Pore-Pressure Prediction in Overconsolidated Shales
Bibliographic record
Abstract
Abstract Several of the existing pore pressure estimation methods are based on establishing a normal compaction trend curve with depth. The rationale is that as fine grained sediments are buried with depth the increase in overburden stress compact the sediments therefrom the normal compaction curve can be developed. Common methods using a normal compaction trend as the basis is the equivalent depth method, or the Eaton ratio method which uses sonic, density or resistivity logs as inputs. When fluid overpressure develops caused by other sources than mechanical compaction, such as fluid mechanical entrapment, rock molecular transformation, migration or hydrocarbon generation, Bowers method can be applied. Bowers extended the above mentioned methods to calculate over pressure where sediments are unloaded during pore pressure build up. However, these methods require that you establish a normal compaction curve for sediments without overpressure. In many onshore basins, such as the Mid-Alberta basin, which has thick eroded sections and therefore the sediments are over-consolidated, it is not possible to establish a normal compaction curve. To be able to predict the pore pressure in overpressured Mid-Alberta shales, we developed a pore pressure prediction method based on over-consolidation theory. We tested the method with sonic logs and the method gave promising results for establishing pore pressures in the over-consolidated Alberta basin.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".