Successful Field Application of Surfactant Additives to Enhance Thermal Recovery of Heavy Oil
Bibliographic record
Abstract
Abstract In this paper, we will describe successful field applications of a class of surfactants called Thin Film Spreading Agents (TFSA) to enhance thermal recovery of heavy oil. TFSA has been successfully applied in cyclic steam stimulation (CSS) applications in California and Alberta, Canada and three such case histories will be presented. In Canada, three oilsands CSS wells were treated with 250ppm of TFSA. After a certain number of steam cycles, all wells decline in production relative to the previous steam cycle. After 500 days of production the TFSA treated wells only declined 1.0%. The primary control group of wells declined 39%. The cumlative increase in production of the TFSA treated wells was 20% (6718 bbls/well) greater than the control group. In California, 17 vertical CSS wells in a sandstone formation were treated with TFSA to realize incremental oil recovery. Out of 17 wells, 14 showed an average incremental oil recovery of 5411bbls translating into a success rate of 82%. The average payout period was 13 days. The producer realized a net revenue increase of USD 26 million. In another field application in California, 44 vertical CSS wells in a sandstone formation were treated with TFSA to realize incremental oil recovery. Out of 44 wells, 32 showed an average incremental oil recovery of 720bbls translating into a success rate of 73%. The average payout period was three days and the producer realized a net revenue increase of USD 11.8 million. These field results establish the efficacy of TFSA in increasing recovery in thermal applications.
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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".