An Enhanced Oil Recovery Technology as a Follow Up to Cold Heavy Oil Production with Sand
Bibliographic record
Abstract
Abstract Lloydminster area that straddles Alberta and Saskatchewan border contains vast amounts of heavy oil deposits in thin unconsolidated formations. Cold Heavy Oil Production with Sand (CHOPS) has been successfully implemented in these reservoirs. However, primary recovery is still low averaging below 10%. How to economically recover the large amount of remaining oil in place is a challenge. Therefore, an effective follow up recovery process is required. Steam injection technologies cannot be widely applied because most of the Lloydminster heavy oil reservoirs are thin and the heat losses to overburden and under burden make the process uneconomic. Alternative solvent methods are not commercial yet due to uncertain oil recovery rates and low solvent recovery. Hybrid application of the aforementioned two technologies using hot water together with solvents could be an economic post CHOPS recovery process. The wormholes created during the primary recovery can be used to contact large reservoir volumes with hot water and solvent. This paper contains the results of hot water and solvent oil recovery experiments conducted in preserved heavy oil cores. Experimental work consisted of three phases. Cores were immersed in hot water in the first phase to pre-heat the formation. Next, cores were exposed to heptane as hydrocarbon solvent. Finally, cores were immersed in hot water again to recover the oil as well as the solvent. The ultimate oil recoveries varied between 42% and 88% OOIP and, the asphaltene precipitation varied between 2.5 wt% and 11.7 wt%. Experiments were also carried out with a distillate from Husky's Lloydminster upgrader used for heavy oil transportation in the pipelines. Better results were obtained if the distillate was used instead of the pure hydrocarbon solvent. It was observed that oil recovery at the end of the initial hot water injection phase due to thermal expansion and viscosity reduction was negligible compared to the ultimate recovery. However, the first phase serves to condition the reservoir for better diffusion in the second phase when the solvent is injected. The final phase of hot water injection causes the water to strongly imbibe into the matrix enhancing the oil and the solvent recovery.
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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 | 0.000 |
| 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) | 0.001 | 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".