Use of Volume-Based 3-D Seismic Attribute Analysis to Characterize Physical-Property Distribution: A Case Study to Delineate Sedimentologic Heterogeneity at the Appleton Field, Southwestern Alabama, U.S.A.
Bibliographic record
Abstract
Abstract In this paper we illustrate the use of 3-D seismic attribute studies for predicting the distribution of physical properties in the subsurface. Using a data set consisting primarily of digital logs and seismic data, we show how correlations can be made between seismic attributes and physical properties (porosity), and how those relationships can be exploited to predict the distribution of the property of interest in three dimensions. The results of these studies: (1) provide quantitative, site-specific 3-D models of physical properties that are of more use for applied studies than qualitative 2-D models commonly derived from facies modeling or sequence stratigraphic analysis, (2) are generally more geologically reasonable than studies based on geostatistics alone, (3) can provide sedimentary geologists with fundamental insights into depositional and/or diagenetic controls on the distribution of properties of interest, (4) need to be rigorously evaluated by integrating other types of data and analyses, and (5) are best thought of as supplementing, rather than replacing, conventional geologic analyses. The concepts and methods we illustrate may have application in various branches of sedimentary geology. Our study area is Appleton Field in southern Alabama. At this location we predict the 3-D distribution of porosity in carbonates of the Upper Jurassic Smackover Formation using a probabilistic neural network and a combination of four attributes. Our results suggest that porosity was best developed, and preserved, in thrombolite facies of the reef front.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".