Application of instantaneous-frequency attribute and gamma-ray wireline logs in the delineation of lithology in Serbin field, Southeast Texas: A case study
Bibliographic record
Abstract
Although Serbin field in Southeast Texas was discovered in 1987, lithologic and petrophysical properties in the southeastern part of the field have not been fully evaluated. We have generated instantaneous frequency from 3D seismic data and predicted gamma-ray response volume from seismic attributes. By extracting maps of the instantaneous frequency and gamma-ray response along interpreted horizons, and crossplotting the instantaneous frequency against gamma-ray logs and integrating core data, we generated lithology maps to identify shale-prone zones that stratigraphically trapped hydrocarbons in the southeastern part of the field. We determine that Serbin field is separated into two areas: (1) a high-frequency, high-gamma-ray, and high-acoustic-impedance area in the northwest and (2) a low-frequency, low-gamma-ray, and low-acoustic-impedance area located in the southeast. By developing a lithologic map and relating it to the corresponding instantaneous-frequency map and log data, we also find that the southeastern part of the field can be divided into three zones: (1) zone 1, composed of approximately 0.7–2.7 m (approximately 2–8 ft) thick sandstone-rich beds of moderate frequency (25–30 Hz); (2) zone 2, composed of high-frequency (33–60 Hz) shale-rich zones that serve as stratigraphic-trapping-mechanisms; and (3) zone 3, composed of approximately 1.7–4 m (approximately 5–13 ft) thick sandstone-rich beds of low frequency (0–18 Hz) and relatively high porosity. These methods can be applied in other areas of the field with limited well control.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".