Petrophysical Quantification of Multiple Porosities in Shale-Petroleum Reservoirs With the Use of Modified Pickett Plots
Bibliographic record
Abstract
Summary Previous petrographic work has shown that shale-petroleum reservoirs at discovery are characterized by a quad-porosity system. In this work, a petrophysical model is built that allows quantification of storage capabilities in shales through determination of adsorbed porosity (ϕads_c), organic porosity (ϕorg), inorganic porosity (ϕm), and fracture porosity (ϕ2). All these porosities are important because they provide reasonable input to physics-based numerical simulators for shale-petroleum reservoirs and thus more-realistic projections of reservoir performance and recoveries. Pattern recognition is used in a modified Pickett plot for distinguishing key shale components such as total organic carbon (TOC) and level of organic metamorphism (LOM) and to distinguish between viscous and diffusion-like flow. Results from the model compare well with laboratory data. The petrophysical model is easy to use, yet it is robust because it can handle at the same time the four porosities mentioned previously, but it can also handle simultaneously three, two, or only one of those porosities depending on the characteristics of the reservoir at a given depth. It is concluded that the petrophysical model presented in this paper constitutes a valuable tool for physics-based characterization of shale-petroleum reservoirs.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".