Scale Inhibitor Squeeze Treatment Design in an Acid Stimulated Carbonate Reservoir
Bibliographic record
Abstract
Abstract The aim of this paper is to describe a methodology to simulate scale squeeze treatments in acid stimulated carbonate reservoir. Acid stimulation treatments refer to matrix acidizing, which aims to improve productivity in tight carbonate reservoirs. They generally show a high porosity, but very low permeability. Matrix acidizing treatments have been applied successfully in numerous occasions, they consist of the injection of an acid, generally, hydrochloric acid, mainly because of the high reactivity with carbonate formations. When the acid is injected into the formation, a number of wormholes or highly conductive channels are formed, which are the result of two very distinctive, but interconnected processes. On the one hand, there are chemical reactions between the carbonate minerals and the acid and on the other hand, there are the fluid dynamics of the injected acid, i.e. the fluid loss from wormhole to formation and the fluid distribution in possibly highly complex wormhole geometries. There have been numerous studies investigating the geometry of the wormhole growth pattern in radial and linear laboratory experiments, as well as stochastic simulation. They concluded that wormholes grow in a certain pattern in both axial and angular directions. Although the exact wormhole pattern will strongly depend on the permeability anisotropy and heterogeneity, it is reasonable to assume that the dominant wormholes are expected to grow symmetrically, and that the region dominated by each wormhole is approximately 90° around the wellbore. To simulate squeeze treatments in an acid stimulated well with a corresponding wormhole pattern, as described above, a reservoir simulator is used. The reservoir simulator describes the pressure field and consequently the propagation of scale inhibitor (SI) along the wormholes, but also into the matrix. The final step is to determine how deep the SI propagates into the matrix, which was used to determine fully a specialized near wellbore model for scale treatment design.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".