Enhance Microscopic Sweep Efficiency by Smart Water in Tight and Very Tight Oil Reservoirs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In the literature, improvement of oil recovery in smart water injection schemes has been shown to be mediated by wettability alteration. This process reduces residual oil saturation, which in turn affects the microscopic sweep efficiency and leads to subsequent enhancement of overall waterflood performance (Willhite, 1986). Tight and very tight oil reservoirs are often associated with high clay content and significant Cation Exchange Capacity (CEC) values (Breeuwsma et al., 1986). CEC directly influences smart waterflood behavior as it controls ion exchangeability between the solid and aqueous phases, which then regulates the double layer thickness and the wettability of the system (Nasralla and Nasr-El-Din, 2014). This study presents the effect of lithology on CEC value. Experimental studies on smart waterflooding in tight oil cores have reported reduction of residual oil saturation by as high as five percent and improvement of microscopic sweep efficiency by six percent (Xie et al., 2015a, 2015b). The promising potential of smart water in tight and very tight oil reservoirs is similarly shown in numerical simulations, in which oil recovery is improved by three percent. Smart water may additionally retard water production by reducing water relative permeability. Furthermore, it enhances effective porosity/permeability through mineral dissolution. However, in tight oil reservoirs, pressure maintenance efficacy could be an issue. Simulation results display a significant pressure drop in the reservoir, which could lead to gas phase liberation and liquid relative permeability reduction. Currently, few studies on smart waterflood in tight and very tight oil reservoirs exist. This work examines smart waterflood opportunities in these reservoirs from both an experimental and a numerical perspective.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.001 | 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 it