Research and Development at Universities: Worldwide Change in Petroleum Engineering
Bibliographic record
Abstract
The fundamental properties of hydrocarbons and the basics of drilling will always remain the same. Nevertheless, innovation in petroleum engineering by researchers at universities around the world is changing the face of oil and gas drilling and production in the 21st century. In addition to traditional approaches, technologies for enhanced production from mature fields; production methods for unconventional resources such as tight gas, methane hydrate, and heavy oil; and experimental nanotechnology applications today are critical and increasingly are a part of petroleum engineering research and technology development. Schulich School of Engineering, University of Calgary, Canada The upstream research at Schulich’s Department of Chemical and Petroleum Engineering concentrates on improving the exploitation of Canadian unconventional oil and gas resources such as heavy oil and bitumen, and tight gas formations. The department has a dozen research teams, said professor Brij Maini. The In Situ Combustion Research Group specializes in laboratory and numerical simulation studies of the in-situ combustion of heavy oils, and high-pressure air injection in light oil reservoirs. The faculty’s EnCana/Paleontological Society of Canadian Institute of Mining, Metallurgy, and Petroleum Endowed Chair heads the department’s joint industry project studying the geomechanics of shale gas. The CMG Foundation Chair in Reservoir Simulation is developing fast and reliable reservoir simulators for the recovery of oil, gas, and coal. The NSERC Industrial Research Chair in Heavy Oil Properties and Processing is researching the properties and phase behavior of heavy oil and solvents. Schulich’s SHARP (Solvent/Heat Assisted Recovery Processes) consortium is supported by 11 local and international companies. The improved heavy oil science and technology group is studying various aspects of heavy oil recovery technology, including cold heavy oil production with sand, and solvent-based recovery processes and steam additives for steam injection-based processes.
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 | 0.027 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.018 | 0.014 |
| Insufficient payload (model declined to judge) | 0.029 | 0.012 |
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".