Bibliographic record
Abstract
Mark McCallum, Z-Seis Corporation, Calgary, Canada provides some case studies to highlight the benefits of crosswell seismic in making exploration and development decisions. New challenges are being faced by oil and gas producers as conventional hydrocarbon resources continue to dwindle. The focus in the industry is shifting from developing new conventional reserves to maximizing the potential of existing reserves. Exploration is now shifting to either unconventional reserves such as oil sands, tight gas, and coal bed methane or remote locations such as the arctic and deep water offshore. In both cases, development of new resources and redevelopment of mature fields, crosswell seismic can deliver unprecedented resolution to solve reservoir issues in the interwell space. At critical junctures in the life of a reservoir, development decisions are made that can dramatically increase value, if they are based on a precise understanding of reservoir architecture. During the last decade, 3D surface seismic has supplied information about the reservoir that has driven exploration and development decisions. However, today crosswell seismic technology is emerging as an effective new tool in characterizing the unconventional resource reservoir and in optimizing the development of mature fields.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".