A Variable Shape Distribution (VSD) Model for Characterization of Pore Throat Radii, Drill Cuttings, Fracture Apertures and Petrophysical Properties in Tight, Shale and Conventional Reservoirs
Bibliographic record
Abstract
Abstract Fractal and power law distributions have been found in the past to be useful for modeling some reservoir properties following the assumptions of constant shape and self-similarity. This study shows, however, that pore throat apertures, fracture apertures, petrophysical and drill cuttings properties of unconventional formations are better matched with a variable shape distribution model (as opposed to constant shape). This permits better reservoir characterization and forecasting of reservoir performance. Pore throat apertures, fracture apertures, petrophysical properties and drill cutting sizes from tight and shale reservoirs are shown to follow trends that match the variable shape distribution model (VSD) with coefficients of determination (R2) that are generally larger than 0.99. The good fit of the actual data with the VSD allows more rigorous characterization of these properties for use in mathematical models. Data that could not be described previously by a single equation can now be matched uniquely by the VSD. Examples are presented using data from conventional, tight and shale formations found in Canada, the United States, China, Mexico and Australia. In addition, the study shows that the size of cuttings drilled in vertical and horizontal wells can also be matched with the VSD. This allows the use of drill cuttings, an important direct source of information, for quantitative evaluation of reservoir and rock mechanics properties. The results can be used for improved design of stimulation jobs including multi stage hydraulic fracturing in horizontal wells. This is important as the amount of information collected in horizontal wells drilled through out tight formations, including cores and well logs, is limited in most cases. It is concluded that the VSD is a valuable tool that has significant potential for applications in conventional, low and ultra-low permeability formations and for evaluating distribution of rock properties at the micro and nano-scale.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".