Experimental Study of Switchable Nonionic to Cationic Surfactants for Acid Diversion in High-Temperature Matrix Stimulation
Bibliographic record
Abstract
Abstract Matrix well stimulation by dissolving part of acid soluble materials from the matrix is a proven technique to improve production from carbonate formations. However, acid placement and diversion remains a challenge to the operators, when dealing with heterogeneous formations with zonal permeability contrast. Inefficient acid placement leads to an unsuccessful treatment as most of the injected acid will flow through the high permeability zone, whereas the main target is usually the low permeability layer. Foam-acid diversion is a common practice for acid placement. The success of any foam acid diversion depends highly on the careful selection of suitable surfactant(s) and testing its performance at relevant reservoir conditions. In this paper, we present a systematic study on surfactant screening, foam-bulk stability, and foam behavior inside the porous medium. Glutamic acid diacetic acid (GLDA) was used as acid. The surfactant screening was performed with an initial list of 29 surfactants at 25°C and 80°C with and without GLDA present in the surfactant formulations. The addition of GLDA resulted in foam collapse for most of the surfactants. However, some bulk-foam stability tests showed improved foamability at 80°C for selected surfactant formulations containing GLDA. Surfactant formulations passing the initial screening were further tested in a 76cm-long high permeability glass beads packed bed. The performance of the selected formulations was next studied in a series of high pressure foam-flooding experiments at 130°C. Foam-quality scan curves were developed to examine the influence of foam quality on foam strength. The mobility reduction factor (MRF) was considered as direct measure of foam strength. Foam coreflood experiments revealed that for most of the formulations comprising cationic surfactants, the foam was most viscous and collapsed at 85% quality.
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.
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 | 0.000 |
| 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".