Introduction to the Special Edition from the 2014 Gussow Conference on Advances in Applied Geomodeling
Bibliographic record
Abstract
Geomodeling has proliferated among earth science and engineering professionals as a body of techniques, software packages, and workflows for subsurface reservoir characterization. Although not a recognized professional discipline or university degree option, geomodeling is a multidisciplinary subject with a growing technical community. Geomodeling is treated as an enabling technical field and focal point in the petroleum industry subsurface teams, with major software development dedicated to the subject and practitioners assuming the role and title. The broad subject typically draws from the fields of geology, geophysics, geostatistics, petrophysics, reservoir engineering, and increasingly geomechanics, computer science, and data analytics. The field of geostatistics is a fundamental aspect of geomodeling, providing many core algorithms. The other associated fields provide concepts, context, inputs, constraints, and direction for the technology applications and for multidisciplinary team efforts to deliver meaningful models and results. The motivation for companies is to pursue exploration, development, and production with increased efficiency and sustainability. The geomodeling proposition is to add value through improved reservoir management decisions. More accurate and precise geomodels lead to improved well planning and prediction of the behavior of alternative extraction technologies. Thus, geomodeling will improve recovery and reduce risk. Yet, gaps exist between the application of geomodeling, geostatistical methods, capabilities of software tools, and appropriate practice and ease of use. With …
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".