Brazos A-105 D-Sand Reservoir Modeling by Integration of Seismic Elastic Inversion Results with Geostatistical Techniques
Bibliographic record
Abstract
Abstract An integrated reservoir modeling study of the Bigenerina humblei (Big Hum) Miocene D-sand at Brazos A-105 field, offshore Texas, was conducted to predict the lateral extent of the reservoir, to build a porosity model for use in flow simulation and reserve evaluation, and to assess the uncertainty of the reserve estimation. Several geostatistical techniques for integrating well-log porosity with quantitative average porosity derived from a forward elastic model-based inversion method for three-dimensional seismic data were applied in this reservoir modeling study. Elastic modeling was necessary to predict the correct porosity-amplitude relationship for this reservoir because it is a class 2 type amplitude-vs.-offset reflection. The results of the study showed that if a reservoir is seismically resolved and properly imaged, elastic model-based inversion of the type employed can be used in conjunction with geostatistical methods to obtain a more complete reservoir description. These techniques were determined to have direct application to reservoir-flow modeling and hydrocarbon reserve volume estimation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".