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.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".