Steam-over-Solvent Injection in Fractured Reservoirs (SOS-FR) Technique as a New Approach for Heavy-Oil and Bitumen Recovery: An Overview of the Method
Bibliographic record
Abstract
Heavy-oil recovery from tight naturally fractured carbonate reservoirs is a great challenge to the oil industry, yet no well-established recovery technique has been adopted by the oil industry. Steam-over-solvent injection in fractured reservoirs (SOS-FR) is a new technology proposed as an alternative method to the sole injection of steam or solvent, and this paper introduces this method through extensive experimental evidence and analysis. The method consists of three phases: (1) steam (or hot water), (2) solvent, and (3) steam (hot water) injection. Phase 1 produces heavy oil by thermal expansion and conditions the oil for phase 2, which is solvent injection. Phase 3 is applied mainly to retrieve the solvent. Several static experiments were conducted mainly to test four critical parameters: (1) wettability of the matrix, (2) solvent type, (3) initial water saturation, and (4) matrix boundary conditions. This was followed by several dynamic experiments, which were aimed at testing the effect of the solvent injection rate on the process. All three phases yielded above 90% recovery, with 85–90% solvent retrieval if the matrix boundary conditions were favorable (large surface area per volume for effective solvent diffusion), when heptane was used as the solvent, regardless of the wettability of the matrix. After provision of the experimental results, we discuss the up- and downsides of the technology and suggest ways to improve it. The importance of this work is that it provides a novel perspective on the interaction between steam/solvent and heavy oil in the matrix and presents an alternative technique for heavy-oil recovery from deep naturally fractured reservoirs with a tight and oil-wet matrix.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".