Combined Low Salinity Water Injection and Foam Flooding in Sandstone Reservoir Rock: A New Hybrid EOR
Bibliographic record
Abstract
Abstract To date, several methods have been designed and implemented either individually or as combination to increase oil recovery. The combined methods also called hybrid Enhanced Oil Recovery (EOR) pose great challenges in the oil and gas industry. This is also confirmed from the Industry Technology Facilitator (ITF)'s Gulf Cooperation Council (GCC) Technology roadmap that pinpointed hybrid EOR as the top challenge. This work investigates the potential of a new hybrid EOR method termed Low Salinity Water Assisted Foam flooding (LSWAF). Typically, it consists of injecting Low Salinity Water (LSW) followed by an alternated injection of a Surfactant Aqueous Solution (SAS) and CO2 gas. The selection of LSW was based on its ability to produce significant changes of rock wettability. While the SAS was made, so that it results in a considerable reduction of IFT and produce foam that will remain considerably stable in the presence of oil under reservoir conditions. The selection of the best LSW and SAS followed a screening phase whereby formulations of different ion compositions and salinities were prepared and their performances were compared against each other. Tests were carried out by using two types of light crude oil with different Total Acid Number and Base Number, namely Crude oil A and B. In terms of changes in wettability, for both crude oils A and B, the experimental results revealed that much changes were experienced by the LSW of KCl composition followed by that of NaCl, then CaCl2, MgCl2, Mix composition, and finally the formation water (FW) that presented negligible changes. The changes with crude oil B were greater than crude oil A. However, for crude oil A and B, the core flooding experiments showed that the highest residual oil recovery was achieved by the LSWAF of KCl composition (87.45% of OOIP) compared to the MgCl2 and FW.
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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".