Laboratory Permeability and Diffusivity Measurements of Unconventional Reservoirs: Useless or Full of Information? A Montney Example from the Western Canada Sedimentary Basin
Bibliographic record
Abstract
Abstract Permeability is one of the most critical parameters characterizing unconventional (shale or tight) gas and oil reservoirs for resource evaluation and exploitation. Permeability is also perhaps the most difficult parameter to be accurately characterized because it is not just a single number but a complicate property with attributes that depend on many factors (namely sampling or testing scale, pore shape and size and distribution, different transport mechanisms, different test fluids, pore pressure, effective stress, and even temperature). Laboratory permeability measurements are mainly conducted on cores or smaller samples. The size and scale of laboratory measurements are hence severely limited as compared to the meters or kilometers scale of exploration or producing fields. Even in the scale of centimeters or less, for the same core samples, laboratory measurements likely yield variable permeability spanning several orders of magnitudes, leading to seemingly useless laboratory permeability for field applications. In this study, using samples from the Montney Formation in the Western Canada Sedimentary Basin as an example, we first present contrasting laboratory permeability measurements with different methods or experimental conditions. Explanations to the seemingly contradictory permeability measurements are then provided in context of different transport modes in highly heterogeneous microporous unconventional reservoir rocks, highlighting that the contrast laboratory measurements are not useless but full of information for understanding the complex characteristics of microporous unconventional rocks. Appropriate experiments to determine the appropriate permeability and their application to field problems are also discussed, which is helpful for petroleum geologists and engineers to better understand the unique permeability system of unconventional reservoirs and hence to make optimal decisions for successful unconventional resource exploitation.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".