New Agent for Formation-Damage Mitigation in Heavy-Oil Reservoir: Mechanism and Application
Bibliographic record
Abstract
Abstract Migration of formation fines has been shown to cause production decline in many wells, especially for sand production wells in heavy oil reservoir. Filter cakes in wire wrapped liner, which were formed by the attachment of viscous crude oil blended with formation fines, may block the flow paths of viscous oil. The solution to this problem is appropriate treatment to mitigate this type of formation damage. In this paper the performance at laboratory-scale of a new type of agent for formation damage mitigation is presented and some guidelines for its application including the injected pore volume and injection concentration are provided. The mechanism for damage mitigation with this type of agent in heavy oil reservoir was introduced in detail, it mainly include that this type of agent can reduce interfacial tension between crude oil and water and change the wettability of rock surface, which may lead to the breakaway of resins and asphaltenes attached to the rock surface. By simulation experiments and core flood tests the effectiveness of this type of agent to mitigate the damage in heavy oil reservoir was identified. Simulation experiment results show that, damage mitigation in cores with the permeability higher than 1μm2, is more effective than those with the permeability lower than 1μm2, and core flood experiment results also indicate that this type of agent with the concentration of higher than 5% can remarkably increase recovery factor for cores with the permeability higher than 1μm2. Finally some results on the behaviour of its application in heavy oil reservoir are presented.
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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".