling in certain cases. The results of this study will be important to professionals involved in laboratory measurement of matrix permeability in unconventional gas reservoirs, modeling well performance, and forecasting shale gas recovery.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.012 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".