Bibliographic record
Abstract
Technology Focus With interest in heavy oil continuing to grow rapidly worldwide, SPE has responded to the need for increased dissemination of technical information related to the exploitation of these unconventional hydrocarbon resources. In one new initiative, SPE joined forces with the Canadian Society for Unconventional Gas to organize the 2010 Canadian Unconventional Resources and International Petroleum Conference in Calgary, in October. SPE also organized heavy-oil advanced-technology workshops and expanded heavy-oil content of regularly scheduled conferences in several Middle East countries, Russia, and China in 2010, signaling the growing importance of heavy-oil reserves to these regions. I often get asked: “What is the best recovery method to use for this heavy-oil reservoir?” The answer can vary substantially, depending on the reservoir setting and fluid properties. Therefore, as with any new field, the process of determining the preferred development strategy begins with a detailed and accurate characterization of the reservoir and fluid properties. However, in the case of heavy oil, unanticipated technical challenges are encountered routinely in accomplishing this basic exercise. For example, while tools and equipment are readily available and proved for capturing live downhole-fluid samples in conventional-oil reservoirs, this is not the case for heavy oils, especially those with in-situ viscosities exceeding several hundred centipoises, let alone thousands or tens of thousands of centipoises. Many heavy-oil reservoirs consist of unconsolidated sand formations, which also makes it difficult to acquire either fluid or undisturbed core samples and then to obtain accurate permeability and porosity data. For thermal projects, determining accurate rock and fluid properties as a function of temperature is important but is not an easy task. Specialized equipment and field-sampling/laboratory-testing techniques along with ample experience typically are required to obtain reliable data. It is also worth noting that the trend over the past few years has been to give much more attention during initial development planning to the sequencing of different enhanced-oil-recovery (EOR) strategies to maximize recovery from heavy-oil reservoirs. On the basis of the many papers written this past year related to polymer flooding of heavy-oil reservoirs, it appears that recent technological advancements and application successes have led to this becoming a viable EOR alternative for a wide range of in-situ fluid viscosities. Finally, the need for conducting pilot operations to establish actual reservoir and well performance and to validate expectations cannot be emphasized enough. Heavy Oil additional reading available at OnePetro: www.onepetro.org SPE 137639 “Thermal Properties of Formations From Core Analysis: Evolution in Measurement Methods, Equipment, and Experimental Data in Relation to Thermal EOR” by Y.A. Popov, Schlumberger, et al. SPE 134849 “In-Situ Heavy-Oil Fluid-Density and -Viscosity Determination Using Wireline Formation Testers in Carbonates Drilled With Water-Based Mud” by Ridvan Akkurt, Saudi Aramco, et al. SPE 136665 “Viscosity of Foamy Oils” by A.B. Alshmakhy, SPE, Weatherford, et al.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.230 | 0.118 |
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".