Research and application of acid fracturing stimulation mechanism in ultra-deep subsalt dolomite reservoir in Tarim Basin
Bibliographic record
Abstract
Abstract Deep and ultra-deep carbonate reservoirs are the focus of exploration and development in future. However, the problems of high pressures in the treatment process, a limited effective etching distance of acid, great acid leak-off, and poor adaptability of the acid system are encountered in this type of oil and gas reservoir. The mechanism of acid fracturing stimulation under different processes and parameters is not clear. Aiming at these issues, the treatment schemes, process optimization, parameter optimization, and liquid system screening are studied in this paper, try to clarify the acid fracturing stimulation mechanism, and the following conclusions are drawn: The acid network fracturing could activate natural fracture to generate a complex fracture network to the greatest extent, and thereby a high output could be achieved; By using of weighted fracturing fluid, the wellhead injection pressure, as well as the performance of equipment required, could be effectively reduced; With 20% gelling acid and 20% retarded acid system, the non-uniform etching could be realized to improve the effective etching distance of acid liquid. The conclusions in this paper shed light on the acid fracturing treatment of deep and ultra-deep carbonate rocks.
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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 | 0.001 | 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".