Effectz of Technology on the Oil and Gas Industry
Bibliographic record
Abstract
Today and for the foreseeable future, oil will be the cornerstone of society’s energy needs. Because of this, the oil and gas exploration and production business constantly requires new and more innovative methods to extract petroleum and natural gas. New technologies have evolved over the last 30 years which include 3D seismic imaging, horizontal well drilling, multi stage hydraulic fracturing, and steam assisted gravity drainage (SAGD) recovery methods. Because of these technological advances, the recovery of oil and gas is becoming more efficient and cost effective. Canada holds the world’s third largest accumulation of petroleum resources, the Athabasca Oil Sands, located in north east Alberta. Up to the start of the 21st century, oil and bitumen recovery from these resources were limited to mining methods. Through the development of progressive cavity pumps and SAGD technology, these resources are now being produced where mining technology is not feasible, providing the United States with a significant portion of their petroleum requirements. RII North America has developed a new, patent pending technology called STRIP which is thermally efficient, cost effective and environmentally friendly. Pilot testing of the technology will commence within the next year, and if successful, will result in a significant technological advancement for the oil and gas industry.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.009 |
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".