Experimental Study of Factors Affecting Heavy Oil Recovery in Solvent Floods
Bibliographic record
Abstract
Abstract The recovery factor during solvent injection is affected by pore structure of porous medium, gravity force, solvent type and its imposed flow rate. However, there is still an incomplete understanding of the pore structure orientation effect of the solvent flooding and how these lead to improve oil recovery. This can be accomplished through the micromodel experiments. In this work a series of experiments performed whereby hydrocarbon solvents (heptane, octane, decane) displaced heavy oil in micromodel pattern of different pore structure orientations. Successive Images of invasion of solvents in heavy oil were taken at desired interval time during injection process. Image analysis technique is used to measure the oil recovery factor as a function of injected pore volume of solvents. The concentration calibration curves of solvents in heavy oil are used for evaluating the solvent concentration. The oil recovery factor versus pore volume of different injected solvents were plotted for different micromodel flow patterns, at three different flow rates in both horizontal and vertical geometry. It has been found that the pore structure orientation have significant effect on the oil recovery during solvent injection process this could be due to the enhancement or detraction of the longitudinal and transverse dispersion in the porous media. The results also indicated that gravity enhances the oil recovery in the case of vertical injection compared to the horizontal one. In addition solvents of lower molecular weight resulted in higher oil recovery. Introduction With the era of conventional oil appearing to be coming to anend, attention has turned to heavy oil production and enhanced oil recovery (EOR). Heavy oil and oil sands are destined to play an increasingly important role in the oil supply in the world and they will be in center-stage in the development of the oil industry in Canada. One third of the world's oil is in Canada in the form of heavy oil and bitumen. The heavy oil resource of the world total about ten trillion barrels, nearly three times the conventional oil-in-place in the world. Alberta contains nearly two trillion barrels of oil. Approximately one fourth of the oil production of Canada is from oil sands. In-situ recovery processes can be classified into two categories: thermal and non-thermal. Thermal recovery processes utilize heat to reduce the viscosity of the heavy oil in situ, thus mobilizing the heavy oil. Examples of thermal processes are Cyclic Steam Simulation (CSS), Steam Assisted Gravity Drainage (SAGD) and Steam Flooding. On the other hand, non-thermal processes rely on dilution of the oil rather than heat to reduce the heavy oil viscosity. Examples of such processes are CO2 Injection, Miscible Floods and Vapour Extraction (VAPEX). Displacement in the case of immiscible floods, such as water flooding, is generally not feasible, but a fluid can be displaced completely from the pores by another fluid that is miscible with it in all proportions. In the case of miscible fluids there are no residual saturations. Hence, solvent floods present an attractive in-situ process for the recovery of the heavy oil.
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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 | 0.001 | 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".