Removing Formation Damage and Stimulation of Deep Illitic-Sandstone Reservoirs Using Green Fluids
Bibliographic record
Abstract
Abstract Illitic-sandstone reservoirs are very sensitive to HCl-based fluids. When HCl contacts illitic-sandstone it breaks down and causes fines migration and formation damage. The migration of fines through the porous media will block the pores, reduce permeability and decrease the production rate of oil and gas wells. Literature showed that all clay minerals are essentially unstable in HCl at temperatures greater than 300 °F. In turn, there is an essential need to look for stimulation fluids other than HCl to stimulate deep sandstone reservoirs. Alternative fluids to HCl/HF mud acids were introduced to stimulate and remove the damage from illitic-sandstone reservoirs. These fluids are chelating agents such as EDTA (ethylene diamine tetraacetic acid), HEDTA (hydroxyl ethylene diaminetriacetic acid), and GLDA (glutamic acid-N, N-diacetic acid). In this study, sandstone cores with different illite contents were examined. Illite content of 1, 10, 14, and 18 wt% of the sandstone cores were used in the coreflood experiments at 300 °F. Different combinations of GLDA/HF were tested to determine the optimum ratio of GLDA/HF. CT scan and permeability measurements before and after the treatment were used to assess the effectiveness of each fluid in removing the damage and stimulation of sandstone cores. Our results showed that 15 wt% HCl caused severe damage to sandstone cores with different illite contents. GLDA, HEDTA, and EDTA showed a good compatibility with the illitic-sandstone cores at 300 °F. Permeability measurements and CT scan results showed that GLDA performed better than HEDTA and EDTA at pH of 4. The optimum ratio of GLDA/HF concentration was found to be 20 wt% GLDA/1 wt% HF, which gives the maximum increase in core permeability. The three fluids tested in this study showed good compatibility with illite. They can be used to stimulate illitic-sandstone reservoirs alone or in combination with HF acid. GLDA was found to be compatible with the sandstone cores that contained up to 18 wt%. No sands deconsolidation was noted with any of the three fluids. The results obtained from this study will significantly improve the outcome of acid treatments in illitic-sandstone reservoirs at high temperatures.
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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".