A Comprehensive Evaluation of Alkaline Surfactant Polymer Flooding and Hybrid Process for Enhanced Oil Recovery
Bibliographic record
Abstract
Abstract This paper presents recent advances in the subject of modeling and optimization of ASP (Alkaline, Surfactant and Polymer) flooding with: (1) a critical review of the state-of-the-art development of ASP flooding; (2) an efficient and accurate novel approach for ASP modeling for robust simulation of chemical processes in conjunction with oil, gas, and water flash calculations using an equation of state (EOS) simulator; (3) systematic validation of the new modeling approach with laboratory studies; (4) evaluation of a hybrid Low Salinity ASP recovery process; and (5) robust optimization of ASP field-scale design under geological uncertainties. We used a new approach that can model the behavior of the surfactant-oil-water-microemulsion system based on solubility data. In the Type III system, the emulsion is distributed judiciously between the oil and water phases without the need to introduce a third liquid phase. This model captures most of the important physical and chemical phenomena in the ASP process. The model was then validated with numerous coreflooding experiments conducted by different research institutes as well as with a specialized chemical flood simulator. The newly proposed model is tested using different injection schemes and chemical formulations including negative salinity gradient, non-negative salinity gradient, and a series of benchmark coreflooding experiments. Excellent agreements between the model and the experiments in terms of oil recovery and pressure drop were achieved for all corefloods. In addition, the model was also proven to be highly consistent with both UTCHEM-EQBATCH and UTCHEM-IPHREEQC. More importantly, previous results obtained without the explicit modeling of Type III indicated that the recovery factor deviates significantly from the experimental data, whereas the pseudo two-phase approach in this paper gives an excellent match in all cases. This model has also been successfully applied to match the recovery of Alkaline-CoSolvent-Polymer flooding, which is a promising recovery approach. We investigated the potential of hybrid low salinity ASP flooding in which Low Salinity Waterflooding (LSW) was implemented in secondary production and followed by ASP flooding. This approach can provide a superior performance compared to the conventional chemical flooding because it provides better oil recovery in the secondary stage and promotes the synergy between low salinity environment and ASP slugs. Finally, the proposed robust optimization workflow helps to increasea project NPV and significantly reduces the uncertainty range associated with geology.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".