Estimation of the Shale Oil/Gas Potential of a Paleocene–Eocene Succession: A Case Study from the Meyal Area, Potwar Basin, Pakistan
Bibliographic record
Abstract
Abstract The successful exploration and production of shale‐gas resources in the United States and Canada sets a new possible solution towards the energy crisis presently affecting most countries of Asia. This study focuses on the use of well log and 2D seismic data for the characterization of the shale oil/gas potential of a Paleocene‐Eocene succession — the Meyal area in the Potwar Basin of Pakistan. Two shaly plays are identified in Paleocene‐Eocene strata in well logs using Δ LogRand modified Δ LogRcross‐plot techniques. The results indicate that Paleocene shale (the Patala Formation) and the lower shaly part of Eocene limestone (Sakesar Formation) can be potentially mature source rocks. However, the thermal maturity modelling proves that only the Paleocene shale is mature. Our results also suggest that the maturity responses on Δ LogRmodels for the lower shaly part of the Eocene limestone are due to trapped hydrocarbons in the intra‐formational fractures. Petroelastic/petrophysical analysis of the Patala Formation reveals two potential shale oil/gas zones on the basis of Young's modulus, Poisson's ratio, Brittleness index and Total Organic Content at an exploitation depth of 3980–3988 m. This work can provide valuable insight for estimating shale oil/gas potential in highly deformed basins not only in Asia but in other parts of the world.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".