Investigation of Changing Pore Topology and Porosity During Matrix Acidizing using Different Chelating Agents
Bibliographic record
Abstract
Core flooding acidizing experiments on sandstone/carbonate formation are usually performed in the laboratory to observe different physical phenomena and to design acidizing stimulation jobs for the field. During the tests, some key parameters are analyzed such as pore volume required for breakthrough as well as pressure. Hydrochloric acid (HCl) is commonly used in the carbonate matrix acidizing while Mud acid (HF: HCl) is usually applied during the sandstone acidizing to remove damage around the well bore. However, many problems are associated with the application of these acids, such as fast reaction, corrosion and incompatibility of HCl with some minerals (illite). To overcome these problems, chelating agents (HEDTA, EDTA and GLDA) were used in this research. Colton tight sandstone and Guelph Dolomite core samples were used in this study. The experiments usually are defined in terms of porosity, permeability, dissolution and pore topology. Effluent samples were analyzed to determine dissolved iron, sodium, potassium, calcium and other positive ions using Inductively Coupled Plasma (ICP). Meanwhile Nuclear Magnetic Resonance (NMR) was employed to determine porosity and pore structure of the core sample. Core flood experiments on Berea sandstone cores and dolomite samples with dimensions of 1.5 in × 3 in were conducted at a flow rate of 1 cc/min under 150oF temperature. NMR and porosity analysis concluded that applied chemicals are effective in creating fresh pore spaces. ICP analysis concluded that HEDTA showed good ability to chelate calcium, sodium, magnesium, potassium and iron. It can be established from the analysis that HEDTA can increase more amount of permeability as compared to other chelates.
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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".