Experimental Study of Surfactant Flooding on Organic Shale with Integrated Characterization
Bibliographic record
Abstract
Abstract Enhanced oil recovery from organic shale reservoirs has increasingly gained interest from oil and gas industry in recent years. The recovery factor of organic shale oil production depends on formation wettability and pore fluid trapping mechanisms. A combination of hydraulic fracturing and surfactant flooding can be used to reduce oil trapping and increase oil recovery by reducing the interfacial tension and decreasing oil wettability. A novel experimental workflow has been developed based on fluid flow monitoring and NMR characterization to study the effect of surfactant flooding on organic-rich shales in the lab. Two blends of surfactants (cationic and nonionic) were carefully selected from prior contact angle (CA) and interfacial tension (IFT) measurements for the surfactant flooding tests. Micro-CT screening was used to select fracture-free samples for these tests. Prior to flooding we acquired nuclear magnetic resonance (NMR) T1-T2 measurements on as-received core samples to establish base-line water and oil saturations. Next, the core samples were pressure-saturated with crude oil at reservoir pressure and temperature, and we continued the aging process for a given time. Following aging, core samples were flooded using continuous crude oil injection from one end of the core sample whilst monitoring fluid flow rate, temperature, and pressure. Robust initial effective oil permeability was computed when the flow system reached steady state. Next, fracturing fluids -with and without surfactants- were injected from the opposite end of the core plugs to simulate the forced imbibition of fracturing fluid along with hydraulic fracturing in real field operations. Finally, the injection of crude oil was resumed from the original end of the core sample to establish the flowback effective oil permeability after hydraulic fracturing and surfactant flooding. We acquired NMR data after each fluid injection step to monitor fluid saturation and wettability changes in the core samples. Additionally, porosity and saturation measurements, X-ray diffraction (XRD), rock-eval pyrolysis and mercury injection capillary pressure (MICP) tests are performed to characterize fluid distribution, mineralogy and pore throat sizes of the rock samples. The results of fracturing fluid injection in all core samples clearly indicate that the water from the fracturing fluid does partially displace the crude oil in the core, effectively making this oil recoverable. Samples injected with the blend of cationic surfactants show less than 3% incremental recovery over samples with no surfactant injection. The flowback effective oil permeabilities of all core samples are much lower than the initial effective oil permeabilities prior to fracturing fluid injection. This observation is corroborated by the differences in MICP results before and after fracturing fluid injection, showing smaller pore throat sizes after fracturing fluid injection. Our novel workflow has successfully characterized the impact of surfactant flooding on organic-rich shale samples in lab-scale tests. and can be used for screening of surfactant enhanced oil recovery before running more expensive field trials.
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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".