Bibliographic record
Abstract
Abstract With the annual growth of development scale and production, the research of heavy oil and oil sands become more and more important. Due to the characteristics of heavy oil and oil sands, the degree of reserves recovery is restricted by development mode. So there is a big difference in classification criterions and evaluation techniques of reserves in the world. In this paper, using the major heavy oil and oil sands accumulation areas —Venezuela heavy oil belt and Canada oil sands as example, we analyse the reservoir characteristic of heavy oil and oil sands, and then discuss the classification and evaluation techniques of reserves suitable for different development modes. The heavy oil belt in Venezuela and oil sands in Canada are all characterized by huge thickness, middle-fine sandstone, loose cementation, high porosity and high permeability. To adapt different development mode, we establish corresponding evaluation criterions for reservoir, including porosity, permeability, shale content, saturation, barrier and interbed, the thickness and width of continuous oil layer and so on. We classify the reserves as OOIP, economic reserves, reserves for horizontal well, reserves for steam flooding (SF)/ cyclic steam stimulation (CSS) and reserves for SAGD. Using logging and geophysical techniques, we evaluate and classify the reserves. 1.Introduction Heavy oil and oil sands are characterized by shallow buried depth, large thickness, huge reserves and unconsolidated structures. Because crude oil has high viscosity, conventional vertical wells generally have no or extremely low productivity. Currently, these reservoirs are predominantly developed by horizontal wells in two stages: cold production and thermal production. All these conditions have presented new requirements on contents and methods of reserve evaluation. In this essay, through study on reservoir features and corresponding development techniques for heavy oil in Venezuela and oil sands in Canada, we put forward reserve assessment methods and techniques by class for various development modes. 2.Reservoir evaluation and selection criteria for heavy oil and oil sands development Currently, cold production (CP) is the prevailing development method adopted for heavy oil in Venezuela, at the same time, cyclic steam stimulation (CSS), steam flooding (SF) and SAGD have been tested. Oil sands in Canada are mostly developed by SAGD and open mining. A series of reservoir selection criteria have been proposed to cope with requirements of different development methods with horizontal wells on oil layers (Table 1).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".