Determination of Optimal Conditions for Addition of Foam to Steam for Conformance Control
Bibliographic record
Abstract
Abstract Heat is transferred into the heavy oil reservoirs through steam injection. It reduces the viscosity of the heavy oil and bitumen and makes them mobile for production. Steam oil ratio (SOR) directly affects the cost of operation and is an index of the process efficiency. Steam processes could be optimized through addition of chemical additives to steam to selectively prohibit unfavorable channeling, gravity override and steam loss to the high permeable thief zones. Injection of foam with steam for conformance control is considered as a solution for increasing steam flooding efficiency and optimizing reservoir performance. In this experimental study, a candidate surfactant is used to evaluate the optimal conditions for steam- foam application. Through a separate dedicated screening study at steam condition, one surfactant was identified which passed the required tests on solubility in injecting brine, foam generation, foam stability, surfactant thermal stability and its loss to reservoir rock surface, due to adsorption. This surfactant was co-injected in aqueous phase with steam to produce foam in porous medium. Core flooding tests were conducted at 260°C to evaluate the performance of foam with steam. The focus of this study was on determination of the optimal foam performance in different steam qualities and injection rates using steam (not non-condensable gases). Our tests were conducted in absence of oil and in a 30-cm core of less than 5 mD. Foam performance was monitored through differential pressures along the core as well as analysis of the foam in the effluent. Mobility Reduction Factor (MRF) allowed us to compare the performance of foam steam with steam-only process. Shear velocities of as high as velocity at well perforations and as low as one meter per day were considered and tested at different steam qualities. The range of steam qualities tested was from 3 5% all the way to 100%, which was a slug-format test. Our candidate surfactant produced most foam around 50% quality which seems to be a good balance between the proportion of gas and liquid that produce stable foam texture. Higher qualities leads to drying out the lamella and lower qualities do not introduce sufficient gas to liquid for foam generation. Our tests reveal that there is a critical velocity beyond which foam generation starts and the foam and surfactant fronts are moving separately. MRFs of larger than 10 was determined in our optimal conditions. Different steam qualities are tested in this study using real steam which condenses due to pressure rise along the core. Through these tests, operators could add significantly to their knowledge on how to best operate in adding foam to steam for a better reservoir performance.
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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".