Investigation of change in different properties of sandstone and dolomite samples during matrix acidizing using chelating agents
Bibliographic record
Abstract
Properties of rock, such as effective porosity, permeability and pore size distribution (PSD), are generally referred to as petrophysical properties. These properties are among the most significant for reservoir evaluation. Acid stimulation treatments are usually used in sandstones to mitigate the impact of formation damage, with the aim of restoring or enhancing the natural matrix permeability and consequently boosting the well productivity. Hydrochloric acid (HCl) is commonly used in the preflush stage to remove calcium and other metal ions, preventing the development of calcium fluoride (CaF 2 ) and other silicate precipitates that could block the pore throats, while an acid mixture (HF–HCl combination) is usually preferred as the main stimulation fluid for the removal of quartz and remaining metal ions. However, sometimes the application of these acids can lead to other problems, including fast reactions, corrosion of pipes, environmental hazards, precipitation reactions and formation damage due to the incompatibility of HCl with clay minerals, so chelating agents have been proposed as an alternative for matrix stimulation fluids. In this study, three different chelating agents, ethylenediaminetetraacetic acid (EDTA), N -(2-hydroxyethyl) ethylenediamine- N , N ′, N ′-triacetic acid (HEDTA) and N -acetyl- l -glutamic acid (GLDA), have been used to stimulate Berea sandstone, Colton tight sandstone and Guelph dolomite samples. Core flood experiments were conducted on 1.5 × 3 (in 2 ) core plugs, at a temperature below 180 °F. A slow injection rate of (1–0.5 cc/min) was chosen for the treatment fluid, promoting the dissolution of ions by increasing the contact time between the fluid and the rock. Furthermore, nuclear magnetic resonance, wettability and micro-computed tomography (CT scan) analyses were employed to evaluate the effect of the acid treatment on formation properties such as porosity, PSD, pore topology, wettability and pore structure. After exposing the samples to HEDTA, large wormholes were detected in their pore network, demonstrating that HEDTA has the highest potential to create new pore spaces when compared to GLDA and EDTA when reacted with both types of samples.
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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".