Assessing the impact of geochemical mechanism and interpolation factor selection on the precision of low-salinity waterflooding modelling: a comparative study
Bibliographic record
Abstract
The efficacy of low salinity waterflooding (LSWF) as an enhanced oil recovery (EOR) method in carbonate formations has been well established; however, its geochemical modelling in such reservoirs remains insufficiently explored. This study aims to bridge this gap by conducting a comparative analysis of three well-recognised geochemical mechanisms including fines migration, rock dissolution/precipitation, and multi-ion exchange (MIE). Our research encompasses two parts: experimental and modelling. First, we conducted four coreflooding experiments to study the effect of LSWF on oil recovery in core scale as well as on rock wettability by measuring the contact angle of the crude oil-brine-rock system. In the next step, we created a one-dimensional compositional model in CMG-GEMTM for LSWF, validated it using the obtained experimental results, and compared the accuracy of the three geochemical mechanisms one-by-one. The results of the experimental section, confirm the positive effect of LSWF on oil recovery: 34.3% with seawater (40,000 ppm), 42.5% with LoSal-01 (10,000 ppm), 49.7% with LoSal-02 (5,000 ppm), and 60.9% with LoSal-03 (2,000 ppm). The experiments also show the wettability alteration through the reduction in the contact angle. Furthermore, the modelling results reveal that choosing MIE as the governing mechanism and sulphate concentration ([SO42−]) as the relevant interpolation factor (IF) leads to the most accurate LSWF model. After identifying the most accurate mechanism, we detected the wettability alteration in the model by showing the movement of oil and water relative permeability curves intersection point on the Kr-Sw diagram toward the right side.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.000 |
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".