Acid retardation for deeper stimulation—Revisiting the chemistry and evaluation methods
Bibliographic record
Abstract
Abstract Acid fracturing treatment in upstream oil and gas production is done to extend the connectivity of the wellbore deep into the reservoir. This is achieved through a dissolution process using acid in conjunction with hydraulic fracturing. A key to success is to impart favourable reaction kinetics between the acid and rock matrix. If the reaction proceeds too rapidly, it results in large voids in the near‐wellbore area but insufficient dissolution in the reservoir. Hydrochloric acid (HCl) is ubiquitous in the petroleum industry for acidizing applications due to its high dissolving capacity towards carbonate minerals, soluble reaction products, and low cost. Its corrosive nature coupled with rapid reaction kinetics have forced the industry to search for mechanisms to address these limitations. The concepts for slowing down reaction rate centre around (1) reducing mass transfer of hydrogen ions, H + ; (2) limiting H + ion dissociation; and (3) altering the wetting property of the rock surface. Reaction kinetics data is critical to guiding the selection of the suitable acid formulations for acid fracturing process. They are commonly measured in the lab using instruments such as a rotating disk reactor, flow cells, and diffusion cell. Though routinely applied, these experimental methods, including their setups and procedures, are rarely questioned. The experimental artefacts can lead to wrong conclusions. Deeper understanding of the fundamental assumptions behind the apparatus design and test procedures is critical. This paper discusses the chemistries employed in acid retardation, and more importantly the attention needed when extending the experimental data to field treatment.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".