Experimental Study of Gas Solvent Flooding for Lloydminster Heavy Oil Reservoirs
Bibliographic record
Abstract
Abstract How to economically recover the vast heavy oil/bitumen resources is still a major challenge of petroleum industry in Canada. In recent years, light hydrocarbon solvent-based heavy il recovery methods have attracted much attention as alternatives for thermal techniques. The objective of this paper is to experimentally investigate the propane flooding efficiency and applicability for Lloydminster type heavy oil reservoirs. A series of model tests were conducted to simulate the performance obtained when ravity effects were present. Based on the fact that the target reservoirs pressure ranges from 500 kPa to 1000 kPa, and the propane dew point pressure under reservoir temperature is around 870 kPa, both pure propane injection and mixture gas injection of methane and propane were investigated. For the pure propane flooding process, three different operation pressures were tested. For each test both horizontal and vertical mode injections were investigated. The results showed that during horizontal mode injection, poor flood conformance and unfavorable gravity segregation effects dominated. Gravity segregation and displacement instability effects occurred due to the natural density and viscosity differences between the injected solvent and reservoir oil. The oil recovery efficiency was very low for all tested pressures, and the SOR was high. However, when the models were changed to vertical mode injection, much more incremental oil recovery was obtained under all operation pressures, and the SOR dramatically decreased. For the gas mixture flooding, vertical mode injection was directly applied based on the poor performance of horizontal mode injection of pure propane flooding, and the effect of solvent injection rates was investigated. The results of this study indicate that gravity force is more important than viscous force for solvent gas flooding. Maintaining gravity stable is crucial for solvent gas flooding success. There is potential to use solvent gas or solvent mixture gas to in situ recover Lloydminster type heavy oil reservoirs. Introduction Canada has a vast heavy oil/bitumen reserves distributed in Alberta and Saskatchewan. How to economically and effectively recover those reserves is still a major challenge of the petroleum industry. Heavy oil/bitumen is characterized by high viscosities and low degree API gravities.1 For example, in some Athabasca reservoirs, the oil viscosity is in the millions of mPa.s at reservoir conditions. Although surface mining has high recovery efficiency, mining susceptible reserves only occupy 10% of the total reserves.2 Therefore, considerable technology effort has to be focused on in-situ recovery processes. Generally, in-situ recovery processes are classified into two categories: thermal recovery processes and non-thermal recovery processes. Thermal recovery processes use heat to reduce the heavy oil/bitumen viscosity in-situ. Examples of thermal recovery processes are Cyclic Steam Simulation (CSS), In-Situ Combustion (ISC), Steam Assisted Gravity Drainage (SAGD) and Steam Flooding. Thermal recovery methods seem to be very effective as the oil viscosity is very sensitive to temperature. The SAGD process has been commercially used by several oil companies. However, some economic constraints for the SAGD process arise if the high cost of steam generation and excessive heat losses in some thin oil reservoirs are considered.3
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".