Low Tension Gas Process in High Salinity and Low Permeability Reservoirs
Bibliographic record
Abstract
Abstract Chemical enhanced oil recovery (EOR) in carbonate reservoirs has always been technically and economically challenging. Conventional Alkaline-Surfactant-Polymer (ASP) flooding has limited application in low permeability (2-20 mD) and high salinity formations (~200,000 ppm TDS) with a large concentration of divalent cations. Also injectivity into such low permeability reservoirs can be a significant problem with polymer solutions. The process of low tension gas (LTG) in tight carbonates has exhibited good microscopic displacement and mobility control. It combines interfacial tension (IFT) reduction with improved mobility control by in-situ generation of foam in low-permeable heterogeneous formations. This process has been tested in the lab for a Middle Eastern carbonate reservoir, which is the subject of this paper. This strategy has been tested through either co-injection or alternating injection of slug/drive surfactant solution and gas (CO2, N2, or hydrocarbon) at low foam quality (high water content). A successful surfactant screening was performed to select the optimum surfactant formula that exhibits ultra-low IFT, good aqueous stability, and low microemulsion viscosity. The formulation allows tailoring of optimal salinity for ultra-low oil-water IFT to the variation of formation and produced water salinity. Core flood experiments have been performed, which demonstrated favorable mobilization and displacement of residual oil. Tertiary recoveries of up to 85% on remaining oil were achieved for cores with permeability less than 10 mD. An innovative experimental method was also developed to achieve high initial oil saturation in tight rocks.
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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".