Role of Monovalent and Divalent Ions in Low-Salinity Water Flood in Carbonate Reservoirs: An Integrated Analysis through Zeta Potentiometric and Simulation Studies
Bibliographic record
Abstract
The presence of principal ions in the water injected is essential for enhanced oil recovery by formation of water-wet state in carbonates. This study reaffirms this and presents an evaluation of the positive influence of both divalent as wells as monovalent ions on wettability alteration mechanisms during low salinity waterflooding using brines of varying ionic composition, referred to as “smart brines”. Zeta potentiometric analysis and reservoir simulation studies were conducted with diluted and smart brines that were prepared by varying the composition of principal ions. Surface charge of oil-saturated whole core samples of rock in the presence of various diluted and smart brines was estimated by zeta potential measurements. A comprehensive analysis of zeta potentiometric and reservoir simulation studies was done to establish and investigate the linkage between the recovery mechanism and the incremental recovery achieved. It is noted that zeta potential increases with the increasing level of dilution and it can be attributed to electric double-layer mechanism. On the contrary, simulation studies implied a different mechanism where an increase in effluent’s pH and Ca2+ mole fraction along with decrease in moles of minerals and saturation index implied rock dissolution was dominant mechanism. Moreover, the effect of mineral dissolution beyond the injection block is highly doubtful. This study demonstrates that an integrated approach from both zeta potentiometric and simulation studies can be used to provide insights into the underlying science of interactions at pore scale during a low salinity waterflood using smart brines. With the aid of an adequately designed upscaling procedure and protocol, the laboratory results can be further used towards developing field-scale models to obtain with realistic recovery factors with optimized brine composition and salinity.
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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 | 0.001 | 0.001 |
| 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".