Reservoir characterization through the application of seismic attributes : multiattribute and unsupervised seismic facies analyses
Bibliographic record
Abstract
Seismic attributes are a descriptive and quantifiable characteristic of seismic data, and so they represent subsets of the total information contained in the original seismic data. Consequently, seismic attrjbutes can be important qualitative and quantitative predictors of physical reservoir properties. Seismic attribute analysis began with the search for bright spots in the late 1960s and early 1970s. Subsequent work in the last decades has established seismic attribute analysis as a valuable tool in reservoir characterization studies. I present in this thesis two seismic attribute analyses for the interpretation of the characteristics of the seismic trace to predict significant geological features (e.g., lithology, rock properties and/or fluid content). The first study is an example of the applicability of seismic attributes to predict geologic controls on production data in a coal bed methane reservoir. Based on the calibration of log data with seismic data procedure, I defined a window of analysis, from which I computed amplitude, frequency and phase related attributes. Then, I used a quantitative multiattribute analysis to derive a seismic attribute-based model that predicts coal thickness. Thereafter, I used with a curvature attribute analysis with the objective to map subtle structural features and predict areas with enhanced permeability. The integration of production data with results achieved from both attribute analyses indicates that best production is associated with thicker coal and the immediate vicinity to seismically defined structures. [...]
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".