Intergrasi Seismik Menggunakan Atribut Seismik dan Amplitude Variation with Frequency (AVF) pada Lapangan Penobscot untuk Mengidentifikasi Fluida pada Tight Sand
Bibliographic record
Abstract
This study aims to identify the presence of hydrocarbon-bearing fluids within the tight sand intervals of the Mississauga Formation in the Penobscot Field, Scotia Basin, Canada, through the analysis of amplitude- and frequency-based seismic attributes. The primary dataset comprises 3D seismic volumes processed using Pre-Stack Time Migration (PSTM), supported by well data from Penobscot L-30 and B-41 as lithology and fluid controls. A suite of seismic attributes—including RMS Amplitude, Envelope, Sweetness, Instantaneous Q, Intercept (A), Gradient (B), Low-Frequency Amplitude (15 Hz), and Amplitude Variation with Frequency (AVF)—was computed to evaluate lithological variations and fluid responses within the target interval. Among these, four key attributes—Sweetness, Envelope, Low-Frequency Amplitude (15 Hz), and AVF—were selected for integrated analysis using a multiplicative workflow following data normalization. The integration results reveal pronounced low-frequency (15–25 Hz) amplitude anomalies associated with an anticlinal crest in the central–southern part of the study area. This zone is interpreted as a gas-saturated sandstone layer characterized by low acoustic impedance and significant poroelastic effects. The integrated, normalized multi-attribute approach proves effective in enhancing seismic responses attributable to fluid presence and provides a robust basis for reservoir characterization in unconventional systems, particularly tight-sand environments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".