Compaction trends for shale and clean sandstone in shallow sediments, Gulf of Mexico
Bibliographic record
Abstract
Compaction depth trends are important in drilling, basin modeling, and seismic exploration for several purposes: (1) to de-tect overpressure and hydrocarbon zones and distinguish them from seismic velocity anomalies; (2) to calculate interval velocities and depth conversion involving seismic data and Earth models; (3) to predict seismic signatures of sand-shale interfaces as a function of depth; and (4) to recognize over-compacted zones due to uplift. Several authors have studied the effects of compaction on the porosity of sands and shales (e.g., Magara, 1980; Ramm and Bjorlykke, 1994). The effects of compaction on velocity-depth trends have been provided by different authors (e.g., Al-Chalabi, 1997; Faust, 1951; Japsen, 2000). However, porosity and velocity depth trends in the shallow section are not well established. The main challenge in computing such trends is the paucity of well-log data in the shallow subsurface. Figure 1, a typical well log from the Gulf of Mexico, lacks measurements in the shallow section (< 3000 ft or ∼1000 m) due to riser-less drilling, and the log response from the deeper section cannot be used to compute the normal compaction trend due to overpressure. One way to overcome this challenge is to integrate data from multiple sources. In this paper, we compute porosity and velocity depth trends by integrating data from multiple sources including well logs, geotechnical borehole data, and core measurements from shallow sections of the Gulf of Mexico, and laboratory measurements at low effective pressure.
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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 | 0.000 | 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".