Bibliographic record
Abstract
Technology Focus What does the term “heavy oil” mean to you, the broad JPT readership? There are of course the various formal definitions that are tied mainly to API-gravity values (i.e., commonly 22°API), while others reflect both oil density and viscosity (preferentially referring to in-situ conditions). In general, however, recent literature suggests that heavy oil means quite different things to different people throughout this industry because it is used to cover a wide spectrum of reservoir conditions and field developments. Only a decade or so ago, papers written about heavy oil typically referred to developments in Canada, China, USA (California), or Venezuela. Now, however, papers describing heavy-oil projects cover a much broader range, both geographically and in terms of the reservoir conditions involved, with a key distinction between onshore and offshore developments. Now, various types of onshore heavy-oil developments are under way in many countries and regions including the Middle East (Kuwait, Oman, and Saudi Arabia), Russia, Africa (Chad, Nigeria, Tunisia, and Libya), USA (Alaska), India, and Europe. Over the past few years, interest has grown rapidly in developing heavy-oil deposits off-shore Brazil, Mexico, Africa, China, Italy, Trinidad, Cuba, and elsewhere. These offshore fields include both carbonate and sandstone reservoirs with in-situ oil viscosities typically less than 400 cp. While such viscosity values are quite low relative to traditional onshore heavy-oil developments, they certainly can present significant technical challenges in an offshore environment. The diversity among these recent heavy-oil developments is significant in terms of the recovery strategies being pursued and the technical challenges that must be overcome. These challenges include choice of recovery method, reservoir characterization and performance, complex-well-design and well-integrity considerations, production and artificial-lift difficulties, as well as fluid-processing difficulties. The papers included in this feature were selected to showcase the wide range of heavy-oil developments under way throughout the world and the novel strategies and technology pursuits that go along with them. Heavy Oil additional reading available at OnePetro: www.onepetro.org PETSOC 2009-067 • “Evaluation of Recovery Technologies for the Grosmont Carbonate Reservoirs” by Q. Jiang, SPE, Osum Oil Sands Corporation, et al. SPE 113625 • “The Zatchi B Heavy-Oil-Reservoir Development: The Unique Challenges of the TAML6 Multilateral Well ZAM-408ML” by L. Tealdi, SPE, Eni Congo, et al. SPE 115240 • “Key Technologies of Polymer Flooding in Offshore Oil Field of Bohai Bay” by W. Zhou, China National Offshore Oil Corporation, et al. SPE 121381 • “When Water Means Oil: Block 16 Case History” by C. Correa Feria, Repsol, 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.321 | 0.248 |
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".