Characterization of Rock and Fluid Properties for Low-Salinity Water Flooding of Highly Paraffinic Oil in a Deep Low-Permeability High-Pressure High-Temperature Offshore Carbonate Reservoir
Bibliographic record
Abstract
Abstract Characterization of the rock and fluids is an essential step in screening a reservoir for Low-Salinity Water Flooding (LSWF). A detailed characterization of rock and fluid properties using appropriate methods is being presented for LSWF in a low-permeability deep carbonate reservoir together with a critical analysis of findings. The techniques used are assessed against other possible alternative methods, with inferences drawn on advantages and disadvantages of each to better interpret and apply data so gathered. In so doing, discussions on their key features as to how they can be used effectively and efficiently to screen a reservoir for LSWF are also provided. Such integration of results with other available reservoir and production data should result in a comprehensive description of the target reservoir, and it will help interpret the mechanisms and process dynamics more reliably during a low-salinity waterflood. This integration should allow us not only to gain confidence on the experimental studies but could also help optimize the key parameters responsible for formulating a more robust, reliable and representative regime for tests relevant to the LSWF prior to its eventual implementation in the field. To authors’ knowledge, such integration of experimental studies has not yet been reported in the literature, particularly for the tight carbonate reservoirs with highly paraffinic oil.
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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 | 0.001 |
| 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".