Improving the Performance of Smart Waterflooding Through Surfactant-Assisted Process for a Carbonate Oil Reservoir
Bibliographic record
Abstract
Abstract Enhanced oil recovery (EOR) techniques utilizing low-salinity water (LSW) are advancing owing to their favorable technical and economic viability. LSW flooding entails the injection of a modified-composition brine into oil reservoirs with a lower concentration of the potential determining ions (PDIs), specifically Ca2+, Mg2+, and SO42− ions compared to high-salinity connate water or injected seawater. Achieving an optimum concentration of the PDIs in the injected water provides further potential for enhancing oil recovery, which is denoted as smart waterflooding. Surfactants can be used to reinforce the smart waterflooding performance by reducing oil-water interfacial tension (IFT) and enhancing the rock surface wettability alteration. In this research, a comprehensive laboratory study is conducted to investigate the optimum surfactant-assisted smart water formulation for a carbonate rock. The initial step of this study involves the evaluation of fluid-fluid interactions using IFT via spinning drop tensiometer. The subsequent step involves studying the rock-fluid interactions using zeta potential experiments, wettability alteration in a specifically-designed HPHT imbibition cell and reservoir-condition HPHT coreflooding tests in composite cores. The results of IFT experiments showed more effective oil-water interactions of the smart brine when the sulfate concentration increased. The zeta potential experiments using the streaming potential method showed a clear trend of yielding more negative values for the smart water solutions when the surfactant was added to the system. The rock surface charge was found sensitive to the sulfate concentration and by adsorption of this ion, the positive charge of the rock surface is reduced. The presence of the surfactant in smart water system has improved the wettability alteration mechanism and reduced the contact angle by 12° which indicated the further alteration of wettability of the carbonate rock from oil-wet to water-wet. The outcomes of the coreflooding revealed an additional oil recovery of 7.72% achieved via the addition of the A-1 surfactant to smart waterflooding. The findings of this study are expected to enhance the understanding of the application of smart waterflooding in carbonate reservoirs and the future perspective of hybrid application of water-based EOR processes.
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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".