COMBINING GEOSTATISTICS AND MULTI‐ATTRIBUTE TRANSFORMS: A CHANNEL SAND CASE STUDY, <i>BLACKFOOT</i> OILFIELD (ALBERTA)
Bibliographic record
Abstract
In this paper, we combine the methods of geostatistics and multi‐attribute reservoir parameter prediction (the multi‐attribute transform) for the integration of seismic and well log data, and illustrate this new procedure with a case study involving the prediction of porosity at the Blackfoot oilfield, central Alberta. The objectives of the survey were to delineate incised, valley‐fill sediments within the Early Cretaceous Glauconitic Formation at this field and to distinguish between sand‐fill and shale‐fill. The input consisted of twelve porosity logs together with a 3D seismic volume and the inversion of this volume. Although an excellent correlation was found between porosity and the initial inverted acoustic impedance volume, the combination of traditional geostatistics and the multi‐attribute transform produced an improved final result. Our approach uses well logs to “train” the multi‐attribute transform algorithm. We first extract average porosity values over the depth zone of interest, and compare these values to average seismic attributes over the same zone. Cross‐validation is used to show which attributes are significant. We then apply the results of the training and cross‐validation to data slices derived from both the seismic data cube and the inverted cube to produce an initial porosity map. Finally, we improve the fit between the well‐log values and the porosity map using cokriging.
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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 | 0.001 | 0.002 |
| 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".