Bibliographic record
Abstract
Technology Focus Over a 6-month time frame in 2016, I was able to attend three SPE conferences on heavy oil in different countries spanning three continents (Canada, Peru, and Kuwait). Despite regional differences in the applications, potentials, problems, and technological needs, the common theme in all conferences was “low cost.” Cost optimization in heavy-oil production was discussed from technical and economic perspectives, not only in the technical sessions but also in numerous panel discussions. Such optimization can be achieved through numerical modeling to suggest general optimal strategies and development plans or by using proper real-time data acquisition (production optimization) for prompt decisions while operations are ongoing. This requires continuous monitoring of the processes as seen in many steam-assisted-gravity-drainage operations or other types of steam-injection applications. I selected two papers about advanced monitoring techniques as suggested reading in this issue. Moreover, chemical and nanomaterial additives to water and steam have received a great deal of attention. Low-interfacial-tension (microemulsion) and low-salinity injection in heavy oils in sands and carbonates and wettability alteration in carbonates were common topics at conferences held over the past year. I selected one review paper for additional reading and one experimental work as a summary paper on this subject. Apparently, modeling efforts on advanced (but unconventional) technologies such as electromagnetic heating have continued. You will find a detailed mathematical analysis of the process in one of the papers summarized. Despite the recent economic downturn, we were able to hear the outcome of current field practices at pilot or demonstration scale. Papers detailing small-scale cyclic-steam-injection applications in Kuwait and Oman were worth reading, and one article on this specific subject is included here. Considering these activities in the Middle East, effective transfer of technologies from North America to that part of the world will become highly critical in the near future. Recommended additional reading at OnePetro: www.onepetro.org. SPE 181160 State-of-the-Art Review of the Steam Foam Process by Eric Delamaide, IFP Technologies Canada, et al. SPE 180732 An Integrated Probabilistic Work Flow for Primary and Thermal Performance Prediction of a Large Extraheavy-Oil Field by Raushan Kumar, Chevron, et al. SPE 181431 Horizontal Steam-Injection Flow Profiling Using Fiber Optics by Mahdy Shirdel, Chevron Energy Technology Company, et al. SPE 180726 SAGD Production Observations Using Fiber-Optic Distributed Acoustic and Temperature Sensing: SAGD DAS—Listening to Wells To Improve Understanding of Inflow by Warren MacPhail, Devon, et al.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.174 | 0.083 |
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".